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Development and Evaluation of

the FUEL Program

Ida Lysdahl Fahrenholtz

Doctoral Dissertations at

the University of Agder 531

Development and Evaluation of

the FUEL Program

Ida Lysdahl Fahrenholtz

Development and Evaluation of

the FUEL Program

A Sports Nutrition Intervention for Female Endurance Athletes at

Risk of Relative Energy Deficiency in Sport (REDs)

Dissertation for the degree philosophiae doctor (ph.d.)

University of Agder

Faculty of Health and Sport Sciences

2025

Doctoral dissertations at the University of Agder, No: 531

ISSN: 1504-9272

ISBN: 978-82-8427-249-8

Printed by Make!Graphics

Kristiansand

Preface

This doctoral thesis and the corresponding research, attests to the author's

academic contributions during the period of employment at the University of

Agder (UiA), Department of Sport Science and Physical Education. The

multicenter study is developed and conducted with resources from UiA, the

Norwegian Olympic Sports Center, Linnæus University, Technical University of

Munich, and Sport Ireland Institute.

The aim of this PhD project was to develop and evaluate the Food and nUtrition

for Endurance athletes – a Learning (FUEL) program, a practice-orientated

learning and counseling program for female endurance athletes at risk of the

syndrome Relative Energy Deficiency in Sport (REDs). The FUEL study was a

multi-country research collaboration including Norway, Sweden, Ireland, and

Germany.

Data collection occurred during the COVID-19 pandemic, which significantly

impacted the study design and methodology. Initially, we planned to collect data

through extensive laboratory measurements across various locations in the

participating countries. However, due to the physical restrictions imposed by the

pandemic, we had to promptly redesign the study’s measurement methods.

The thesis is based on four papers from the FUEL study (see list of papers) and

results have been communicated at international conferences; the Nordic Eating

Disorder Conference in Oslo in 2022, the British Dietetic Association Sports

Nutrition Specialist Group study day in 2022, and at the European College of

Sports Science Congress in Seville in 2022 and in Paris in 2023.

Acknowledgements

Many people have supported and assisted me in various ways throughout my Ph.D.

journey. It is paramount to extend heartfelt appreciation to these people without

whom this project would not have been accomplished.

First and foremost, I would like to express my deepest gratitude to my main

supervisor, Professor (and super-woman) Monica Klungland Torstveit. Your

unwavering commitment to this field of research and your exceptional guidance

have profoundly shaped my development as a researcher, teacher, and supervisor.

Beyond your supervision and professional support, you showed immense personal

care during pivotal moments in my life: When I moved to Norway and needed help

finding a place to live, during my pregnancy-related discomfort, when I mourned

the loss of my dearly beloved grandmother, and when unforeseen circumstances

required us to move back to Denmark. Your kindness and support have been

invaluable.

Next, I would like to thank my co-supervisor Professor Anna Katarina Melin.

Your dedication and expertise are truly inspiring, and you have supported me and

worked hard on the project, even though life has been hard on you. I already had

the pleasure to work with you during my master’s thesis and later as your research

assistant at the University of Copenhagen. You recognized my potential and

believed in my ability to pursue a Ph.D. long before I did. There is no doubt that

your encouragement and support have been instrumental in my ability to complete

this thesis.

I would also like to thank my other co-supervisor Ina Garthe, specifically for

sharing your expertise on working closely with top-level athletes. Your

competences have been indispensable for the development of the project and in

my growth both as sports science researcher and practitioner. Thank you for your

positive mindset, your warm smiles, and for engaging me in Olympiatoppen’s

important work.

Thank you, Bård Erlend Solstad, for conducting the qualitative interviews and

executing the qualitative analyses. Your guidance has broadened my scientific

perspective, and your contributions have been invaluable to this project.

Thanks to my fantastic collaborators abroad for all your valuable contributions to

the FUEL project. Karsten Köhler and Paulina Wasserfurth from Technical

vii

University of Munich – thank you for an exciting week in Munich with many

interesting discussions about the FUEL study and for introducing me to your

research group. Paulina, my friend, thank you for your emotional support and for

cheering me on when I needed it. Sharon Madigan and Danielle Logue from

Sport Ireland Institute – thank you for inviting me to Birmingham and for

introducing me to other British researchers committed to sports nutrition. Thanks

to Andreas Ivarsson and Andreas Stenling for statistical guidance and to Maria

Gräfnings for being a dedicated master’s student during the recruitment and data

collection and for now further developing the FUEL program in your own Ph.D.

Thanks to Siri Marte Hollekim-Strand, Heidi Holmlund, Kristin Lundestad,

Emma Martner, Josefine Dahlqvist, Sara Ring, and Petra Lundström for all

your valuable contributions to the project.

Thanks to all the participants in Norway, Sweden, Ireland, and Germany for all

your effort related to the project, making this thesis possible, and thanks to the

dedicated master’s students Ingvild Brattekleiv, Miriam Myhren Bouchleh,

Viktoria Snev Såland, Rasmus Fjeld and Marlene Sophie Wåland.

Thank you, Professor and Head of the Department, Sveinung Berntsen Stølevik,

for your support and the trust you have shown me, both as a Ph.D. candidate and

in my teaching endeavors.

Thank you, Solveig Pedersen and Gjermund Nesland, for welcoming me with

open arms to Olympiatoppen Sør. I am very grateful for my time as a nutrition

consultant where I had the chance to work with you and all the dedicated athletes.

Thanks to Thomas Stenqvist for our time as office mates in Spicheren, for

interesting scientific discussions, and not least, for teaching me how to be a Dane

in Norway. Also, a heartfelt thank you to my dear colleague and friend Lena

Malnes for sharing the ups and downs during this journey and for becoming a

mother during the Ph.D. Cecilie Beinert, thank you my friend for the overnight

stays, for our engaging scientific nutrition discussions, and for cheering me on.

A hearty warm thank you to the family Kind “the Kind Ones” for a fantastic

neighborship in Kristiansand. You provided a space for rest and made me feel at

home after long days at work.

I have many Danish friends that in different ways have supported me and many

took the long way to Norway and visited me there. But I especially want to thank

viii

you, Anne Sofie Fedders, for many valuable conversations, both professionally

and emotionally.

Last, but not least, I would like to thank my family for their indispensable support

throughout this long Ph.D. journey. To my parents Karen Fahrenholtz and

Morten Lysdahl, thank you for fostering my curiosity and supporting me, even

when I moved to another country and chose my career with my heart and not my

mind. To my mother-in-law Doris Lind for babysitting during Ph.D. writing and

for long drives to Hirtshals. To my farther-in-law Ole Lind, for providing the office

space that allowed me to complete this thesis. Thanks to my brother Emil Lysdahl

Fahrenholtz and Rebekka Stensgaard Klausen for linguistic guidance. To my

sister Mette Lysdahl Fahrenholtz for psychology-related discussions and

Alexander Birch for sharing experiences with your own Ph.D.

To my life partner and the father of my daughters: Nis Lind, thank you for leaving

your safe environment in Denmark to embark on this Norwegian adventure with

me. You have been an indispensable support on this hilly road. Cheers to all our

sports science discussions over the dinner table, amidst diaper changes and with

oatmeal on the walls. Thank you for all your warm hugs and kisses that gave me

energy during the writing process. Freja and Asta: my most important

achievements during this Ph.D. period, thank you for your patience when

mummy’s brain was overloaded. Indeed, I er mine øjesten. As you may experience

over time, it is not always easy to be a woman. Nature has provided us with the

hardest conditions when it comes to pain and hormonal fluctuations. But resistance

makes us stronger. Endurance allows us to go far. With this dissertation, I hope I

have contributed with a small but significant piece to a larger puzzle, giving you

the opportunity to grow up as active females in a healthier environment.

Ida Lysdahl Fahrenholtz,

November 2024

List of papers

Paper I: Fahrenholtz, I. L., Melin, A. K., Wasserfurth, P., Stenling, A., Logue,

D., Garthe, I., Koehler, K., Gräfnings, M., Lichtenstein, M. B.,

Madigan, S., & Torstveit, M. K. (2022). Risk of Low Energy

Availability, Disordered Eating, Exercise Addiction, and Food

Intolerances in Female Endurance Athletes. Frontiers in Sports and

Active Living, 4. Doi: 10.3389/fspor.2022.869594

Paper II: Fahrenholtz, I. L., Melin, A. K., Garthe, I., Hollekim-Strand, S. M.,

Ivarsson, A., Koehler, K., Logue, D., Lundström, P., Madigan, S.,

Wasserfurth, P., & Torstveit, M. K. (2023). Effects of a 16-Week

Digital Intervention on Sports Nutrition Knowledge and Behavior in

Female Endurance Athletes with Risk of Relative Energy Deficiency

in Sport (REDs). Nutrients, 15(5), 1082. Doi: 10.3390/nu15051082

Paper III: Fahrenholtz, I. L., Melin, A. K., Garthe, I., Wasserfurth, P., Ivarsson,

A., Hollekim-Strand, S. M., Koehler, K., Logue, D., Madigan, S.,

Gräfnings, M., & Torstveit, M. K. (2023). Short-term effects and

long-term changes of FUEL—a digital sports nutrition intervention

on REDs related symptoms in female athletes. Frontiers in Sports

and Active Living, 5. Doi: 10.3389/fspor.2023.1254210

Paper IV: Solstad, B.E., Fahrenholtz, I.L., Melin, A., Garthe, I., Torstveit, M.K.

Participant Evaluations of the FUEL Intervention Designed for

Female Endurance Athletes at Risk of REDs: A Mixed Methods

Approach. Submitted to Sports Psychiatry – Journal of Sports and

Exercise Psychiatry.

Summary

Female endurance athletes are frequently reported to not get enough fuel from food

to meet the energy demands of physiological processes important for optimal

health and performance. Hence, female endurance athletes are considered a high‐

risk group for problematic low energy availability (LEA) and thereby the

syndrome Relative Energy Deficiency in Sport (REDs) with multiple health and

performance consequences. Different causes of LEA and REDs have been

identified, including disordered eating (DE) behavior. Since LEA and symptoms

of REDs are frequently reported without DE, other potential origins need to be

investigated. In addition, the high prevalence calls for intervention studies,

targeting and evaluating the effects of the management of REDs.

The aim of this dissertations was to develop and evaluate the Food and nUtrition

for Endurance athletes – a Learning (FUEL) program, a practice-orientated sports

nutrition intervention for female endurance athletes at risk of REDs. The aim of

Paper I was to identify the risk of LEA/REDs and potential risk factors and

explore associations between them. The aim of Paper II was to investigate the

effects of the FUEL intervention on sports nutrition knowledge and dietary intake,

while the aim of Paper III was to investigate immediate effects and long-term

changes on physiological REDs symptoms and symptoms of DE and exercise

addiction. Finally, the aim of Paper IV was to evaluate athletes’ experiences after

participating in the FUEL study.

Female endurance athletes, 18-35 years of age, training at least five times a week

were recruited in Norway (n = 60), Sweden (n = 84), Ireland (n = 17), and Germany

(n = 47) during the COVID-19 pandemic. Participants completed an online-survey

comprising the LEA in Females Questionnaire (LEAF-Q), Exercise Addiction

Inventory (EAI), Eating Disorder Examination Questionnaire (EDE-Q), and

questions regarding food intolerances. This initial survey constituted the screening

for the FUEL intervention consisting of 16 weekly digital sports nutrition lectures

and individual athlete-centered nutrition counseling every other week. Fifty

athletes at risk of REDs (risk of LEA: LEAF-Q score ≥ 8), with low risk of DE

(EDE-Q score < 2.5) and no use of hormonal contraceptives were allocated to

either the FUEL intervention (n = 32; FUEL) or a 16‐week control period with

subsequent FUEL intervention with or without individual counseling (n = 18;

CON). Three athletes were directly allocated to the FUEL intervention without

individual counseling. FUEL and CON completed an additional survey with

xii

questions regarding sports nutrition related behavior and self‐perceived sports

nutrition knowledge. In the same week that a seven‐day dietary, physical activity

and training record was conducted, a telephone interview with questions regarding

sports nutrition knowledge was performed. After 16‐weeks, athletes once again

completed the two online surveys, the telephone interview, and a seven-day dietary

and activity record which was followed by a participant evaluation consisting of a

questionnaire and a qualitative interview. Six and 12 months after the intervention,

FUEL athletes completed a survey comprising the LEAF-Q, EDE-Q, and EAI.

In total, 31 and 15 athletes completed FUEL and CON, respectively and were

included in Paper II and III. After CON, two athletes completed the FUEL

intervention with lectures and individual counseling. In total, 11 completed the

FUEL intervention with lectures only. Of the 44 athletes who completed the FUEL

intervention with or without individual counseling, 36 completed the evaluation

questionnaire, and ten participated in the qualitative interview and were included

in Paper IV.

In Paper I, 65% were categorized as being at risk of LEA/REDs, 21% were

categorized with DE and 23% with exercise addiction. Athletes at risk of

LEA/REDs had higher EDE-Q global score and EAI score compared to athletes

with low risk. The EAI total score remained higher for athletes at risk of

LEA/REDs after excluding athletes with DE. There was no difference in the

prevalence of food intolerance between athletes at risk of LEA/REDs compared to

athletes at low risk of LEA/REDs, although athletes reporting food intolerances

had higher LEAF-Q total score compared to athletes without food intolerances.

Using a logistic regression analysis, BMI and EDE-Q global score were

significantly associated with the risk of LEA/REDs.

Using Baysian ANOVA analyses in Paper II, strong evidence was found for

improvements in sports nutrition knowledge, assessed via interviews, and

moderate to strong evidence in the ratings concerning self‐perceived sports

nutrition knowledge in FUEL versus CON. Analyses of the food record and

questions related to sports nutrition related behavior, found weak evidence for

better improvements in energy and macronutrient intake in FUEL versus CON.

In Paper III, no evidence for difference in change in LEAF-Q or EAI total scores

between groups was detected at posttest, while weak evidence for EDE-Q global

was found. Six and 12-months follow-up revealed strong evidence for improved

xiii

LEAF-Q total, LEAF-Q menstrual score, and EAI total score and weak evidence

for improved gastrointestinal score after FUEL. The decline in EDE-Q score

remained at six and 12-months follow-up among FUEL athletes.

Using a mixed methods approach, quantitative and qualitative findings in Paper

IV revealed a high participant satisfaction. Participants found the FUEL content

motivating, educational, and appropriate in duration, difficulty level, and

frequency. The digital format of the intervention was not considered as a weakness

but rather as an advantage. Finally, the participants reported experiencing several

positive bodily and mental changes and emphasized that the combination of digital

lectures and athlete-centered nutrition counseling was effective in helping them

apply newly acquired knowledge in practice.

The findings of this thesis reflect that REDs is a common and complex syndrome

among female endurance athletes. Behavioral changes and improvement of REDs

symptoms take time and may vary according to the individual. Nevertheless, the

positive participant evaluation indicates the relevance of further development and

implementation of the FUEL program.

xiv

Sammendrag (Norwegian summary)

Kvinnelige utholdenhetsutøvere har ofte et utilstrekkelig energiinntak til å dekke

energibehovene som er nødvendige for optimal helse og prestasjon. Derfor anses

de som en høyrisikogruppe for lav energitilgjengelighet (LEA), som kan føre til

syndromet Relativ energimangel i idrett [Relative Energy Deficiency in Sport

(REDs)] med flere helse- og prestasjonsmessige konsekvenser. Ulike årsaker til

LEA og REDs er identifisert, inkludert forstyrret spiseadferd. Siden LEA og

symptomer på REDs også kan forekomme uten forstyrret spiseatferd, bør også

andre mulige risikofaktorer undersøkes. Den høye forekomsten av LEA og REDs

krever dessuten intervensjoner som er designet for målgruppen.

Formålet med denne avhandlingen var å utvikle og evaluere FUEL-programmet

(Forstå UtholdenhetsidrettsErnæring – et Læringsprogram), en praksisorientert

idrettsernæringsintervensjon for kvinnelige utholdenhetsutøvere med risiko for

REDs. Hensikten med Artikkel I var å identifisere risikoen for LEA/REDs og

mulige risikofaktorer, samt å undersøke assosiasjoner mellom dem. Hensikten med

Artikkel II var å undersøke effekten av FUEL-intervensjonen på kunnskap om

idrettsernæring og kostinntak, mens hensikten med Artikkel III var å undersøke

umiddelbare effekter og langsiktige endringer på fysiologiske REDs-symptomer

samt symptomer på forstyrret spise- og treningsatferd. Til slutt var hensikten med

Artikkel IV å evaluere utøvernes erfaringer med å ha deltatt i FUEL-studien.

Kvinnelige utholdenhetsutøvere i alderen 18-35 år, som trente minst fem ganger i

uken, ble rekruttert i Norge (n = 60), Sverige (n = 84), Irland (n = 17) og Tyskland

(n = 47) under COVID-19-pandemien. Deltakerne fullførte en digital

spørreskjemaundersøkelse med LEA in Females Questionnaire (LEAF-Q),

Exercise Addiction Inventory (EAI), Eating Disorder Examination Questionnaire

(EDE-Q) og spørsmål om matintoleranse. Denne første undersøkelsen utgjorde

utvelgelsen for FUEL-intervensjonen, bestående av 16 ukentlige nettbaserte

idrettsernæringsforelesninger og individuell utøversentrert ernæringsveiledning

annenhver uke. Femti utøvere med risiko for REDs (risiko for LEA: LEAF-Q-

score ≥ 8) og med lav risiko for forstyrret spiseadferd (EDE-Q-score < 2,5), uten

bruk av hormonelle prevensjonsmidler ble tildelt enten FUEL-intervensjonen (n =

32; FUEL) eller en 16-ukers kontrollperiode med etterfølgende FUEL-

intervensjon med eller uten individuell ernæringsveiledning (n = 18; CON).

Samme uke som det ble gjennomført syv dagers kostholds-, fysisk aktivitet- og

treningsregistrering, ble det utført et telefonintervju med spørsmål relatert til

kunnskap om idrettsernæring og deltakerne besvarte et spørreskjema omhandlende

idrettsernæringsrelatert atferd og selvopplevd idrettsernæringskunnskap. Etter 16

uker gjennomførte deltakerne posttest med samme målemetoder som på pretest

samt en deltakerevaluering bestående av et spørreskjema og et kvalitativt intervju.

LEAF-Q, EDE-Q og EAI ble gjennomført seks og 12 måneder etter FUEL-

intervensjonen.

Videre fullførte 31 og 15 utøvere henholdsvis FUEL og CON og ble inkludert i

Artikkel II og III. Etter CON fullførte to utøvere FUEL-intervensjonen med

forelesninger og individuell veiledning. Totalt fullførte 11 FUEL-intervensjonen

kun med forelesninger. Av de 44 utøvere som fullførte FUEL-intervensjonen med

eller uten individuell veiledning, fullførte 36 evalueringsskjemaet, og ti deltok i

det kvalitative intervjuet og ble inkludert i Artikkel IV.

I Artikkel I ble 65% av utøverne kategorisert med risiko for LEA/REDs, 21% med

forstyrret spiseatferd og 23% med treningsavhengighet. Utøvere med risiko for

LEA/REDs hadde høyere EDE-Q- og EAI-score sammenlignet med utøvere med

lav risiko for LEA. EAI-scoren forble høyere hos utøvere med risiko for

LEA/REDs etter ekskludering av utøvere med forstyrret spiseatferd. Det var ingen

forskjell i forekomsten av matintoleranser mellom utøvere med risiko for

LEA/REDs og utøvere med lav risiko for LEA/REDs, selv om utøvere med

matintoleranser, hadde høyere LEAF-Q sammenlignet med utøvere uten

matintoleranser. Ved bruk av en logistisk regresjonsanalyse var

kroppsmasseindeks og EDE-Q global score signifikant assosiert med økt risiko for

LEA/REDs.

Ved bruk av en Bayesian ANOVA analyse, ble det i Artikkel II funnet sterke bevis

for en større økning i målt idrettsernæringskunnskap hos FUEL sammenlignet med

CON vurdert via telefonintervjuer, og moderate til sterke bevis i vurderingene av

selvopplevd idrettsernæringskunnskap. Analyser av kostholdsregistreringen og

spørsmål knyttet til idrettsernæringsvaner fant svake bevis for større forbedringer

i energi- og makronæringsstoffinntakk hos FUEL sammenlignet med CON.

I Artikkel III ble det ikke funnet noen bevis for forskjell i endring i LEAF-Q eller

EAI total score mellom gruppene ved posttest, mens det ble funnet svake bevis for

EDE-Q global score. Seks- og 12-månedersoppfølgning viste sterke bevis for

forbedret LEAF-Q total, LEAF-Q menstruasjonsscore og EAI total score, samt

xvi

svake bevis for forbedret gastrointestinal score etter FUEL. Reduksjonen i EDE-

Q score vedvarte ved seks- og 12-måneders oppfølging blant FUEL.

Ved å kombinere kvantitative og kvalitative analyser i Artikkel IV ble det funnet

en høy grad av deltakertilfredshet. Deltakerne fant intervensjonsinnholdet

motiverende, lærerikt og passende i varighet, nivå og frekvens. Det digitale

formatet til intervensjonen ble ikke ansett som en svakhet, men snarere som en

fordel. Til slutt rapporterte deltakerne at de opplevde flere positive kroppslige og

mentale endringer og understreket at kombinasjonen av digitale forelesninger og

utøversentrert ernæringsrådgivning var effektiv for å hjelpe dem å anvende

nyervervet kunnskap i praksis.

Funnene i denne avhandlingen avspeiler at REDs er et vanlig og komplekst

syndrom blant kvinnelige utholdenhetsutøvere. Adferdsendringer og forbedring av

REDs-symptomer tar tid og kan variere avhengig av den enkelte. Ikke desto

mindre viser den positive deltakerevalueringen til relevansen av videreutvikling

og implementering av FUEL-programmet.

xvii

Abbreviations

ACSM American College of Sports Medicine

BF Bayes factor

BMI Body mass index

CON athletes Athletes participating in the control condition

COVID-19 Coronavirus disease 2019

DE Disordered eating

E% Energy percentage

EDE-Q Eating Disorder Examination Questionnaire

EAI Exercise Addiction Inventory

FFM Fat free mass

FHA Functional hypothalamic amenorrhea

FSH Follicle-stimulating hormone

FUEL Food and nUtrition for Endurance athletes—a Learning

program

FUEL athletes Athletes participating in the FUEL intervention

GnRH Gonadotropin-releasing hormone

HPO Hypothalamic-pituitary-ovarian

IOC International Olympic Committee

LEA Low energy availability

LEAF-Q Low Energy Availability in Females Questionnaire

LH Luteinizing hormone

REDs Relative Energy Deficiency in Sport

IOC REDs CAT International Olympic Committee Relative Energy

Deficiency in Sport Clinical Assessment Tool

TSD Services for Sensitive Data

xviii

Definitions

Athlete A person training in sports aiming to improve her

performance/results and participates in sport

competitions. Expected to be registered in a local,

regional or national sport federation and has training

and competition as her major activity or focus of

personal interest (Araújo & Scharhag, 2016; McKay

et al., 2022).

BF The probability of the (null) hypothesis, the ratio of

the likelihood of one particular hypothesis to the

likelihood of another (van de Schoot et al., 2014).

BFincl Bayes factor for inclusion of group * time interaction

(van den Bergh et al., 2020).

DE Abnormal eating behaviors including restrictive

eating, compulsive eating or irregular or inflexible

eating patterns that do not meet clinical criteria for an

eating disorder (Ackerman et al., 2023; American

Psychiatric Association, 2013). An EDE-Q global

score ≥ 2.5 indicates DE (Rø et al., 2015).

Eating disorder A mental disorder characterized by pathological

disturbance of attitudes and behaviors related to food

meeting the Diagnostic and Statistical Manual of

Mental Disorders criteria (American Psychiatric

Association, 2013).

Endurance athlete An athlete engaging in a sport where performance is

dependent on the ability to sustain a high rate of

energy expenditure for prolonged periods of time.

Disciplines include running >1500 m, cycling,

triathlon, biathlon, and cross-country skiing (Joyner &

Coyle, 2008).

Energy availability Available energy to support basic biological functions

required by the body to maintain optimal health and

xix

reproductive function after accounting for the energy

expended from exercise: Energy availability (kcal/kg

FFM/day) = energy intake (kcal) - exercise energy

expenditure (kcal) / fat-free mass (kg) / day (Loucks

et al., 2011; Mountjoy et al., 2023).

Eumenorrhea Menstrual cycle length ≥ 21 days and ≤ 35 days

resulting in 9 or more consecutive periods per year.

For clinical verification evidence of LH surge, plus

correct hormonal profile and no hormonal

contraceptive use for three months is needed (Elliott-

Sale et al., 2021).

Exercise addiction An abnormal reliance on exercise behavior to cope

with chronic stress, featuring the core components of

addiction found in more established addictions.

Consequently, it is a behavioral process that can

provide either pleasure (compulsion) or relief

(dependence) from internal discomfort. An EAI score

≥ 24 indicates exercise addiction (Berczik et al., 2012;

Lichtenstein et al., 2017; Terry et al., 2004).

LEA Any mismatch between dietary energy intake and

energy expended in exercise that leaves the body’s

total energy needs unmet, resulting in inadequate

energy to support the biological functions required for

maintaining optimal health and performance

(Mountjoy et al., 2023).

LEA, adaptable Involves a reduction in energy availability that leads

to benign effects, such as mild and quickly reversible

changes in biomarkers across various body systems.

These changes indicate an adaptive partitioning of

energy and demonstrate the plasticity of human

physiology (Mountjoy et al., 2023).

LEA, problematic Exposure to LEA that is associated with greater and

potentially persistent disruption of various body

systems, often presenting with signs and/or

symptoms, and represents a maladaptive response.

The characteristics of problematic LEA exposure

(e.g., duration, magnitude, frequency) may vary

according to the body system and the individual

(Mountjoy et al., 2023).

LEA, risk of A LEAF-Q score ≥ 8 (Melin et al., 2014). Since the

LEAF-Q assesses symptoms related to problematic

LEA, an elevated LEAF-Q score indicates risk of

REDs (see REDs definition).

Menstrual dysfunction Menstrual dysfunction related to energy deficiency

ranged from subclinical anovulation [ovulation cannot

be detected by urinary LH surge or confirmed by

hormone concentrations via blood sample analysis,

but menstrual bleeding occurs] and luteal phase

defects [luteal phase length <10 days and/or

progesterone concentration <16 nmol/L when a single

luteal phase progesterone measurement is taken] to

clinical oligomenorrhea [cycle length >35 days] and

functional hypothalamic amenorrhea [improper

functioning of the HPO axis, not caused by any

disease] (Allaway et al., 2016; Elliott-Sale et al.,

2021).

Nutrition counseling A supportive process delivered by a qualified

professional who guides the client/athlete to set

priorities, establish goals, and create individualized

action plans to facilitate sustainable behavior change.

Specifically, sports nutrition counseling guides the

athletes in nutritional needs to support the sports

specific demands of exercise stimuli from training and

competition with a performance-enhancing focus,

whereas clinical nutrition counseling focus on helping

with nutritional related challenges or diagnosis such

as celiac disease, diabetes, allergies or food

intolerances (Fiorini et al., 2023; Rozga et al., 2020).

xxi

Nutrition education A formal process to improve an athlete’s knowledge

about food and physical activity (Fiorini et al., 2023).

REDs A syndrome of impaired physiological and/or

psychological functioning experienced by athletes that

is caused by exposure to problematic LEA (Mountjoy

et al., 2023).

REDs CAT2 A three-step REDs prevention process with screening,

severity/risk stratification, and a physician-led final

diagnosis and treatment plan developed by the IOC

(Mountjoy et al., 2023; Stellingwerff et al., 2023)

REDs signs Any REDs primary, secondary or potential indicator

parameter(s) that a clinician identifies on the IOC

REDs CAT2 Severity/Risk Assessment Tool

(Mountjoy et al., 2023).

REDs symptoms Any REDs primary, secondary or potential indicator

parameter(s) that an athlete directly reports or

experiences in the IOC REDs CAT2 Severity/Risk

Assessment and Stratification Tool (Mountjoy et al.,

2023).

xxii

Overview of thesis

Table 1. A brief description of the main findings and practical implications of the four papers.

Main findings

Practical implications

Paper I

In this sample of female endurance athletes, the

risk of LEA/REDs, DE, and exercise addiction

was 65%, 21, and 23%, respectively. Athletes at

risk of LEA/REDs reported more symptoms of

DE and exercise addiction. There was no

difference in food intolerance prevalence

between high and low-risk athletes, but those

with food intolerance had higher LEAF-Q

scores. A logistic regression analysis found that

lower BMI and higher EDE-Q scores were

associated with an increased risk of LEA/REDs.

Low BMI and DE may be

indicators of REDs among

female endurance athletes.

However, exercise addiction

and food intolerances may

also play a role in the

development of REDs in

some athletes and should

therefore not be overlooked

in the prevention of REDs.

Paper II

The study provided strong evidence that the

FUEL intervention enhanced sports nutrition

knowledge among female endurance athletes at

risk of REDs. However, the study found only

weak evidence for improvements in sports

nutrition behavior, total energy, and

macronutrient intake.

Digital nutrition lectures and

athlete-centered counseling

may improve sports nutrition

knowledge. However,

nutritional behavioral

changes are complex, even in

the absence of DE.

Paper III

The study found no immediate effects of the

FUEL intervention on physiological REDs

symptoms but showed strong evidence for long-

term improvements in menstrual function and

weak evidence for gastrointestinal function.

Immediate positive effects and long-term

changes in DE symptoms were also found, along

with reduced exercise addiction symptoms at

long-term follow-up.

Although improvements of

symptoms take time, the

FUEL program seems to be a

promising intervention as a

part of the management of

REDs symptoms among

female endurance athletes

without negatively affecting

DE symptoms.

Paper IV

Analyses of quantitative and qualitative

participant evaluations found overall high

satisfaction with the FUEL program. Athletes

reported several positive experiences and

provided suggestions for improving the content.

Positive participant

evaluations laid the

foundation for further

development of the FUEL

program.

Abbreviations: BMI: body mass index, DE: disordered eating behavior, EDE-Q: Eating

Disorder Examination Questionnaire, FUEL: Food and nUtrition for Endurance athletes – a

Learning program, LEA: low energy availability, LEAF-Q: Low Energy Availability in Females

Questionnaire, REDs: Relative Energy Deficiency in Sport.

 
 xxiii    
 
Table of contents 
 
Preface .................................................................................................................... v 
Acknowledgements ............................................................................................... vi 
List of papers ......................................................................................................... ix 
Summary ................................................................................................................ xi 
Sammendrag (Norwegian summary) ................................................................... xiv 
Abbreviations...................................................................................................... xvii 
Definitions ........................................................................................................ xviii 
Overview of thesis .............................................................................................. xxii 
List of figures..................................................................................................... xxvi 
List of tables ..................................................................................................... xxvii 
1 Introduction .................................................................................................... 1 
1.1 Aim ......................................................................................................... 3 
2 Background ..................................................................................................... 5 
2.1 Nutritional recommendations for endurance athletes ............................. 5 
2.2 Relative Energy Deficiency in Sport ...................................................... 8 
2.2.1 Definitions .......................................................................................... 9 
2.2.2 Risk factors ....................................................................................... 13 
2.2.3 Identification ..................................................................................... 14 
2.2.4 Prevalence ......................................................................................... 16 
2.2.5 Health consequences ........................................................................ 20 
2.2.6 Performance consequences ............................................................... 26 
2.2.7 Prevention ......................................................................................... 27 
2.3 Previous interventions .......................................................................... 31 
2.3.1 Prevention of REDs signs and symptoms in female athletes ........... 31 
2.3.2 Promoting dietary behavior change in female athletes .................... 34 
2.4 Theoretical rationale for a REDs prevention program ......................... 40 
3 Scientific approach ....................................................................................... 45 

 
 xxiv    
 
Methods ........................................................................................................ 49 
1 Study design ......................................................................................... 49 
2 Participants ........................................................................................... 51 
3 Measurements ....................................................................................... 55 
3.1 Low energy availability .................................................................... 55 
3.2 Disordered eating .............................................................................. 55 
3.3 Exercise addiction ............................................................................ 56 
3.4 Food intolerances .............................................................................. 57 
3.5 Sports nutrition knowledge............................................................... 57 
3.6 Sports nutrition‐related behavior and dietary intake ........................ 57 
3.7 Physical activity and training ........................................................... 58 
3.8 Participant evaluation ....................................................................... 59 
4 The FUEL intervention ......................................................................... 60 
4.1 Sports nutrition lectures .................................................................... 60 
4.2 Athlete‐centered nutrition counseling .............................................. 61 
5 Ethical considerations ........................................................................... 63 
6 Analyses................................................................................................ 64 
6.1 Quantitative analyses ........................................................................ 64 
6.2 Qualitative analyses .......................................................................... 66 
Main results .................................................................................................. 67 
1 Risk of low energy availability ............................................................. 67 
2 Risk of disordered eating ...................................................................... 67 
3 Risk of exercise addiction .................................................................... 68 
4 Food intolerances .................................................................................. 69 
5 Effect of the FUEL intervention on sports nutrition knowledge .......... 69 
6 Effect of the FUEL intervention on dietary intake ............................... 70 
7 Effect of the FUEL intervention on LEA symptoms ............................ 71 
8 Long-term changes of the FUEL intervention on LEA symptoms ...... 73 
9 Participant evaluation of the FUEL intervention .................................. 74 

xxv

6 Discussion ..................................................................................................... 79

6.1 Discussion of main results .................................................................... 79

6.2 Methodological considerations ............................................................. 90

6.2.1 The influence of COVID-19 ........................................................... 104

7 Conclusion .................................................................................................. 107

8 Practical implications and future directions ............................................... 109

References .......................................................................................................... 113

Paper I-IV

Appendix I-X

xxvi

List of figures

Figure 1. Thesis at a glance…………………………...……………………...……4

Figure 2. Overview of the REDs research development…………….…..………...8

Figure 3. REDs Health Conceptual Model………………………………………10

Figure 4. REDs Performance Conceptual Model………………………………...11

Figure 5. Determinants of athletes’ food choice and eating behavior………….…14

Figure 6. IOC REDs CAT-2 three step model……………………………………29

Figure 7. Scientific approach in the four included papers………………………47

Figure 8. Overview of the FUEL study…………………………………………50

Figure 9. Flowchart of the recruitment and inclusion of subjects………………...53

Figure 10. Overview of the FUEL lectures and consultations……..……………..60

Figure 11. Promoting athlete‐centered communication in the FUEL study……...62

Figure 12. The interaction between the risk of low energy availability, disordered

eating behavior, and exercise addiction…………………………………………68

Figure 13. Self-perceived sports nutrition knowledge…………………………..70

Figure 14. Self-reported eumenorrhea at pre- and posttest…………………......72

Figure 15. Long-term changes in LEAF-Q scores……………………………….73

Figure 16. Participant evaluation of the FUEL study…………………………...74

Figure 17. Key areas to improve the FUEL study……………………………..110

Figure 18. Broadening the FUEL study's perspective for future research...…...111

xxvii

List of tables

Table 1. Overview of thesis……………………………………………......…..xxii

Table 2. Prevalence of directly assessed LEA / high LEAF-Q score in female

endurance athletes……………………………………………………………….18

Table 3. Nutritional interventions in female athletes and exercising women with

menstrual dysfunction…………………………………………………………...33

Table 4. Intervention studies promoting dietary behavior change in female

athletes…………………………………………………………………………..35

Table 5. Overview of study design and participants in all four papers………...…54

Table 6. Dietary characteristics for the FUEL and CON group………………..…71

xxviii

1 Introduction

There is strong scientific evidence for a wide range of positive health effects when

engaging in regular physical activity (Blair et al., 2012; Warburton et al., 2006).

However, to achieve the optimal benefits of an active lifestyle, adequate nutrition

is required. Indeed, competitive athletes need sufficient fuel through food to

support energy demands of the physiological processes important for optimal

performance and to stay healthy and injury free (Thomas et al., 2016).

Individuals with a high training load may be at risk of Relative Energy Deficiency

in Sport (REDs), a syndrome with impaired physiological and/or psychological

functioning caused by problematic (long-term/severe) low energy availability

(LEA) with or without disordered eating (DE) or eating disorders (Mountjoy et al.,

2014, 2018, 2023). Potential negative outcomes of problematic LEA include

menstrual dysfunction, impaired gastrointestinal function, and reduced bone health

with the latter including potential irreversible consequences (Keen & Drinkwater,

1997; Nose-Ogura et al., 2018). Individually and synergistically, these and other

detrimental outcomes can lead to impaired well-being, increased risk of injuries,

and reduced sports performance (Mountjoy et al., 2023). Although REDs

symptoms have been reported both in males (Lundy et al., 2022; Nattiv et al., 2021;

Stenqvist et al., 2021, 2023) and females (Melin et al., 2015; Taim et al., 2023),

elite athletes and recreational active individuals (Logue et al., 2019; Torstveit &

Sundgot-Borgen, 2005b), across various sport disciplines (Drew et al., 2018;

Torstveit & Sundgot-Borgen, 2005a), female athletes participating in endurance

sports appear to be particularly vulnerable, with an estimated prevalence of

symptoms between 10% and 80% (Dambacher et al., 2023; Dervish et al., 2023;

Folscher et al., 2015; Heikura et al., 2017; Henninger et al., 2023; Ihalainen et al.,

2021; Jesus et al., 2021; Karlsson et al., 2023; Melin et al., 2014; Tektunalı Akman

et al., 2024; Wilwand et al., 2024; Witkoś et al., 2023).

The increased participation of women in endurance sports (International Olympic

Committee, 2024; Pauline, 2014), underscores the importance of research aimed

at facilitating optimal health and performance for female endurance athletes.

Unfortunately, females are underrepresented as participants in sports nutrition

(Kuikman et al., 2023; Smith et al., 2022) and sports medicine (Anderson et al.,

2023; Costello et al., 2014) research. Although research on REDs has emerged

(Mountjoy et al., 2014, 2023), there are still several knowledge gaps to be filled.

Most studies are conducted with a cross-sectional design and demonstrates a

persistent problem of LEA and REDs among female endurance athletes (Mountjoy

et al., 2023), suggesting inadequate prevention and treatment of the syndrome.

To effectively target the prevention and treatment of this syndrome, associated risk

factors need to be investigated. While eating disorders and DE are well-known risk

factors for LEA and REDs (Nattiv et al., 2007; Stellingwerff et al., 2023), a

prevalence of 60% of clinically verified functional hypothalamic amenorrhea

(FHA) has been reported in female endurance athletes without eating disorders or

DE (Melin et al., 2016). Therefore, there may be other less investigated risk

factors. There is some evidence suggesting that exercise addiction is associated

with REDs in male endurance athletes (Torstveit et al., 2019), but more research

is needed, including research in female athletes. Food allergies or intolerances may

also contribute to LEA if food groups are eliminated from the diet without proper

replacement or due to suboptimal nutrient absorption (Lis et al., 2019). Whether

food intolerances are associated with REDs needs, however, to be investigated.

Therefore, there is an urgent need to develop and test potentially effective

programs that can assist in the prevention and treatment of REDs (Ackerman et

al., 2020; Mountjoy et al., 2018; Torstveit et al., 2023). Nutrition education

initiatives has been requested (Ackerman et al., 2020; Mountjoy et al., 2018),

which has the potential to reach widely and act as an important component in both

primary, secondary, and tertiary prevention (Baumann & Ylinen, 2017; Torstveit

et al., 2023; Wells et al., 2020). While there is a need for REDs prevention at all

levels, involving both the athlete health and performance team, athlete entourage,

and sport organizations (Mountjoy et al., 2018; Torstveit et al., 2023), targeting

high-risk groups, namely female endurance athletes at risk of REDs (i.e., multiple

symptom exhibition), appears to be a good starting point for investigating such

strategies. Since individualized treatment is recommended (Mountjoy et al.,

2018), incorporating individual sports nutrition counseling that utilizes multiple

behavior change techniques beyond nutrition education (Bentley et al., 2020;

Fiorini et al., 2023; Michie et al., 2013) appears to be an important supplement in

the treatment of REDs. Finally, no previous study has conducted a thorough

evaluation of participants’ experiences of taking part in an intervention aiming to

improve REDs symptoms. Such evaluation may, however, be crucial for

improving the intervention content for future studies or implementation and may

even be an important part of the clinical evidence (Bakland et al., 2020).

1.1 Aim

The overall aim of this project was to develop and evaluate a nutrition intervention

for female endurance athletes at risk of REDs, consisting of weekly lectures in

sports nutrition combined with individual consultations every other week (the

FUEL program). To accommodate this aim, the project was divided into three

overall phases/studies: a cross-sectional study, an intervention study, and a mixed

methods participant evaluation study (Figure 1). To develop relevant REDs

prevention programs, continuously research of potentially (unexplored) risk

factors for REDs in female endurance athletes is needed. Hence, the cross-

sectional study aimed to:

I: Identify the risk of LEA/REDs

and potential risk factors and to

explore associations between them, including comparing DE,

exercise addiction, and food intolerances in female endurance

athletes at risk versus low risk of LEA/REDs (Paper I).

Furthermore, very few REDs prevention programs have been developed, and none

of those published to date have thoroughly been evaluated. Therefore, the

intervention study aimed to:

II: Investigate the effect of the FUEL program on sports nutrition

knowledge and dietary intake among female endurance athletes at

risk of REDs (Paper II).

III: Investigate immediate effects and long-term changes of the FUEL

program on physiological REDs symptoms and symptoms of DE and

exercise addiction among female endurance athletes at risk of REDs

(Paper III).

Finally, the participant evaluation study aimed to:

IV: Investigate the experiences and evaluations of female endurance

athletes at risk of REDs who participating in the FUEL program

using a mixed-methods study design (Paper IV).

The term “risk of LEA” was used in Paper I, since it was based on the Low Energy Availability in Females

Questionnaire (LEAF-Q) and published before the latest IOC consensus statement where the term

“problematic LEA” was introduced. However, since the LEAF-Q assesses symptoms of REDs (as a result

of problematic LEA), the term risk of “LEA/REDs” will be used in this thesis. The term “risk of REDs” is

used for athletes included in the intervention and hence data concerning Paper II-IV.

Figure 1. Thesis at a glance. Illustrating the link between the four included papers. Paper I was

part of the development of the FUEL intervention, constituting the screening phase and aimed at

investigating the risk rate of low energy availability (LEA)/Relative Energy Deficiency in Sport

(REDs) and associated risk factors. Paper II, III, and IV represent the evaluation of the FUEL

program. Paper II aimed to investigate the effects of the FUEL program on sports nutrition

knowledge and dietary intake. Paper III focused on the immediate effects and long-term changes

of the FUEL program on physiological REDs symptoms, disordered eating (DE) symptoms, and

exercise addiction. Finally, Paper IV aimed to evaluate athletes’ experiences after participating in

the FUEL program using a mixed-methods approach. Abbreviations: Food and nUtrition for

Endurance athletes – a Learning program, REDs: Relative Energy Deficiency in Sport.

2 Background

2.1 Nutritional recommendations for endurance athletes

An adequate energy intake is crucial for all athletes to maintain health and

physiological mechanisms important for optimal performance during training and

competition (Mountjoy et al., 2023; Thomas et al., 2016). Total energy expenditure

and thereby energy requirements are highly individual depended on the sport

where endurance athletes represent the high end of the energy expenditure

spectrum (Manore, 2002). For instance, total energy expenditure determined by

doubly labeled water has been reported to be between 3611 and 4830 kcal/day in

female cross-country skiers (Sjödin et al., 1994), suggesting energy requirements

twice as what is recommended for normal active women (Blomhoff et al., 2023).

Key factors for performance in endurance sports are maximal oxygen uptake (sets

the upper limit for aerobe capacity), the second lactate threshold (establishes the

maximum intensity at which prolonged high-intensity exercise can be sustained)

and movement economy (determines the energy cost of movement at a given

power or speed) (Joyner & Coyle, 2008). Although, endurance athletes can

compete in a wide range of events, from few minutes like middle-distance running

to several hours such as Ironman distance triathlons, most competitive endurance

athletes must be capable of maintaining exercise intensities above 75% of their

maximal oxygen uptake, primarily relying on carbohydrate-based energy sources

(Burke et al., 2019; Joyner & Coyle, 2008). Endurance athletes’ training regimes

typically include significant amounts of aerobic or submaximal training conducted

at or below the first lactate threshold in combination with high-intensity workouts,

cross and strength training within a periodized training schedule (Issurin, 2010;

Joyner & Coyle, 2008; Tønnessen et al., 2024).

Periodizing energy and nutrient intake is considered a cornerstone of an endurance

athlete’s diet (Burke et al., 2019; Stellingwerff et al., 2019). This approach includes

adjusting the intake of energy and nutrients to align with different phases of

training and competition (Burke et al., 2019; Stellingwerff et al., 2019). In addition

to adjusting nutritional intake to the annual training plan with monthly adjustments

(macro-level), endurance athletes are recommended to periodize at meso (weeks)

and micro-levels (days/within-day) to meet the nutritional demands of training

(Burke et al., 2019; Stellingwerff et al., 2019). However, studies indicate that

female endurance athletes often struggle with nutrition periodization (Henninger

et al., 2023; Jesus et al., 2022; Viner et al., 2015) and within-day energy deficiency

(Fahrenholtz et al., 2018) which may lead to long-term energy and nutrient

deficiency with consequences for health and performance (Mountjoy et al., 2023).

Carbohydrate is considered the most important source of energy for endurance

athletes and has rightfully received a great deal of attention in sports nutrition

(Thomas et al., 2016; Vitale & Getzin, 2019). Carbohydrate provides a key fuel for

the brain and central nervous system and is a valuable substrate for the working

muscles as it can support exercise over a large range of intensities due to its

utilization by both anaerobic and oxidative pathways (Burke et al., 2019; Rollo et

al., 2020; Thomas et al., 2016). An adequate carbohydrate intake is therefore

paramount for optimal performance and adaptation to training (Burke et al., 2019;

Thomas et al., 2016). Carbohydrate stores in the body are, however, relatively

limited and continuously supply is therefore needed with a recommended daily

intake between 3-12 g/kg body weight/day, depending on periodization of the

training program (Burke et al., 2019; Kerksick et al., 2018; Stellingwerff et al.,

2019; Thomas et al., 2016; Vitale & Getzin, 2019). Studies have, however, reported

that female endurance athletes do not meet these recommendations when

considering current training load (Carr et al., 2019; Melin et al., 2016; Snead et al.,

1992). Recommendations for acute carbohydrate fueling strategies include 1-4 g

carbohydrate/kg body weight 1-4 hours before training or competition and up to

90 g carbohydrate/hour during long-term training sessions or competitions (Burke

et al., 2019; Rollo et al., 2020). In addition, endurance athletes are advised to

normalize glycogen levels by consuming 7-12 g carbohydrate/kg body weight/day

for 24 hours before events like a half marathon (Burke et al., 2019). For marathon

events or similar, a carbohydrate loading strategy of 10-12 g/kg body weight/day

for 36-48 hours is recommended (Burke et al., 2019; Vitale & Getzin, 2019). It

should be noted, however, that these recommendations are largely based on studies

with male athletes (Holtzman & Ackerman, 2021), although there are some

evidence to suggest that females can benefit equally from carbohydrate

supplementation during endurance exercise as men (Wallis et al., 2006).

A special type of carbohydrate is dietary fiber with a recommended intake of 25-

35 g/day (Blomhoff et al., 2023). There are no specific recommendations for

dietary fiber for athletes, except for an endorsement to refrain from fiber in time

periods around training sessions due to the risk of gastrointestinal issues (Burke et

al., 2019). Female endurance athletes have, however, been reported to exceed the

dietary fiber recommendation and a high dietary fiber intake has been associated

with menstrual dysfunction (Barron et al., 2016; Melin et al., 2016).

When energy intake is sufficient, amino acid oxidation during endurance training

is limited, especially in women (Tarnopolsky, 2004). The higher protein

recommendation for endurance athletes of 1.2-1.7 g/kg body weight/day (Tipton

& Wolfe, 2004; Vitale & Getzin, 2019) compared to the 0.8 g/kg body weight/day

for the general population (Blomhoff et al., 2023) is easily achieved through the

higher energy intake with a mixed diet without further need for supplementation

(Tarnopolsky, 2004). Female endurance athletes may even exceed protein

recommendations despite insufficient energy intake (Melin et al., 2016).

Fat is an important part of endurance athletes’ diet, since this macronutrient has a

high energy density and thereby supports optimal energy availability, provides

essential fatty acids and assist in the absorption fat-soluble vitamins (Thomas et

al., 2016; Vitale & Getzin, 2019). There are no absolute recommendations for fat

as for carbohydrate and protein, but athletes are discouraged from fat intakes below

20 E% (Thomas et al., 2016; Vitale & Getzin, 2019), yet it is recommended to limit

fat intake in the hours before training according to risk of gut issues. Low-energy

density, characterized by a low-fat intake, seems to be a dietary concern for female

endurance athletes associated with menstrual dysfunction (Melin et al., 2016).

In general, micronutrient recommendations for female endurance athletes are in

accordance with the recommendations for the general population, except for iron

where athletes are recommended regularly screening and may need to aim for > 18

mg/day (Holtzman & Ackerman, 2021; Thomas et al., 2016). Deficiency of iron,

vitamin D, and calcium are common among female athletes (Holtzman &

Ackerman, 2021). Especially distance runners are at high risk of iron deficiency

and athletes living in Nordic countries are at risk of vitamin D deficiency (Thomas

et al., 2016). To prevent these deficiencies, it is recommended to increase

consumption of diverse foods and potential supplementation (Holtzman &

Ackerman, 2021; Thomas et al., 2016).

Although micronutrients and macronutrient distribution are important nutritional

considerations in female athletes, ensuring sufficient energy intake and thereby

optimal energy availability is the foundation of a healthy diet for female athletes.

As such, energy availability constitutes the basis of the hierarchy of nutritional

considerations and needs for female athletes (Holtzman & Ackerman, 2021).

2.2 Relative Energy Deficiency in Sport

Historical overview

By doing anything athletic, women will lose their femininity. This was the mindset

in the 1960’s, where the first woman ran a marathon in defiance of the laws of the

day (Pauline, 2014). Since then, women's participation in sports, including

endurance sport events, has continued to increase and at the Olympic Games in

Paris 2024 there was for the first time in Olympic history full gender parity

(International Olympic Committee, 2024). Despite a drastic increase in female

sport participation, females are still underrepresented in sports and exercise

medicine research (Anderson et al., 2023; Costello et al., 2014; Cowley et al.,

2021; Holtzman & Ackerman, 2021). The only female-centric component of

exercise physiology and sports medicine to undergo a significant development is

the consequence of insufficient energy intake on different body systems, first

identified as the Female Athlete Triad (Holtzman & Ackerman, 2021). See figure

2 for an illustrated summary of the scientific development.

Figure 2. Overview of the REDs research development. Abbreviations: ACSM: American

College of Sports Medicine, FUEL: Food and nUtrition for Endurance athletes – a Learning

program, IOC: International Olympic Committee, REDs: Relative Energy Deficiency in Sport,

Triad: Female Athlete Triad.

In the 1970s, researchers reported insufficient body fat stores as a plausible cause

for menstrual dysfunction (Frisch & McArthur, 1974). It was also found that

athletes had delayed menarche compared to non-athletes (Malina et al., 1978).

Observations in the 1980s found reduced bone mineral density in amenorrheic

compared to eumenorrheic athletes, despite comparable body fat percentage

(Drinkwater et al., 1984) in addition to associations between menstrual

dysfunction, stress fractures, and DE (Barrow & Saha, 1988). In 1992, the

American College of Sports Medicine (ACSM) established a panel of experts to

address the growing concern in sports medicine, resulting in the characterization

of the Female Athlete Triad

; an interrelationship between DE, amenorrhea, and

osteoporosis (Yeager et al., 1993). In 1997, the first ACSM position stand

concerning the Female Athlete Triad was published (Otis et al., 1997) followed by

an updated version in 2007 (Nattiv et al., 2007) and again in 2014 (De Souza,

Nattiv, et al., 2014). The Triad was now described as an interrelationship between

energy availability, menstrual function, and bone health, resolved around a

continuum ranging from optimal energy availability, eumenorrhea, and bone health

to the severest consequences being LEA (with or without an eating disorder), FHA,

and osteoporosis (Nattiv et al., 2007). To acknowledge the growing body of

evidence regarding the consequences of LEA on multiple body systems beyond

bone and reproductive function and that these consequences do not just affect

female but also male athletes, the IOC published a consensus statement and

introduced the new terminology Relative Energy Deficiency in Sport, abbreviated

RED-S at the time (Mountjoy et al., 2014). The IOC consensus statement

stimulated activity in the research field, which resulted in updates in 2018

(Mountjoy et al., 2018) and latest in 2023 (Mountjoy et al., 2023). The 2023 IOC

consensus statement presented new terminologies, updated health and

performance conceptual models and an updated clinical assessment tool

(Mountjoy et al., 2023).

2.2.1 Definitions

In 2014 the syndrome of REDs was defined by the IOC as “…impaired

physiological function including, but not limited to, metabolic rate, menstrual

function, bone health, immunity, protein synthesis, cardiovascular health caused

by relative energy deficiency” (Mountjoy et al., 2014, page 1). With the latest

consensus statement in 2023, the syndrome got a broader definition, and the term

problematic LEA was introduced in order to distinguish between prolonged and/or

long-term LEA and short-term (adaptable) LEA (Mountjoy et al., 2023).

As specified in box 1, page 1074 (Mountjoy et al., 2023):

In this dissertation the terminology of REDs will be used.

A syndrome of impaired physiological and/or psychological functioning

experienced by female and male athletes that is caused by exposure to

problematic (prolonged and/or severe) LEA. The detrimental outcomes

include, but are not limited to, decreases in energy metabolism,

reproductive function, musculoskeletal health, immunity, glycogen synthesis

and cardiovascular and haematological health, which can all individually

and synergistically lead to impaired well-being, increased injury risk and

decreased sports performance.

The health and performance consequences of problematic LEA are illustrated in

Figure 3 and 4.

Figure 3. REDs Health Conceptual Model. Reproduced from Mountjoy et al. (2023) with

permission. Alteration from white to red arrows indicates that low energy availability exists on a

continuum: from mild and transient (adaptable) to problematic with a variety of adverse health

consequences. *Mental health issues can precede or be a result of REDs.

Figure 4. REDs Performance Conceptual Model. Reproduced from Mountjoy et al. (2023) with

permission. Alteration from white to red arrows indicates that low energy availability exists on a

continuum: from mild and transient (adaptable) to problematic with a variety of adverse

performance consequences.

Like other multifactorial diseases, REDs has multiple and diverse potential risk

factors, has numerous signs and symptoms, is influences by the individual’s

environment, gender and age, may present more in certain ethnic groups, and could

be genetically influenced (Stellingwerff et al., 2023).

While both “the critical fat theory” and the hypothesis regarding stress of exercise

as an isolated cause of menstrual dysfunction has been rejected (Hilton & Loucks,

2000), problematic LEA (i.e. prolonged and/or severe inadequate energy intake

relative to exercise energy expenditure) is now acknowledged as the underlying

cause of REDs (Mountjoy et al., 2023).

Energy availability is the amount of ingested energy remaining for all other

metabolic processes after subtracting the energy cost of exercise expenditure and

can be expressed by the mathematical formula: energy availability (kcal/kg fat free

mass [FFM]) = energy intake (kcal) – exercise energy expenditure (kcal) (Loucks,

2004). In opposition to energy balance, this calculation method is considered more

appropriate to assess energy status in athletes (Loucks, 2014; Nattiv et al., 2007),

since metabolic adaptations caused by problematic LEA, will result in a more

neutral energy balance (Loucks et al., 2011). Based on laboratory studies

investigating sedentary women, energy availability ≥ 45 kcal/kg FFM/day has been

considered as a healthy level, while 30-44 kcal/kg FFM/day, and < 30 kcal/kg

FFM/day have been considered to be reduced and low, respectively (Ihle &

Loucks, 2004; Loucks et al., 1998; Loucks & Thuma, 2003). While short-term

experimental laboratory studies have defined thresholds for energy availability and

demonstrated dose-response relationships between energy availability and

hormones involved in reproduction and bone metabolism (Ihle & Loucks, 2004;

Loucks & Thuma, 2003), this metric may for several reasons not be transferable to

athletes under free-living conditions (Burke et al., 2018; Heikura et al., 2022;

Salamunes et al., 2024). Therefore, the use of self-reported symptoms (including

menstrual status) in combination with signs identified by a clinician (e.g. blood

markers) are recommended (Heikura et al., 2022; Mountjoy et al., 2023;

Stellingwerff et al., 2023).

Difficulties in accurately assessing and controlling for energy availability in

prospective research means that the scientific understanding of the time-course of

LEA leading to physiological and psychological signs/symptoms still emerges

(Mountjoy et al., 2023). Currently, short-term LEA is defined as a few days to

weeks, medium-term as weeks to months and long-term as months to years

(Heikura et al., 2022; Mountjoy et al., 2023). Alongside this, a distinguish is made

between adaptable LEA and problematic LEA. Adaptable LEA is short-term with

minimal (or no) impact on long-term health and performance, whereas problematic

LEA is long-term and/or has a more severe magnitude and/or frequency with

greater and potentially persistent disturbances of various body systems, often

presenting with signs and/or symptoms (Mountjoy et al., 2023). In this regard,

REDs symptoms are understood as any REDs indicator that an athlete directly

reports or experiences, while REDs signs are any indicator that a clinician

identifies (Mountjoy et al., 2023).

2.2.2 Risk factors

Various reasons for inadequate energy intake in athletes exist and range from

unintentional undereating to severe eating disorders (Jagim et al., 2022;

Wasserfurth et al., 2020; Wells et al., 2020). Although DE and eating disorders are

common in sports, especially among female endurance athletes (Barrack et al.,

2021; Borowiec et al., 2023; Melin et al., 2015; Sundgot-Borgen & Torstveit,

2004), unintentional undereating may be a more common cause than first thought

(see Historical overview, page 9). For instance, a study found that 60% of female

endurance athletes showed signs of REDs, even after excluding those with DE

(Melin et al., 2015). Inadvertent risk factors may include suppression of appetite

after training (Howe et al., 2016; Larson-Meyer et al., 2012) and/or lack of

knowledge of optimal sports nutrition and the consequences of inadequate fueling

(Lodge et al., 2021; Logue et al., 2020). A variety of special diets has gained

popularity, also among athletes, hoping to achieve health and performance benefits

(Lis et al., 2019). However, these misconceptions of optimal sports nutrition may

do more harm than good (Jeukendrup & Killer, 2011; Lis et al., 2019; Rosenbloom,

2017). Limited finances, time constraints, neglect of REDs symptoms, and social

media have also been suggested to play a role in the development and maintenance

of problematic LEA (Adam et al., 2022; Beals & Meyer, 2007; Jagim et al., 2022;

Logue et al., 2021; S. M. Miller et al., 2012; Verhoef et al., 2021; Wasserfurth et

al., 2020). However, even more factors may act as determinants for athletes’ food

choice and eating behavior (Bentley et al., 2021; Birkenhead & Slater, 2015;

Heaney et al., 2008; Janiczak et al., 2022; Trakman et al., 2016) and could

potentially contribute to an athlete’s overall risk of LEA and REDs (Figure 5).

Dietary characteristics associated with LEA and REDs in female athletes have

been reported to be high intake of fiber and low intake of fat and carbohydrate-rich

foods (Barron et al., 2016; Heikura et al., 2017; Laughlin & Yen, 1996; Melin et

al., 2016). While the intake of protein and dietary fiber exceed general and sports

nutrition recommendations, potentially increasing the feeling of satiety and reduce

the motivation to eat (Carr et al., 2019; Melin et al., 2016), the carbohydrate intake

is often reported to be below sports nutrition recommendations (Carr et al., 2019;

Matt et al., 2021; Melin et al., 2016). Indeed, recent evidence suggest that low

carbohydrate availability act as an additive and independent cause for the

development of REDs (Lodge et al., 2023; Mountjoy et al., 2023), although more

research in female athletes is needed (Lodge et al., 2023).

Figure 5. Determinants of athletes’ food choice and eating behavior. Food choice and eating

behavior are complex and influenced by a large range of determinants, including personal factors

(e.g. taste), personal resources (e.g. knowledge and skills) and environmental factors (e.g. training

culture and time a year/day). Developed based on Bentley et al. (2021); Birkenhead & Slater

(2015); Burke et al. (2018); Heaney et al. (2008); Jagim et al. (2022); Jeukendrup & Killer (2011);

Logue et al. 2020; 2021; Mountjoy et al. (2023); Pelly et al. (2022); Wasserfurth et al. (2020).

2.2.3 Identification

Early identification of LEA seems to be paramount for athletes’ health and

performance (De Souza, Nattiv, et al., 2014; Mountjoy et al., 2014, 2018, 2023).

Athletes’ entourage (e.g. coaches, parents, managers) and health and performance

team are in the position to identify athletes with prominent and/or reported

indicators of REDs (e.g. restrictive eating, frequent illness and injuries, lack of

training progress, and amenorrhea). The identification may, however, be

complicated by the negligence or ignorance of athletes’, coaches’ (Beals & Meyer,

2007; Brown et al., 2014; Feldmann et al., 2011; Lodge et al., 2021; S. M. Miller

et al., 2012; Mukherjee et al., 2016; Verhoef al., 2021), and even medical staff

(Curry et al., 2015). The complexity of REDs requires the exclusion of other

potential etiologies (e.g. food intolerance in the case of gastrointestinal problems

or poly cystic ovarian syndrome in the case of oligomenorrhea or amenorrhea) and

to date, no singular, validated diagnostic test for REDs exist (Stellingwerff et al.,

2023).

Although assessment of energy availability has been used to guide and monitor

athletic practices and support a diagnosis of REDs, several barriers prohibit the

direct measurement of energy availability from being a practical and reliable

option (Burke et al., 2018; Heikura et al., 2017; Mountjoy et al., 2023; Salamunes

et al., 2024). These barriers include the numerous errors associated with accurately

measuring the components of energy availability; energy intake (Burke, 2001),

exercise energy expenditure (Freedson & Miller, 2000), and fat free mass (FFM)

(Nana et al., 2016). In addition, there is lack of a universal protocol for assessing

these components (Burke et al., 2018; Mountjoy et al., 2023) and transferring the

laboratory-based thresholds into real-life settings is questionable due to a likely

individual difference in the susceptibility to energy restriction (Burke et al., 2023;

Guebels et al., 2014; Manore, 2002; Melin et al., 2015). Finally, there is a high

level of burden (time and effort) for the athlete and test-personnel to assess energy

availability (Mountjoy et al., 2023). In addition, factors such as within-day energy

deficiency (Fahrenholtz et al., 2018; Torstveit et al., 2018) and low carbohydrate

availability are not considered when assessing energy availability. Further, the

energy availability cut-offs defined by short-term laboratory studies (Loucks et al.,

1998; Loucks & Heath, 1994; Loucks & Thuma, 2003) are not supported by

randomized controlled long-term studies (Koltun et al., 2020; Lieberman et al.,

2018; Williams et al., 2015). A recently published review concluded that although

energy availability calculation may be used in clinical practice as one of multiple

assessments to investigate the risk of REDs related symptoms, it is not

recommended to be used as a single risk assessment tool (Salamunes et al., 2024).

Questionnaires assessing REDs related symptoms (step 1 in the IOC REDs CAT2)

can therefore be a more reliable method for the screening of problematic LEA

(Heikura et al., 2024; Mountjoy et al., 2023; Stellingwerff et al., 2023; Torstveit et

al., 2023) and are recommended as a part of the secondary prevention of REDs

(later presented in section 2.2.7). The Low Energy Availability in Females

Questionnaire (LEAF-Q) assesses self-reported symptoms of injury frequency, the

past year, gastrointestinal function, and current and past reproductive function via

25 items. The LEAF-Q has been validated in female endurance athletes 18-39

years of age with a total score ≥ 8 producing a sensitivity of 78% and specificity

of 90% for correctly classifying current energy availability and/or reproductive

function and/or bone health (Melin et al., 2014). Since DE is a significant risk

factor for problematic LEA (Mountjoy et al., 2023), questionnaires used to assess

cognitive and behavioral eating disorder symptoms are also widely used as

surrogate markers (Sim & Burns, 2021; Torstveit et al., 2023). The Eating Disorder

Examination Questionnaire (EDE-Q) (Fairburn & Beglin, 1994) is validated

against clinical interview and more recently in an athletic population (Lichtenstein,

Haastrup, et al., 2021). Other validated questionnaires used to assess DE symptoms

in athletes and purposed to be used in REDs screening (Torstveit et al., 2023)

include the Brief Eating Disorder in Athletes Questionnaire, validated in

adolescent female athletes (Martinsen et al., 2014), and the Female Athlete

Screening Tool, validated in female college students (McNulty et al., 2001).

Questionnaires are cheap and convenient and can therefore be used in larger scales

but have lower sensitivity and specificity (Gibson, 2005; Stellingwerff et al.,

2023). Therefore, to correctly identifying athletes at risk of REDs, screening with

questionnaires is recommended to be accompanied with a severity/risk

stratification which may include gynecological examination, Dual-energy X-ray

absorptiometry (DXA) scan for the assessment of bone mineral density, and

assessment of blood hormone biomarkers such as leptin, luteinizing hormone

(LH), and triiodothyronine (Stellingwerff et al., 2023).

2.2.4 Prevalence

REDs signs and symptoms and LEA have been detected in numerous sports with

a wide range in the reported estimated prevalence (Logue et al., 2020; Mountjoy

et al., 2023). However, athletes participating in sports that require a high volume

of training with consequently high energy expenditure, i.e., endurance sports

(Fudge et al., 2006; Hill & Davies, 2002; Sjödin et al., 1994), are likely at a greater

risk, as it may be challenging to consume an adequate amount of energy to

compensate the high energy expenditure from training (Carr et al., 2019; Jesus et

al., 2022; Jeukendrup, 2011; Melin et al., 2016). Although REDs can affect both

males and females, there is evidence to suggest that females are more susceptible

to the syndrome compared to males (Bronson, 1985; Burke et al., 2023; Koehler

et al., 2016; Papageorgiou et al., 2017). Therefore, female endurance athletes seem

to constitute a particularly high risk with an estimated prevalence between 8-73%

when defined as energy availability <30 kcal/kg FFM/day (Beermann et al., 2020;

Day et al., 2016; Goodwin et al., 2014; Heikura et al., 2017; Jesus et al., 2022;

Matt et al., 2022; Mccormack et al., 2019; Melin et al., 2015; Muia et al., 2016;

Tektunalı Akman et al., 2024; Viner et al., 2015) and 10-80% when defined as a

LEAF-Q score ≥ 8 (Dambacher et al., 2023; Dervish et al., 2023; Folscher et al.,

2015; Heikura et al., 2017; Henninger et al., 2023; Ihalainen et al., 2021; Jesus et

al., 2021; Karlsson et al., 2023; Melin et al., 2014; Miles et al., 2024; Tektunalı

Akman et al., 2024; Wilwand et al., 2024; Witkoś et al., 2023).

Reasons for the wide range in the reported LEA/REDs prevalence includes lack of

standardization of research methodologies (Mountjoy et al., 2023). After the

development and validation of the LEAF-Q (Melin et al., 2014), this questionnaire

has been identified as the most common questionnaire as measure for LEA and

risk of REDs (Sim & Burns, 2021) and has also recently been recommended by

the IOC as a part of the REDs screening process (Mountjoy et al., 2023; Torstveit

et al., 2023). Since direct measurement of energy availability only provides a

snapshot of current energy status, typically ≤ one week (Heikura et al., 2022) and

the LEAF-Q assesses symptoms related to problematic LEA (Melin et al., 2014),

there are methodological challenges when comparing studies. In addition, studies

directly assessing energy availability have used many different methods to

measure energy intake, exercise energy expenditure, and FFM (Table 2).

According to a systematic review, male and female endurance athletes may have

higher energy expenditure and FFM during the competition phase compared to the

preparation phase, while energy intake is unchanged among females (Heydenreich

et al., 2017). This suggests that energy availability may fluctuate across the athletic

season among female endurance athletes with lower energy availability during the

competition phase. Few studies (Jesus et al., 2022; Viner et al., 2015) with small

female samples have investigated the prevalence of LEA in endurance athletes

across the athletic season with contradictory results. Viner et al. (2015) reported a

higher prevalence of LEA (< 30 kcal/kg FFM/day) among female and male cyclist

in the competition phase (90%) compared to pre- (70%) and off-season (80%),

while Jesus et al. (2022) reported a higher prevalence of LEA (36%) among male

and female triathletes in the pre-season compared to the competition phase (0%).

Table 2. Prevalence of directly assessed LEA / high LEAF-Q score in female endurance athletes.

Arranged according to year of publication.

Reference

n and sport

Method

Prevalence

Goodwin et al.

(2014)

n = 25 middle and

long-distance runners

3-day food and training diaries

EI: weighed food record

EEE: Actigraph GT3X plus

(GT3X+) tri-axial

accelerometer

FFM: DXA

LEA: 56%

Melin et al. (2014)

n = 45 long-distance

runners and

triathletes

LEAF-Q

LEAF-Q score ≥ 8:

62%

Day et al. (2016)

n = 25 runners

(distance runners,

sprinters, hurdlers,

and jumpers)

3-day food and training diaries

EI: 24-hour recall

EEE: Actigraph GTX3 triaxial

accelerometer

FFM: 3-site skinfold

LEA: 52%

Melin et al. (2015)

n = 40 long-distance

runners and

triathletes

7-day food and training diaries

EI: weighed food record

EEE: heart rate in relation to

individual regression line

FFM: DXA

LEA: 20%

Viner et al. (2015)

n = 4 cyclists

3 days food and training

record/month through one

cycling season

EI: weighed food record

EEE: MET values

FFM: DXA

LEA*:

Pre-season: 70%

Competition: 90%

Off-season: 80%

Muia et al. (2016)

n = 61 middle and

long-distance runners

5-day food and training diaries

EI: diet record developed by

researchers

EEE: MET values

FFM: skinfold

LEA: 18%

Heikura et al.

(2017)

n = 35 middle- and

long-distance runners

7-day food and training diaries +

LEAF-Q

EI: weighed and household

measure dietary record

EEE: MET values

FFM: DXA

LEA: 31%

LEAF-Q score ≥ 8:

Carr et al. (2019)

n = 13 cross-

country skiers

LEAF-Q

LEAF-Q score ≥ 8:

31%

McCormack et al.

(2019)

n = 33 cross-

country runners

FFQ + 3 months training diaries

EI: Block FFQ

EEE: MET

FFM: DXA

LEA: 29%

Beerman et al.

(2020)

n = 20 distance

runners

FFQ + 3 months training diaries

EI: Block FFQ

EEE: MET

FFM: DXA

LEA: 41%

Ihalainen et al.

(2021)

n = 13 distance

runners

7-day food and training diaries

and LEAF-Q

EI: weighed and household

measure dietary record

LEA: 8%

LEAF-Q score ≥ 8:

EEE: MET values

FFM: DXA

Jesus et al. (2021)

n = 83 cross-country

runners

LEAF-Q

LEAF-Q score ≥ 8:

80%

Jesus et al. (2022)

n = 3 triathletes

7 day-period

EI: assuming a neutral energy

balance (i.e., EI-TEE = 0 kcal),

the measurement of TEE with

doubly labeled water was used

as the value of EI.

EEE: MET

FFM: DXA

LEA*:

Pre-season: 36%

Competition: 0%**

Matt et al. (2022)

n = 60 runners

7-day period with different

methods for the two cohorts:

EI: written / online Block FFQ

EEE: heart rate / average weekly

running mileage + equation

FFM: DXA / bioelectrical

impedance analysis

LEA: 60%

Dambacher et al.

(2023)

n = 287 runners

LEAF-Q

LEAF-Q score ≥ 8:

55%

Dervish et al.

(2023)

n = 524 runners

LEAF-Q

LEAF-Q score ≥ 8:

47%

Henninger et al.

(2023)

n = 1445 runners

LEAF-Q

LEAF-Q score ≥ 8:

50%

Karlsson et al. 2023

n = 85 runners

LEAF-Q

LEAF-Q score ≥ 8:

19%

Witkoś et al. (2023)

n = triathletes

LEAF-Q

LEAF-Q score ≥ 8:

10%

Tektunalı Akman et

al. (2024)

Control: n = 38

Intervention: n = 45

football, basketball,

and volleyball

players but defined

as endurance athletes

by the authors

3-day food and training diaries

and LEAF-Q

EI: household measure dietary

record

EEE: MET values

FFM: bioelectrical impedance

analysis

Pretest

Control: LEA:

53%, LEAF-Q:

63%

Intervention: LEA:

73%, LEAF-Q:

51%

Posttest

Control: LEA:

53%, LEAF-Q:

66%

Intervention: LEA:

47%, LEAF-Q:

46%

Wilwand et al.

(2024)

n = 485 runners

LEAF-Q

LEAF-Q score ≥ 8:

60%

Miles et al. (2024)

n = 1923 runners

LEAF-Q

LEAF-Q score ≥ 8:

53%

*NB! Reported collectively for male and female athletes. **Most athletes were in the suboptimal

category (energy availability = 30-44 kcal/kg FFM). Abbreviations: DXA: dual-energy X-ray

Absorptiometry, FFQ: Food Frequency Questionnaire, LEA: low energy availability defined as

energy availability < 30 kcal/kg fat-free mass / day, LEAF-Q: Low Energy Availability in Females

Questionnaire, MET: metabolic equivalent.

2.2.5 Health consequences

This section will present potential health consequences caused by problematic

LEA according to the REDs health conceptual model (Figure 3) with emphasis on

female endurance athletes and outcomes relevant for the present thesis.

Impaired reproductive function

A regular menstrual cycle is used as an important health indicator in female athletes

(Ihalainen et al., 2024; Mountjoy et al., 2023). Eumenorrhea is defined as a

menstrual cycle length between 21-35 days resulting in ≥ 9 consecutive cycles per

year and is due to predictable cyclic changes in sex hormones produced by the

pituitary and ovaries (Elliott-Sale et al., 2021). For clinical verification of

eumenorrhea, evidence of LH surge and correct hormonal profile and no hormonal

contraceptive use for three months is recommended (Elliott-Sale et al., 2021).

Pulses of the peptide hormone gonadotropin-releasing hormone (GnRH),

occurring at hourly intervals during the follicular phase, cause the release of LH

and follicle-stimulating hormone (FSH) from the pituitary glands. These glyco-

proteins stimulate the ovaries to produce the sex steroids progesterone and

estrogen, that in turn regulate the secretion of LH and FSH, negative feedback. The

adipokine leptin plays an integral role in the normal physiology of the reproductive

system with complex interactions at all levels of the hypothalamic-pituitary-

ovarian (HPO) axis (Ihalainen et al., 2024; Pérez-Pérez et al., 2015). Impaired

function of the HPO-axis will cause disruption in this fine-tuned process and result

in menstrual dysfunction (Ihalainen et al., 2024). Since glucose facilitates LH

pulsatility, adequate carbohydrate intake is important to maintain reproductive

health, where laboratory experiments have demonstrated that five days with LEA,

accompanied by substantial reduction in carbohydrate ingestion, reduces the

normal LH pulsatility in eumenorrheic women (Loucks & Thuma, 2003).

Menstrual dysfunction is the strongest, most explored, and well-known

consequence of problematic LEA (Stellingwerff et al., 2023). Nevertheless,

menstrual dysfunction remains to be perceived as a natural consequence of an

intense training program (Adam et al., 2022; Beals & Meyer, 2007; McGawley et

al., 2023; Verhoef et al., 2021). This may be further complicated by a high

prevalence of hormonal contraceptive use among female endurance athletes

(Engseth et al., 2022), thereby masking a potential menstrual disorder (Mountjoy

et al., 2018) and a high prevalence of subclinical menstrual disorders (De Souza et

al., 1998). As such, menstrual dysfunction related to LEA are categorized into

clinical and subclinical disorders (Allaway et al., 2016; Elliott-Sale et al., 2021;

Ihalainen et al., 2024; McGawley et al., 2023; Mountjoy et al., 2014):

• Subclinical

o Luteal phase defects: Short luteal phase length (<10 days) and/or

inadequate phase or a mid-luteal phase blood progesterone

concentration < 16 nmol/L. Asymptomatic and only evident by

measuring sex steroid hormones over an entire menstrual cycle.

o Anovulation: Absence of ovulation due to impaired follicular

development (insufficient increase in LH), monthly bleedings occur.

• Clinical

o Oligomenorrhea: Menstrual cycles between 36–90 days, irregular and

inconsistent, typically nine or fewer menstrual cycles per year.

o Functional hypothalamic amenorrhea (FHA): Improper functioning of

the HPO-axis, not caused by any disease. Can be primary (absence of

menarche at the age of 15) or secondary (absence of menstruation for

minimum three consecutive months).

Hence, menstrual dysfunctions related to LEA occurs on a continuum from

subclinical to clinical with FHA considered as the most severe outcome (Mountjoy

et al., 2023). In brief, the current working model for how LEA affects female

reproductive function is as follows; LEA reduces leptin and increases ghrelin

levels, which increases the expression of neuropeptide Y and agouti-related

protein. This will directly and indirectly through corticotropin-releasing hormone

inhibit GnRH pulsatility. The suppression of GnRH reduces LH pulsatility and

compromises FSH action on the ovaries, resulting in diminished production of

estrogen and progesterone, and disturbs the normal feedback mechanism (Gordon

et al., 2017; Ihalainen et al., 2024; Warren, 2011). Consequently, blood hormones

such as LH and leptin levels are used as markers for problematic LEA (Mountjoy

et al., 2023) and have been used longitudinal in interventions aiming at improved

energy availability in female athletes (Cialdella-Kam et al., 2014; Dueck et al.,

1996; Lagowska, Kapczuk, & Jeszka, 2014; Mallinson et al., 2013).

The estimated prevalence of menstrual dysfunction in athletes ranges from 0% -

64%, depending on the assessment method, diagnostic criteria, and type of sport

(Gibbs et al., 2013; Gimunová et al., 2022), compared to 2-5% in non-athletes

(Torstveit & Sundgot-Borgen, 2005b). In female endurance athletes, a prevalence

of 60% has been reported when using clinical verified gynecological and hormone

level assessments (Melin et al., 2015). Because menstrual dysfunction is an

adaptive result of weeks to months of LEA, most studies using amenorrhea as a

surrogate of LEA have been cross-sectional (Stellingwerff et al., 2023). The

hormonal changes associated with FHA, however, have been reported in controlled

studies after short-term LEA when investigating sedentary eumenorrheic women

(Loucks, 2003; Loucks et al., 1998; Loucks & Thuma, 2003). While the LEA cut-

offs (presented in paragraph 2.2.1) used in these controlled studies frequently have

been extrapolated into real-life settings, longitudinal investigation does not support

the existence of a specific threshold of energy availability below which menstrual

dysfunctions are induced (Lieberman et al., 2018). This may be explained by

several factors that moderate the effects of LEA as stressors on reproduction,

including gynecological age (Loucks, 2006) and psychogenic stress (Ihalainen et

al., 2024; Kuikman, Mountjoy, Stellingwerff, et al., 2021).

As previously described, low body fat stores per se was initially hypothesized as

the cause for menstrual dysfunction. However, menstrual dysfunction can occur at

a wide range of body weight/body fat percentages (Bronson & Manning, 1991)

and as reviewed by Redman & Loucks (2005) most studies have found comparable

body weight and fat percentages comparing amenorrheic and eumenorrheic

athletes (Redman & Loucks, 2005). Also, among patients with anorexia nervosa,

FHA has been found not to correlate strictly with body weight, emphasizing a

multifactorial origin including an individual predisposition (Cacciatore et al.,

2023). The evolutionary rationale is to preserve body tissue during problematic

LEA by reducing energy metabolism to prioritize energy important for immediate

survival (Wade & Jones, 2004). Since the female menstrual cycle is an energy

demanding physiological process that causes resting metabolic rate to fluctuate up

to 10% during a cycle (Henry et al., 2003), amenorrhea can be considered as a

survival mechanism (Wade & Jones, 2004). A lower resting metabolic rate

(Lebenstedt et al., 1999; Melin et al., 2015) as well as increased work efficiency

(Melin et al., 2015) have been reported among female endurance athletes with FHA

compared to eumenorrheic athletes and impaired energy metabolism is a part of

the REDs Health Conceptual Model (Figure 3). A resting metabolic rate 10% lower

than predicted (low resting metabolic rate ratio: < 0.90), low or subclinical low T3

as well as increased cortisol are examples of impairments from problematic LEA

where low or subclinical low T3 is considered a primary indicator for REDs

(Mountjoy et al., 2023).

The optimal assessment of reproductive function in athletes requires pertinent

evaluation of sex hormones and ultrasound evaluation by a skilled gynecologist to

exclude any anatomic disorder (e.g. polycystic ovarian syndrome [PCOS]) and to

establish whether the origin of the menstrual dysfunction is due to a suppression

of the HPO-axis (Gordon et al., 2017). Although the etiology of PCOS is not

related to LEA, this diagnosis has also been reported in female athletes (Hagmar

et al., 2009). Remarkably, features of PCOS have been found to occur in women

with FHA and may be reversable after diet and physical activity modification

(Carmina et al., 2018). Nevertheless, the existents of menstrual dysfunctions not

related to LEA are important to consider when relying on self-reported data.

As an alternative to endocrine and gynecological assessments, the validated

screening tool LEAF-Q has been used to assess menstrual dysfunction in female

endurance athletes, where a menstrual score ≥ 4 is considered providing the highest

sensitivity and specificity for identifying menstrual dysfunction (Melin et al.,

2014). Dambacher et al. (2023) found that 57% of collegiate runners had a LEAF-

Q menstrual score ≥ 4. Studies have utilized the LEAF-Q to report rates of

amenorrhea, based on athletes experiencing more than three months without

menstruation or alterations in their menstruation when increasing training load.

For instance, Heikura et al. (2017) reported a prevalence of amenorrhea of 37%,

while Witkoś et al. (2023) reported 27% of triathletes experiencing less bleeding

than normal and Jesus et al. (2021) reported 65% of runners experiencing cessation

of menstruation when increased intensity, frequency, or duration of training.

Few studies have investigated whether menstrual dysfunction in athletes increases

the risk of future infertility- and pregnancy-related complications (Fujita et al.,

2022; Nose-Ogura et al., 2024; Sigurdardottir et al., 2019). By using a self-report

questionnaire, Fujita et al. (2022) investigated former long-distance runners and

found that amenorrhea or being underweight during the athletic career was not

associated with later problems related to conception and childbirth. Similarly, a

retrospective case–control study comparing birth outcomes of primiparous female

elite athletes engaging in high-impact and low-impact sports compared with non-

athletic controls, reported that elite sport participation in itself was not related to

adverse delivery outcomes (Sigurdardottir et al., 2019). A recent study, however,

found that long-term menstrual dysfunction that persist from active sports careers

to post-retirement may negatively affect fertility (Nose-Ogura et al., 2024).

Impaired bone health

Although the LEA related infertility may be transient (Fujita et al., 2022),

problematic LEA can have serious negative impact on bone health, which may be

irreversible (Barrack et al., 2021; Keen & Drinkwater, 1997; Nose-Ogura et al.,

2018). The effect of energy availability on bone mineral density is hypothesized to

be mediated by endocrine factors with an estrogen-depended pathway increasing

bone resorption and an estrogen-independent pathway decreasing bone formation

(Kameda et al., 1997; Warren, 2011). This is reflected by a high prevalence (67%)

of amenorrheic female endurance athletes with impaired bone health (Melin et al.,

2015) as well as lower serum concentrations of metabolic hormones promoting

bone formation (IGF-1 and T3) in amenorrheic versus eumenorrheic distance

runners (Zanker & Swaine, 1998). While even short-term LEA has been reported

to negatively affect bone turnover markers in recreationally active females (Ihle &

Loucks, 2004; Papageorgiou et al., 2017), low bone mineral density is considered

a long-term and serious consequence of problematic LEA (Mountjoy et al., 2023).

Importantly, other factors than energy availability act as determinants for bone

mineral density, including genetics and history of mechanical loading (Burke et

al., 2023; Nattiv et al., 2007), yet history of menstrual function appears to be an

important contributing factor for bone health in female athletes (Barrack et al.,

2021; Keen & Drinkwater, 1997; Nose-Ogura et al., 2018).

Despite the positive effect of weight bearing exercise on bone health, the estimated

prevalence of low bone mineral density (defined as Z-scores <-1

) in female

endurance athletes ranges from 34-45% (Barrack et al., 2010; Melin et al., 2015;

Pollock et al., 2010; Rauh et al., 2020; Viner et al., 2015). Among female

endurance athletes classified with an elevated LEAF-Q score, however, a

prevalence of 46% has been reported (Melin et al., 2014). A LEAF-Q injury score

≥ 2 is considered problematic in relation to the risk of LEA, providing the highest

This cut-off is debated and a Z-score < 0 has been recommended as an alternative cut-off to enable early

intervention, at least in athletes from high-impact sports (Jonvik et al., 2022).

sensitivity and specificity for identifying risk of low bone mineral density (Melin

et al., 2014). Low bone mineral density constitutes an increased risk for bone stress

injuries, with a reported incidence rate of 21% in track and field athletes

prospectively assessed over 12 months (Bennell et al., 1996). Investigating young

female recreational runners, 51% had experienced at least one stress fracture

during their lifetime, while 33% had experienced at least two stress fractures with

82% being classified with an elevated LEAF-Q score (Wilwand et al., 2024). Stress

fractures are significant injuries that can lead to prolonged absences from sport

participation or even early retirement, highlighting the critical importance of

prevention (Beck & Drysdale, 2021; Cooper et al., 2021; Hutson et al., 2021).

Impaired gastrointestinal function

Impaired gastrointestinal function is another potential consequence of problematic

LEA (Mountjoy et al., 2014, 2018, 2023). LEA may result in mucosal atrophy

characterized by diminished intestinal function and morphological changes

including decreased villous height, crypt depth, surface area, and epithelial cell

numbers (Norris et al., 2016; Shaw et al., 2012). This can be manifested as

abdominal pain, cramps, bloating, and alteration in bowel movements (Mountjoy

et al., 2023), problems commonly reported by endurance athletes (de Oliveira et

al., 2014; Melin et al., 2014). Gastrointestinal health has been evaluated with

questions from the LEAF-Q (Ackerman, Holtzman, et al., 2019; Jesus et al., 2021)

with a ≥ 2 for the item score providing the highest sensitivity and specificity for

identifying risk of current LEA (Melin et al., 2014). Jesus et al. (2021) reported

75% of female runners having a LEAF-Q gastrointestinal score ≥ 2, suggesting a

concerning problem with gastrointestinal well-being in this group.

Other health consequences

In young female endurance athletes, amenorrhea has been associated with

unfavorable lipid profiles and endothelial dysfunction (Rickenlund et al., 2005)

and elevated low-density lipoprotein is considered a secondary REDs indicator

(Mountjoy et al., 2023). In addition, several other health consequences caused by

problematic LEA have been purposed, including reduced immunity, sleep

disturbances, and impaired growth and development (Mountjoy et al., 2023).

2.2.6 Performance consequences

Although less investigated compared to the health consequences, several potential

performance consequences caused by problematic LEA have been identified

(Figure 4). Yet, the performance and health consequences are often interconnected.

Potential REDs outcomes can directly or indirectly have negative effect on athletic

performance due to the physiological changes and decreased athlete availability

due to illness and injuries (Melin et al., 2024).

In a single-blinded crossover study, female endurance athletes were randomized to

begin with either 14 days of LEA or optimal energy availability diet (Jeppesen et

al., 2024). Power output during a 20-min time trial was 8% lower after LEA and

remained 7% lower after three days refueling, compared to before LEA. The same

study found a substantial impact of the 14 days with LEA on the immune system

as evidenced by altered redox balance in peripheral blood mononuclear cells,

altered immune/inflammatory proteome, and a reduced exercise-induced

mobilization of peripheral blood mononuclear cells, suggesting that LEA may

heighten disease susceptibility (Jeppesen et al., 2024), resulting in decreased

athlete availability (Mountjoy et al., 2023). Decreased athlete availability may

reduce training volume and performance (Ihalainen et al., 2021) and contribute to

mood disorders (Lichtenstein et al., 2018; Smith, 1996). Decreased athlete

availability can also be a result of frequent injuries (Mountjoy et al., 2023). In

female endurance athletes, increased risk of injuries has been associated with

menstrual dysfunction/LEA, prospectively using training and injury logs

(Ihalainen et al., 2021) and retrospectively using the LEAF-Q (Melin et al., 2014).

Decreased training response because of LEA have been reported, investigating 16

eumenorrheic long-distance runners entering a four-week intensified training

protocol followed by two weeks of recovery (Schaal et al., 2021). Well-adapted

runners (n = 9) increased energy intake, thereby maintaining baseline energy

availability and improved running performance, while seven runners were non-

functionally overreached because of failure to increase their energy intake during

the intensified training period, resulting in LEA and decreased performance.

Decreased recovery has also been associated with LEA. This has been directly

reported by athletes (Gillbanks et al., 2022) and indirectly assessed as indicated by

a reduced recovery rate for phosphorylated creatine in female endurance athletes

with menstrual dysfunction compared to their eumenorrheic counterparts (Harber

et al., 1998). Tornberg and colleagues reported decreased cognitive performance

as reflected by reduced reaction time as well as decreased muscle strength and

decreased neuromuscular endurance in female endurance athletes with FHA

compared to the eumenorrheic athletes (Tornberg et al., 2017). Investigating

eumenorrheic trained females randomized to LEA or optimal energy availability

for ten days, Oxfeldt et al. (2024) reported reduced repeated sprint ability, reduced

4-min time-trial performance, and rate of force development of the knee extensors

together with reduced body mass and muscle glycogen following LEA compared

to optimal energy availability (Oxfeldt et al., 2024).

Through semi-structured telephone interviews with female and male light-weight

rowers, a range of physical and psychosocial implications of LEA were described,

including decreased performance and recovery, fatigue, impaired sleep,

musculoskeletal pain, injuries, weakened immune systems, poor emotional

regulation, and impaired mood (Gillbanks et al., 2022).

2.2.7 Prevention

As described in previous sections, REDs is a common problem among female

endurance athletes with a wide range of health and performance consequences.

Therefore, focusing on prevention of the development of REDs is of utmost

importance (Mountjoy et al., 2018, 2023). The prevention of a health condition can

be categorized into primary, secondary, and tertiary prevention, as seen in the case

of eating disorders (Gresko et al., 1994). Recently, these definitions have also been

applied to the syndrome of REDs (Torstveit et al., 2023) with the three-step

approach IOC REDs CAT-2 being recommended to operationalize the secondary

and tertiary prevention of REDs (Mountjoy et al., 2023; Stellingwerff et al., 2023).

Primary prevention

The purpose of primary prevention is to prevent a disease from ever occurring, i.e.

the target population is healthy individuals (Baumann & Ylinen, 2017). In the

context of REDs, the purpose of primary prevention is to minimize exposure to

and reduce behaviors associated with LEA (Baumann & Ylinen, 2017; Torstveit et

al., 2023). It is recommended that primary prevention should be targeted at both

the athlete health and performance team (physicians, physiotherapists, dietitians,

psychologists and physiologists), athlete entourage (coaches, parents, managers),

and sport organizations (Torstveit et al., 2023). A previous consensus statement

highlighted education as the best evidence-based method for primary prevention

of DE and eating disorders in high performance sports, where strategies should

focus on increasing awareness of the problem and risk factors, improving body

image, and raising nutrition literacy (Wells et al., 2020). Similarly, it is

recommended that primary prevention strategies for REDs should focus on

education about the importance of sufficient energy intake an reduce risk factors,

although the current evidence of the efficiency of REDs primary prevention

programs is limited (Torstveit et al., 2023).

Secondary prevention

The purpose of secondary prevention is early detection (e.g. by using self-reported

screening instruments and assessment of REDs markers as presented in section

2.2.3) and subsequent management of REDs signs and symptoms to prevent the

development of more serious REDs outcomes, such as impaired bone health

(Torstveit et al., 2023). This is reflected in step one and two in the IOC REDs CAT-

2 (Figure 6).

For female athletes, relevant physical symptoms include menstrual dysfunction,

recurrent illness and injuries, while psychological symptoms may include mood

changes, reduced well-being or even depression. Behavioral symptoms may

include frequent measurements of body weight or body composition and DE

behavior (Torstveit et al., 2023). Because no validated screening instrument

includes all these aspects, a combination of instruments (e.g. the LEAF-Q in

combination with the EDE-Q, and the Exercise Addiction Inventory [EAI]) is

recommended to increase the possibility of optimal secondary prevention of REDs

(Torstveit et al., 2023). Due to the risk of response bias and under-reporting, it is

further recommended to include other information-gathering tools, such as

personal interviews and to include evaluation of multiple REDs signs (step two in

the IOC REDs CAT-2, Figure 6) to accurately diagnose and determine the severity

of REDs (Torstveit et al., 2023).

Figure 6. IOC REDs CAT-2 three step model. Modified from Mountjoy et al. (2023) with

permission. *According to the IOC REDs CAT2 Severity/Risk stratification where green

represent severity/risk of none to very low, yellow: mild, orange: moderate to high and red: very

high/extreme. Abbreviations: CAT: Clinical Assessment Tool, IOC: International Olympic

Committee, REDs: Relative Energy Deficiency in Sport.

Tertiary prevention

Tertiary prevention (treatment) is reflected in step three in the REDs CAT-2, where

the purpose is to prevent or limit long-term severe health consequences (Mountjoy

et al., 2023; Stellingwerff et al., 2023; Torstveit et al., 2023). Traditionally,

estrogen replacement therapy has been used to normalize menstrual cycles but is

no longer recommended since it masks the underlying problem (Gordon et al.,

2017; Mountjoy et al., 2018). In addition, studies report lack of efficacy of oral

estrogens in the recovery of bone mineral density (Cobb et al., 2007; Warren et al.,

2003). Pharmacological treatment alone does not address the underlying causative

factor, and therefore, nonpharmacological treatment should always be the first

approach (Angelidi et al., 2024; Coelho et al., 2021; Gordon et al., 2017;

Kyriakidis et al., 2016). There may be selected cases where pharmacological

treatment is a relevant add-on to improved energy availability. Transdermal E2

therapy with cyclic oral progestin in females without return of menses after a

reasonable (~1 year) nutritional, psychological and/or modified exercise

intervention is recommended to diminish bone mineral loss (Torstveit et al., 2023)

based on randomized clinical trial findings (Ackerman, Singhal, et al., 2019).

Treatment of REDs focuses on increasing energy availability which can be

achieved through increased energy intake, reduced exercise energy expenditure, or

a combination of both. An increased energy intake may require changes in food

choices, energy spread and other dietary characteristics which must be

individualized according to the athlete’s training (Mountjoy et al., 2018).

Increasing current energy intake by ∼300–600 kcal/day with an unchanged energy

expenditure has been recommended as a practical treatment approach to address

LEA (Mountjoy et al., 2014). As previously addressed in section 2.2.2 many

factors can influence athletes’ food choices and dietary behavior, emphasizing the

importance of an individual approach. Meanwhile, dietary characteristics of

female endurance athletes with REDs symptoms have been identified and include

reduced energy density (Melin et al., 2016), high dietary fiber intake (Barron et al.,

2016; Melin et al., 2016) and within-day energy deficiency (Fahrenholtz et al.,

2018) and may therefore be relevant themes when targeting this group of athletes.

In addition, the complexity of REDs involves diverse treatment principles for the

potential outcomes (Torstveit et al., 2023), e.g., the treatment should include

attention to Vitamin D, calcium, and iron. In case of deficiency, the recommended

treatment is to increase food variety and consider supplementation (Holtzman &

Ackerman, 2021; Thomas et al., 2016; Torstveit et al., 2023).

In case of unintentional under-eating, nutritional education has been suggested to

be sufficient for tertiary prevention of REDs (Mountjoy et al., 2018). Although

athletes may have similar general nutrition knowledge and potentially greater

sports nutrition knowledge compared to nonathletes, sports nutrition knowledge in

both male and female athletes are generally reported to be inadequate (Heaney et

al., 2011; Janiczak et al., 2022) in addition to inadequate knowledge about REDs

(Beals & Meyer, 2007; Brown et al., 2014; Feldmann et al., 2011; Lodge et al.,

2021; S. M. Miller et al., 2012; Mukherjee et al., 2016; Verhoef et al., 2021), where

a part of the explanation has been assigned the exposure to conflicting nutrition

information from friends, family, team-mates, and the internet (Tam et al., 2019).

Nutrition knowledge is one of the few determinants of dietary behavior that are

modifiable (Figure 5) and therefore has the potential to affect athletes’ dietary

behavior, health, and performance (Tam et al., 2019, 2022; Trakman et al., 2016).

Systematic reviews suggest a weak, but positive correlation between greater

nutrition knowledge and improved dietary intake among athletes from different

sports (Heaney et al., 2011; Janiczak et al., 2022) and in the general population

(Spronk et al., 2014). Similarly, a more recent published study, reported a positive

association between sports nutrition knowledge, energy availability and

carbohydrate intake in female endurance athletes (Kettunen et al., 2021).

Therefore, improved nutrition knowledge may constitute an important prerequisite

for desirable behavioral changes in female endurance athletes with REDs.

2.3 Previous interventions

This section will focus on previous intervention studies relevant for the aim of the

thesis. As there is no pharmacological agent to treat the underlying cause of REDs,

increasing energy availability is the recommended treatment with only few

interventions focusing on the efficacy of this approach (Mountjoy et al., 2018).

The first part of this section will focus on studies aimed at improving REDs

symptoms in females, encompassing exercising women and athletes across all

sports and competition levels. The second part will broaden the scope to include

studies aimed at promoting dietary behavior change in female athletes, including

those not specifically focused on REDs.

2.3.1 Prevention of REDs signs and symptoms in female athletes

Although the recommended treatment of REDs is to ensure adequate energy intake

relative to energy expenditure (De Souza, Nattiv, et al., 2014; Kuikman, Mountjoy,

Stellingwerff, et al., 2021; Mountjoy et al., 2014, 2018, 2023), few intervention

studies have been conducted to investigate the efficacy of this approach (Table 3).

Intervention studies aiming at improving REDs signs or symptoms in female

athletes or recreationally active women include case studies (Dueck et al., 1996;

Kopp-Woodroffe et al., 1999; Mallinson et al., 2013), interventions without a

control group (Cialdella-Kam et al., 2014; Guebels et al., 2014; Lagowska,

Kapczuk, & Jeszka, 2014; Lagowska, Kapczuk, Friebe, et al., 2014), and a

randomized controlled trial (De Souza et al., 2021a, 2022). These studies have all

included participants with menstrual dysfunction and specifically aimed at

improving menstrual function.

Overall, the available evidence appears promising for non-pharmacological

treatment of REDs with all studies reporting positive outcomes. The heterogeneity

of the studies, however, makes it difficult to identify the optimal intervention. Poor

energy intake, rather than excessive exercise, seems to cause a more pronounced

effect of the physiological perturbations (Hilton & Loucks, 2000; Loucks et al.,

1998) and studies aiming at improving REDs signs or symptoms have primarily

focused on increasing energy intake by administering dietary supplementation

(Cialdella-Kam et al., 2014; Dueck et al., 1996; Kopp-Woodroffe et al., 1999),

dietary counseling (Arends et al., 2012; Lagowska, Kapczuk, & Jeszka, 2014;

Mallinson et al., 2013), or both (De Souza et al., 2021a). Some interventions have

also implemented a reduction in exercise (Dueck et al., 1996; Kopp-Woodroffe et

al., 1999), although compliance may be challenged (Kopp-Woodroffe et al., 1999).

Prospective studies are characterized with a high drop-out rate ranging from 27%

to 60% (Cialdella-Kam et al., 2014; De Souza et al., 2021a, 2022; Lagowska,

Kapczuk, & Jeszka, 2014; Lagowska, Kapczuk, Friebe, et al., 2014). Although the

studies have used different definitions of menstrual recovery, e.g. the occurrence

of a single menses (Cialdella-Kam et al., 2014; Kopp-Woodroffe et al., 1999),

menstrual cycles ≤ 36 for at least three months (Arends et al., 2012; Lagowska,

Kapczuk, & Jeszka, 2014), and increased frequency of menses (De Souza et al.,

2021a), authors suggest that the time to recover is depended on several factors,

including the duration of menstrual dysfunction (Lagowska, Kapczuk, & Jeszka,

2014; Mallinson et al., 2013). Although most cross-sectional data suggest

comparable body weight and fat percentage when comparing athletes with and

without menstrual dysfunction (Redman & Loucks, 2005), improvement of

menstrual status may be associated with increased body fat and weight gain

(Arends et al., 2012; De Souza et al., 2021a; Dueck, Matt, et al., 1996; Kopp-

Woodroffe et al., 1999; Lagowska, Kapczuk, & Jeszka, 2014; Mallinson et al.,

2013). These results may suggest an individual susceptibility to menstrual

dysfunction (Burke et al., 2023) that can occur at a wide range of body weight and

body fat percentages (Bronson & Manning, 1991).

As previous addressed, nutrition education has been highlighted as an important

part of the prevention and treatment of REDs (Mountjoy et al., 2018; Torstveit et

al., 2023) and nutrition counseling has been used to facilitate behavior change in

female athletes with menstrual dysfunction (Arends et al., 2012; De Souza et al.,

2021a; Lagowska, Kapczuk, & Jeszka, 2014; Mallinson et al., 2013). Incorporating

behavior change theories can help understand why people behave as they do and

provide insights into designing successful interventions but also to enable

repeatability (Fiorini et al., 2023; Michie et al., 2018). None of the included studies

in Table 3, however, account for the use of such theories or describe the therapeutic

approach.

Table 3. Nutritional interventions in female athletes and exercising women with menstrual

dysfunction. Arranged according to year of publication.

Reference

Population (n completers)

and identification

Intervention

Main findings

Dueck et al.

(1996)

n = 1 amenorrheic runner.

Amenorrhea identified via

hormonal status, including

pulsative LH pattern

compared to n = 3

eumenorrheic athletes.

15 weeks with the use of

sports nutrition beverage

(360 kcal/day) and a

reduction of training 1

day/week.

Along with improved

EB, the amenorrheic

runner increased her

body fat from 8.2 to

14.4%, increased fasting

LH from 3.9 to 7.3

mIU/ml, and decreased

fasting cortisol from

41.2 to 33.2 µg /dl.

Kopp-

Woodroffe et al.

(1999)

n = 4 athletes (body

building / different forms of

aerobic training.

Self-reported menstrual

status + blood sampling.

20 weeks with the use of

sports nutrition beverage

(360 kcal/day) and a

reduction of training 1

day/week.

BCT NR.

n=3 resumed menstrual

function during or

shortly after the

intervention. All

improved EB, and ≈

+1kg weight gain,

although n=2 did not

reduce total minutes of

weekly exercise.

Arends et al.

(2012)

n = 51 female athletes with

oligomenorrhea or

amenorrhea from various

sports.

Self-reported menstrual

status + blood sampling.

A 5-year retrospective

chart view of individual

counseling by physician

(4.2 ± 0.9 / 4.6 ± 1.2

visits) and sport dietitian

(2.4 ± 1.5 / 4.9 ± 2.0

visits), individual plan

to improve energy

availability.

BCT NR.

17.6% of oligo-

/amenorrheic athletes

experienced restoration

of menses (time to

return: 15.6 ± 2.6

months). Greater body

fat% and weight gain in

those who resumed

menses.

Mallinson et al.

(2013)

n = 2 athletes with

amenorrhea of short (3

months) and long (11

months) duration.

A 4-week baseline period

with menstrual calendar

and daily urine samples.

12 months where

participants were

instructed to gradually

increase energy intake.

BCT NR.

Resumption of menses

occurred 23 and 74 days

into the intervention for

the athletes with short-

and long-term

amenorrhea,

respectively. Energy

intake increased with

13% and 27%, while

body weight increased

with 8% and 5%.

Cialdella-Kam

et al. (2014) /

Guebels et al.

(2014)

n = 8a amenorrheic

endurance athletes

n = 9 eumenorrheic

controls measured at

baseline.

Menstrual status identified

through self-reporting +

ovulation status by

Clearblue Easy®, + blood

hormone levels.

6 months with dietary

supplement (360

kcal/day).

BCT NR.

n=8 resumed menses (7

ovulating), 2.63 ± 2.2

months to first menses,

LH: 5.6 ± 4.1 at

baseline, increased to

15.3 ± 16.3 post-

intervention. Energy

availability 36.7 ± 10.2

at baseline and 45.4 ±

14.7 post-intervention.

Lagowska,

Kapczuk, Friebe

et al. (2014) /

Lagowska,

Kapczuk, &

Jeszka (2014)

n = 31b endurance athletes /

n = 55c endurance athletes

and dancers with

oligomenorrhea or

amenorrhea.

Self-reported menstrual

status and blood hormone

levels.

3 months → extended to

9 months, where dancers

were included.

Individual dietary

counseling by dietitian

with education on health

consequences of dietary

deficiencies.

BCT NR.

No change in menstrual

status after 3 months,

despite increased LH

and energy intake. After

extending the study,

restoration of regular

menses was observed

among n = 10.

De Souza et al.

(2021; 2022)

Exercising women;

n = 20d eumenorrheic

controls

n = 16e controls with

menstrual disturbances

n = 17f intervened women

with menstrual

disturbances.

Menstrual status identified

through urine and blood

hormone assessment.

12 months with

increased energy intake.

The intervention group

was counselled by a

clinical dietician to

increase food intake and

were supplied with

energy bars (220–300

calories) and pre-

measured servings of

nuts, if desired.

BCT NR.

The intervention group

was more likely to

experience menses

during the intervention

than the control group

and had a greater

increase in energy

intake, body weight,

percent body fat and

total triiodothyronine.

64% in the intervention

group improved

menstrual function

compared with 19% in

the control group. Bone

health did not improve.

an = 4 drop-outs, b n = 14 drop-outs, cn = 20 drop-outs, d n = 20 drop-outs, en = 20 drop-outs fn =

25 drop-outs. Abbreviations: BCT NR: Behavior change theory not reported; EB: energy

balance; LH: luteinizing hormone.

2.3.2 Promoting dietary behavior change in female athletes

Other sports nutrition intervention studies have promoted dietary behavior change

in female athletes (Table 4), where few have assessed energy availability or self-

reported REDs symptoms in adolescents (Day et al., 2016; Tektunalı Akman et al.,

2024) and adult (Dickey et al., 2016) athletes, or included information about the

Female Athlete Triad in the curriculum (Doyle-Lucas & Davy, 2011; Roche et al.,

2024). Most studies have included high school or college students (Abood et al.,

2004; Abood & Black, 2000; Anderson, 2010; Chapman et al., 1997; Cleary et al.,

2012; Coccia et al., 2020; Day et al., 2016; Heikkilä et al., 2019; Patton-Lopez et

al., 2018; Philippou et al., 2017; Roche et al., 2024; Tektunalı Akman et al., 2024;

Terenzio et al., 2021; Valliant et al., 2012; Wenzel et al., 2012; Zaman et al., 2021).

In addition, most studies have included ball game athletes (Abood et al., 2004;

Abood & Black, 2000; Anderson, 2010; Chapman et al., 1997; Cleary et al., 2012;

Coccia et al., 2020; Patton-Lopez et al., 2018; Tektunalı Akman et al., 2024;

Valliant et al., 2012; Wenzel et al., 2012), while others have included athletes from

aesthetic (Doyle-Lucas & Davy, 2011; Laramée et al., 2017; Yannakoulia et al.,

2002), endurance sports (Day et al., 2016; Dickey et al., 2016; Heikkilä et al., 2019;

Nowacka et al., 2016; Philippou et al., 2017), or mixed sports (Garthe et al., 2011;

Nascimento et al., 2016; Terenzio et al., 2021).

Overall, the methods used in the studies listed in Table 4 are heterogeneous and

the results are conflicting. For example, the methodologies for assessing eating

behavior and the definitions of a successful outcome vary widely. Of the 22

included studies, 11 did not include a control group. Several studies report an

improvement in nutrition knowledge without a corresponding change in eating

behavior (Abood et al., 2004; Chapman et al., 1997; Dickey et al., 2016; Heikkilä

et al., 2019; Nowacka et al., 2016; Yannakoulia et al., 2002), supporting that

knowledge improvement is not sufficient for behavior change (Heaney et al., 2011;

Pelly et al., 2022; Worsley, 2002).

Table 4. Intervention studies promoting dietary behavior change in female athletes. Arranged

according to year of publication.

Reference

n and sport

Intervention

Knowledge

tool / dietary

behavior

indicators

Main findings

Chapman et

al. (1997)

I: n = 37 softball

players

C: n = 35 softball

players

2 × 45 min

lectures over 6

weeks with focus

on ergogenic aids,

pre-competition

meal, dehydration,

energy sources,

supplements,

weight control.

BCT NR.

AD nutrition

knowledge

questionnaire.

24-hour recall.

Nutrition

knowledge

improved but

not dietary

intake.

Abood et al.

(2000)

I: n = 70 diving,

cross-country,

track, swimming,

softball, basketball,

and volleyball

athletes randomly

assigned to I or C

8 weeks with 2 × 1

hour. Focus on

female athlete

requirements,

nutrition myths,

weight and stress

management, self-

esteem and

goalsetting.

BCT NR.

AD nutrition

knowledge

questionnaire.

The Eating

Disorder

Inventory-2.

Intervention

group reduced

drive for

thinness and

body

dissatisfaction.

Yannakoulia

et al. (2002)

I: n = 32 dancers

12 weekly 2-hour

lessons; lectures,

group discussions

and workshops

focusing on

3-day dietary

record. AD

nutrition

knowledge

questionnaire.

Improved

nutrition

knowledge and

decreased

abnormal eating

applied nutrition

for dancers and

primary prevention

of eating disorders.

BCT NR.

EAT-26 and

DEBQ.

behavior and

dietary restraint

that were

maintained at 6-

months follow-

up. No effect on

energy intake.

Abood et al.

(2004)

I: n = 15 soccer

players

C: n = 15

swimmers

8x1-hour SCT

based group

education session

conducted on a

weekly basis.

Focus on total

energy, macro-

and micronutrients

3-day diet

record. AD

self-efficacy

and nutrition

knowledge

questionnaire.

I improved

nutrition

knowledge and

self-efficacy but

not energy or

macronutrient

intake.

Anderson et

al. (2010)

I: n = 8 volleyball

players

C: n = 8 volleyball

players

Individual and

group

feedback on

dietary intake.

BCT NR.

3-day dietary

record.

Protein, calcium

and vitamin C

increased, but

not energy or

carbohydrate.

Doyle-

Lucas et al.

(2011)

I: n = 231 ballet

dancers

C: n = 90 ballet

dancers

3x30 min DVD

lectures focusing

on basic sports

nutrition and the

Triad, promotion

of self-efficacy for

adopting healthier

dietary habits.

Incorporation of

HBM and SCT.

FFQ.

EAT-26.

SNKBQ.

Compared to C,

I improved

nutrition

knowledge and

self-efficacy,

decreased candy

intake and

increased milk

intake.

Abnormal

eating behavior

increased for I

at 6 week-

follow up.

Garthe et al.

(2011)

I: n = 2

C = n = 2

from mixed sports

Nutritional

counselling once a

week with

prescribed diet

plan during an 8–

12-week weight-

gain period.

BCT NR.

4-day dietary

record.

Energy intake

was higher in I

than in the C

and body weight

increased more

in I than in C.

Cleary et al.

(2012)

I: n = 36 volleyball

players

Educational

intervention (1

slide presentation)

and a prescribed

hydration

intervention.

BCT NR.

Fluid

consumption

indicators.

The education

alone was not

successful in

changing

hydration

behaviors but

prescribing

individualized

hydration

protocols

improved

hydration.

Valliant et

al. (2012) /

Wenzel et

al. (2012)

I: n = 11 volleyball

players

4 × individual

face-to-face

consultations over

4 months with

focus on

individual needs.

BCT NR.

Reilly and

Maughan

sports nutrition

questionnaire.

3-day dietary

record.

Carbohydrate,

protein, and

total energy

intake

increased.

Nutrition

knowledge

improved.

Day et al.

(2016)

I: n = 15 runners

6 x 30 min

classroom/power

point sessions

once a week

focusing on the

Triad taught

by either an

undergraduate

dietetic student or

a Registered

Dietitian.

BCT NR.

3-day diet and

activity record.

A 73-item

questionnaire

was used to

assess

knowledge and

behavior

changes.

Nutrition

knowledge

improved, but

not energy

intake.

Dickey et al.

(2016)

I: n = 8 rowers

3 months with 8 x

35 min individual

sports nutrition

counseling

sessions and 8 co-

active life

coaching sessions.

Client handouts on

Practice-Based

Evidence in

Nutrition.

BCT NR.

Adapted

validated

sports nutrition

knowledge

questionnaire.

3-day dietary

record.

Sports nutrition

knowledge

improved, but

not dietary

behavior and

energy

availability was

still low or

reduced for all

participants

after the

intervention.

Nowacka et

al. (2016)

I: n = 8 slalom

canoeist

2 years with

unknown number

of individual and

group sessions

with nutritional

guidelines and

review of nutrition

mistakes.

BCT NR.

24-hour recall.

No difference

for the female

group.

Nascimento

et al. (2017)

I: n = 6 from mixed

sports

4 x consultations

separated by an

interval of 45 to

60 days. Focus on

hydration, meal

frequency, and

quality. Access to

SoMe group.

BCT NR.

24-hour recall.

Knowledge

assessed by

selecting

questions from

previously

validated

questionnaire.

Improved

nutrition

knowledge,

meal frequency,

and daily water

intake but

results only for

females are not

reported.

Laramée et

al. (2017)

I: n = 37

synchronized

swimmers and

dancers

Both groups: 3

weekly 60 min

sports nutrition

education sessions

focusing on energy

AD nutrition

knowledge

questionnaire.

Psychosocial

determinants:

Both groups

improved

nutrition

knowledge

equally and I

C: n = 33 gymnasts

and cheerleaders.

and carbohydrate

needs, importance

of considering

hunger and satiety

signals, acute

fueling strategies. I

also received 3

TPB based

sessions aimed at

reducing

intention to use

restrictive dietary

behaviors for

losing weight.

Intention,

attitude,

subjective

norm and

perceived

behavioral

control.

maintained a

low intention of

using restrictive

dietary

behaviors for

losing weight

over time

compared to C.

Philippou et

al. (2017)

I: n = 23 swimmers

A half day with

athlete and parent

interactive lectures

and a supermarket

tour provided by

dietitians and a

medical doctor.

Focus on MD,

sports nutrition

and use and

misuse of

supplements.

BCT NR.

KIDMED

index.

AD nutrition

knowledge

questionnaire.

Increased

KIDMED index

score. Improved

nutrition

knowledge, but

this increase

was mainly

driven by males.

A trend for a

lower adherence

to MD in

swimmers

whose parents

did not

participate

compared to

those whose

parents did.

Patton-

Lopez et al.

(2018)

I: n = 94 high

school soccer

players

C: n = 44 high

school soccer

players

7 hours with sports

nutrition lessons,

TBWs, and

experiential

learning (e.g. role

playing, cooking

demonstrations)

over 2 years

delivered by a

registered dietitian

nutritionist. Focus

on acute fueling

strategies, body

composition, and

hydration.

BCT NR.

Validated

sports nutrition

questionnaire.

Attitudes and

beliefs relevant

to sports

nutrition and

self-reported

behaviors.

I increased

nutrition

knowledge with

the greatest

change in the

female I vs C. I

were 3 times

more likely to

report trying to

eat for

performance.

Heikkilä et

al. (2019)

I: n = 17 (education

+ app)

C: n = 18

from cross-country

skiing endurance

running/race-

walking biathlon,

Both groups

received 3x90 min

education sessions

with lectures,

discussions,

exercises, group

and individual

3-day food

dietary record.

Nutrition

knowledge

evaluation

through a

Both groups

improved

nutrition

knowledge

during

intervention

without

orienteering,

triathlon

work with

nutritionist.

Increase in

motivation was

based on Self-

Determination

Theory. I: +mobile

food application.

validated

questionnaire.

improvement in

dietary intake.

Use of the

mobile app did

not improve the

results further.

Coccia et al.

(2020)

I: n = 39

swimmers, baseball

and softball players

6 weeks of social

media education

with “tweets” or

messages

containing text,

photos, and videos

delivered by a

doctoral level

dietitian. HBM

used as the

theoretical

framework.

AD nutrition

knowledge and

self-efficacy

questionnaire.

The 16-item

National

Cancer

Institute fat

screener and

the 9-item

Dietary

Targets

Monitor.

Increased

nutrition

knowledge and

self-efficacy and

reduced fat

intake.

Terenzio et

al. (2021)

I: n = 46 females

from jumping,

100–200 m

distance running,

and 300 m or more

distance running

Athlete and

parents were

educated by a

dietitian focusing

on nutrition

guidelines for

athletes, meal

frequency, fruit

and vegetables,

and reduced

consumption of

sweetened

beverages and

sugary snacks.

BCT NR.

FFQ.

Increased

consumption of

legumes and

fish.

Zaman et al.

(2021)

I: n = 10 university

athletes not further

defined

1-day sports

nutrition class

focusing on

general nutrition,

hydration, and

energy balance.

BCT NR.

3-day dietary

record. KAP

questionnaire.

Nutrition

knowledge

improved and

total energy and

carbohydrate

intake

increased.

Ackman et

al. (2024)

I: n = 45

C: n = 38

football, basketball,

and volleyball

players

6x60-min face-to

face nutrition

education lectures,

conducted every

week by a

registered dietitian

focusing on energy

metabolism,

energy balance,

nutrition before

and after training,

LEA, macro and

3-day dietary

record,

SNKQ,

EAT-26,

LEAF-Q.

Energy

availability,

carbohydrate

intake, SNKQ

and LEAF-Q

scores improved

for I with a

significant

group-by-time

interaction

effect.

micronutrients,

hydration, and

supplements.

BCT NR.

Roche et al.

(2024)

I: n = 45

C: n = 39

I: 5 videos

on Triad/REDs,

nutrition,

menstrual cycle,

bone health and

mental health over

the course of a

week.

C: read pamphlets.

BCT NR.

AD nutrition

knowledge,

material

interest and

potential for

impacting their

behavior.

No difference in

knowledge gain

comparing I and

C. However, I

had stronger

scores on

behavioral

impact,

information

novelty and

interest.

Abbreviations: AD: Author designed, BCT NR: Behavior change theory not reported; C: control

group; DEBQ: Dutch Eating Behaviour Questionnaire; EAT-26: Eating Attitude Test, FFQ: Food

Frequency Questionnaire; HBM: Health Believe Model; KIDMED: Mediterranean Diet Quality

Index for Children and Adolescents; KAP: Knowledge, attitude, practice; LEAF-Q: Low Energy

Availability in Females Questionnaire, I: intervention group, MD: Mediterranean diet; SCT:

Social Cognitive Theory; SNKBQ: Sports Nutrition Knowledge and Behavior Questionnaire;

SNKQ: Sports Nutrition Knowledge Questionnaire; TBW: team-building workshops; Triad:

Female Athlete Triad; TPB: Theory of Planned Behavior.

2.4 Theoretical rationale for a REDs prevention program

Building on the previous sections, this part will focus on compiling the theoretical

foundation for future interventions aimed at female endurance athletes with REDs.

The design and content of this intervention aim to meet relevant theory and

overcome the limitations of previous studies.

Nutrition education may be a crucial component in the prevention and treatment

of REDs without DE (Mountjoy et al., 2018), with nutrition counseling being

utilized to facilitate behavior change in female athletes with REDs (Arends et al.,

2012; De Souza et al., 2021a; Lagowska, Kapczuk, & Jeszka, 2014; Mallinson et

al., 2013). It is possible that this approach can facilitate more sustainable health

behavior change compared to simply administering a dietary supplementation

(Cialdella-Kam et al., 2014; Dueck et al., 1996; Kopp-Woodroffe et al., 1999) as

it may be the case in non-athletic populations (Wong et al., 2022). As reflected in

Table 4, education is a frequently used intervention technique to promote dietary

behavior change in female athletes. While improved nutrition knowledge appears

to be an important prerequisite, it may not be sufficient to achieve behavior change

(Heaney et al., 2011; Pelly et al., 2022; Worsley, 2002). Several behavior change

techniques may, therefore, be necessary to implement to accommodate human

diversity (Bentley et al., 2020; Michie et al., 2013).

An important distinguish is made between nutrition education and nutrition

counseling (Fiorini et al., 2023). Similarly, a distinguish is made between

declarative knowledge (knowledge of “what is”, awareness of things and

processes) and procedural knowledge (knowledge about how to do things)

(Worsley, 2002). Nutrition education is described as a formal process to improve

athletes’ knowledge about food and physical activity and support sound food

choices within a specific target population (Fiorini et al., 2023). In contrast

nutrition counseling is a supportive process delivered by a qualified professional

who guides the athlete(s) to set priorities, establish goals, and create individualized

action plans to facilitate behavior change, supporting both declarative and

procedural knowledge (Fiorini et al., 2023; Worsley, 2002). Nutrition counseling

is a dynamic, two-way interaction that actively involves the athlete(s) where

existing nutrition knowledge is used as a part of the starting point to define and

support key behavioral changes. Nutrition counseling can be delivered both to

individuals and groups. While group counseling has the opportunity to create a

collective awareness and commitment, individual nutrition counseling has the

benefit of being well-adapted to the athletes’ own needs and preferences (Fiorini

et al., 2023). Through communication, individual counseling can incorporate a

wide range of behavior change techniques tailored to the individual factors (Figure

5) influencing the athlete’s eating behavior (Bentley et al., 2020; Fiorini et al.,

2023; Michie et al., 2011, 2013). The combination of sports nutrition education

and individual counseling is therefore attractive to evaluate in female endurance

athletes at risk of REDs.

Nutrition counseling may apply a variety of behavior change theories and/or

models in combination or by themselves (Fiorini et al., 2023; Spahn et al., 2010).

Incorporating behavior change theories and models into nutrition counseling can

help explain why individuals behave as they do and provide insights into designing

effective interventions, as well as ensuring their repeatability (Fiorini et al., 2023;

Michie et al., 2018; Spahn et al., 2010).

The goal of the sports nutrition counseling may change depending on the athletes’

readiness to change (Deci & Ryan, 2012; Fiorini et al., 2023; Miller & Rollnick,

2012; Norcross et al., 2011). Stages of Change, one of the components of the

Transtheoretical Model, operates on the assumption that people do not change

behavior quickly or decisively (Norcross et al., 2011). Rather behavior change

occurs continuously through six stages in a cyclic process. As this model facilitates

an individualized approach, it can be used as a supporting tool to tailor the

conversation and is one of the more widely used models in nutrition counseling

(Fiorini et al., 2023; Spahn et al., 2010).

Cognitive dissonance is a psychological concept referring to the mental discomfort

or stress experienced when a person holds two or more contradictory beliefs,

values, or attitudes simultaneously (Festinger, 1957). The concept suggests that

people have an inherent desire for internal consistency. To reduce the dissonance,

people may change their beliefs, acquire new information, or minimize the

importance of the conflicting belief (Festinger, 1957). According to this theory, an

athlete experiencing REDs who values health and performance would likely feel

discomfort due to the conflict between her current state and her values. To reduce

this cognitive dissonance, the athlete might downplay the health risks (Beals &

Meyer, 2007; Verhoef et al., 2021) or take steps to improve energy availability.

Self-determination theory distinguishes between autonomous motivation and

controlled motivation (Deci, 2017; Deci & Ryan, 1985). Autonomous motivation

refers to participating in activities with a sense of choice and personal

endorsement. It includes both intrinsic (doing something because it is inherently

interesting or enjoyable) and extrinsic motivation (doing something because it

aligns with one’s values and self-identity)

. In contrast, controlled motivation

refers to engaging in activities due to external pressure or demands (Deci, 2017).

When individuals experience cognitive dissonance, their motivation to resolve this

discomfort may be influenced by their dominant motivational orientation

(Lavergne & Pelletier, 2016). Individuals with a high dominance of autonomous

motivation may be more likely to solve cognitive dissonance by changing behavior

to align with their values (Lavergne & Pelletier, 2016).

According to self-determination theory, humans have three fundamental

psychological needs: autonomy, competence, and relatedness (Deci & Ryan, 1985;

Ryan & Deci, 2007). To satisfy these three needs, the counseling approach of

Motivational Interviewing can be employed (Deci & Ryan, 2012). This approach

involves a spirit of collaboration, evoking the client’s own ideas about change and

autonomy, thereby strengthening the alliance between the counselor and client (W.

Hence, all intrinsic motivation is autonomous, but not all autonomous motivation is intrinsic.

R. Miller & Rollnick, 2012). Although originally developed to treat substance use

disorders, Motivational Interviewing has been shown to be effective in several

other areas of health behavior change (Copeland et al., 2015) where it has been

practiced in e.g. sport psychology (Mack et al., 2017), nutrition counseling

(Campbell et al., 2009) and eating disorder treatment among non-athletes (Feld et

al., 2001). To support personalized nutrition counseling inspired by Motivational

Interviewing, the Transtheoretical Model can be included. For instance, exploring

ambivalence by weighing pros and cons is an important Motivational Interviewing

technique used in the contemplation phase, while goal setting and developing a

clear plan are relevant techniques to employ in the preparation phase (Miller &

Rollnick, 2012; Norcross et al., 2011).

To summarize the background chapter: Research on REDs has progressed.

However, most studies are cross-sectional in nature and the optimal treatment is

yet to be identified. Prevalence studies published the last decades demonstrates a

persistent problem of REDs among female endurance athletes suggesting a crucial

need for developing and evaluating prevention and treatment programs. Focusing

on high‐risk groups, such as female endurance athletes with symptoms of REDs,

appears to be a good starting point for investigating such strategies. Nutrition

education initiatives have been requested, where individual nutrition counseling

seems to be an important add on in tertiary REDs prevention as it enables the use

of multiple behavior change techniques, beyond nutrition education. An athlete-

centered nutrition counseling can be implemented using Motivational Interviewing

techniques and the Transtheoretical Model.

3 Scientific approach

By understanding the diversity of perspectives within the research paradigms of

sport and health science, we can get a more purposeful engagement with the

contributions of others (Young & Ryan, 2020). Therefore, this chapter provides a

concise overview of the scientific approach and the underlying philosophy of

science that forms the foundation of this Ph.D. thesis.

Positivism is aligned with the hypotheticodeductive model of science; a circular

process that begins with theory from literature, build testable hypotheses, design

an experiment, and then conduct an empirical study (Park et al., 2020). While

positivism focuses on verifying theories (Park et al., 2020), post-positivism argues

that true scientific theories can be disproven, i.e. falsified (Young & Ryan, 2020).

Hence, according to post-positivism science proceeds through falsification. After

formulating a hypothesis, one works to prove it wrong, rather than making a

discovery and continuing to prove it right (Gelman & Shalizi, 2013; Packer, 2018;

Young & Ryan, 2020). In contrast to positivism, post-positivism argues that a

theory can never be absolutely proven correct, because falsification is the

fundamental tenant (Young & Ryan, 2020). In other words, the epistemological

assumption implies that it is not possible to establish whether a theory is true and

therefore it must be regarded as tentative and open to revision.

Like positivism, post-positivism holds the assumption that there is an objective

truth but acknowledges that we are unlikely ever to find it (Park et al., 2020; Young

& Ryan, 2020). Rather, we build our world within the limitations of our times,

techniques, and currently available knowledge, where observation and

measurement are considered imperfect (Young & Ryan, 2020). Therefore, a

cautious and critical attitude must be adopted (Blaikie & Priest, 2019). Indeed,

theories in the research field of REDs have been revised throughout history (see

page 8-9) and are still debated (De Souza, Williams, et al., 2014; Jeukendrup et al.,

2024; Mountjoy et al., 2015). The historical progression has shifted from the belief

that sports are harmful to women to achieving gender equality at the Olympic

Games and from insufficient body fat stores to problematic LEA being the

underlying cause of menstrual dysfunction and several other REDs outcomes.

This thesis is dominated by the post-positivistic approach and the hypothetico-

deductive method. In Paper I, II, and III theories are tested through statistical

analyses on the sample in question and falsification is then the only certain

statement that can be done (Gelman & Shalizi, 2013; Young & Ryan, 2020).

Two different statistical philosophies exist: the Frequentist and the Bayesian

inference differing in their definition of probability (van de Schoot et al., 2014).

While Frequentist probability is a long-run frequency, Bayesian probability is a

degree of belief. Frequentist statistics compute P-values, defined as the probability

of observing the same or more extreme data assuming that the null hypothesis is

true in the population. Bayesian statistics report Bayes Factors (BF) defined as the

probability of the (null) hypothesis, it is the ratio of the likelihood of one particular

hypothesis to the likelihood of another (van de Schoot et al., 2014). Despite a clear

predominance of Frequentist statistics in the literature, researchers have criticized

the use of this approach and highlighted advantages for using the Bayesian

approach as an alternative (Gigerenzer & Marewski, 2015; van de Schoot et al.,

2014; Wagenmakers et al., 2018). Wagenmakers et al. (2018) suggest several

arguments that may explain the continued popularity of P-values over Bayesian

methods in the literature, including the need for investing serious effort to learn

new methods. Although the Frequentist approach was the author's prerequisite

before starting this Ph.D., recognizing the advantages of Bayesian statistics has

encouraged the author to adopt this method. Hence, the Frequentist approach is

used in Paper I, while the Bayesian approach is used in Paper II-IV.

There is a clear predominance of the post-positivism approach to science in the

research of REDs (Mountjoy et al., 2023). While this scientific approach also was

the author’s prerequisite prior to this Ph.D., a self-critical mindset acknowledges

that this approach is not able to fully cover all scientific questions. As expressed

by Packer (2018, p. 47, line 28-33):

Research in normal science is like playing chess – trying out various moves

but not challenging the rules of the game. To force scientists to conduct only

hypothesis-testing research is to prevent them from challenging the rules of

their game, from questioning or even examining the assumptions of the

prevalent scientific paradigm.

To challenge the rules of the game a qualitative part was added to the present

project, where Paper IV represents a quantitative as well as a qualitative (i.e., a

mixed methods research) approach to evaluate athletes’ experiences after

participating in the intervention. More specifically, constructionism is an

ontological perspective that necessitates a distinct logic of inquiry, differing from

the approaches of positivism and post-positivism (Park et al., 2020; Rees et al.,

2020; Young & Ryan, 2020). Constructionism speaks for multiple realities and

holds the view that we must regard the world as created or constructed by scientific

theorizing (Blaikie & Priest, 2019; Rees et al., 2020). Where post-positivism

claims that knowledge needs to be measured, the epistemology of constructionism

states that reality needs to be interpreted. Our description of the world will always

be historically and socially specific; the structure of science has changed over time

and the world changes depending on how you look at it (Rees et al., 2020). By

valuing language and dialogue and acknowledging multiple realities (Rees et al.,

2020), interviews were used to explore athletes’ experiences with their

participation in the FUEL program. The mixed methods approach allows best to

answer a complex research question and to corroborate findings within a single

study (Ryba et al., 2022) and this approach was therefore chosen for Paper IV.

Critical realism supports the view that quantitative and qualitative research can

work together to address the other’s limitations and is a useful and recommended

approach for evaluation-based studies (Shannon-Baker, 2016).

Figure 7. Scientific approach in the four included papers. Outer layers represent philosophy of

science, next layers represent quantitative/qualitative approach, second innermost layers represent

study design and data collection method, while the innermost layers represent method of analysis.

4 Methods

This dissertation presents data from a project conducted in the years 2020 – 2023

with recruitment from November 2020 to September 2021. Hence, recruitment and

data collection took place during the COVID-19 pandemic, where physical contact

with the participants was prohibited. Therefore, modifications from the original

plan (Appendix X) were made, and the final methodology is strongly influenced

by the pandemic restrictions. Originally, the project was planned and approved to

include REDs related clinical biomarker measurements in the laboratory and a

control group prior to initiation of the intervention (see Appendix I and II).

4.1 Study design

To explore the first aim, the first part of the project was conducted with a cross-

sectional design to investigate the risk of LEA/REDs and potential associated risk

factors in female endurance athletes from four European countries (Paper I). The

second and third aims were investigated with a prospective quasi-experimental

intervention design with two follow-up points to explore effects and changes in

nutrition knowledge, dietary intake, and REDs symptoms after a 16-week nutrition

intervention in female endurance athletes at risk of REDs (Paper II and III). The

intervention was followed by a quantitative and qualitative participant evaluation

(Paper IV).

The FUEL program

Although published after the planning of the present project, Ackerman et al.

(2020) well described the underlying core of our project by emphasizing that

educational initiatives should emphasize the positive aspects of energy, namely,

that food is FUEL and FUEL is needed for performance (Ackerman et al., 2020).

To express the positive aspects of food, we named this study the FUEL study (Food

and nUtrition for Endurance athletes—a Learning program). This non-randomized

multicenter study recruited female endurance athletes from Norway, Sweden,

Ireland, and Germany. The FUEL intervention group was offered one weekly

digital lecture in sports nutrition and individual athlete-centered nutrition

counseling with an experienced sports nutritionist every other week for 16 weeks.

The control group received no lectures or counseling. The risk of imitation of the

intervention was high, because many female endurance athletes know each other

in the countries concerned and they frequently participate in training camps

together. Consequently, seasonal allocation of summer and winter sport disciplines

was prioritized over randomization. Athletes from summer sport disciplines

received the intervention during winter and early spring while athletes from winter

sport discipline received the intervention during summer and early autumn.

An overview of the study is illustrated in Figure 8. The study was initiated with a

screening phase, where interested participants completed an online survey (part 1)

via Nettskjema connected to Services for Sensitive Data (TSD, University of

Oslo). Participants provided written consent prior to accessing the survey. Part 1

collected background information, including training volume, sports participation,

level of competition, education attainment, occupation, age, height, body weight,

diagnosis, and food allergies or intolerances

. Then followed the validated

instruments the LEAF-Q (Melin et al., 2014), EAI (Terry et al., 2004), and EDE-

Q (Fairburn & Beglin, 1994). The survey ended with two questions regarding the

history of eating disorders and a comment section.

Figure 8. Overview of the FUEL study. Modified from Paper II.

Despite the biological difference between food intolerances and food allergies they will here collectively

be termed food intolerances to ease readability.

Athletes’ responses meeting the inclusion criteria for the intervention [risk of

LEA/REDs defined as LEAF‐Q score ≥8 (Melin et al., 2014) and a low risk of DE

behavior (EDE‐Q global score < 2.5) (Rø et al., 2015)] were analyzed in more

detail and athletes were contacted for clarification to exclude suspected false

positive cases. Eligible athletes were then invited to complete an additional survey,

including questions regarding sports nutrition related behavior and self‐perceived

sports nutrition knowledge. In the same week that a seven‐day dietary and training

record (part 2, ≈ four weeks from part 1) was conducted, a telephone interview

with 20 questions regarding sports nutrition knowledge was performed. Athletes

who signed up during their competition season and fulfilled inclusion criteria were

allocated to a waiting list with control group conditions, before offering them the

intervention with or without individual counseling. Study week 0 (pre-test) was

followed by the intervention (part 3), during the athletes’ off‐season or a 16‐week

control period (study week 1–16). After the 16‐week nutrition intervention /

control period, athletes once again completed the two online surveys, the telephone

interview, and a seven-day diet and activity record (part 4, week 17). Finally,

athletes were asked to complete an evaluation questionnaire concerning their

experience with participating in the FUEL study in week 18 and invited to

participate in a qualitative interview. Six and 12 months after the intervention,

FUEL athletes were asked to complete the LEAF-Q, EAI and EDE-Q.

4.2 Participants

The study population was recruited from Norwegian, Swedish, Irish, and German

endurance competitive clubs, the Norwegian Olympic Centre, Sport Ireland

Institute, Swedish Olympic Committee, German Ski Federation, German Olympic

Sport Confederation, and affiliated social media with a link to the project website

with participant information (Appendix III) and the online survey. Inclusion

criteria for Paper I were: 18-35 years of age, competitive female endurance

athletes from cycling, running, orienteering, triathlon, biathlon, or cross-country

skiing, and training at least five times a week. Exclusion criteria for participating

in the intervention and thereby Paper II-IV were use of hormonal contraceptives,

chronic diseases, pregnancy, menstrual dysfunction not related to LEA, and

suspected false positive identification by LEAF-Q (e.g. LEAF-Q score ≥ 8 due to

acute injuries and previous menstrual dysfunction). Athletes using hormonal

contraceptives had the chance to be included in the intervention if they

discontinued the use at least six weeks before the screening phase (part 1).

In total, 208 participants completed part 1 of the study and were assessed for

eligibility (see flowchart, Figure 9): n = 2 were male athletes, n = 3 were <18 or

>35 years of age, and n = 1 was a badminton player and therefore excluded.

Additionally, n = 135 athletes were excluded from further participation in the

FUEL intervention due to one or more exclusion criteria. Categorization was based

on the primary reason for exclusion as follows: n = 3 had a chronic disease [n = 1:

Crohn’s disease, n = 1: Hashimoto’s thyroiditis, n = 1: hypothyroidism]; n = 55

used hormonal contraceptive; n = 23 had an EDE‐Q global score ≥ 2.5; n = 51 had

a LEAF‐Q score < 8, and n = 3 had not provided any contact information. All

excluded participants with available contact information (n = 138) were contacted

by e‐mail and given the opportunity to receive the reason for their exclusion

through telephone call from the researchers or in an encrypted file sent by email.

Participants with EDE‐Q ≥ 2.5 (n = 43) were informed and encouraged to contact

their general practitioner for further examination. They were also provided with

links to relevant websites, including voluntary associations that offer support for

individuals with DE and eating disorders in their respective countries.

After thoroughly analyzing n = 67 LEAF-Q responses, athletes were contacted for

clarification of answers if needed. Consequently, n = 7 athletes were excluded due

to a suspected false positive identification of the risk of REDs. Further, n = 4

athletes were unavailable and n = 3 athletes declared severe illness ahead of the

pretest measurements (e.g., abdominal surgery and COVID‐19 infection). Three

responded too late in relation to intervention start‐up and allocation to sports

nutritionists and were offered the intervention without individual consultations. n

= 32 athletes were directly allocated to the FUEL intervention with video lectures

and individual consultations, while n = 1 terminated participation in the project in

week 13 due to experiencing too much work related to the project. In total, n = 18

athletes were allocated to a 16‐week waiting list control condition of which n = 15

athletes completed (n = 1 wished to use hormonal contraceptives and n = 2 were

unreachable) and subsequently offered the FUEL intervention with or without

individual consultations, depending on the available resources at the recruitment

site. Accordingly, n = 33 (97%) completed the FUEL intervention with sports

nutrition lectures and individual consultations (in Paper II and III: FUEL / in

Paper IV: FUELcombined), n = 15 (83%) completed the control condition (CON),

while n = 11 completed the FUEL intervention with digital lectures only

(FUELlectures). Of notice, n = 2 who completed the intervention, acts as controls in

Paper II and Paper III. One athlete missed the postintervention survey but

completed all other measurements. Data from athletes who participated in the

FUEL intervention with lectures only, is solely presented in Paper IV.

Figure 9. Flowchart of the recruitment and inclusion of subjects in Paper I-III. Abbreviations:

CON: control group, FUEL: Food and nUtrition for Endurance athletes – a Learning program

intervention group, REDs: Relative Energy Deficiency in Sport.

Athletes were categorized in terms of level of competition (club, national, or

professional) with the majority competing at the club level (Table 5). After the

recruitment, McKay et al. (2022) published a 6-tiered participant classification

framework whereby all individuals across a spectrum of exercise backgrounds and

athletic abilities can be classified. According to this classification, the included

participants were defined as tier 2-4 athletes, with the majority being tier 2

(trained/developmental) followed by tier 3 (highly trained/national level), and tier

4 (elite/international level) athletes (McKay et al., 2022).

Table 5. Overview of study design and participants in all four papers.

Paper

Design

Sport (n)

Level (n)

Nationality (n)

Cross-sectional

202

Running: 54

Orienteering: 18

Triathlon: 50

Cycling: 45

XC skiing: 15

Biathlon: 20

Club: 134

National: 27

Prof: 29

Other: 12

Norway: 54

Sweden: 83

Ireland: 17

Germany: 45

Intervention

Running: 14

Orienteering: 7

Triathlon: 12

Cycling: 6

XC skiing: 1

Biathlon: 6

Club: 33

National: 7

Prof: 4

Other: 2

Norway: 11

Sweden: 18

Ireland: 5

Germany: 12

III

Intervention

with long-term

follow-up

Running: 14

Orienteering: 7

Triathlon: 12

Cycling: 5

XC skiing:1

Biathlon: 6

Club: 32

National: 7

Prof: 4

Other: 2

Norway: 11

Sweden: 17

Ireland: 5

Germany: 12

Mixed methods

evaluation:

Quan/Qual

36/

Running: 14/4

Orienteering: 5/1

Triathlon: 7/2

Cycling: 6/2

XC skiing: 0/0

Biathlon: 4/1

Club: 23/8

National: 7/2

Prof: 4/0

Other: 2/0

Norway: 10/5

Sweden: 15/2

Ireland: 5/2

Germany: 6/1

Abbreviations: Level: level of competition, National: national team, Prof: professional, Qual:

qualitative part, Quan: quantitative part; XC: cross-country skiing.

4.3 Measurements

4.3.1 Low energy availability

The validated screening tool the LEAF-Q was used to assess self-reported

symptoms of problematic LEA

, namely injury frequency the past year, current

gastrointestinal function, and current and past reproductive function (Melin et al.,

2014). Although hormonal contraceptives can mask underlying menstrual

dysfunctions (Gordon et al., 2017; Mountjoy et al., 2018), the LEAF-Q can provide

valuable information about the risk of problematic LEA, despite the use of

hormonal contraceptives, i.e., if athletes give the reason that they use it to avoid

amenorrhea. Therefore, hormonal contraceptives were not an exclusion criterion

for Paper I. Athletes completed the LEAF-Q at pre- and posttest and again at six

and 12-months follow-up for the intervention group. The 25-item LEAF-Q was

originally validated in female endurance athletes and produced an acceptable

sensitivity (78%) and specificity (90%) to correctly classify current energy

availability and/or reproductive function and/or bone health with a total score ≥ 8

as cutoff (Melin et al., 2014). In collaboration with the developer of the LEAF-Q,

small clarifications, not affecting the scoring key, were added to question A2:1

[“Specify how old you were when you started taking oral contraceptives and for

how long? (Months or years in total)”], C6 [added answer option: “0-4 weeks”

(scoring 0 points), to the answer option “I am pregnant...,” “... /I am

breastfeeding...” was added] and D [“.../breastfeeding” following pregnancy].

Menstrual dysfunction diagnosis

To minimize potential false positive categorization of the risk of LEA, participants

were asked in part 1: “Do you have any diagnosis related to menstruation? [For

example, polycystic ovary syndrome (PCO/PCOS)]? With the possibility to

answer “no” or “yes” and an open answer option to specify any potential diagnosis.

4.3.2 Disordered eating

The EDE-Q was used to measure behavioral and cognitive symptoms of DE the

past 28 days (Fairburn & Beglin, 1994) at pre- and posttest, and again at six and

This term was introduced after the publication of the LEAF-Q validation and Paper I.

12-months follow-up for the intervention group. The EDE-Q is based on the Eating

Disorder Examination Interview which is considered as the gold standard in eating

disorder assessment (Guest, 2000) and underwent a validation in an athletic

population during the planning of the current study (Lichtenstein et al., 2021). The

questionnaire consists of 28 items and can be assessed with four subscales

(restraint, eating concern, shape concern, and weight concern) and a global score

by averaging the subscales that can be used as cut-off for eating disorder pathology.

In the current study, a global EDE-Q score of ≥2.5 was used to classify athletes

with DE behavior (Rø et al., 2015). A diagnostic differentiation between eating

disorders and DE behavior exists (Reardon et al., 2019; Wells et al., 2020), but

since this study was not designed to diagnose eating disorders (no clinical

interviews were conducted), the term DE will be used for EDE-Q score ≥2.5.

Self-constructed questions about eating disorders

In accordance with previous studies investigating DE in female athletes (Sundgot-

Borgen & Torstveit, 2004), the survey in part 1 included two self-constructed

questions about eating disorder history: “Have you ever been diagnosed with an

eating disorder?” If “yes,” the following question was “What eating disorder(s)

have you been diagnosed with?” with the answer options “Anorexia Nervosa,”

Bulimia Nervosa” “Binge Eating Disorder,” or “Eating Disorder Not Otherwise

Specified/Other Specified Feeding or Eating Disorders (e.g., atypical Anorexia or

Bulimia Nervosa)” (it was possible to tick more than one diagnosis). If “no”: “Do

you think you have had an eating disorder even though you have not been

diagnosed?” with the possibility to answer: “yes,” “no,” or “I do not know.”

4.3.3 Exercise addiction

The EAI was used to assess symptoms of exercise addiction at pre- and posttest,

and again at six and 12-months follow-up for the intervention group. It includes

six general components describing the degree of addiction rated on a five-point

Likert scale: salience (exercise is the most important thing in life), conflicts (e.g.,

interpersonal conflicts due to the exercise behavior), mood modification (a coping

strategy to regulate emotions), tolerance (increasing amounts of exercise is needed

to achieve effect), withdrawal symptoms (e.g., irritability when an exercise session

is missed), and relapse (reversions to earlier patterns). The risk of addiction was

defined as an EAI score ≥ 24 (Griffiths et al., 2005) (primary exercise addiction:

EAI score ≥ 24 and EDE-Q global score < 2.5 and secondary exercise addiction:

EAI score ≥ 24 and EDE-Q global score ≥ 2.5). The EAI was originally validated

in recreational exercisers but underwent a validation in an athletic population

during the planning of the current study (Lichtenstein, Melin, et al., 2021).

4.3.4 Food intolerances

In part 1 of the study, athletes were asked: “Do you have any food allergies or

intolerances?” if “yes”: “Please specify your food allergy/intolerance” with an

open response option.

4.3.5 Sports nutrition knowledge

Sports nutrition knowledge was assessed at pre- and posttest. Although

questionnaires to assess nutrition knowledge have been developed and validated

in endurance athletes (Heikkilä et al., 2018), this type of measurement was

considered inappropriate for the FUEL study, because physical attendance was

prohibited, making it impossible for the research team to control if participants

searched for the correct answers online or in books. Instead, 20 statements that

were suitable to be read out during a telephone interview were developed with the

possibility of answering “true”, “false”, or “unsure” (Appendix VII). In addition,

participants were asked to rank their sports nutrition knowledge on five statements

in the online survey on a scale from 1 to 10 (1 = totally disagree, 10 = fully agree).

Both methods were pilot tested within a small group of female endurance athletes

in terms of relevance and readability before initiation of the study.

4.3.6 Sports nutrition‐related behavior and dietary intake

Ten questions concerning sports nutrition behavior with a scoring system were

developed (Appendix VIII) and answered by the athletes at pre- and posttest. These

questions were developed based on current sports nutrition guidelines for

endurance athletes (Burke et al., 2019; Kerksick et al., 2018; Thomas et al., 2016).

Dietary intake was assessed from a seven-day weighed food record. Athletes

received a kitchen scale via the postal service, including a comprehensive user’s

manual with instructions and a demonstration with pictures on how food and drink

should be weighed and recorded. In addition, all athletes received a telephone call

to ensure that they understood how to conduct the dietary registration correctly.

All completed dietary records were reviewed by a project member, who asked the

athlete for in‐depth answers where needed (i.e., when under‐ or overreporting was

suspected). The Norwegian and Swedish participants registered all food and

beverages in the Dietist Net Matdagbok (Kost och Näringsdata AB, Bromma,

Sweden), in which the participants were unable to see any nutritional content when

registering the food and beverages. The data were later analyzed using Dietist Net

Pro. The Irish and German participants registered food and beverages in paper

form before project members, at the respective sites, entered the data into Nutritics

(2019, Research Edition v5.09, Dublin, Ireland) and EBISpro (2016, University of

Hohenheim, Stuttgart, Germany), respectively. To account for athletes’ micro-

periodization, i.e. day-to-day variation in training volume (Stellingwerff et al.,

2019), current carbohydrate intake (g/kg body weight/day) was compared to

carbohydrate recommendations for endurance athletes (Burke et al., 2011, 2019;

Kerksick et al., 2018; Thomas et al., 2016). Accordingly, the daily carbohydrate

intake was assessed relative to the training volume of the day in question. The

following criteria for meeting carbohydrate intake recommendations were used:

training < 0 h/day: minimum 4 g/kg; training 0.5–1.5 h/day: minimum 6 g/kg;

training 1.6–3.9 h/day: min. 7 g/kg; training ≥ 4 h/day: min. 9 g/kg (Burke et al.,

2011, 2019; Kerksick et al., 2018; Thomas et al., 2016).

4.3.7 Physical activity and training

In part 1, athletes reported their average training volume, while athletes in Part 2

and 4 (see Figure 8) were instructed to use a chest‐worn heart rate monitor during

all training sessions and describe in detail all training in the online training diary

Bestr

(www.bestr.no), the same seven days as the weighed food record. Athletes,

who within the last year, had performed a maximal heart rate test in a laboratory

setting (n = 13), entered their maximal heart rate (HRmax) manually in Bestr. All

As a part of the development of the FUEL program, Bestr was translated from Norwegian into English,

Swedish, and German to enhance user-friendliness for the participants.

other athletes had their HRmax estimated via the equation integrated in Bestr: HRmax

= 208 − 0.7 × age (Tanaka et al., 2001). The time in five intensity zones was

calculated in Bestr: I1: 60–72% of HRmax; I2: 60–72% of HRmax; 72–82% of

HRmax; I3: 82–87% of HRmax; I4: 87–92% of HRmax; I5: 92–97% of HRmax. Non-

exercise activity thermogenesis was assessed with Actigraphy (ActiGraph

GT3X®, Pensacola, FL, USA) and the data analysis software ActiLife 5

(ActiGraph). Athletes were instructed to wear an accelerometer on their hip from

getting up in the morning until bedtime, and only take it off during showering,

swimming, and training. Participants received a user’s manual in print with

instructions and demonstrations with pictures on how to wear the accelerometer

and record training in Bestr. In addition, all participants received a telephone call

to ensure that they had understood the written instructions.

4.3.8 Participant evaluation

Quantitative assessment – evaluation questionnaire

One week after the post-intervention measurements, FUEL athletes were asked to

complete a questionnaire evaluating their experiences with their participation in

the FUEL intervention (Figure 8). The evaluation questionnaire consisted of 17

closed-ended and 12 open-ended questions. Of the 33 athletes who completed the

FUEL intervention with sports nutrition lectures combined with individual

counseling (FUELcombined), n = 29 (88%) filled out the evaluation questionnaire,

while seven of the 11 (64%) who completed the FUEL intervention without

individual counseling (FUELlectures) filled out the evaluation questionnaire.

Qualitative assessment – semi-structured interview

After completing the FUEL intervention, athletes were invited to participate in a

qualitative interview to elaborate on their experiences with participating in the

study (for participant information see Appendix III). The interviews were

performed by a male researcher who had not participated in either the development

of the intervention or been part of the nutrition counselor team. The interviews

were conducted using the teleconferencing platform Zoom, Zoom Video

Communication, Inc. (San Jose, California, USA). Ten athletes agreed on

participating in a qualitative interview, which was planned to take approximately

30 minutes. The interviews were conducted 1-3 months after the intervention,

giving the participants the possibility to reflect on their experiences with

intervention. The semi-structured interview guide (Appendix IX) was developed

by the project management team in collaboration with the interviewer and

contained eight general questions including supplementary questions.

4.4 The FUEL intervention

The FUEL intervention was 16 weeks of duration and involved weekly digital

lectures in sports nutrition targeting female endurance athletes at risk of REDs and

individual athlete-centered nutrition counseling every other week (Figure 10).

Figure 10. Overview of the FUEL lectures and consultations. Abbreviations: FUEL: Food and

nUtrition for Endurance athletes – a Learning program, REDs: Relative Energy Deficiency in

Sport.

4.4.1 Sports nutrition lectures

The sports nutrition lectures were grounded in evidence-based information and

guidelines (Burke et al., 2019; Mountjoy et al., 2018; Thomas et al., 2016). The

content was carefully selected based on nutrition challenges associated with REDs

in female endurance athletes, including inadequate carbohydrate and acute fueling

strategies (Carr et al., 2019; Melin et al., 2016; Snead et al., 1992), low-energy

density and high fiber intake (Barron et al., 2016; Melin et al., 2016), within-day

energy deficiency (Fahrenholtz et al., 2018), and body weight concerns (Melin et

al., 2015; Torstveit & Sundgot-Borgen, 2005b). The lectures were developed by

the Ph.D. candidate in collaboration with her three supervisors (all researchers and

practicing sports nutritionists), initially in Norwegian and Swedish, including a

comprehensive manuscript for each session, and subsequently translated into

English and German. All lectures were comprehensively reviewed and finally

approved by all four researchers. In the development of the lectures, emphasis was

placed on the dissemination of sports nutrition research in easily understandable

language, scientific sports nutrition recommendations with practical examples,

relevant and explanatory pictures, case stories, the benefits of optimal nutrition for

health and performance, and potential consequences of inadequate fueling. The

lectures enabled behavior change techniques, including “4.1 instruction on how to

perform the behavior”, “4.2 information about antecedents”, “5.1 information

about health consequences”, “5.2 salience of consequences”, and “5.5. anticipated

regret”, according to the behavior change technique taxonomy (v1) (Michie et al.,

2013). The lectures were recorded by a native speaking female sports nutrition

researcher in Norwegian, Swedish, English, and German. The lectures averaged

25.0 ± 8.4 minutes (range: 15–43; total duration: 400 minutes). Athletes received

an email with a link and password to the lecture of the week located on a closed

online platform and had the opportunity to watch the lectures when they wanted.

4.4.2 Athlete‐centered nutrition counseling

Every other week for 16 weeks athletes in the FUEL intervention group received

an individual nutrition counseling through the teleconferencing platform Zoom,

Zoom Video Communication, Inc. (San Jose, California, USA). The first

consultation was planned in project week 1 and scheduled to run for 1.5 hours,

while the following seven consultations were scheduled to run for approximately

1 hour. The actual mean duration for the first consultation was 73 ± 15 minutes,

and 55 ± 6 minutes for the remaining consultations. Three Norwegian, four

Swedish, two Irish, and one German highly experienced sports nutritionists made

up the FUEL counseling team. The four researchers designing the study (the Ph.D.

candidate and her supervisors) were not a part of the counseling team.

Several initiatives were implemented to improve standardization of the counseling

sessions, yet with room for the individual needs of each athlete. A comprehensive

FUEL counseling manual was developed, and three webinars were conducted

ahead of the intervention. FUEL counselors filled out the athlete’s journal

(Appendix V) during/after each consultation and were instructed to follow the

FUEL decision tree (Appendix VI) to encourage athletes to seek help for further

examination in the health care system when needed. Finally, weekly Zoom

meetings among the FUEL counselor team during the intervention gave rise to

discuss cases and follow the study guidelines. The weekly counselor meetings

were led by the head of nutrition at the Norwegian Olympic and Paralympic

Committee and the Confederation of Sports together with the Ph.D. candidate.

Autonomy

, competence

, and relatedness

were key components in the FUEL

counseling, which according to self-determination theory are the three innate

psychological needs of goal directed behaviors (Ryan & Deci, 2007; Sheeran et

al., 2020). To meet these human needs in the FUEL counseling sessions, a client‐

based, empathic communication approach, inspired by core skills in motivational

interviewing, was utilized (Miller & Rollnick, 2012). To guide the individual

athlete based on her individual and dynamic readiness to change (that is, change

behavior by increasing energy intake), the Transtheoretical Model of behavior

change (Norcross et al., 2011) was introduced in lecture 1 and utilized in the

individual consultations. The athlete was asked to define her readiness to change,

depending on the behavior(s) in question, by placing herself in the transtheoretical

model of behavior change and/or the FUEL counselor placed the athlete in the

model based on her assessment of the athlete.

Figure 11. Promoting athlete‐centered communication in the FUEL study. The communication

and structure of the consultations were inspired by Self-Determination Theory, Motivational

Interview, The Transtheoretical Theory of health behavior change, and The Four Habits Model.

Figure modified from Paper II.

The need to feel ownership of one’s behavior

The need to produce desired outcomes and to experience mastery

The need to feel accepted by, and meaningfully related to others

The structure of the FUEL consultations was inspired by the Four Habits Model

(Frankel & Stein, 1999) with the goal to build trust rapidly, facilitate effective

exchange of information, and demonstrate caring and concern. An illustration of

how to promote athlete‐centered communication is presented in Figure 11.

4.5 Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki at all sites

and the study was approved by the regional ethics committee in Norway (31640)

(Appendix I), Sweden (2019‐04809), and by the Norwegian Centre for Research

Data (968634) (Appendix II). Originally, the study was planned and approved to

include a wide range of REDs clinical biomarkers and measurements, including

blood markers, DXA-scans, and resting metabolic rate measurements (see

Appendix X) and a control group prior to initiation of the intervention. Due to the

COVID‐19 pandemic all physical contact with the participants was prohibited and

since the final research plan included no laboratory procedures, the study was

considered exempt from additional ethical approval at the other study sites

(Germany and Ireland).

Participants were thoroughly informed about the project aim (Appendix III) and

informed consent was obtained from all participants. All project staff were asked

to sign a confidentiality agreement (Appendix IV). Services for Sensitive Data

(TSD) were used to handle data collection and data storage in a secure

environment. The study was hypothesized to generate several health benefits and

no side effects, except for the time the participants had to invest in the project. Yet,

the intervention was adapted to each participant’s everyday life.

While this study was not designed to treat athletes with DE which requires an

interdisciplinary treatment approach (Bratland-Sanda & Sundgot-Borgen, 2013;

Joy et al., 2016), participants with elevated EDE-Q global score

were guided with

help and support for further treatment in the health care system. In addition, a

medical responsible (a psychiatrist specialized in eating disorders) was affiliated

to the project.

Following the analysis of EDE-Q data before and after the FUEL intervention, an additional ethical

application was submitted and approved. Consequently, a sub-study was conducted for athletes identified

with elevated EDE-Q scores. Data from this sub-study are not included in the present thesis.

All athletes who took part in the first phase of the study were offered personal

feedback if they were not included for further participation. The feedback included

suggestions for further progress and the opportunity to call the research team. In

addition, the encouragement to seek additional examination in the health care

system during the intervention was based on ethical considerations (see FUEL

decision tree Appendix VI). Finally, CON athletes were offered the FUEL

intervention with video lectures after the 16-week control period.

4.6 Analyses

4.6.1 Quantitative analyses

Data are presented as frequencies with percentages for binary and categorical data,

as means ± standard deviation (SD) for continuous normally distributed data, and

as median and interquartile range for non-normally distributed data. Histograms,

skewness, and kurtosis were used to verify normality of distribution of continuous

variables.

Data analyses were conducted using STATA software (version 16.0, StataCorp,

College Station, TX 77845, USA) for Paper I and JASP (version 0.16.3.0) for

Paper II, III, and IV. Analyses were conducted within the Frequentist framework

in Paper I and the Bayesian framework in Paper II, III, and IV. In comparison to

the Frequentist approach, Bayesian statistics is less sensitive to multiple testing,

thereby reducing the risk of type I errors, and is also less sensitive to small sample

sizes (Van de Schoot et al., 2014; Wagenmakers et al., 2018). Hence, Bayesian

statistics may produce reasonable results even with small to moderate sample sizes

(van de Schoot & Miočević, 2020).

In Paper I, comparisons between two independent groups were made using

unpaired Student’s t-test for normally distributed data and the Wilcoxon rank-sum

test for non-normally distributed data. Levene’s test was applied to test for equality

of variances. The chi-square test for independence was used to test for differences

between categorical outcomes between two independent groups. Pearson’s

correlation coefficient was calculated to explore associations between continuous

variables for normally distributed data, while Spearman’s correlation coefficient

was calculated for non-normally distributed data. Logistic regression models using

Firth’s bias reduction method was used to explore possible associations with the

LEA/REDs risk defined as a LEAF-Q score ≥ 8 or < 8 as the dependent variable.

Odds ratios and confidence intervals were used to explore associations in the

logistic regression model. The Wald χ2 test and accompanying P-values were used

to examine model fit. A two-tailed significance level of <0.05 was applied.

For Paper II and III, group comparisons of the baseline characteristics were

conducted using the Bayesian independent samples t‐test for normally distributed

data and the Mann–Whitney test for non‐normally distributed data, while Bayesian

contingency table tests were used to compare groups for categorical data. Group

comparisons from pre- to post-intervention were conducted using a Bayesian

repeated measures analysis of variance (ANOVA) with default priors and

compared to the null model. Non-normally distributed data were transformed using

Statistical Package for the Social Sciences (SPSS, version 28.0.1.1) but did not

change the interpretations of the results compared to analyzing the non-

transformed data. A group × time interaction effect was hypothesized, i.e., that the

FUEL intervention and the control group’s LEAF-Q scores would change

differently over time (alternative hypothesis). To calculate the BF for the

interaction effect, inclusion probabilities for matched models were considered (van

den Bergh et al., 2020).

BFs between 1 and 3 were considered to indicate weak evidence for the alternative

hypothesis, BFs between 3 and 10 were considered moderate evidence for the

alternative hypothesis, while BFs greater than 10 were considered as strong

evidence for the alternative hypothesis (van Doorn et al., 2021). Examples of

interpreting the BF: If the BFincl = 1.03 then the data is in favor of the alternative

hypothesis by being 0.03 times more likely under the alternative hypothesis than

under the null hypothesis. If the BFincl = 9 then the data is in favor of the alternative

hypothesis and the data is nine times more likely under the alternative hypothesis

than under the null hypothesis.

In Paper III, menstrual function for individual questions from the LEAF-Q was

presented in a descriptive manner due to unsatisfactory number of certain

rows/columns, thereby violating the assumption of the contingency table tests.

Within group comparisons for LEAF-Q, EDE-Q, and EAI scores for all four

measurement time points (pre-, post-intervention, six and 12-months follow-up)

were conducted using a Bayesian repeated measures ANOVA.

In Paper IV, comparisons between the FUEL intervention groups (FUELcombined

versus FUELlectures) for continuous data were investigated using the Bayesian

Mann-Whitney test.

4.6.2 Qualitative analyses

Interviews were transcribed using intelligent verbatim transcription. In total, 106

pages of single-spaced transcripts for the semi-structured interviews were

generated. Transcripts were read repeatedly by two researchers (the interviewer

and a research assistant), who were highly experienced in qualitative methodology

and methods. Given that a mixed methods approach was utilized in Paper IV,

critical realism was used as a philosophical and methodological framework

(Fletcher, 2017; Mukumbang, 2023; Ryba et al., 2022). Critical realism is useful

when researchers want to engage in explanation and causal analysis, which, in turn,

makes it useful for analyzing social problems and suggesting solutions for social

change (Fletcher, 2017). In the process of critical realism data analysis, a deductive

yet flexible coding process that drew on existing theory and literature was

primarily used (Bentley et al., 2020; Mountjoy et al., 2014, 2018, 2023). During

the coding process, the two researchers independently and systematically coded

the transcripts, and then discussed the codes to verify their interpretations of the

transcribed data material. After the coding process, the next step was the process

of abduction (Halpin & Richard, 2021). Specifically, the aim was to combine the

strengths of both inductive and deductive inquiry by reasoning from the

transcribed data material and, thus, use the data material to extend, refine, or refute

existing theories or propositions (Halpin & Richard, 2021). The final step in the

critical realism data analysis was retroduction, which focuses on causal

mechanisms and conditions (Fletcher, 2017; Mukumbang, 2023). In the critical

realism data analysis, retroduction was used to investigate the causal mechanisms

and conditions influencing female endurance athletes while participating in the

FUEL intervention. Thus, a key factor was to investigate the psychological

mechanism of athletes’ perceptions and interpretations of the FUEL intervention

content, thereby acknowledging that a psychological mechanism will not always

produce the same outcome (Mukumbang, 2023).

5 Main results

This section briefly summarizes the main findings related to the aim of the thesis.

Paper I, II, III, and IV with specific results can be found at the end of the thesis.

5.1 Risk of low energy availability

Of the 202 athletes, 65% were categorized as being at risk of LEA/REDs (Figure

12). Athletes at risk of LEA/REDs had lower body weight and BMI compared to

athletes with low risk of LEA/REDs. Menarche ≥ 15 years of age was reported by

25% of the athletes, while n = 2 had never menstruated at the age of 19 and 25,

respectively. Twenty-nine percent reported using hormonal contraceptives with 9%

citing the avoidance of amenorrhea as their reason. When excluding all hormonal

contraceptive users, 62% were at risk of LEA/REDs. Among non-hormonal

contraceptive users, 26% reported not having normal menstruation, while 12%

answered that they were unaware whether their menstruation was normal or not.

In the group of non-hormonal contraceptive users who had reported normal

menstruation (n = 90), 19% reported irregular periods and 33% reported

menstruation stoppage when exercise intensity, frequency, or duration increased.

In contrast, only two of the 202 athletes reported having a diagnosed menstrual

dysfunction.

5.2 Risk of disordered eating

Forty-three (21%) athletes were categorized with DE (Figure 12) with a higher

frequency among athletes at risk of LEA/REDs compared to low-risk athletes

(27% vs. 11%, P = 0.013). Athletes with DE had higher LEAF-Q score compared

to athletes without DE (12.6 ± 5.8 vs. 8.9 ± 4.4, P < 0.001), which was due to a

higher gastrointestinal function score (4.0 ± 2.4 vs. 1.9 ± 1.3, P < 0.001).

Correspondingly, athletes at risk of LEA/REDs had higher EDE-Q global score

compared to athletes at low risk of LEA/REDs [1.2 (0.6–2.7) versus 0.7 (0.3–1.5),

P = 0.001]. In the group of athletes categorized with DE, 42% reported having a

previous diagnosed eating disorder (Anorexia Nervosa: 21%, Bulimia Nervosa:

16%, Binge Eating Disorder: 5%, and other eating disorders: 14%), while 12% of

the athletes categorized without DE responded that they had been diagnosed with

an eating disorder in the past (Anorexia Nervosa: 6%, Bulimia Nervosa: 3%, Binge

Eating Disorder: 1%, and other eating disorders: 6%). Eight athletes (4%) reported

that they had been diagnosed with more than one eating disorder.

5.3 Risk of exercise addiction

In total, 23% (n = 47) were at risk of exercise addiction, of which 10% (n = 21)

were classified with primary exercise addiction and 13% (n = 26) were classified

with secondary exercise addiction (Figure 12). Athletes at risk of LEA/REDs had

higher EAI score compared to athletes at low risk (21.4 ± 3.5 vs. 19.8 ± 3.3, P =

0.002) even after excluding athletes with DE (20.6 ± 3.0 vs. 19.4 ± 3.1, P = 0.017).

Figure 12. The interaction between the risk of low energy availability, disordered eating behavior,

and exercise addiction. The risk of LEA/REDs is here abbreviated as LEA. In total, 65% were

classified as being at risk of low energy availability, 21% with disordered eating, and 23% with

exercise addiction. Abbreviations: DE: disordered eating, EA: exercise addiction, LEA: low

energy availability.

5.4 Food intolerances

Fourteen percent of the athletes reported having at least one food intolerance, with

no difference in prevalence between those at risk of LEA/REDs and those at low

risk (16.7% vs. 10.0%, P = 0.198). However, athletes reporting food intolerances

had a higher LEAF-Q score compared to athletes not reporting food intolerances

(11.3 ± 4.9 vs. 9.4 ± 4.9, P = 0.048) due to a higher gastrointestinal function score

(3.8 ± 1.9 vs. 2.1 ± 1.9, P < 0.001).

Logistic regression analysis

The logistic regression analysis indicated that lower BMI (OR = 0.69, P < 0.001)

and a higher EDE-Q score (OR = 1.72, P = 0.001) were significantly associated

with an increased risk of LEA/REDs. None of the other potential risk factors (EAI

score, food intolerances, and training volume) had a statistically significant

association with LEA/REDs.

5.5 Effect of the FUEL intervention on sports nutrition knowledge

The number of correct answers from the telephone interview differed at pretest

comparing the FUEL and CON athletes (FUEL pre: 14.3 ± 2.6 vs CON pre: 12.1

± 2.6, BF10 = 4.23). Nevertheless, strong evidence for the alternative hypothesis

was found in the repeated measures ANOVA analysis (BFincl = 216.93), i.e., an

interaction effect between the groups and measurement time point was present

(FUEL post: 18.3 ± 1.5; CON post: 12.6 ± 2.2).

Correspondingly, there was moderate to strong evidence for the alternative

hypothesis for four of the five questions concerning self-perceived sports nutrition

knowledge (Figure 13). The only self-rated sports nutrition knowledge statement

without an interaction effect between the groups and measurement time point was

“There has been agreement between how I have eaten and my theoretical

knowledge of sports nutrition” (BFincl = 0.94).

Figure 13. Self-perceived sports nutrition knowledge. Divided by intervention (FUEL) and

control (CON) group. Data are presented as mean ± SD. Abbreviations: BFincl: Bayes factor for

inclusion of group * time interaction, CON: control group, FUEL: FUEL intervention group.

5.6 Effect of the FUEL intervention on dietary intake

Weak evoidence for an interaction effect between group and measurement time

point was found for total energy intake, and carbohydrates (g/day and g/kg/day),

protein (g/day and g/kg/day), and fat (E%) intake (Table 6). Among FUEL athletes,

74% increased their carbohydrate intake compared to 44% in the control group

(BFincl = 1.27), while 61% in the FUEL intervention group increased their total

energy intake compared to 56% in the control group (BFincl = 0.34). When each

participant’s carbohydrate intake was compared to the current guidelines, the

FUEL intervention group met the carbohydrate recommendations 1.2 ± 1.1

days/week at pretest and 2.6 ± 1.8 days/week at posttest, while the control group

met the carbohydrate recommendations 1.4 ± 1.2 at pretest and 1.8 ± 1.8 at posttest

(BFincl = 1.20 for the group x time interaction effect).

For the sports nutrition global score, there was weak evidence (BFincl = 2.75) for the

interaction effect between group and measurement time point (FUEL: 1.7 ± 0.5 at

pretest and 2.2 ± 0.4 at posttest; CON: 1.9 ± 0.4 at pretest and 2.0 ± 0.4 at posttest).

Table 6. Dietary characteristics for the FUEL and CON group.

Data are presented as mean ± SD. Abbreviations: BFincl = Bayes factor for inclusion of group *

time interaction, CON: control group, FUEL: FUEL intervention group.

5.7 Effect of the FUEL intervention on LEA symptoms

FUEL athletes reduced the LEAF-Q total score from 12.0 ± 2.8 to 9.8 ± 4.3 (BF10

= 20.92) compared to CON athletes reducing the LEAF-Q total score from 11.0 ±

3.0 to 10.3 ± 2.5 (BF10 = 0.79) with no evidence for difference in change between

groups (BFincl = 0.85). At posttest, total LEAF-Q score was <8 for 37% (n = 11) of

the FUEL athletes and 13% (n = 2) of the CON athletes (BF10 = 1.27).

Figure 14 illustrates the number of athletes reporting eumenorrhea, which for the

FUEL group increased from 30% (n = 9 athletes) to 67% (n = 20 athletes) and

decreased for the CON group from 73% (n = 11) to 53% (n = 8). The number of

athletes reporting reduced or absence of menstrual bleedings with increased

training load decreased from n = 21 (70%) to n = 14 (47%) among FUEL athletes

while the number was unchanged among CON athletes (n = 14/73%).

Five of the 14 (36%) FUEL athletes, who reported menstrual dysfunction at pretest,

reported eumenorrhea at posttest. Among the FUEL athletes who reported

menstrual dysfunction at pretest and eumenorrhea at posttest, all had experienced

their most recent menstruation within the last 0–3 months prior to pretest. Three

FUEL athletes and one CON athlete reported secondary amenorrhea at pretest.

None of these athletes improved their menstrual function from pre- to posttest.

FUEL

CON

BFincl

Dietary intake

Week 0

Week 17

Week 0

Week 17

Energy intake (kcal/day)

2588 ± 528

2726 ± 547

2455 ± 482

2300 ± 449

1.03

Carbohydrates (g/day)

Carbohydrates (g/kg/day)

Carbohydrates (E%)

290 ± 68

326 ± 88

285 ± 65

280 ± 74

1.09

4.8 ± 1.0

5.5 ± 1.4

4.8 ± 1.0

4.7 ± 1.2

1.04

47 ± 8

50 ± 8

49 ± 5

51 ± 6

0.38

Dietary fibers (g/day)

37.5 ± 12.5

36.7 ± 12.7

36 ± 9

37 ± 15

0.37

Dietary fibers (g/1000 kcal)

14.4 ± 3.8

13.3 ± 3.1

14.9 ± 3.6

15.9 ± 5.2

0.88

Protein (g/day)

107 ± 30

115 ± 31

95 ± 19

88 ± 24

1.06

Protein (g/kg/day)

1.8 ± 0.5

1.9 ± 0.5

1.6 ± 0.3

1.5 ± 0.4

1.12

Protein (E%)

17 ± 4

18 ± 4

17 ± 4

16 ± 4

0.42

Fat (g/day)

106 ± 35

96 ± 26

97 ± 24

88 ± 23

0.36

Fat (g/kg/day)

1.8 ± 0.6

1.6 ± 0.4

1.6 ± 0.3

1.4 ± 0.4

0.38

Fat (E%)

37 ± 9

32 ± 6

35 ± 5

34 ± 7

1.43

Figure 14. Self-reported eumenorrhea at pre- and posttest. In the Low Energy Availability

Questionnaire (LEAF-Q), participants were asked: “Do you have normal menstruation?” with

the possibility to answer yes, no, or I don’t know. Self-reported eumenorrhea increased among

FUEL athletes (from 30% to 67%), while it decreased among CON athletes (from 73% to 53%).

At pretest, 23% of FUEL athletes and 20% of CON athletes were unaware whether they had

normal menstruation. All FUEL athletes were able to define whether they had normal

menstruation at posttest, while the number was unchanged among CON athletes. Abbreviations:

CON: the control group, FUEL: the FUEL intervention group.

Disordered eating and exercise addiction symptoms

The EDE-Q global score decreased from 1.03 ± 0.73 to 0.72 ± 0.69 (BF10 = 11.84)

among FUEL athletes and was unchanged among CON athletes (0.80 ± 0.74 at

pretest and 0.96 ± 0.85 at posttest, BF10 = 0.41) with weak evidence for a difference

in change between groups (BFincl = 1.86). The EDE-Q global score increased above

the 2.5 threshold at posttest for two (7%) FUEL athletes and one (7%) CON athlete

to EDE-Q global scores of 2.8, 3.0, and 2.5, respectively.

No difference in change between FUEL and CON was found for the EAI total

score (FUEL at pretest: 20.7 ± 3.0, FUEL at posttest: 20.8 ± 2.7 vs. CON at pretest:

20.6 ± 3.0, CON posttest: 21.1 ± 2.9) or any of the six EAI item scores (BFincl < 1).

5.8 Long-term changes of the FUEL intervention on LEA symptoms

As illustrated in Figure 15, strong evidence for improvement in LEAF-Q total

score was found at six (BFincl = 441) and 12-months (BFincl = 123) follow-up, which

was explained by improvements in the menstrual score (six months: BFincl = 4,486,

12-months: BFincl = 840) and the gastrointestinal score (six months: BFincl = 9.5,

12-months: BFincl = 2.3). There was weak evidence for an improvement in the

gastrointestinal score from six to 12-months follow-up (BF10= 1.2) while no

evidence was found for improvement in LEAF-Q total score, menstrual score or

injury score when comparing six and 12-months follow-up (BF10< 1).

Figure 15. Long-term changes in LEAF-Q scores. Modified from Paper III. The figure illustrates

changes in LEAF-Q total and subscale scores at pre- and post-intervention and at six and 12-

months follow-up. Changes in LEAF-Q total score (a), and the LEAF-Q subscale scores menstrual

(b), gastrointestinal (c), and injury (d) score for the FUEL athletes at pre- and postintervention

and at six and 12-months follow-up. Data are presented as mean and 95% credible intervals.

Twenty-six FUEL athletes completed the six months follow-up. Three athletes had started using

hormonal contraceptives, one reported pregnancy/breastfeeding and were therefore excluded

from the six months follow-up. Twenty-three FUEL athletes completed the 12-months follow-up.

Additional two athletes had started using hormonal contraceptives and one reported

pregnancy/breastfeeding and was therefore excluded from the12-months follow-up analysis.

Abbreviations: BFincl: bayes factor for inclusion of time interaction; LEAF-Q: Low Energy

Availability in Females Questionnaire.

Disordered eating and exercise addiction symptoms

For the EDE-Q score there was moderate evidence (BFincl = 5.18) for a reduction

comparing the four measuring points, while strong evidence was found for the EAI

score (BFincl = 31.50).

5.9 Participant evaluation of the FUEL intervention

Quantitative findings

On a scale from 1 to 10, the overall satisfaction with participating in the FUEL

project was 9.1 ± 1.1, min: 5 – max: 10 (Figure 16), with weak evidence for a

higher satisfaction among FUELcombined compared to FUELlectures (9.3 ± 0.9, min: 7,

max: 10 versus 8.0 ± 1.5, min: 5, max: 10, BF10=1.00). No evidence was found for

a difference in participants’ motivation for watching the lectures when comparing

the two groups (FUELcombined: 7.7 ± 1.9 versus FUELlectures: 7.3 ± 1.3, BF10 = 0.41).

On a scale from 1 to 10, the satisfaction level and perceived motivation for

participating in the individual nutrition consultation for FUELcombined was 9.4 ± 1.2

(min: 7, max:10).

Figure 16. Participant evaluation of the FUEL study. On a scale from 1 to 10 participants were

asked 1) Overall, how satisfied were you with your participation in the FUEL project? 2) How

did you experience your motivation for watching the FUEL lectures? 3) How satisfied were you

with the individual consultations in the FUEL project? and 4) How did you experience your

motivation for participating in the individual consultations in the FUEL project? The two latter

questions were only relevant for FUELcombined (athletes receiving FUEL lectures combined with

individual athlete-centered counseling) Abbreviations: FUEL: Food and nUtrition for Endurance

athletes – a Learning program.

Most athletes found the duration of the intervention (FUELcombined: 94%;

FUELlectures: 86%), the duration (FUELcombined: 90%; FUELlectures: 100%) and

difficulty level (FUELcombined: 72%; FUELlectures: 71%) of the lectures appropriate.

On a scale from 1 to 10 (1 being not limiting at all and 10 being very limiting),

twelve (41%) participants found it not limiting at all that the consultations took

place digitally instead of physically, while one athlete found it limiting by reporting

a score of 8. On average, the perceived limitation of the digitalized consultation

was 2.4 ± 1.7.

Qualitative findings

Three themes illustrated the breadth and depth of the participants’ experiences: 1)

the personal experiences of participating, 2) the personal benefits of participating,

and 3) suggestions for further improvements of the FUEL intervention.

Topic 1: The personal experiences of participating

Although the participants had different personal experiences prior to the study and

different motivations for their participating, all of them perceived the intervention

content to be personally relevant, educational, and helpful in their daily life as

endurance athletes. Accordingly, several participants highlighted the value of

weekly digital lectures, “It was structured super well” (P2). Another point

highlighted was that, despite variations in the perceived difficulty of the content

due to differences in participants’ age, background, and education level, many

appreciated the ability to repeatedly view the digital lectures. Moreover, a key

factor, which facilitated learning among the participants, was that the structure of

the digital lecture content and topics was presented in an orderly manner. As

expressed by one participant, “If it is precise and right to the point, you are more

likely to take experience from it” (P1). However, it is important to note that the

participants expressed some individual preferences regarding the length of the

digital lectures. For example, one participant expressed, “I would have liked to go

deeper and have longer lectures” (P4) while another participant expressed,

“Manageable, but rather short than long; 20-30 minutes would be suitable” (P5).

All participants emphasized the value of the lecture content in facilitating

consultations with the sports nutritionist, “The experience was good;

complementary and relevant learning” (P2).

All participants were pleased with the nutrition counseling, e.g. “My experience

was 10 out of 10 to be honest. She was brilliant. I cannot give her enough credit”

(P1). The participants expressed willingness to talk more freely about their

previous experiences with sports nutrition, “I’ve never liked to talk about it, but I

found it getting easier and easier” (P7). The participants emphasized the

counselors’ ability to tailor the counseling sessions to individual needs, effectively

pressing the right buttons. As one participant explained in more detail:

I really felt that I received a very personal follow-up in the counseling

sessions. In the beginning, I sort of felt that she wanted to get to know me

and how I was feeling, what I did, what I liked, and that kind of things. I

didn’t really feel that she had a question standard that she followed, but I

felt that it was personalized according to what we talked about at the last

session and what we should work on next. (P9)

Several participants noted that the digital counseling sessions, adapted to their busy

schedules, made consultations more accessible and personal. As expressed by one

participant, “Very good with Zoom since I’m busy” (P3). Yet, several participants

stated that it would have been preferable to have some in-person meetings, “The

digital counseling sessions worked, but I would have appreciated some in-person

sessions, especially in the beginning” (P6). Another participant further nuanced it

by arguing, “If I had been a full-time athlete, I would have liked to have an in-

person meeting, but for me, Zoom was absolutely top notch” (P3).

Most participants were satisfied with the counseling sessions being every-other-

week, though, one participant expressed, “Every-two-week was appropriate, but

not more frequently. Every-three-week had also worked, but not every-four-week”

(P5). The participants were in full agreement that the lectures gave them in-depth

knowledge about sports nutrition and the counseling sessions provided them with

goals and direction in their life, e.g.:

Oh yeah, 100%. Because if you just had the online lectures, you wouldn’t be

able to, if you didn’t understand something you are on your own. Whereas

if you only had the counseling sessions, you might not go into so much

depth, and it’s probably, it would go over your head because you wouldn’t

have time to talk about stuff and go to the PowerPoint-presentation as well.

So, I think the two together is a good balance and both equally important.

(P2)

Topic 2: The personal benefits of participating

The duration of the FUEL intervention was a key factor contributing to the athletes’

personal benefits of participating in the intervention. Indeed, they were close to

unanimous in saying that a 16-week intervention period was an appropriate length

of time, thereby considering the duration of the FUEL intervention to be

sustainable to build new habits in relation to sports nutrition. As expressed by one

participant, “Fantastic, because you got to incorporate it into your lifestyle” (P2).

This was a personal experience that was multifaceted due to the newly acquired

knowledge, adaptations of daily life routines, and positive bodily changes

throughout the intervention period. Accordingly, the week-to-week interplay

between digital lectures, sports nutrition counseling sessions, and testing of the

new acquired knowledge maintained the participants’ motivation and engagement,

which, in turn, led the participants to experience positive bodily changes of the

new acquired knowledge related to sports nutrition and recovery and make changes

in their daily lives. While one participant expressed, “I’ve become even more

conscious of what I already knew” (P6), another participant elaborated by saying,

“It has made me somewhat less stressed in relation to dietary intake and helped

me with meal timing” (P8). Additionally, one participant went as far as expressing,

“My patterns have changed completely since the FUEL intervention” (P1). Several

athletes were surprised about the amount of carbohydrates they needed to eat to

meet sports nutrition recommendations. One participant said, “I used to fear

carbohydrates, but now I like them” (P4). Another participant elaborated:

I would say that I’ve learned a lot, and it has given me a lot and I have

become much more confident about what is good to eat and how much is

good to eat. And it has been very good that there have been academic and

knowledgeable people who have used research to support the claims they

have made. (P9)

Finally, the participants considered who would benefit most from the intervention.

One participant expressed it this way:

You can certainly manage to get an athlete onto the right path, I think, but

I think an athlete who really struggles and has challenges with REDs and

who eats too little, and even has an eating disorder, I suspect needs to have

follow-up over a longer period. But if it’s likely that you eat relatively okay,

but it goes a bit up and down, and you don’t have huge challenges, then I

think this intervention can be enough to stabilize the athlete. (P7)

Topic 3: Suggestions for further improvements of the FUEL intervention

Although the participants were satisfied with the intervention content, some

offered suggestions for improvement. Specifically, some participants argued that

the FUEL intervention should have placed greater emphasis on promoting a

healthy body image as athletes work to adjust their body weight, e.g.:

Performing in training and in everyday life is demanding for me, and at the

same time manage to lose body weight because I’ve been too heavy and

manage to maintain a good and healthy body image at the same time as I

lose weight, is quite difficult. It has a lot to do with one’s body image. (P8)

One participant notably expressed it this way: “I’ve been reluctant to talk about it

since cycling has a weight focus” (P7).

Another suggestion for improving the FUEL intervention was to involve the

athletes’ entourage. Indeed, one of the participants expressed:

I would assume that spreading the information also to coaches, as well as

the entourage surrounding the participant could be very appropriate, and

for my part, it would be nice if my family could have some information

material regarding food type, etc. (P8)

When the participants talked about their coaches, the participants had mixed

experiences. For instance, one participant expressed:

My coach, the coach at the university, he was the one who encouraged me

to join the intervention. Even though I wasn’t able to have so much training

with him during Covid-19, he is very aware of sports nutrition as well as

enhancing performance and recovery. (P2)

While another participant put it:

My coach doesn’t talk much about it, but I think maybe if I had asked him, he would

have knowledge that could help me. But he doesn’t mention it himself. (P6)

Collectively, the findings suggest improvements could be made by placing greater

emphasis on a healthy body image and involving athletes’ entourage.

6 Discussion

The first part of the discussion will address the main results from the four included

papers, while the second part will focus on the methodological considerations.

6.1 Discussion of main results

Risk of LEA/REDs and associated risk factors

The finding in Paper I that 65% (62% when excluding hormonal contraceptive

users) of athletes were at risk of LEA/REDs is comparable to the previously

reported 61-62%, which was identified using clinically verified methods to detect

signs caused by problematic LEA in female endurance athletes (Høeg et al., 2022;

Melin et al., 2014). This indicates that despite intensified research in REDs, current

prevention strategies are too few and/or ineffective. In general, however, there is a

wide range in the reported estimated prevalence of LEA/REDs indicators, as

reflected in Table 2 and addressed by the IOC and several factors complicate the

basis of comparison between the studies (Mountjoy et al., 2023). Using the LEAF-

Q, lower risk rate (31%) has been reported when studying a smaller population (n

= 13) of cross-country skiers (Carr et al., 2019), while a higher risk rate (80%) has

been reported among female cross-country runners (Jesus et al., 2021), which is

comparable to the risk rate among runners (85%) and orienteers (78%) in the

present study. Several factors may have influenced the risk-rate reported in Paper

I, including recruitment bias if the study specifically attracted athletes

experiencing REDs symptoms (thereby overestimating the LEA/REDs risk-rate).

On the other hand, some athletes may not have participated due to fear of being

confronted with their symptoms (thereby underestimating the LEA/REDs risk-

rate). Previous studies have reported that athletes tend to neglect symptoms related

to REDs or are unaware of the reason for their symptoms (Beals & Meyer, 2007;

Brown et al., 2014; Feldmann et al., 2011; Lodge et al., 2021; S. M. Miller et al.,

2012; Mukherjee et al., 2016; Verhoef al., 2021). Therefore, it is also possible that

recruitment bias had a minor influence on the risk-rate, although a brief description

of REDs in relation to the project aim was available on the project website

(Appendix III).

Noteworthy in Paper I, 12% answered that they were unaware whether their

menstruation was normal or not, while 19% reported irregular periods, and 33%

reported menstruation stoppage when exercise intensity, frequency, or duration

increased, although they had stated that they had a normal menstruation in a

previous question. This controversy indicates a need for initiatives aiming at

improved menstrual health literacy in sport (McGawley et al., 2023). The FUEL

intervention may be such an initiative, as all participants were able to identify

whether they had normal menstruation or not after the intervention as reported in

Paper III (Figure 12).

Eating disorders and DE

are well-known risk factors for LEA and REDs

(Wasserfurth et al., 2020), supported by the results in Paper I. However, potential

underlying causes for LEA and REDs are manifold (Mountjoy et al., 2018, 2023;

Wasserfurth et al., 2020), where psychological factors are just one category of

several determinants that may influence athletes’ food choice and eating behavior

and thereby the risk of REDs (Figure 5). In the present study, approximately one

fourth of athletes at risk of LEA/REDs were categorized with DE, supported by

previous findings, where 29% of female endurance athletes with LEAF-Q score ≥

8 had a clinically verified eating disorder or DE (Melin et al., 2014). This suggests

that for most female endurance athletes, LEA is due to unintentional origins.

There may be other, less investigated, psychological factors increasing the risk of

REDs, including exercise addiction. In the present study, primary exercise

addiction coexisted with the risk of LEA/REDs in 7% of the cases (Figure 12),

suggesting that exercise addiction should be considered when treating female

endurance athletes with REDs. It is intuitive to hypothesize that increased training

volume increases the risk of exercise addiction (Lichtenstein et al., 2017), that may

result in LEA and REDs. However, this study found no associations between

training volume and EAI score nor was the item tolerance (increasing amounts of

exercise is needed to achieve effect) associated with the risk of LEA/REDs. One

possible explanation is that all participants in the present study had a high training

volume and frequency, since being a competitive endurance athlete and training at

least five times a week was one of the inclusion criteria. For these athletes,

increasing exercise volume is a natural part of their training protocol to enhance

performance and findings from this and other studies (Granziol et al., 2023;

Lichtenstein, Melin, et al., 2021) suggest, that exercise volume appears to be

It is noteworthy, that among the athletes categorized with DE, only 42% reported a previous diagnosed

eating disorder. Although this discrepancy may be a result of current versus previous eating disorder

symptoms, it may also indicate that approximately half of all individuals with an eating disorder are not

identified by the healthcare system (Joy et al., 2016).

unrelated to exercise addiction. Rather, the association between LEA/REDs

symptoms and exercise addiction was attributed to the EAI items salience

(exercise is the most important thing in life), conflicts (e.g. interpersonal conflicts

due to exercise behavior), and withdrawal symptoms (e.g. irritability when an

exercise session is missed). These personality traits related to exercise addiction

may therefore be more pronounced in female endurance athletes at risk of

LEA/REDs compared to athletes at low risk. Psychogenic stress may act

synergistically with the stress caused by LEA in disturbing the GnRH drive

(Ihalainen et al., 2024; Kuikman, Mountjoy, Stellingwerff, et al., 2021). Therefore,

it can be speculated that stress caused by e.g. interpersonal conflicts increases the

susceptibility to REDs and thus contributes to the explanation for the relationship

between risk of LEA/REDs and exercise addiction. It should be noted, however,

that the EAI score was not associated with the risk of LEA/REDs in the logistic

regression analysis. The difference in EAI score comparing athletes at risk of

LEA/REDs versus low risk of LEA/REDs was 1.6 (1.2 when excluding athletes

with DE). While this difference was statistically significant, more research is

needed to explore whether this difference is clinically relevant.

Injuries are a potential consequence of both exercise addiction (Lichtenstein et al.,

2017) and problematic LEA (Melin et al., 2014; Mountjoy et al., 2018, 2023),

which is consistent with the higher injury score found in athletes at risk of exercise

addiction compared to athletes with low risk of exercise addiction (Paper I). Since

the recruitment of the FUEL study, the EAI has been revised and expanded where

the item “I am inclined to train when (or before completely recovered from) illness

or injury” has been included (Granziol et al., 2023). It would therefore be

interesting to explore the association between LEA/REDs and exercise addiction

in female endurance athletes with this revised version of the EAI, in addition to

explore potential social harm of exercise addiction in this group of athletes.

There is inconsistent evidence when it comes to the association between BMI and

signs/symptoms of REDs. Some studies report lower BMI among female

endurance athletes with menstrual dysfunction compared to their eumenorrheic

counterparts (Christo et al., 2008; Tornberg et al., 2017), while others report no

difference (Laughlin & Yen, 1996; Melin et al., 2016). Higher BMI has been

reported among ovarian-suppressed swimmers compared to cyclic swimmers

(Vanheest et al., 2014). In the present study, athletes at risk of LEA/REDs had

lower BMI compared to athletes at low risk of LEA/REDs and a logistic regression

analysis found lower BMI being associated with the risk of LEA/REDs (Paper I).

However, it is important to notice that the vast majority (> 90%) of athletes at risk

of LEA/REDs had a BMI within the normal range. This supports that athletes with

a normal BMI should not be overlooked when screening for REDs due to potential

metabolic compensatory mechanisms (Mountjoy et al., 2014). In the latest IOC

consensus statement, low BMI is classified as a potential REDs indicator, meaning

that it is intentionally vague in its quantification and awaits further research

(Mountjoy et al., 2023).

The risk of under fueling increases when food groups are removed from the diet if

omitting proper replacement (Lis et al., 2019). Therefore, food intolerance could

be another potential origin to LEA and REDs. However, in contrast to the

hypothesis, the present study found no differences in the frequency of reported

food intolerances when comparing athletes at risk of LEA/REDs with athletes at

low risk of LEA/REDs (Paper I). Nor were food intolerances associated with the

risk of LEA/REDs in the logistic regression analysis. These findings may indicate

that athletes who report food intolerances are already aware of finding dietary

alternatives. However, athletes with food intolerances had higher LEAF-Q scores

due to elevated gastrointestinal scores compared to those without intolerances. Due

to the reliance on self-reported data, it is uncertain whether the gastrointestinal

problems are due to food intolerances or gastrointestinal adaptations resulting from

LEA with athletes possibly misinterpreting the symptoms.

Effects and long-term changes of the FUEL intervention

The FUEL study is the first sports nutrition intervention study aiming to

collectively improve nutrition knowledge, dietary behavior, and LEA/REDs

symptoms in female endurance athletes with REDs. The FUEL study utilized

theoretical behavior change techniques and standardized procedures combining

weekly digital lectures and individual consultations every other week for 16 weeks.

In Paper II, strong evidence for improved sports nutrition knowledge as well as

weak evidence for an improvement in dietary behavior after the FUEL intervention

was found. In Paper III, weak evidence was found for a lower LEA/REDs risk

rate (LEAF-Q score <8) after participating in the FUEL intervention compared to

the control condition. However, no evidence was found for better improvement in

LEAF-Q total or subscale scores comparing FUEL and CON athletes. Six and 12-

months follow-up revealed strong evidence for improved LEAF-Q total and

menstrual score while weak evidence was found for improved gastrointestinal

score among athletes participating in the FUEL intervention.

Nutrition knowledge and dietary intake

Systematic reviews report weak-to-moderate, positive associations between

nutrition knowledge and positive dietary behaviors (Heaney et al., 2011; Janiczak

et al., 2022). Education is a frequently used behavioral strategy to promote dietary

behavior change in athletes (Table 4) and improved nutrition knowledge may be

considered as an important prerequisite for behavioral change (Worsley, 2002).

Even studies targeting the treatment of FHA in non-athletes through cognitive

behavior therapy have included guidance (education) on healthy eating and

exercise habits for the participants (Berga et al., 2003; Michopoulos et al., 2013).

Therefore, education programs have been requested within the REDs prevention

field (Ackerman et al., 2020; Mountjoy et al., 2014, 2018).

During the 16-week intervention period, athletes in the present study improved

their sports nutrition knowledge with 28%, which is higher than the mean increase

(16%) based on the results of 32 studies reviewed by Tam and colleagues,

investigating the effectiveness of educational interventions designed to improve

nutrition knowledge in athletes (Tam et al., 2019). The studies in this systematic

review used interventions that were primarily delivered in a face‐to‐face group

setting, with a variety of session frequencies (down to one single session) and had

a typically intervention duration of less than four weeks with a total contact time

less than 300 minutes. Studies using online content had a lower attrition range,

while the knowledge scores increased compared to non‐technology‐based

education protocols (Tam et al., 2019). Therefore, the longer duration of the

intervention period, the higher overall contact time, and the use of a digital

approach may partly explain the greater improvement in sports nutrition

knowledge observed in this study compared to previous studies aimed at enhancing

nutrition knowledge among athletes (Tam et al., 2019). In addition, the

combination of lectures and individual athlete-centered counseling may stimulate

both declarative and procedural knowledge (Worsley, 2002), since this approach

allows athletes to ask questions about the lectures, discuss individual challenges,

and receive tools to implement the acquired knowledge into their everyday lives

(Paper IV). Potential advantages of a digital approach and the combination of

lectures and individual athlete-centered consultations will be elaborated later in the

section Participant evaluation of the FUEL intervention.

Previous intervention studies investigating endurance athletes have reported

improved sports nutrition knowledge without corresponding improvement in

dietary intake (Day et al., 2016; Dickey et al., 2016; Heikkilä et al., 2019). While

the present study did find evidence for an interaction effect between group and

measurement time point for sports nutrition related behavior (sports nutrition

global score and carbohydrate intake compared to current sports nutrition

recommendations), total energy intake, carbohydrate (g/day and g/kg/day), protein

(g/day and g/kg/day), and fat (E%) intake, the evidence was weak. This supports

that nutrition knowledge may be necessary, but not sufficient for behavior change

(Heaney et al., 2011; Janiczak et al., 2022; Pelly et al., 2022; Worsley, 2002), since

multiple determinants affect athletes’ food choice and eating behavior (Figure 5).

How improvements in nutrition knowledge better translate to dietary intake and

subsequent outcomes in athletes needs further investigation (Janiczak et al., 2024).

In the present study, a sub-analysis found weak evidence for a negative correlation

between the difference in nutrition knowledge (post-pre) and the LEAF-Q total

score (post-pre) (r = -0.323, BF10 = 1.394, unpublished data). This may indicate

that greater improvements in nutrition knowledge led to more significant

reductions in LEAF-Q score, thus a reduction of symptoms related to LEA/REDs.

Similarly, moderate evidence was found for a negative correlation between the

difference in total energy intake and in LEAF-Q total score (r = -0.367, BF10 =

2.540, unpublished data) along with weak evidence for a negative correlation

between the difference in carbohydrate intake (g/kg/day) (post-pre) and LEAF-Q

total score (post-pre) (r = -0.327, BF10 = 1.453, unpublished data). Collectively,

these sub-analyses and results from Paper II and Paper III, support previous

research linking sports nutrition knowledge with energy availability and

carbohydrate intake in female endurance athletes (Kettunen et al., 2021) and the

hypothesis that improved nutrition knowledge is an important prerequisite for

behavioral change (Worsley, 2002). While the evidence and associations (Heaney

et al., 2011; Janiczak et al., 2022) are weak-to-moderate, findings from Paper IV

suggest that the FUEL intervention may be sufficient for improving behavior in

some athletes, as will be discussed later.

Dietary intake and REDs symptoms

As reported in Paper II, the increase in energy intake was modest among FUEL

athletes after 16 weeks (138 ± 453 kcal/day, corresponding to an increase of 5%),

which may in part explain the lack of evidence for a difference in change in the

LEAF-Q score between groups (Paper III). Previous intervention studies in active

females and female athletes with REDs have reported an increase in energy intake

of 17% after six months (Cialdella-Kam et al., 2014), 18% after nine months

(Lagowska, Kapczuk, & Jeszka, 2014), and 18% after a 12-months intervention

period (De Souza et al., 2021a). In these studies, 23% (Lagowska, Kapczuk, &

Jeszka, 2014), 64% (De Souza et al., 2021a), and 88% (Cialdella-Kam et al., 2014),

respectively, restored regular menstruation after the intervention. In the present

study, 36% of the athletes reporting an irregular menstrual cycle at pretest, reported

eumenorrhea at posttest while the 16-weeks intervention was insufficient to re-

establish regular bleeding in the three athletes reporting secondary amenorrhea. In

addition, FUEL athletes had a further improvement in the menstrual score at six

months follow-up, suggesting a substantial improved energy availability. The

findings of Paper II and III may not only reflect the complexity and longtime

horizons for behavior change but also that a certain amount of time is required to

improve REDs symptoms, depending on the severity and individual factors like

the susceptibility to LEA (Mountjoy et al., 2023). Indeed, recovery of menstrual

function may take more than a year (Arends et al., 2012).

Of concern, 23% and 20% of FUEL and CON athletes, respectively, were unable

to define their menstrual status at pretest (Paper III). This response option results

in one point in the LEAF-Q, thus contributing to the difference reported in Paper

I (12%) where athletes with and without risk of LEA/REDs were included in the

analysis. Nevertheless, the findings suggest that menstrual health literacy is low

among female athletes and are supported by a recently published review

(McGawley et al., 2023) where the authors highlight that few evidence-informed

education or implementation strategies exist to improve menstrual health literacy

in sport. An important finding of Paper III is that all FUEL athletes could define

their menstrual status at posttest, with unchanged results for CON athletes,

suggesting that the FUEL intervention succeeded in increasing the awareness of

the menstrual cycle among the participants. The seven FUEL athletes who were

unaware of their menstrual status at pretest all reported eumenorrhea at posttest.

Due to the reliance of self-reported data, it is unclear whether these athletes

experienced improved menstrual function or simply became aware that they were

eumenorrheic. Therefore, the increase of eumenorrhea from pre- to posttest

[increasing from 30% (n = 9 athletes) to 67% (n = 20 athletes)] must be interpreted

with caution. Nevertheless, being aware of one’s menstrual cycle is an essential

first step in the prevention of REDs (Torstveit et al., 2023) with the FUEL

intervention being a possible strategy that is requested for improving menstrual

health literacy among athletes (McGawley et al., 2023).

Psychological factors associated with REDs

With an intervention that includes nutrition education, there is a concern that

increased nutrition awareness among participants could elevate the risk of DE, as

previously reported when investigating female ballet dancers (Doyle-Lucas &

Davy, 2011). Importantly, the FUEL intervention did not negatively affect DE or

exercise addiction symptoms (Paper III). Rather, there was weak evidence for

reduced EDE-Q global and subscale scores after the FUEL intervention compared

to the control condition and the reduction in eating disorder symptoms for FUEL

athletes remained at six and 12-month follow-up. Importantly, increased nutrition

awareness can lead to positive changes, such as higher carbohydrate intake, which

was the aim of the FUEL study. As one participant expressed it in the evaluation

questionnaire: “No, except that you think more about food. Shortly after certain

videos, more attention was paid to the intake of carbohydrates” (Paper IV). A key

element in the FUEL program was to present food as fuel rather than focusing on

body weight and to emphasize that there are no ‘good’ or ‘bad’ foods.

Dissemination of this view may therefore be fundamental in a sports nutrition

intervention for female endurance athletes.

As described in a systematic review (Sandgren et al., 2020), other successful

interventions addressing eating psychopathology in athletes have had high

participant retention rates, intervention durations of more than three weeks, and

weekly sessions, similar to the FUEL study. In addition, it was a deliberate decision

not to ask participants about body weight beyond the initial phase of the study (part

1) to maintain consistency with the intervention’s teachings, which aimed to shift

focus away from body weight and avoid provoking undesired thoughts about

weight and dieting. This ethical decision, however, was at the expense of

investigating potential associations with improved REDs symptoms, where

previous studies have reported body weight gain being associated with the

resumption of menstruation (De Souza et al., 2021a; Lagowska, Kapczuk, &

Jeszka, 2014). In addition, potential changes in body weight could influence the

calculation of relative macronutrient intake.

Participant evaluation of the FUEL intervention

This is the first study to thoroughly evaluate participants’ experiences in an

intervention aimed at improving REDs symptoms in athletes. Quantitative and

qualitative findings from Paper IV showed that the athletes were satisfied with

their participation in the FUEL intervention. They reported high motivation for

watching the lectures and participating in the counseling sessions. Additionally,

they found the duration of the intervention, the difficulty level of the lectures, and

the frequency and duration of the consultations to be appropriate. Noteworthy, the

participants did not experience the digital format of the intervention as a weakness

but rather as an advantage in a busy daily life.

The high participant satisfaction may partly explain the lower drop-out rate in the

present study (3-17%) compared to previous studies (27-60%) (Cialdella-Kam et

al., 2014; De Souza et al., 2021a, 2022; Lagowska, Kapczuk, & Jeszka, 2014;

Lagowska, Kapczuk, Friebe, et al., 2014). This may be attributed to the digital

approach and the appropriate intervention duration as indicated by the quantitative

and qualitative analyses (topic 2), the personal follow-up in the counseling sessions

(e.g., P9), and the combination of frequent sports nutrition lectures and individual

counseling (topic 1, e.g., P2), as evidenced by a lower dropout and a slightly higher

satisfaction level among participants in FUELcombined compared with FUELlectures.

Findings from Paper IV suggest that the participants valued the 16-week duration

of the intervention because it gave them the opportunity to absorb the new

knowledge, apply it in their daily life routines and training practices, become more

body conscious, and reflect on their new positive bodily experiences (topic 2, e.g.

P1, P2, and P4). This is interesting, as the results from Paper II and III found

weak or no evidence when it comes to the effectiveness of the FUEL intervention,

which may in part be due to a too short intervention duration to statistically detect

an effect in the measured outcomes. Interestingly, findings from Paper IV indicate

that the FUEL intervention may be suitable for improving behavior in some

athletes. As one athlete expressed in the evaluation questionnaire: “…I have also

received help to incorporate what we have learned in practice... Seemingly small

changes in not only food choices, but also the timing of them, have proven to be

valuable.” While others expressed in the interview: “My patterns have changed

completely since the FUEL intervention” (P1) and “I used to fear carbohydrates,

but now I like them” (P4).

Quantitative and qualitative analyses in Paper IV highlighted the benefits of the

digital approach. This contrasts with the findings by Solly et al. (2023),

investigating nutrition education preferences of Australian competitive athletes

from mixed sports (55% females). This study found that only 13% were interested

in exclusively online delivery (Solly et al., 2023). As this study also was conducted

during COVID-19, the authors speculated whether the participants were

experiencing screen fatigue which may have influenced their nutrition education

delivery preferences. Apparently, screen fatigue during the pandemic was not an

issue for the participants in the FUEL study. The included participants in the FUEL

study are likely to have significant time challenges due to a high training load in

combination with either work or studies. Hence, digital lectures that can be seen

whenever suitable in the athletes’ everyday lives may offer a sustainable means of

supporting athlete education, as discussed in a systematic review (Tam et al.,

2019). Even in non-pandemic times, the digital approach, that in this study also

included individual consultations, may be beneficial by improving retention rates,

as athletes can avoid transportation time spent to meet their nutrition

counselor/lecturer. As expressed by one of the participants in the interview: “Very

good with Zoom since I’m busy” (P3). Another expressed in the questionnaire

concerning the digital lectures: “Great that they were recorded, could watch in my

own time.” Some participants, though, would have appreciated having a few in-

person sessions at the beginning (P6).

Noteworthy, findings from Paper IV showed that the participants were surprised

by the daily amount of carbohydrates they needed to meet the recommendations,

suggesting that female endurance athletes need more evidence-based sports

nutrition and tools to implement it in their daily routines, supported by previous

studies (Carr et al., 2019; Janiczak et al., 2022; Lodge et al., 2021; Matt et al.,

2021; Melin et al., 2016; Snead et al., 1992) and results from Paper II, showing

that carbohydrate recommendations were only met 1.3 days/week at pretest. In this

context, it is worth noting that the participants found the lectures on carbohydrates

(lecture 6), REDs and performance (lecture 4), and the menstrual cycle (lecture 16)

most valuable. Emphasizing these topics could be relevant for future studies.

Suggestions for improving the FUEL program included placing greater emphasis

on promoting a positive and healthy body image. This is interesting because

athletes at risk of DE were excluded from the intervention, 44% of athletes

reported improved body satisfaction (Paper IV), and EDE-Q score decreased

following the FUEL intervention (Paper III). Still, female endurance athletes at

risk of REDs appear to be concerned about their body appearance. This is also

indicated by a previous study with female endurance athletes without eating

disorders or DE, where athletes with FHA/oligomenorrhea had a higher EDI-3

Drive for Thinness score compared to eumenorrheic athletes (Melin et al., 2016).

In leanness-focused sports, such as endurance sports, there appear to be unwritten

expectations to maintain a certain appearance or body type (Jagim et al., 2022;

Nattiv et al., 2007). Apparently, this pressure regarding body weight can be

observed even in athletes who are not classified as having DE. Lecture 14 in the

FUEL study covered a presentation of different body composition methods and

their limitations, a critical view of the “perfect body composition” to avoid

comparisons, weight regulation methods and potential physiological and mental

consequences of weight loss, and finally how and when weight loss or body

composition manipulation are relevant. To accommodate the participants’

suggestions for improving the FUEL intervention, topics like appreciations of the

body’s abilities and mindful self-compassion could be added to this lecture, themes

valuable for promoting a positive body image in female athletes (Roche et al.,

2024; Voelker et al., 2019, 2020).

Finally, participants in the present study suggested to involve athletes’ coaches and

family in the FUEL intervention (e.g., topic 3, P6 and P8). Including the athletes’

family in a sports nutrition intervention was done in a previous study with female

adolescent swimmers (Philippou et al., 2017) and the importance of including

athletes’ entourage in REDs prevention is supported by the latest IOC consensus

statement (Mountjoy et al., 2023; Torstveit et al., 2023). These individuals might

have a decisive role in the athletes’ everyday life and can, thus, be either a

promoting or inhibiting factor for behavior change. Forming new habits

necessitates focusing on factors that promote cue-dependent automaticity, such as

consistent repetition in a stable, low-friction environment, as argued in a recent

review (Verplanken & Orbell, 2022).

6.2 Methodological considerations

The methods used in the four included papers involve both strengths and

weaknesses, which may have influenced the results. These methodological

considerations will be discussed in the following section.

Study design

Paper I represents a cross-sectional design, which is ideal for determining

prevalence rates and examining the associations between multiple exposures and

outcomes (Wang & Cheng, 2020). However, a cross-sectional design is unable to

assess incidence and to make a causal inference (Wang & Cheng, 2020). Therefore,

the multiple associations presented in Paper I should be regarded as explorative,

and more studies are needed to gather further evidence on the associations between

exercise addiction, food intolerance, and the risk of LEA/REDs. Indeed, the

observed reduction in EAI score, along with the decreased LEAF-Q scores among

FUEL athletes at long-term follow-up (Paper III), suggests that further research

in this area would be valuable.

Paper II and III represent a quasi-experimental study, i.e. a study investigating

the impact of an intervention without random assignment. The justification for not

using randomization or block randomization in the FUEL study was due to the

high risk of intervention imitation, limited resources at recruitment sites, and the

priority of offering the intervention to athletes before the competition phase to

enhance compliance and facilitate the practice of nutrition strategies before a

competition. Summer sport athletes (triathletes, cyclists, runners, and orienteers)

received the intervention during winter and early spring, while winter sport athletes

received the intervention during summer and early autumn with an attempt to

target the athletes’ transition and preparation phase. It must, however, be

acknowledged that the annual plan for each individual athlete is dependent on

when the main competition takes place during the year (e.g. in April or October)

as well as the number of peaks (e.g., one or more main competitions) (Heydenreich

et al., 2017; Issurin, 2010; Stellingwerff et al., 2019). To increase the likelihood of

intervening all athletes during their preparation phase, the time point should ideally

have been tailored to each participant’s competition schedule. Unfortunately, this

was not possible due to the unpredictable circumstances and competition

cancellations caused by COVID-19 (Washif et al., 2022).

Despite our efforts to minimize the risk of intervention imitation, the lack of

random assignment may compromise internal validity, as the intervention and

control groups might not be comparable at baseline (Andrade, 2021). As evident

in Paper II, the FUEL athletes had a higher nutrition knowledge at baseline

compared to CON athletes. While the reason for this difference is unknown, level

of education could be a part of the explanation. Although not statistically

significant, a somewhat higher proportion of FUEL athletes had a university or

college degree compared to CON athletes. Given the higher baseline nutrition

knowledge score among FUEL athletes, one could argue that there is less room for

improvement. Still, strong evidence for a greater improvement in nutrition

knowledge was found for FUEL athletes compared to CON athletes.

The seasonal assignment may reduce comparability between the intervention and

the control group as the data assessment was conducted at different phases of the

athletic season for the two groups, considering training periodization and the

recommended periodization of energy and carbohydrate intake (Stellingwerff et

al., 2019). Nevertheless, an observational study, reported persistent suboptimal

energy availability throughout the season among triathletes, but not among athletes

from other sports (Jesus et al., 2022), suggesting that endurance athletes do not

necessarily periodize their energy intake. This is supported by the findings from a

systematic review, which reported that neither absolute nor relative energy intake

differed between seasonal training phases among female endurance athletes,

despite variations in total energy expenditure (Heydenreich et al., 2017).

Furthermore, the present study found no difference in self-reported or measured

baseline training or activity between FUEL and CON athletes, nor in energy or

carbohydrate intake and both groups had similar reduction in training volume from

pre- to posttest (Paper II).

Another concern of the study design is the lack of six and 12-month follow-up for

the control group, making it impossible to assess the long-term effects of the FUEL

intervention. Based on ethical considerations and time restrictions, CON athletes

were offered the intervention directly after the 16-week control period. This

enabled the inclusion of the FUELlectures group in Paper IV but was at the expense

of the methodological strength in Paper III. On the other hand, it is possible that

immediate initiation of the intervention after the post-testing ensured higher

retention, considering other intervention studies with a high dropout rate among

the control group (De Souza et al., 2021a, 2022). In the present study, an alternative

comparison could have been an expected less-optimal intervention to meet the

ethical considerations for control group and enabled long-term follow-up. This was

done by Roche et al. (2024) who compared the effect of watching educational

videos with reading pamphlets on Triad/REDs, nutrition, menstrual cycle, bone

health, and mental health (Roche et al., 2024).

The repeated testing with pre- and post-testing, six and 12-months follow-up may

have affected the internal validity (Paper II and III), since there is a risk that

participants got familiar with the questionnaires and accordingly responded based

on knowledge on the outcome they measure. In addition to a seven-day dietary and

training record, several questionnaires were included and may have resulted in

fatigue, making the participants respond less reflective or honest (Egleston et al.,

2011). Additionally, FUEL athletes also had to watch the lectures and prepare for

the individual consultations, resulting in an overall higher participant load.

Further, respondent biases must be considered (Gibson, 2005), as there is a risk

that participants in the intervention group have felt an obligation or need to respond

more favorable than pertinent to please the project members. To remedy this

potential problem, the project management group (the Ph.D. candidate and her

supervisors who had access to the participants data) did not conduct the qualitative

interviews or were a part of the nutrition counselor team. One strength of the FUEL

study, in contrast to other studies aiming to improve REDs signs/symptoms in

female athletes (Table 3) is the inclusion of a mixed-methods evaluation. Findings

from participant evaluations may contribute to the interpretation of the results and,

importantly, they are a highly valuable source to make changes and adjustments to

improve the treatment in question (Bakland et al., 2019). However, prospective

evaluation methods, such as bi-weekly interviews and immediate post-lecture

evaluation questions, could have provided a more nuanced assessment of the

FUEL study by mitigating the limitations of retrospective evaluation, which

heavily relies on participants’ memory (Gibson, 2005). Given the trade-off

between participant burden and study design quality, future studies could

incorporate prospective evaluation methods while simultaneously considering

ways to reduce participant burden in other areas.

Considering the framework Multiphase Optimization Strategy (MOST) utilized to

optimize multi-component behavioral and biobehavioral interventions (Collins,

2018), it could have been valuable to include the FUELlectures group in Paper II and

III, thereby separating the components in the FUEL intervention and investigating

differences in changes by comparing the three group. Separating intervention

components is important to ensure optimal efficiency of the intervention by not

wasting time, money, or other valuable resources in a potential future

implementation (Collins, 2018). Nevertheless, quantitative findings from Paper

IV indicated a slightly higher participant satisfaction when the individual nutrition

counseling was included. Additionally, interviews with athletes emphasized the

value of integrating both lectures and individual counseling.

Participants

First, it should be considered whether the included participants are representative

of female endurance athletes at risk of REDs. In addition to the aforementioned

risk of recruitment bias in phase 1, the risk of recruitment bias should also be

acknowledged for the qualitative part of the study, given that only 23% (10 out of

44) of all athletes completing the FUEL intervention chose to participate in this

part of the study (Demark-Wahnefried et al., 2011). While there is a concern that

the most satisfied participants chose to participate in an interview, the quantitative

part of the evaluation study had a higher response rate (82%) and demonstrated

similar positive experiences to those found in the qualitative data.

Although the inclusion criteria ensured homogeneity in aspects such as training

volume, symptoms of REDs, low risk of DE, and the absence of hormonal

contraceptive use, the sample was heterogeneous in other areas. While some

heterogeneity is needed for the sample to represent European competitive female

endurance athletes, it is important to consider these differences among participants

and their potential implications for the findings. The included participants were

tier 2-4 athletes according to the definitions by McKay et al. (2022). Some of the

athletes had job(s) or were studying, while others were full-time athletes, reflecting

a different everyday life among the participants. A systematic review found some

evidence, that elite athletes have better nutrition knowledge compared to

recreational athletes (Heaney et al., 2011), which may be explained by a greater

access to sports nutrition resources. This is, however, in contrast to the findings of

a sub-analysis in the present study with no evidence for a difference in baseline

nutrition knowledge score comparing national and professional athletes with

recreational athletes. In addition, there was no evidence for an interaction effect

between groups and measurement time point when splitting the intervention group

into these two groups. This suggests that female endurance athletes may benefit

equally from the FUEL intervention regardless of their level of competition.

Likewise, the athletes were 18-35 years old with different levels of education, and

while all were endurance athletes, they represented six different sports and four

different European countries, which in turn speaks for the individual approach.

This is supported by the results of Paper IV, reporting no cross-cultural

differences in participants’ experiences and highlight the benefits of individual

consultations customized to the individual athlete.

Originally, the project was planned with the Frequentist inference where a power

calculation is required to ensure a detection of the smallest true effect. The power

calculation suggested 42 participants in each group with LH as primary outcome

[power: 80% (0.84), level of significance: 5% (1,96)]. The expected improvement

in LH was set at 1.55 IU/L with a SD of 2.53, based on findings from athletes with

menstrual dysfunction after three months of interventions to improve energy

availability (Lagowska, Kapczuk, & Jeszka, 2014). After the cancellation of the

blood sampling, due to the pandemic restrictions, total LEAF-Q score was chosen

as an alternative primary outcome. Since no other studies had investigated changes

in LEAF-Q, an initial analysis was conducted during the recruitment phase. Based

on a SD of 4 for athletes with high LEAF-Q, the assumption of minimum clinically

relevant change of 3, a type I error (α) of 5%, and a type II error (β) of 20%, the

power calculation suggested 28 athletes in each group.

Although the expected improvement in the LEAF-Q score is theoretically founded

without any previous studies to lean on, the power calculation may indicate an

insufficient number of CON athletes in the present study. Since Bayesian statistics

is less sensitive to small sample sizes and multiple testing in comparison to

classical statistics (Van de Schoot et al., 2014; Wagenmakers et al., 2018), the

Bayesian framework was chosen for analyzing the data in Paper II and III. In

addition, the Bayesian inference is not depended on a power calculation, since the

goal is to update prior beliefs about the null hypothesis with the data and therefore

choosing Bayesian statistics is an advantage if similar studies are conducted in the

future (Van de Schoot et al., 2014; Wagenmakers et al., 2018). Nevertheless, even

though the applied analyses may be suitable for small sample sizes, small samples

are prone to contain less information compared to larger sample sizes, especially

without the possibility for specifying any prior knowledge (van de Schoot &

Miočević, 2020).

Measurements

The Low Energy Availability in Females Questionnaire

The LEAF-Q was used in Paper I to assess the risk of problematic LEA among

202 female endurance athletes and to investigate possible associated risk factors.

Only athletes with a LEAF-Q total score ≥ 8 were considered for further

participation in the FUEL intervention study where intervention effects are

presented in Paper II and III, including changes in LEAF-Q total and subscale

scores in the latter. Thus, a large part of this dissertation depends on the validity

and reliability of the LEAF-Q. Participants included in the original validation of

the LEAF-Q were Danish, Swedish, and English-speaking female elite endurance

athletes and dancers, between 18-39 years of age, who trained ≥ 5 times/week

(Melin et al., 2014), which is comparable to the population in the present study.

This enhances the chance of accurate athlete classification, achieving a sensitivity

of 78% and a specificity of 90% (Melin et al., 2014). In comparison, studies on

female athletes from other sports using the LEAF-Q have reported lower

specificity (Rogers et al., 2021). However, even with acceptable sensitivity and

specificity (Melin et al., 2014), there are risks of both false positive and false

negative cases (Gibson, 2005). The sensitivity of the LEAF-Q indicates that eight

out of 10 female athletes with current LEA and/or oligomenorrhea/FHA and/or

low bone mineral density will be correctly characterized at risk of LEA/REDs,

while the specificity indicates that nine out of 10 athletes with current higher

energy availability, eumenorrhea, and normal bone mineral density will be

correctly classified as being at low risk of LEA/REDs (Melin et al., 2014). We

tried to reduce the risk of including false positives in the intervention by adding

questions concerning self-reported diagnosis in part 1 of the study and assessments

that were possible within the restriction of the COVID-19 pandemic (see

flowchart, Figure 9). Despite these actions, false positives could still have been

included and could potentially have affected the results of Paper II and III, likely

by suggesting a lower effect of the intervention. False negative classification is

also a possibility, resulting in a lower LEA risk rate in Paper I, and potentially

lower n in Paper II-IV. In Paper I risk assessments were calculated both with and

without hormonal contraceptive users but were largely unchanged (65% versus

62%). In the analysis including hormonal contraceptive users, false negatives may

occur if the contraceptives mask an underlying menstrual dysfunction. Other forms

of hormonal contraceptives may, on the other hand, result in absence of bleeding,

which accordingly can falsely be interpreted as a menstrual dysfunction.

Accordingly, due to the absence of a true menstrual cycle, athletes using hormonal

contraceptives were excluding from the intervention. Additionally, it can be

speculated, whether the complexities and various symptoms that athletes may

exhibit due to problematic LEA (Burke et al., 2023) can result in false negatives,

if these athletes exhibit symptoms that are not captured by the LEAF-Q. Although

the validation process of the LEAF-Q considered other potential indicators of LEA

but excluded illness, dizziness, and cold sensitivity in the final questionnaire

(Melin et al., 2014), other symptom combinations may exist (Figure 3 and 4).

Although the LEAF-Q is useful for screening purposes, it cannot be used to

diagnose REDs and additional assessments is recommended (Melin et al., 2014;

Mountjoy et al., 2018, 2023; Stellingwerff et al., 2023; Torstveit et al., 2023). In

the present study, laboratory measurements with DXA scan and blood hormone

analyses were not possible due to the pandemic restrictions and the second step in

the risk-assessment relied on telephone-interviews. Therefore, to increase

sensitivity, future studies are encouraged to use the IOC REDs CAT-2 three step

model (Heikura et al., 2024; Mountjoy et al., 2023; Stellingwerff et al., 2023).

Although its use has been criticized (Agbo, 2010; Taber, 2018), Cronbach’s α

remains a frequently used measure of internal reliability. The goal is for each

individual question to correlate as highly as possible with every other question

(Taber, 2018). In the original validation of the LEAF-Q, Cronbach’s α was 0.79,

0.75, 0.61, and 0.71 for the injury, gastrointestinal function, menstrual function,

and test scale, respectively (Melin et al., 2014), while the FUEL study found

Cronbach’s α values of 0.68, 0.60, 0.51, and 0.53 for the same scales, suggesting

a lower internal reliability. One explanation for the lower Cronbach’s α may be

ascribed to the complexity of REDs where symptoms of problematic LEA can be

manifested in different ways (Burke et al., 2023; Mountjoy et al., 2023). A critique

point for the reliance on Cronbach’s α is concerned with the assumption that each

test item measures the same latent trait on the same scale (Tavakol & Dennick,

2011). Therefore, if multiple factors underlie the items on a scale, this assumption

is violated and Cronbach’s α underestimates the reliability of the test (Taber, 2018;

Tavakol & Dennick, 2011).

The Eating Disorder Examination Questionnaire

In Paper I, the risk rate of DE using an EDE-Q global score of 2.5 as cut-off was

21% compared to 24-25% earlier reported in elite female endurance athletes using

the gold standard Eating Disorder Examination Interview (Melin et al., 2015;

Sundgot-Borgen & Torstveit, 2004). Other studies have used the EDE-Q global

cut-off score ≥ 4 (Barrack et al., 2021; Kristjánsdóttir et al., 2019; Rauh et al.,

2014) based on original and clinically derived recommendations of the Eating

Disorder Examination Interview (Fairburn & Beglin, 1994) with lower (11%)

reported risk rates for DE behavior in female endurance athletes (Kristjánsdóttir et

al., 2019; Rauh et al., 2014). When utilized for screening purposes, a cut-off of 4

may underestimate the prevalence of eating disorders and a cut-off of 2.5 has been

reported as a better alternative (Rø et al., 2015) and was therefore chosen for the

present study. After the FUEL study had been conducted, a clinical interview study

including elite athletes (predominately females) reported that the EDE-Q with 2.3

as a global cut-off score detected 90% of athletes with eating disorders and no

false-positives (Lichtenstein et al., 2022). With the 2023 IOC consensus statement,

an EDE-Q global score > 2.3 is now recommended as a primary indicator of REDs

for female athletes (Mountjoy et al., 2023; Stellingwerff et al., 2023). By using this

cut-off, a later analysis revealed that seven more athletes would have been

categorized with DE in Paper I, thereby increasing the risk rate from 21% to 25%.

One of these seven athletes was included in the FUEL intervention (the six others

were excluded for other reasons) and decreased her EDE-Q global score from 2.4

at pretest to 0.7 at posttest and 0.6 at six months follow-up. At 12-month follow-

up her EDE-Q global score was 0.8. This example is interesting considering the

other EDE-Q results by generating a hypothesis that the FUEL program can assist

in eating disorder prevention among female endurance athletes.

The risk of athletes underreporting eating disorder symptoms when using

questionnaires has previously been addressed (Lichtenstein et al., 2022; Staal et

al., 2018; Sundgot-Borgen, 1993; Torstveit et al., 2008). In the present study, it is,

therefore, possible that some athletes underreported their symptoms out of fear of

being excluded from the intervention, which may have affected the risk rate

reported in Paper I. Nonetheless, the risk rate is comparable to previous findings

in female endurance athletes using the gold standard Eating Disorder Examination

Interview (Melin et al., 2015; Sundgot-Borgen & Torstveit, 2004).

The Exercise Addiction Inventory

Different questionnaires have been developed to measure negative exercise

attitudes and beliefs, which all have different advantages and limitations, and there

is no consensus about which is most appropriate (Lichtenstein et al., 2017). Other

relevant questionnaires beyond the EAI include the Exercise Dependence Scale

developed based on the seven diagnostic criteria for alcohol dependence

(Hausenblas & Downs, 2002) and the Running Addiction Scale (Chapman & De

Castro, 1990). While Kuikman et al. (2021) used the Exercise Dependence Scale

to examine the relationship between exercise dependence, DE, and LEA

(Kuikman, Mountjoy, & Burr, 2021), the EAI was chosen for the present study,

since exercise addiction addresses both the elements of compulsion and

dependence

(Berczik et al., 2012; Lichtenstein et al., 2017), it has a clear defined

cut-off, and has been suggested as the best alternative to screen for early detection

of the syndrome among endurance athletes by identifying a higher proportion of

athletes at risk (Di Lodovico et al., 2019). While the Exercise Dependence Scale

consists of 21 items, the EAI only consists of six items, thereby reducing the

participant load. In addition, the EAI can be used for multiple sports in contrast to

e.g. the Running Addiction Scale, which only targets runners.

Based on theoretical concepts of behavioral addiction, the EAI was developed and

originally validated in habitual exercisers (Terry et al., 2004). Researchers has

suggested that exercise addiction is not the same phenomenon in competitive

athletes as in non-athlete (de la Vega et al., 2016), questioning the high [23%

compared to 6% among habitual exercisers (Lichtenstein et al., 2014)] prevalence

of exercise addiction reported in Paper I. Specially for professional athletes versus

non-professional, the internal structure of the scale and the underlying construct

may differ because professional athletes are expected to perceive exercise as the

most important thing in their lives. Training and competition are their job, and they

must train daily to obtain their goals and keep the job (Lichtenstein, Melin, et al.,

2021). The present study, however, found no differences in EAI scores due to level

of competition (unreported data) and the EAI has now also been validated in a

sample of elite athletes, suggesting that the scale also is appropriate for measuring

While exercise dependence, compulsion, and addiction share some commonalities and often are used

interchangeable, they mean different things. Exercise dependence is an analogue to exercise addiction,

but it lacks the element of compulsion, since addictions involve both dependence and compulsion.

Similarly, compulsive exercise is an analogue to exercise addiction but lacks the element of dependence.

(Berczik et al., 2012).

the risk of exercise addiction in athletes (Lichtenstein, Melin, et al., 2021).

Moreover, the main reason for using the EAI was to explore associations with LEA

within a group of competitive athletes, and therefore this questionnaire seems

appropriate for this purpose.

Finally, the EAI has recently been revised and expanded with different cut-offs for

recreation exercisers and athletes (Chhabra et al., 2024; Granziol et al., 2023),

where a large cross-sectional study with 15 countries, found that exercise addiction

was twice as high among athletes compared to leisure exercisers (Chhabra et al.,

2024). It should, however, be noticed that clinical interviewing is recommended as

a supplement to questionnaire assessments of exercise addiction to examine false

positives (Lichtenstein et al., 2017), which was not a part of the present study.

Sports nutrition knowledge

In the FUEL study, 20 statements on sports nutrition specifically relevant for

female endurance athletes with REDs were developed. These statements were

inspired by a validated nutrition knowledge questionnaire for endurance athletes

(Heikkilä et al., 2018). However, they had to be suitable for a short telephone

interview due to the COVID-19 restrictions, which prohibited physical contact

with the participants. This modification was necessary to minimize the risk of

participants looking up answers in books or on the internet, which would have been

more straightforward with an online questionnaire. The nutrition knowledge

assessment tool underwent an initial content and face validation through

development by four experts within sports nutrition and later reviewed by four

other experts within the field. It was pilot tested, first among two female endurance

athletes for comprehensibility and later in a group of German sport science

students before and after watching the first five FUEL lectures, whereafter minor

adjustments were made. However, to ensure representative scores, future studies

should consider structured validations, including assessments of construct validity,

test-retest reliability, and internal consistency, as previous validation studies have

done (Heikkilä et al., 2018). Systematic reviews have criticized the use of a wide

range of unvalidated nutrition knowledge tools, which makes it difficult to

compare results across studies (Heaney et al., 2011; Janiczak et al., 2022; Tam et

al., 2019). Although it may be unfeasible to compare the sports nutrition

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knowledge scores with previous studies, the FUEL study aimed to investigate

changes from pre- to posttest, suggesting that the applied method is appropriate.

Dietary behavior

To assess dietary behavior, a seven-day prospective weighed food record was

conducted. In addition, a series of questions was developed to assess sports

nutrition related behavior and compared with current sports nutrition

recommendations. Studies using self-reported food diaries are always challenged

by errors of validity and reliability, which may include inaccurate recording of

participants’ dietary intake to improve the perception of what they eat, errors of

quantification or description in the recording, and/or alteration in dietary intake

during the recording period so that it fails to reflect the participants’ habitual intake

(Burke, 2001; Gibson, 2005; Heydenreich et al., 2017; Magkos et al., 2003).

Indeed, under-reporting has been found to account for approximately 10–45% of

total energy expenditure in male and female athletes from mixed sports when using

doubly labeled water and is mainly due to under-recording rather than under-eating

(Magkos et al., 2003). Previous studies in female endurance athletes have used the

Goldberg cut-offs to exclude under reporters (Melin et al., 2015, 2016) but this

calculation is depended on measuring resting metabolic rate and was therefore not

possible in the present study. Instead, all dietary records were thoroughly reviewed

by a FUEL project member who asked the athletes for in‐depth answers (e.g., when

under-recording was suspected) and then corrected the dietary records when

needed. This method has been found effective to reduce under-recording among

athletes, as the estimated energy intake closely matches the doubly labeled water-

determined energy expenditure (Magkos et al., 2003). However, the

aforementioned potential errors are likely to be evenly distributed between the two

groups, thereby increasing the chance of obtaining valid results relevant for the

study aim. However, there may be a risk that the nutrition education and counseling

have made the FUEL athletes more aware of the food recording process compared

to CON athletes.

The questions developed to assess sports nutrition related behavior (Appendix

VIII) were based on current sports nutrition recommendations (Burke et al., 2019;

Thomas et al., 2016). These questions aimed to complement the seven-day dietary

record in assessing athletes’ sports nutrition behavior over the last four weeks at

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pre- and posttest. The questions were pilot tested within a small group of female

endurance athletes in terms of relevance and readability but were not validated.

The goal of the FUEL study was to facilitate improved energy availability in

female endurance athletes with REDs symptoms by targeting personal

determinants of athletes’ food choice and eating behavior (Figure 5). More

specifically, the FUEL study aimed to change behaviors among female endurance

athletes to enhance energy availability, with a focus on improving sports nutrition

knowledge, but also the psychological determinants autonomous motivation and

self-efficacy. Therefore, it is a limitation that we did not measure the development

of these two latter outcomes. Intrinsic motivation (Hollembeak & Amorose, 2005)

and self-efficacy (Abood et al., 2004; Zagórska & Guszkowska, 2014) have been

assessed in sport settings but to the author’s knowledge, no instruments exist that

specifically relate to energy availability. Abood et al. (2004) used self-designed

questions to assess the development in self-efficacy after a nutrition education

intervention for female athletes. In the present study, athletes’ readiness to change

was assessed (Paper II) and their motivation for watching the FUEL lectures and

for participating in the consultations were assessed retrospectively (Paper IV).

Prospective measurement of psychological determinants for improved energy

availability, including autonomous versus controlled motivation (Clancy et al.,

2017), constitutes an opportunity for future studies.

The FUEL intervention

Previous studies targeting the improvement of REDs symptoms have provided the

participants with a prescribed diet plan (De Souza et al., 2021a, 2022; Łagowska

et al., 2014; Lagowska, Kapczuk, & Jeszka, 2014). This approach may enhance

standardization and repeatability, e.g., by prescribing an energy intake 20–40%

above baseline energy requirements (De Souza et al., 2021a, 2022). However, in a

group vulnerable to eating disorders and perfectionism, there may be a risk that

diet plans can result in an inflexible eating pattern and increase the risk of DE

(Mancine et al., 2020; Melin et al., 2015; Sundgot-Borgen & Torstveit, 2004;

Torstveit et al., 2008). Therefore, a diet plan was not the first-line approach in the

present study. Rather, the FUEL intervention utilized an athlete-centered nutrition

counseling approach inspired by core skills in Motivational Interviewing (Miller

& Rollnick, 2012). Three webinars with the nutrition counselors were conducted

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and they were provided with a FUEL counseling guide including a standardized

structure of the consultations and examples of how to approach the athlete,

depending on her readiness to change. Although these initiatives were intended to

improve standardization, they come with certain limitations that need to be

acknowledged. Motivational Interviewing has been widely implemented to help

people to change behavior, including dietary changes and eating disorder

treatment, but most studies focus on its use in smoking cessation and substance

misuse with varying quality of evidence (Frost et al., 2018). Indeed, the success of

Motivational Interviewing depends on the skill and fidelity of the counselor (Miller

& Rollnick, 2012), and since the FUEL consultations were not recorded, it is

infeasible to verify to what extent Motivational Interview techniques were used.

Recording the consultations would have introduced additional ethical concerns, as

some athletes might have felt less comfortable sharing sensitive information with

their counselor. The Transtheoretical Model has its roots within studies of smoking

cessation, but it has also been applied to a wide range of other health behaviors

(Norcross et al., 2011). While the inclusion of the model aimed to assist in tailoring

the individual consultations, its application may be challenging in practice, e.g.,

because human behavior may be more fluid and dynamic than the model accounts

for (Whitelaw et al., 2000).

Another initiative to more thoroughly describe the FUEL intervention compared

to previous studies (Table 3) was the identification of utilized behavior change

techniques (Michie et al., 2013). While the FUEL lectures enabled behavior change

techniques such as “4.1 instruction on how to perform the behavior” and “5.1

information about health consequences”, the FUEL consultations enabled behavior

change techniques such as “1.1 goal setting (behavior)”, “1.2 problem solving”,

and “3.3 social support (emotional)” (Michie et al., 2013). Recognizing that

individuals are motivated in diverse ways, the FUEL study was open for various

behavior change techniques tailored to each athlete’s needs and preferences. This

approach places great demands on the sports nutritionists’ ability to accurately

identify and address those needs and due to the lack of recording, it is not possible

to quantify which behavior change techniques were most frequently used or

determine the effectiveness of each technique. Other behavior change techniques

were less applicable to the FUEL intervention. Although we tried to implement “6.

Comparison of Behaviour” (e.g., “modelling” in lecture 16 through a case story)

(Michie et al., 2013), a group setting would more naturally have facilitated “6.2

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Social comparison” as described by Abood et al. (2004) evaluating the efficacy of

a nutrition education intervention for female college swimmers and soccer players

(Abood et al., 2004). “3.2 Social support (practical)” would have possessed an

option if family members and/or coaches were involved in the intervention, as

suggested by the participants in Paper IV. Another example of behavior change

techniques not applicable to the FUEL intervention, is “12.5 Adding objects to the

environment” (Michie et al., 2013). In non-athletic populations, access has been

identified as having large effect on behavior change, according to a recent review

synthesizing meta-analyses (Albarracín et al., 2024). Therefore, investigating the

effect of providing free access to items such as sports drinks in training arenas for

female endurance athletes would be interesting for future research.

Although all ten FUEL counselors were highly experienced in working with

athletes with REDs, individual personalities vary. Consequently, it is uncertain

whether some athletes would have responded differently to the intervention if they

had been paired with another sports nutritionist, potentially resulting in a better or

worse personal match. Nevertheless, athletes generally reported high satisfaction

with the individual consultations (Paper IV). Additionally, this variability likely

reflects real-world conditions, and the inclusion of multiple sports nutritionists

enhances the study’s external validity compared to relying on a single sports

nutritionist.

The duration of the FUEL intervention was 16 weeks, which is shorter than most

previous prospective nutritional intervention studies aimed at improving

symptoms of REDs, which lasted up to one year (Table 3). While 16 weeks may

have been too short to detect changes in REDs symptoms (Paper III) with the

applied methods, it appears sufficient for improving sports nutrition knowledge

(Paper II). In addition, the 16-week duration was found appropriate for the

participants (Paper IV). A longer intervention duration may increase the drop-out

rate, as observed in studies lasting a year (De Souza et al., 2021a, 2022). While

lack of information makes it impossible to compare the overall contact time with

the studies presented in Table 3, the FUEL intervention had a higher overall contact

time (558 minutes versus <300 minutes) compared to the studies reviewed by Tam

et al., investigating the effectiveness of educational interventions designed to

improve nutrition knowledge in male and female athletes (Tam et al., 2019). On

one hand, this may enhance the improvement in nutrition knowledge, as discussed

in section 6.1. However, if a longer intervention duration is prioritized, reducing

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the overall contact time should be considered to decrease the load on the

participants, as indicated by P5 in Paper IV. Finally, the flexibility of a digital

approach may be a favorable solution, although it may be beneficial to conduct the

first consultation as an in-person meeting (Paper IV) in non-pandemic times.

While the participant found the intervention content educative and instructive

(Paper IV), it is important to note, that the content was based on current sports

nutrition recommendations, where research primarily has been conducted with

male participants and the few studies that include females have not provided

sufficient information on menstrual status (Kuikman et al., 2023). This calls for

increased high-quality sports nutrition research that consider female physiology to

ensure that the recommendations provided to female athletes are evidence based

(Kuikman et al., 2023). Since future sports nutrition recommendations may

become more tailored to females, it is important that future interventions take any

revisions into account.

6.2.1 The influence of COVID-19

Although several points have been addressed throughout this thesis as to how the

study design and methods have been affected by the COVID-19 pandemic

(Appendix X), some additional reflections will be mentioned here.

Despite the intention of designing a practice orientated intention, the

circumstances under the COVID-19 pandemic may in different ways have

compromised the ecological validity (Mara & Peugh, 2020). First, athletes’ mental

health, eating habits and training habits may have been affected by the pandemic

(Bigalke et al., 2020; Makarowski et al., 2022; Mitra et al., 2023; Washif et al.,

2021, 2022). While the long period of isolation may have led to more restricted

eating behaviors and/or changes in training attitudes for some athletes, it is also

possible that the pandemic had the opposite effect on others. Secondly, a large

study involving 142 countries with 12,526 male and female athletes from different

sports reported that most athletes trained alone and focused on general health and

well-being rather than with sports discipline specificity during lock-down (Washif

et al., 2022). Training data from the FUEL study (Paper II) may therefore have

been influenced by the athletes’ lack of resources such as space, equipment, and

facilities but also motivation amplified by the lack of competition (Washif et al.,

2022). Thirdly, the pandemic may have influenced the recruitment of participants.

105

While some athletes may have experienced less time due to extra family-related

home tasks (Mitra et al., 2023) or reduced motivation (Washif et al., 2022), others

may have had extra time during the lockdown period or had more focus on general

health (Washif et al., 2022) and were therefore more willing to participate in a

nutrition-related research project. Conversely, three athletes withdrew in the period

between the screening process and the intervention start due to COVID-19

infection.

The sports nutritionists were instructed to follow the FUEL decision tree

(Appendix VI) to encourage athletes to seek help for further examination in the

health care system when needed, e.g., in the presence of menstrual dysfunction to

rule out other causes than FHA/oligomenorrhea. However, during the pandemic,

preventive measures against infection led to increased waiting lists for non-urgent

situations and fear of healthcare settings in some people (Radell & McGuire,

2021). In future studies it would be relevant to track the number of referrals.

Finally, changes in several health outcomes have been detected related to the

behavioral changes experienced during the pandemic including anxiety, stress,

reduced sleep quality, menstrual irregularity, reduced libido, low mood/depression,

and fatigue (Bigalke et al., 2020; Makarowski et al., 2022; Mitra et al., 2023), all

symptoms that are associated with REDs (Mountjoy et al., 2023). Indeed, the

COVID-19 lockdowns were unpredictable and were implemented at different time

points across the countries included in the FUEL study (Askim & Bergström,

2022). Consequently, potential health changes due to the pandemic may have

affected participants at various points during the measurement period, further

complicating the overall picture.

In summary, several factors caused by the COVID-19 pandemic have influenced

the FUEL study, including its methodology. Additionally, there are several external

factors that can be speculated to have impacted the data. Therefore, the results and

related interpretations should be viewed in the context of the pandemic, and an

optimized version of the FUEL study is encouraged in non-pandemic times.

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107

7 Conclusion

The overall aim of the thesis was to develop and evaluate a nutrition intervention

for female endurance athletes at risk of REDs. To accommodate this aim, a cross-

sectional study, an intervention study, and a mixed methods participant evaluation

study were conducted, resulting in four papers. The main conclusions of the thesis

are summarized below.

I: In this sample of female endurance athletes, the risk of LEA/REDs,

DE, and exercise addiction was 65%, 21%, and 23%, respectively.

Athletes at risk of LEA/REDs had more symptoms of DE and

exercise addiction compared to those at low risk of LEA/REDs.

There was no difference in the prevalence of food intolerance

between athletes at risk of LEA/REDs compared to athletes at low

risk of LEA/REDs.

II: The study provided strong evidence that the FUEL intervention

enhanced sports nutrition knowledge and weak evidence for

improvements in dietary intake and sports nutrition related behavior

among female endurance athletes at risk of REDs.

III: The study found no immediate effects of the FUEL intervention on

physiological REDs symptoms. However, there was strong evidence

for long-term improvements in menstrual function and weak

evidence for improvements in gastrointestinal function. Immediate

positive effects and long-term changes in DE symptoms, and reduced

exercise addiction symptoms at long-term follow-up were found.

IV: The participants were satisfied with the FUEL intervention, with

several positive experiences being reported both in the quantitative

and qualitative evaluations. A slightly higher satisfaction was

observed when the FUEL lectures were combined with athlete-

centered counseling compared to the FUEL lectures alone and the

benefits of this combination were highlighted. The participants found

the duration of the intervention appropriate for incorporating their

newly acquired sports nutrition knowledge into new behaviors.

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109

8 Practical implications and future directions

The cross-sectional findings of this study highlight the complexity of REDs, which

involves multiple possible risk factors and symptoms. The findings support that

DE is an important indicator when screening for REDs, but also that low BMI,

classified as a potential indicator (Mountjoy et al., 2023), should be considered.

Importantly, however, the syndrome may be even more prevalent in the absence of

DE or low BMI. According to the present study, athletes at risk of LEA/REDs seem

to have more symptoms of exercise addiction compared to athletes at low risk, but

more research is needed to clarify the role of exercise addiction as a potential REDs

indicator (Mountjoy et al., 2023), including studies using the revised EAI. Athletes

reporting food intolerance show more symptoms of LEA/REDs compared to those

without, but medical verification in practical settings and future studies is needed

to determine the cause of gastrointestinal complications.

Although the statistical evidence for increased energy intake from dietary records

was weak, participant evaluations revealed high satisfaction with the intervention

and reported both behavior changes and reduced REDs symptoms. This

controversy not only emphasizes the complexity of nutritional behavior changes

but also suggests individual differences in the intervention’s effectiveness.

Therefore, despite tailored lectures and individualized counseling, additional

intervention strategies may be necessary.

The COVID-19 restrictions have been lifted, and a new IOC REDs consensus

statement has been published (Mountjoy et al., 2023). Therefore, future studies are

encouraged to use the IOC REDs CAT2 (Heikura et al., 2024; Stellingwerff et al.,

2023) when recruiting athletes to participate in a tertiary prevention program for

REDs (Figure 17).

Although the FUEL study received positive participant experiences and aligned

with several nutrition education preferences identified in other research (Solly et

al., 2023), conducting qualitative interviews in the planning phase could give

deeper insight into athletes’ needs and preferences. This would also enable

involvement of athletes in the development of the intervention through co-design

as recommended by other researchers (Janiczak et al., 2024). Based on the results

from the qualitative analyses of the present study, future studies are encouraged to

put more emphasis on the participants’ mental health and the focus on body weight

in endurance sport (Figure 17).

110

Figure 17. Key areas to improve the FUEL study. To improve methodology and verify findings,

the FUEL study should be conducted in non-pandemic times. Focus group interviews with the

athletes during the planning enable co-design and comparison with findings from Paper IV.

Alternative study design includes three group comparisons. Prospective evaluation with questions

after watching the lectures and frequent interviews may provide a more nuanced participant

evaluation. The figure is developed based on chapters 6 and 8 with sub-elements from Colourbox.

Acknowledging that behavior change interventions can encompass a wide range

of functions beyond those utilized in the FUEL study (Michie et al., 2011),

environmental restructuring represents an additional possibility. Indeed, a recent

review conducting synthesis of meta-analyses of behavior change interventions

(non-athletic populations) concluded that access constitutes the most effective

intervention function (Albarracín et al., 2024). Therefore, it would be interesting

to investigate the effects of increased fuel access, for instance in the athletes’ sports

arenas (Figure 18).

Furthermore, broadening the perspective, prevention programs on all levels are

important in the management of REDs, involving both female, male, and

adolescent athletes, as well as athletes from sports other than endurance sports

(Torstveit et al., 2023). Additionally, prevention programs should engage coaches,

parents, managers, health and performance teams (Torstveit et al., 2023). Beyond

REDs information, primary intervention studies could include cultural changes,

including investigating the effect of strengthening protective risk factors (such as

positive body image) and reduce risk factors (such as peer pressure and

111

internalization of ideal body type). Secondary prevention studies may include

investigating the effect of regular screening within sport clubs (Torstveit et al.,

2023). Finally, deeper insights into factors that may modify the effects of LEA are

wanted, including psychogenic stress, low carbohydrate availability, and a high

intake of dietary fibers (Kuikman, Mountjoy, Stellingwerff, et al., 2021).

As a concluding remark, it is important to emphasize that REDs is a complex

syndrome (Burke et al., 2023; Jeukendrup et al., 2024). Establishing the causality

of LEA on various outcomes (as illustrated in Figures 3 and 4) is likely complicated

by both genetic and environmental factors (Stellingwerff et al., 2023), and

manipulation of energy availability in longitudinal studies (months/years) is both

time consuming, complex, costly, and unethical (Jeukendrup et al., 2024). The

REDs model was first introduced 10 years ago (Mountjoy et al., 2014) and future

research is expected to provide us with much greater insight into the syndrome, as

outlined in the applied scientific approach (Chapter 3).

Figure 18. Broadening the FUEL study's perspective for future research. The figure illustrates

examples of how the FUEL study stimulates future research in four different ways: 1) Modify

intervention function, 2) Adapt to other target groups, 3) Adjust level of prevention, and 4)

Investigate potential modifiable factors. The figure is developed based on chapter 6, Torstveit et

al. (2023) and Kuikman et al. (2021) with sub-elements from Colourbox.

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Paper I

Risk of Low Energy Availability, Disordered Eating, Exercise Addiction, and

Food Intolerances in Female Endurance Athletes

Fahrenholtz, I. L., Melin, A. K., Wasserfurth, P., Stenling, A., Logue, D., Garthe, I.,

Koehler, K., Gräfnings, M., Lichtenstein, M. B., Madigan, S., & Torstveit, M. K.

Published in Frontiers in Sports and Active Living 2022; 4: 869594

ORIGINAL RESEARCH

published: 03 May 2022

doi: 10.3389/fspor.2022.869594

Frontiers in Sports and Active Living | www.frontiersin.org 1May 2022 | Volume 4 | Article 869594

Edited by:

Stacy T. Sims,

Auckland University of Technology,

New Zealand

Reviewed by:

José Miguel Martínez Sanz,

University of Alicante, Spain

Claire Badenhorst,

Massey University, New Zealand

Dane Baker,

University of Otago, New Zealand

*Correspondence:

Ida Lysdahl Fahrenholtz

ida.fahrenholtz@uia.no

Specialty section:

This article was submitted to

Sport and Exercise Nutrition,

a section of the journal

Frontiers in Sports and Active Living

Received: 04 February 2022

Accepted: 04 April 2022

Published: 03 May 2022

Citation:

Fahrenholtz IL, Melin AK,

Wasserfurth P, Stenling A, Logue D,

Garthe I, Koehler K, Gräfnings M,

Lichtenstein MB, Madigan S and

Torstveit MK (2022) Risk of Low

Energy Availability, Disordered Eating,

Exercise Addiction, and Food

Intolerances in Female Endurance

Athletes.

Front. Sports Act. Living 4:869594.

doi: 10.3389/fspor.2022.869594

Risk of Low Energy Availability,

Disordered Eating, Exercise

Addiction, and Food Intolerances in

Female Endurance Athletes

Ida Lysdahl Fahrenholtz 1

*, Anna Katarina Melin 2, Paulina Wasserfurth 3,

Andreas Stenling 1,4, Danielle Logue 5, Ina Garthe 6, Karsten Koehler 3, Maria Gräfnings 7,

Mia Beck Lichtenstein 8, Sharon Madigan 5and Monica Klungland Torstveit 1

1Department of Sport Science and Physical Education, University of Agder, Kristiansand, Norway, 2Department of Sport

Science, Linnaeus University, Kalmar, Växjö, Sweden, 3Department of Sport and Health Sciences, Technical University of

Munich, Munich, Germany, 4Department of Psychology, Umeå University, Umeå, Sweden, 5Sport Ireland Institute, National

Sports Campus, Dublin, Ireland, 6The Norwegian Olympic and Paralympic Committee and Confederation of Sport, Oslo,

Norway, 7Department of Medical Science, Dalarna University, Falun, Sweden, 8Centre for Telepsychiatry, Mental Health

Services in the Region of Southern Denmark, Department of Clinical Research, University of Southern Denmark, Odense,

Denmark

Relative energy deﬁciency in sport (RED-S) is a complex syndrome describing health

and performance consequences of low energy availability (LEA) and is common among

female endurance athletes. Various underlying causes of LEA have been reported,

including disordered eating behavior (DE), but studies investigating the association with

exercise addiction and food intolerances are lacking. Therefore, the aim of this cross-

sectional study was to investigate the association between DE, exercise addiction and

food intolerances in athletes at risk of LEA compared to those with low risk. Female

endurance athletes, 18–35 years, training ≥5 times/week were recruited in Norway,

Sweden, Ireland, and Germany. Participants completed an online-survey comprising

the LEA in Females Questionnaire (LEAF-Q), Exercise Addiction Inventory (EAI), Eating

Disorder Examination Questionnaire (EDE-Q), and questions regarding food intolerances.

Of the 202 participants who met the inclusion criteria and completed the online survey,

65% were at risk of LEA, 23% were at risk of exercise addiction, and 21% had DE.

Athletes at risk of LEA had higher EDE-Q and EAI scores compared to athletes with

low risk. EAI score remained higher in athletes with risk of LEA after excluding athletes

with DE. Athletes at risk of LEA did not report more food intolerances (17 vs. 10%,

P=0.198), but were more frequently reported by athletes with DE (28 vs. 11%, P=

0.004). In conclusion, these athletes had a high risk of LEA, exercise addiction, and DE.

Exercise addiction should be considered as an additional risk factor in the prevention,

early detection, and targeted treatment of RED-S among female endurance athletes.

Keywords: Low Energy Availability in Females Questionnaire, compulsive exercise, endurance training, Relative

Energy Deﬁciency in Sport (RED-S), eating disorder, restrictive eating behavior

INTRODUCTION

The syndrome Relative Energy Deﬁciency in Sport (RED-S) describes impairments of multiple

physiological functions including energy metabolism, reproductive function, bone health, immune

function, protein synthesis, and cardiovascular health (Mountjoy et al., 2014, 2018). The etiology

behind this syndrome is low energy availability (LEA), which can occur with or without disordered

Fahrenholtz et al. LEA in Female Endurance Athletes

eating behavior or eating disorders (Mountjoy et al., 2014, 2018).

Psychological factors can therefore precede RED-S, but LEA may

also result in signiﬁcant psychological distress (Mountjoy et al.,

2014; Ackerman et al., 2019; Langbein et al., 2021; Rogers et al.,

2021). LEA seems to aﬀect both males and females, elite athletes

and recreational exercisers (Torstveit and Sundgot-Borgen, 2005;

Mountjoy et al., 2014, 2018), able-bodied athletes and para-

athletes (Brook et al., 2019) and across all age groups (Sharps

et al., 2021).

Although any athlete can suﬀer from RED-S, athletes

participating in weight-sensitive sports (i.e., endurance sports,

combat sports, and aesthetic sports) seem to be particularly

at risk. Endurance athletes also have high exercise energy

expenditure (Sjödin et al., 1994) as an additional risk factor

for LEA (Burke et al., 2018). Furthermore, some studies report

gender diﬀerences in the sensitivity of LEA as well as the

endocrine and metabolic responses, indicating a vulnerability of

LEA related to females compared to males (Koehler et al., 2016).

Therefore, female endurance athletes seem to be a high-risk

group for RED-S.

In female endurance athletes, the reported prevalence of LEA

ranges between 8 and 56% when deﬁned as energy availability

<30 kcal/kg fat-free mass/day assessed with food and training

records (Monyeki et al., 2014; Melin et al., 2015; Day et al., 2016;

Muia et al., 2016; Heikura et al., 2017; Mccormack et al., 2019;

Beermann et al., 2020). It has been suggested that assessing self-

reported physiological symptoms of LEA using questionnaires,

such as the Low Energy Availability in Females Questionnaire

(LEAF-Q), provides a better assessment of the overall health

status of an athlete than a snapshot of current energy availability

using error-prone assessments of dietary energy intake and

exercise energy expenditure (Heikura et al., 2017; Burke et al.,

2018; Sim and Burns, 2021). When assessed as a LEAF-Q score

≥8, the LEA risk rate ranges from 31 to 80% in female endurance

athletes (Melin et al., 2014; Folscher et al., 2015; Heikura et al.,

2017; Carr et al., 2019; Ihalainen et al., 2021; Jesus et al.,

2021).

Sustained research on factors associated with LEA is

important for practitioners and other health professionals

for early detection and aid in the nutrition interventions

to prevent potential decrements in health and performance

(Melin et al., 2019; Ackerman et al., 2020). The association

between disordered eating behavior and health consequences as

a result of LEA in female athletes is well-researched and suggest

that particular athletes from weight-sensitive sports, including

endurance sports, may be at higher risk (Gibbs et al., 2013).

While the prevalence of disordered eating behavior, which may

lead to LEA, is reported to be higher among athletes competing

in weight-sensitive sports compared with controls (Torstveit

et al., 2008; Wasserfurth et al., 2020), LEA without disordered

eating behavior is also common among female endurance athletes

(Melin et al., 2016).

Another potential reason for LEA might be the exclusion or

avoidance of diﬀerent foods due to food allergies or intolerances.

Although self-reported incidences of food intolerances and

adherence to special diets are commonly reported in athletes

(Lis et al., 2015, 2019; Logue et al., 2019), associations with

LEA and RED-S among female endurance athletes have not been

examined yet.

High levels of exercise energy expenditure without a

corresponding increase in energy intake can result in LEA (Burke

et al., 2018). It has been proposed that athletes with propensity

to exercise addiction, characterized as excessive exercise behavior

with potential negative consequences such as injuries and mental

health problems (Griﬃths et al., 2005; Lichtenstein et al., 2017),

could increase the vulnerability for RED-S (Turton et al., 2017).

We have previously reported an association between symptoms

of exercise addiction and markers of RED-S in male endurance

athletes (Torstveit et al., 2019). However, research investigating

the association between exercise addiction and symptoms of

RED-S among female endurance athletes is scarce with only one

study investigating this association in a group representing 63%

female endurance athletes (Kuikman et al., 2021). This study

reported an exacerbated risk of LEA when disordered eating is

accompanied with exercise addiction compared to disordered

eating in isolation (Kuikman et al., 2021).

Therefore, the aim of the present cross-sectional study was

to identify the risk of LEA and associated risk factors in a

multi-country cohort of competitive female endurance athletes.

Speciﬁcally, we aimed to compare disordered eating behavior,

exercise addiction, and food intolerances in athletes at risk of

LEA vs. low risk of LEA. Finally, we aimed to assess explanatory

variables for the risk of LEA in this cohort. We hypothesized that

athletes with risk of LEA would report more disordered eating

behavior, exercise addiction, and food intolerances, have lower

BMI and higher training volume compared to athletes with low

risk of LEA.

METHODS

The present analysis is based on cross-sectional data collected

during the screening and inclusion phase of an international

multicenter intervention study aiming to induce health behavior

change and improve nutritional status in female endurance

athletes with symptoms of RED-S.

Recruitment

Recruitment and data collection took place during the COVID-

19 pandemic. Participants were recruited via Norwegian,

Swedish, Irish, and German endurance competitive clubs, the

Norwegian Olympic Sport Centre, Sport Ireland Institute,

Swedish Olympic Committee, German Ski Federation, German

Olympic Sport Confederation, and social media with a link to

the project website and an online survey. Participants had to

be 18–35 years of age, competitive female endurance athletes

from cycling, long-distance running, orienteering, triathlon,

biathlon, or cross-country skiing, training ≥5 times a week.

The study was approved by the regional ethics committee in

Norway (31640), Sweden (2019-04809), and Norwegian Centre

for Research Data (968634). Because data collection occurred

remotely and included no medical procedures, the study was

considered exempt from additional approval at the other study

sites. Regardless, the study was conducted in full accordance with

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Fahrenholtz et al. LEA in Female Endurance Athletes

the Declaration of Helsinki at all sites. All data were stored and

analyzed in Services for Sensitive Data (University of Oslo, 2022).

Online Survey

All participants provided written consent to their participation

before they were given access to the survey. The survey questions

concerned the participants’ background information, including

current and past sports participation, level of competition,

best competition results, education attainment, occupation,

training volume, age, height, body weight, menstrual dysfunction

diagnosis, and food intolerances. This was followed by the

validated instruments LEAF-Q (Melin et al., 2014), Exercise

Addiction Inventory (EAI) (Terry et al., 2004), Eating Disorder

Evaluation Questionnaire (EDE-Q) (Fairburn and Beglin, 1994),

two self-constructed questions regarding history of eating

disorders, and concluded with a comment section.

Low Energy Availability in Females Questionnaire

By assessing injury frequency, the past year, current

gastrointestinal function, and current and past reproductive

function the LEAF-Q was used to consider LEA related

symptoms. The LEAF-Q has been validated in female endurance

athletes with Cronbach’s Alpha =0.61–0.79 (Melin et al., 2014).

A total score ≥8 was used to classify athletes at risk of LEA

(Melin et al., 2014). Minor clariﬁcations from the original

LEAF-Q were added to question A2:1 [“Specify how old you

were when you started taking oral contraceptives and for how

long? (Months or years in total)”], C6 [added answer option:

“0-4 weeks” (scoring 0 points), to the answer option “I am

pregnant. . . ,” “. . . /I am breastfeeding. . . ” was added] and D

[“.. . /breastfeeding” following pregnancy]. These additions do

not aﬀect the scoring key and were approved by the ﬁrst author

(AKM) of the development and validation of the LEAF-Q.

Eating Disorder Evaluation Questionnaire

The EDE-Q 6.0 was used to measure behavioral and cognitive

symptoms of eating disorders the past 28 days (Fairburn and

Beglin, 1994). The EDE-Q is based on the Eating Disorder

Examination Interview which is considered as the gold standard

in eating disorder assessment (Guest, 2000) and is one of the most

used instruments to screen for eating disorder symptoms and

risk of LEA/RED-S (Sim and Burns, 2021). It consists of 28 items

which were divided into four subscales (restraint, eating concern,

shape concern, and weight concern) and a global score averaging

the subscales, used as cut-oﬀ for eating disorder pathology. A

global EDE-Q score ≥2.5 was used to classify athletes with

disordered eating behavior (Rø et al., 2015; Kuikman et al., 2021).

The EDE-Q has been validated in an athletic population with

Cronbach’s Alpha coeﬃcients ranging from 0.81 to 0.91 in the

subscales (Lichtenstein et al., 2021a).

Self-Constructed Questions About Eating Disorders

Based on previous studies investigating disordered eating

behavior in female athletes (Sundgot-Borgen and Torstveit,

2004), the EDE-Q was followed by two self-constructed questions

regarding eating disorder history: “Have you ever been diagnosed

with an eating disorder?” If the participants answered “yes,”

the following question was “What eating disorder(s) have you

been diagnosed with?” with the answer options “Anorexia

Nervosa,”Bulimia Nervosa” “Binge Eating Disorder,” or “Eating

Disorder Not Otherwise Speciﬁed/Other Speciﬁed Feeding or

Eating Disorders (e.g., atypical Anorexia or Bulimia Nervosa)”

(multiple answers allowed). If the participants’ answered “no”

to the ﬁrst question, the following question was “Do you think

you have had an eating disorder even though you have not been

diagnosed?” with the following answer option: “yes,” “no,” or “I

do not know.”

Exercise Addiction Inventory

The EAI was used to assess the risk of exercise addiction (Terry

et al., 2004), since this tool is suggested to be more appropriate

to screen the risk of exercise addiction in speciﬁc populations

of exercisers compared to the other frequently used screening

instrument, i.e., the Exercise Dependence Scale (Di Lodovico

et al., 2019). It consists of six general components describing the

degree of addiction rated on a ﬁve-point Likert scale: salience

(exercise is the most important thing in life) conﬂicts (e.g.,

interpersonal conﬂicts due to the exercise behavior), mood

modiﬁcation (a coping strategy to regulate emotions), tolerance

(increasing amounts of exercise is needed to achieve eﬀect),

withdrawal symptoms (e.g., irritability when a exercise session is

missed), relapse (reversions to earlier patterns). Risk of addiction

was deﬁned as an EAI score ≥24 (Griﬃths et al., 2005). The EAI

was originally validated in recreational exercisers and has later

been validated in elite athletes with a Cronbach’s Alpha =0.72

(Lichtenstein et al., 2021b). Athletes were classiﬁed with primary

exercise addiction (EAI score ≥24 and EDE-Q global score <

2.5) and secondary exercise addiction (EAI score ≥24 and EDE-Q

global score ≥2.5).

Menstrual Dysfunction Diagnosis

Participants were asked: “Do you have any diagnosis related

to menstruation? [For example, polycystic ovary syndrome

(PCO/PCOS)]? With the possibility to answer “yes” or “no.”

Food Intolerances

To measure food intolerances or allergies, participants

were asked: “Do you have any food intolerance or allergy?.”

If the participants answered “yes” they were asked to

answer the following question: “Please specify your food

intolerance(s)/allergy.” To ease the readability in the paper

reported food intolerances and/or allergies are here collectively

termed food intolerances.

The online survey was pilot tested in a group of ten

females who were current or former endurance athletes and

subsequently adjusted where needed (minor clariﬁcations). The

survey took ∼15 mins to complete. A total of 208 athletes

answered the survey. After exclusion based on non-endurance

sport (badminton, n=1), age (n=2<18 years, n=1>35

years), and sex (n=2), a total of 202 responses were eligible

for analysis.

Body Mass Index

Body mass index (BMI) was calculated as weight (kg) divided by

height squared (m2). Low BMI was deﬁned as BMI <18.5 kg/m2

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Fahrenholtz et al. LEA in Female Endurance Athletes

as recommended when screening athletes for risk of LEA (Joy and

Nattiv, 2017).

Statistical Analysis

Statistical analysis was undertaken using STATA software version

16.0 (StataCorp, College Station, TX 77845, USA) with a

two-tailed signiﬁcance level of <0.05. Histograms were used

to verify normality of distribution of continuous variables.

Data are presented as mean ±standard deviation (SD) for

normally distributed data and as median and interquartile range

(IQ 25 and IQ 75 percentiles) for non-normally distributed

values. For normally distributed data, comparisons between two

independent groups were made using unpaired Student’s t-test.

To test for equality of variances, Levene’s Test was applied.

For non-normally distributed data, the Wilcoxon rank-sum test

was used to compare two independent groups. The chi-square

test for independence was used to test for diﬀerences between

categorical outcomes between two independent groups. Pearson’s

correlation coeﬃcient was calculated to explore associations

between continuous variables for normal distributed data,

while Spearman’s correlation coeﬃcient was calculated for non-

normally distributed data. Finally, logistic regression models

using Firth’s bias reduction method (Firth, 1993; Heinze and

Schemper, 2002) was used to explore possible risk factors of LEA

deﬁned as a LEAF-Q score ≥or <8 as the dependent variable.

Odds ratios and conﬁdence intervals were used to examine

associations in the logistic regression model. The Wald χ2test

and its accompanying P-value were used to examine model ﬁt.

RESULTS

Subject Characteristics and Risk of Low

Energy Availability

Subject characteristics are presented in Table 1. Endurance

athletes from Norway (n=57), Sweden (n=83), Ireland (n=

17), and Germany (n=45) were included from the following

endurance disciplines: running (n=54), orienteering (n=18),

triathlon (n=50), cycling (n=45), cross country skiing (n=

15), and biathlon (n=20). In total 65.0% of the participants were

categorized as being at risk of LEA (running: 85.2%, orienteering:

77.8%, triathlon: 50.0%, cycling: 57.8%, cross country skiing:

53.8%, biathlon: 65.0%). Athletes at risk of LEA had a lower

body weight and BMI compared to athletes with low risk of

LEA (Table 1). There were no diﬀerences in age, height, training

volume, level of competition or education comparing athletes

with risk of LEA and low risk of LEA. Nor was there a diﬀerence

in the risk rate of LEA between the countries (Norway: 73.7%,

Sweden: 67.5%, Ireland: 64.7%, Germany: 51.1%, P=0.114).

All correlations with LEA and potential risk factors for LEA

are oﬀered in Supplementary Table S1.

Twenty-ﬁve percent of the participants reported menarche

at 15 years of age or older and two had never menstruated

(current age 19 and 25 years). Twenty-nine percent (n=58)

reported using hormonal contraceptives of which 9.1% reported

using hormonal contraceptives to avoid amenorrhea. After

excluding all hormonal contraceptive users, 62% were at risk of

LEA. Among non-hormonal contraceptive users, 25.7% reported

not having normal menstruation, while 11.8%% answered that

they did not know whether their menstruation was normal or

not. Of the 62.5% who reported normal menstruation among

non-hormonal contraceptive users, 18.7% reported irregular

periods. Thirty-three percent of the non-hormonal contraceptive

users reported menstruation stoppage when exercise intensity,

frequency, or duration increased. There was no diﬀerence in

LEAF-Q score when comparing hormonal contraceptive users

with non-users (10.2 ±5.2 vs. 9.4 ±4.9, P=0.309), and

therefore hormonal contraceptive users were included in the

same manor when categorization of being at risk of LEA

vs. low risk. Among all participants, two reported having a

diagnosed menstrual dysfunction (polycystic ovary syndrome

and unspeciﬁed secondary amenorrhea).

Disordered Eating Behavior

As presented in Table 2, a total of 43 athletes (21.3%) had

disordered eating behavior with a higher frequency among those

at risk of LEA compared with those with low LEA risk (26.5

vs. 11.4%, P=0.013). Athletes with disordered eating behavior

had higher LEAF-Q total score compared to athletes without

disordered eating behavior (12.6 ±5.8 vs. 8.9 ±4.4, P<0.001),

due to a higher gastrointestinal function score (4.0 ±2.4 vs. 1.9 ±

1.3, P<0.001). There was a positive correlation between EDE-Q

score and BMI (r=0.22, P=0.002) and LEAF-Q score (r=0.37,

P<0.001). All EDE-Q subscales correlated with total LEAF-Q

score (restraint: r=0.287, P<0.001, eating concern: r=0.297,

P<0.001, shape concern: r=0.280, P<0.001, weight concern:

r=0.284, P<0.001).

Of the 43 athletes with disordered eating behavior, 41.9%

of participants reported a previous eating disorder diagnosis

(Anorexia Nervosa: 20.9%, Bulimia Nervosa: 16.3%, Binge

Eating Disorder: 4.7%, and Other EDs: 14.0%). Among the 159

athletes with low EDE-Q score, 12.0% responded they had been

diagnosed with an eating disorder in the past (Anorexia Nervosa:

5.7%, Bulimia Nervosa: 2.5%, Binge Eating Disorder: 0.6%, and

Other EDs: 5.7%).

Risk of Exercise Addiction

Athletes at risk of LEA had higher EAI score compared to athletes

with low risk (Table 3). In total, 23.3% of the athletes were at risk

of exercise addiction with a higher LEAF-Q total score compared

to athletes with low risk of exercise addiction (12.1 ±5.6 vs. 8.9

±4.5, P<0.001), due to a higher injury (3.1 ±2.2 vs. 2.2 ±2.2, P

=0.013) and gastrointestinal function score (3.5 ±2.3 vs. 2.1 ±

1.8, P<0.001). Among athletes with disordered eating behavior,

60.5% (n=26) were at risk of exercise addiction compared

to 13.2% (n=21) among athletes without disordered eating

behavior (P<0.001). That is, 10.4% (n=21) were classiﬁed

with primary exercise addiction, 12.9% (n=26) with secondary

exercise addiction, and 8.4% (n=17) had disordered without

exercise addiction. The higher EAI score in athletes with risk of

LEA compared to athletes with low risk remained however after

excluding athletes with disordered eating behavior (20.6 ±3.0

vs. 19.4 ±3.1, P=0.017). After the exclusion of athletes with

disordered eating behavior, 71.4% with risk of exercise addiction

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Fahrenholtz et al. LEA in Female Endurance Athletes

TABLE 1 | Description of subjects characterized by energy availability status.

All LEAF-Q score <8 LEAF-Q score ≥8P-value*

n=202 n=70 n=132

Age (y) 25 (21–30) 24 (19–30) 26 (21–30) 0.323

Height (cm) 169.4 ±6.3 168.8 ±5.6 169.6 ±6.6 0.394

Body weight (kg) 61.2 ±6.9 62.9 ±6.9 60.3 ±6.7 0.010

BMI (kg/m2) 21.3 ±2.0 22.1 ±2.2 20.9 ±1.8 <0.001

BMI <18.5 kg/m2(%) 7.9 4.3 9.9 0.164

Training volume (h/month) 48.2 ±19.7 45.3 ±19.5 49.8 ±19.8 0.119

Full time athlete (%) 20.8 21.4 20.5 0.871

Level of competition

Club (%) 66.4 65.7 66.7 0.994

National team (%) 13.4 14.3 12.9

Professional (%) 14.4 14.3 14.4

Other (%) 5.9 5.7 6.1

Level of education

Primary school (%) 2.0 4.3 0.8 0.248

Secondary school (%) 32.2 31.4 32.6

University/college <4 years (%) 32.2 27.1 34.9

University/college ≥4 years (%) 33.7 37.1 31.8

BMI, Body Mass Index; LEAF-Q, Low Energy in Females Questionnaire; LEAF-Q score ≥8 indicates risk of LEA. *Difference between athletes at risk of LEA and low risk of LEA.

TABLE 2 | Symptoms of disordered eating behavior characterized by energy availability status.

All LEAF-Q score <8 LEAF-Q score ≥8P-value*

n=202 n=70 n=132

EDE-Q scales

Restraint 0.6 (0.2–2.2) 0.5 (0.0–1.2) 0.9 (0.2–2.6) 0.003

Eating concern 0.6 (0.2–1.4) 0.4 (0.2–0.8) 0.7 (0.2–1.8) 0.005

Shape concern 1.5 (0.8–3.0) 1.3 (0.5–2.3) 1.8 (1.0–3.3) 0.004

Weight concern 1.4 (0.6–2.6) 1.0 (0.4–2.0) 1.6 (0.6–2.9) 0.004

EDE-Q global score 1.0 (0.5–2.4) 0.7 (0.3–1.5) 1.2 (0.6–2.7) 0.001

EDE-Q ≥2.5 (%) 21.3 11.4 26.5 0.013

EDE-Q, Eating Disorder Examination Questionnaire; LEAF-Q, Low Energy in Females Questionnaire.

LEAF-Q score ≥8 indicates risk of LEA. EDE-Q ≥2.5 indicates risk of disordered eating behavior. *Difference between athletes at risk of LEA and low risk of LEA.

TABLE 3 | Symptoms of exercise addiction characterized by energy availability status.

Exercise addiction

inventory items

All LEAF-Q score <8 LEAF-Q score ≥8P-value*

n=202 n=70 n=132

Salience 3.7 ±1.0 3.5 ±1.0 3.9 ±0.9 0.019

Conﬂicts 2.7 ±1.3 2.3 ±1.1 2.9 ±1.4 0.002

Mood modiﬁcation 3.6 ±1.0 3.5 ±0.9 3.7 ±1.0 0.434

Tolerance 4.0 ±0.9 4.0 ±0.8 4.0 ±0.9 0.733

Withdrawal symptoms 3.6 ±1.1 3.3 ±1.1 3.8 ±1.0 <0.001

Relapse 3.3 ±1.1 3.2 ±1.1 3.3 ±1.1 0.700

EAI total score 20.8 ±3.5 19.8 ±3.3 21.4 ±3.5 0.002

EAI ≥24 (%) 23.3 15.7 27.3 0.064

EAI, Exercise Addiction Inventory.

EAI ≥24 indicates risk of exercise addiction; LEAF-Q, Low Energy in Females Questionnaire.

LEAF-Q score ≥8 indicates risk of LEA. *Difference between athletes at risk of LEA and low risk of LEA.

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Fahrenholtz et al. LEA in Female Endurance Athletes

were also at risk of LEA compared to 59.4% without risk of

exercise addiction (P=0.293).

Total EAI score was positively correlated with the EDE-Q

global score (r=0.47, P<0.001). Further, the EAI score was

positively correlated with the LEAF-Q score (r=0.32, P<0.001),

even after excluding athletes with disordered eating behavior (r

=0.21, P=0.007). There was no association between training

volume and EAI score.

Distribution of scores on the EAI is oﬀered in

Supplementary Table S2.

Food-Intolerances

In total, 14.4% of the athletes responded having one or more

food intolerances, with gluten and lactose intolerance being the

most common, and a higher total LEAF-Q score compared to

athletes not reporting food intolerances (11.3 ±4.9 vs. 9.4 ±

4.9, P=0.048) due to a higher gastrointestinal function score

(3.8 ±1.9 vs. 2.1 ±1.9, P<0.001). There was no diﬀerence

in the prevalence of food intolerance between athletes at risk

of LEA compared to those with low risk (16.7% vs. 10.0%, P

=0.198). Food intolerances were more frequent among athletes

with disordered eating behavior compared to athletes without

disordered eating behavior (27.9% vs. 10.7%, P=0.004), and

among athletes at risk of exercise addiction compared to athletes

with low risk of exercise addiction (29.8 vs. 9.7%, P=0.001).

Food intolerances were, however, not more frequent among

athletes with exercise addiction compared to athletes with low

risk of exercise addiction after excluding athletes with disordered

eating behavior (19.0 vs. 9.4%, P=0.183). Among the 29

athletes who reported food intolerances, 38.0% (n=11) had

simultaneously both disordered eating behavior and were at risk

of exercise addiction.

Explanatory Variables for the Risk of LEA

Results from the logistic regression analysis are presented in

Table 4. The statistically signiﬁcant Wald χ2test indicated a

better model ﬁt when including the predictors in the model. The

logistic regression analysis showed that lower BMI (OR =0.69, P

<0.001) and higher EDE-Q score (OR =1.72, P=0.001) were

associated with risk of LEA. None of the other predictors had a

statistically signiﬁcant association with LEA.

DISCUSSION

The main purpose of the study was to identify the risk of LEA

and associated risk factors in this group of athletes, including

exercise addiction and food intolerances where research has been

lacking. Speciﬁcally, we aimed to compare disordered eating

behavior, exercise addiction, and food intolerances in athletes at

risk of LEA and low risk of LEA. A key novel ﬁnding of the

study was that athletes at risk of LEA were more likely to exhibit

symptoms of exercise addiction related to salience, conﬂicts, and

withdrawal symptoms.

By using the LEAF-Q we found that 65.0% of the athletes were

at risk of LEA, which is similar to the 62.2% clinically veriﬁed

LEA in female endurance athletes by Melin et al. (2014). When

compared to other studies which used the LEAF-Q in female

TABLE 4 | Risk factors for low energy availability.

n=202 Wald χ2=P<0.001

Variables OR 95% CI P-value

BMI 0.69 0.57 – 0.83 <0.001

Training volume 1.01 0.99 – 1.03 0.262

EDE-Q global score 1.72 1.22 – 2.41 0.001

EAI total score 1.03 0.93 – 1.14 0.615

Food intolerances 1.31 0.49 – 3.49 0.538

A statistically signiﬁcant P value (<0.05) indicates that excluding the predictors from the

model will substantially worsen model ﬁt (i.e., the predictors add something to the model),

whereas a non-signiﬁcant P-value indicates that excluding the predictors will not worsen

model ﬁt (i.e., they do not add anything to the model).

BMI, Body Mass Index; EAI, Exercise Addiction Inventory; EDE-Q, Eating Disorder

Examination Questionnaire; LEAF-Q: Low Energy in Females Questionnaire.

endurance athletes, Carr et al. (2019), reported a lower number

(31.0%) than the present study and Jesus et al. (2021) a higher

number (79.5%.) In contrast to the present study, the study by

Jesus et al. (2021) recruited only female athletes from cross-

county running (n=83) and when comparing their reported

risk rate of LEA, it is similar to the risk rate among runners

(85.2%) and orienteers (77.8%) in the present study. Carr et al.

(2019) also had a lower number of participants (n=13) only

from cross-country skiing, which may partly explain the lower

reported risk rate of LEA. Of note, the current cross-sectional

study served as a screening phase for an intervention study

aiming to improve dietary habits in athletes at risk of RED-S.

Therefore, the recruitment may have attracted athletes who were

interested in improving knowledge related to sports nutrition.

Conversely, others may have not participated due to fears or

anxiety coming from their LEA, disordered eating behavior

and/or exercise addiction.

In this study we did not directly assess energy availability,

which requires assessments of body composition, energy intake,

and exercise energy expenditure. Although it is well-recognized

that LEA is the etiological factor underpinning the syndrome

of RED-S (Mountjoy et al., 2014), several barriers prohibit the

direct measurement of energy availability from being a practical

and reliable option (Heikura et al., 2017; Burke et al., 2018;

Fahrenholtz et al., 2018; Mountjoy et al., 2018). Questionnaires

can therefore be a convenient method for screening, with

the LEAF-Q (Melin et al., 2014) and the EDE-Q (Fairburn

and Beglin, 1994) being the most widely used and validated

questionnaires in the research of RED-S (Sim and Burns, 2021).

We emphasize that although the LEAF-Q is useful for

screening purposes it cannot be used as a diagnostic tool and

additional individual evaluation is necessary in intervention

contexts (Melin et al., 2014; Mountjoy et al., 2018). Despite

acceptable sensitivity (78%) and speciﬁcity (90%) (Melin et al.,

2014), there are risks of either false positive or false negative

classiﬁcations. For instance, false positive cases may be due to

injuries, gastrointestinal problems, and menstrual dysfunctions

not related to LEA, including acute injuries, irritable bowel

syndrome, or polycystic ovary syndrome, although features of

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Fahrenholtz et al. LEA in Female Endurance Athletes

polycystic ovary syndrome have been found to be prevalent

in women with functional hypothalamic amenorrhea (Carmina

et al., 2018). False negative cases may occur if participants use

oral contraceptives, that may mask an underlying menstrual

dysfunction. Other forms of hormonal contraceptives may, on

the other hand, result in absence of bleeding, which accordingly

can falsely be interpreted as a menstrual dysfunction.

Of note, 12.0% answered that they did not know whether

they had a normal cycle and 18.7% of those who reported

normal menstruation answered that they had irregular periods.

This controversy indicates that a relatively high frequency

of adult female endurance athletes lacks basic knowledge of

female reproduction. In addition, only two athletes reported

a diagnosed menstrual dysfunction, despite a high occurrence

of amenorrhea and oligomenorrhea, which may relate to a

perception that menstrual dysfunction often is accepted as a

natural consequence of intense training programs (Beals and

Meyer, 2007) or lack of awareness about the consequences of

LEA among medical professionals (Curry et al., 2015). This is

concerning since the menstrual cycle is an important health

marker and calls for educational interventions on menstrual cycle

function for female endurance athletes but also awareness among

medical professionals.

In the present study, athletes at risk of LEA had lower BMI

compared to athletes with a low risk of LEA and lower BMI

was identiﬁed as a risk factor in the logistic regression analysis.

Similar to our ﬁndings Christo et al. (2008) reported lower BMI

in female endurance athletes with amenorrhea compared to their

eumenorrheic counterparts. In contrast, Vanheest et al. (2014)

reported higher BMI in amenorrhoeic swimmers compared to

eumenorrheic swimmers, while Melin et al. (2014, 2015) found

no diﬀerence in BMI comparing female endurance athletes at risk

of LEA with those with a low risk of LEA. Thus, research seems

conﬂicting when it comes to the relationship between BMI and

LEA. In addition, it is important to notice, that the vast majority

of athletes at risk of LEA in the current study had a BMI within

the normal range, supporting that BMI alone should not be used

for screening of RED-S due to potential metabolic compensatory

mechanisms as a result of LEA (Mountjoy et al., 2014).

In this study, athletes at risk of LEA had higher EAI scores

compared to athletes with low risk of LEA. Our results indicate

that female endurance athletes with LEA are more likely to

have symptoms of exercise addiction. However, in approximately

half of the cases of athletes exceeding the cut-oﬀ point, exercise

addiction was accompanied with disordered eating behavior.

Therefore, both dietary patterns and exercise behavior should be

addressed when treatment interventions for athletes with RED-

S are developed. Although exercise addiction is often associated

with excessive exercise (Lichtenstein et al., 2017) that may result

in LEA, we found no association between training volume

and EAI score, which is consistent with previous research in

athletes (Lichtenstein et al., 2021b). Nor did we ﬁnd a diﬀerence

in the EAI item concerning increasing exercise amount when

comparing athletes at high vs. low risk of LEA. One explanation

for this can be that all participants in the current study had a

high training volume and frequency, since this was a part of the

inclusion criteria (being a competitive endurance athletes and

training at least ﬁve times per week). For competitive endurance

athletes it is a natural part of their training protocol to increase

exercise amount to improve performance. However, we found

an association between LEA and the EAI items concerning the

importance exercise is attributed in life, conﬂicts with family

and friends, and negative feelings if an exercise session is

missed. The ﬁndings suggest that these exercise addiction related

behaviors are more pronounced in athletes at risk of LEA, and

therefore practitioners should pay attention to these personality

characteristics when screening for LEA and RED-S.

Injuries are a potential consequence of both exercise addiction

(Lichtenstein et al., 2017) and LEA (Mountjoy et al., 2014) and

questions related to injuries are a part of the LEAF-Q due to its

association to low bone mineral density (Melin et al., 2014). We

found a higher injury score in athletes at risk of exercise addiction

compared to athletes with low risk of exercise addiction, which

may be a part of the explanation for the association between risk

of LEA and exercise addiction in this group.

As reviewed by Di Lodovico et al. (2019) endurance sports

are characterized with the highest risk of exercise addiction

with a weighted average of 14.2% compared to 10.2% in mixed

disciplines and 3.0% in the general population, although the

reported risk rate spans from 0.5 to 43.0%. The risk rate of 23.3%

reported in the present study is therefore considerably higher

compared to previous research. The review did not distinguish

between male and female responders, although female triathletes

have been found to be at higher risk of exercise addiction

compared to males (Griﬃths et al., 2015). Hence, one potential

reason for the diﬀerences in the results may be that our studied

population is at high risk of disordered eating behavior. Exercise

addiction is a frequent comorbid condition in female patients

with eating disorders, often termed secondary exercise addiction,

and therefore, eating disorder symptoms should be considered

separately in the assessment of exercise addiction (Cook and

Luke, 2017). Among athletes without disordered eating behavior

(n=159), 13.2% (n=21) were at risk of exercise addiction,

suggesting a risk rate of 10.4% for primary exercise addiction

for the total sample. This is higher than the 4% among female

athletes reported by Kuikman et al. (2021). Beyond recruiting

athletes from diﬀerent sports with no upper age limit, the study

by Kuikman et al. (2021) diﬀers from the present study by using

the Exercise Dependence Scale, which generally identiﬁes a lower

proportion of individuals at risk of exercise addiction compared

to the EAI (Di Lodovico et al., 2019). However, the occurrence of

secondary exercise addiction in the present study (13%, n=26)

is similar to the 13% reported by Kuikman et al. (2021).

Since the EAI was originally validated in habitual exercisers,

the interpretation of the questions by athletes may diﬀer and

researchers have suggested that exercise addiction is not the

same phenomenon in competitive athletes as in non-competitive

athletes (de la Vega et al., 2016). However, a recent study reported

adequate psychometric properties of the EAI in a sample of elite

athletes, suggesting that the EAI is useful for assessing symptoms

of exercise addiction in competitive athletes (Lichtenstein et al.,

2021b).

Disordered eating behavior and eating disorders are a well-

known risk factors for LEA (Wasserfurth et al., 2020) supported

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Fahrenholtz et al. LEA in Female Endurance Athletes

by the ﬁndings in the present study. Nevertheless, the reasons

for LEA are manifold (Wasserfurth et al., 2020) and in the

present study, only 26.5% of the athletes at risk of LEA had

disordered eating behavior, supporting the ﬁndings of Melin

et al. (2014) where 28.6% of the female endurance athletes with

a total LEAF-Q ≥8 had a clinically veriﬁed eating disorder or

disordered eating behavior. Our results suggests that for the

majority of the athletes, LEA is due to unintentional origins

such as suppression of appetite post exercise (Larson-Meyer

et al., 2012; Howe et al., 2016), low energy-dense diets (Melin

et al., 2015), lack of knowledge regarding optimal sports nutrition

(Benardot, 2013; Trakman et al., 2016; Heikkilä et al., 2018; Sim

and Burns, 2021), lack of knowledge about the consequences

of LEA (Folscher et al., 2015; Condo et al., 2019; Tosi et al.,

2019; Logue et al., 2020) or a busy lifestyle with frequent

traveling where lack of time and access to food become important

barriers to adequately fueling (Benardot, 2013; Burke et al., 2018;

Logue et al., 2021). Nevertheless, disordered eating behavior

was common among this group of female endurance athletes

with a risk rate of 21.3% using a EDE-Q global score 2.5 as

cut-oﬀ compared to 24-25% earlier reported in elite female

endurance athletes (Sundgot-Borgen and Torstveit, 2004; Melin

et al., 2015) using the gold standard EDE Interview (Fairburn and

Beglin, 1994). Collectively, these results suggest a concerning and

persistently high prevalence of disordered eating among female

endurance athletes.

Another potential origin to LEA could be food intolerances,

since the risk of energy deﬁciency increases when food groups

are removed from the diet, if proper replacements are not made

(Lis et al., 2019). However, in contrast to our hypothesis, we

did not ﬁnd any diﬀerences of the frequency of reported food

intolerances when comparing athletes at risk of LEA with athletes

at low risk. This may indicate that athletes who report food

intolerances are already familiar with ﬁnding dietary alternatives

to compensate for potential deﬁciencies. We did, however, ﬁnd

a higher LEAF-Q total score among athletes reporting food

intolerances, which was due to a higher gastrointestinal score.

Since the study is based on self-reported data, we can only

speculate whether the gastrointestinal problems are due to food

intolerances or due to the gastrointestinal alterations as a result

of LEA, where the symptoms may have been misinterpreted by

some athletes.

Food intolerances were, however, more frequently reported

by athletes with disordered eating behavior compared to athletes

with low EDE-Q score, and also among athletes with risk

of exercise addiction compared to athletes with low risk of

exercise addiction. While there is evidence to suggest that a

diagnosed food allergy increases the likelihood of developing a

subsequent eating disorder (Jafri et al., 2021), it is also possible

that the fear and anxiety regarding food and eating in a pre-

existing eating disorder may lead to self-diagnosis to gain social

acceptance for food exclusion. Likewise, the association in the

present study between exercise addiction and food intolerances

can potentially be explained by increased attention to the body

and its reactions in athletes at risk of exercise addiction, since

exercise addiction may develop as a way of dealing with diﬃcult

emotions and low self-esteem where one tries to get better

mentally by ﬁnding simple physical explanations (Wågan et al.,

2021). Therefore, some athletes, speciﬁcally those with secondary

exercise addiction, may use food elimination as a part of their

coping strategy.

Strength and Limitations

The present study was conducted in female endurance athletes,

which is the population in which the LEAF-Q was initially

validated. In addition, inclusion of athletes from four diﬀerent

European countries increases the generalizability of the ﬁndings.

To our knowledge, it is the ﬁrst study to investigate the

association between food intolerances and risk of LEA in female

endurance athletes and one of few (Kuikman et al., 2021) to

investigate the association between exercise addiction and LEA

in female endurance athletes.

Studies based on self-reported data are vulnerable to response

bias, denial, and inaccurate reporting, for instance when it

comes to anthropometric data. However, evidence suggests

that endurance athletes may more accurately self-perceive

and/or report their anthropometric characteristics compared

to the general population (Nikolaidis and Knechtle, 2020)

and a systematic review and meta-analysis concluded that the

magnitude of which self-reported data over- or underestimated

the real value by women of reproductive age is negligible

regarding clinical and research use (Seijo et al., 2018). Concerning

the data of food intolerances, previous studies have reported

a high adherence to special diets by athletes (Lis et al., 2016,

2019; Logue et al., 2019) despite lack of medical rationale

(Lis et al., 2015) and it is a limitation that we did not ask

whether participants who reported food intolerances had a

medical veriﬁcation or not. Additionally, although the EDE-Q is

frequently used for screening in larger samples, it was originally

developed for clinical use. The validity of the EDE-Q in athletes

needs to be investigated in future studies.

We acknowledge that analysis of biomarkers of RED-S from

blood samples, e.g., cortisol and triiodothyronine (Elliott-Sale

et al., 2018) and measurements of bone health and resting

metabolic rate (Mountjoy et al., 2018) would have strengthened

the validity of the study. Unfortunately, this was not possible

due to the COVID-19 pandemic. On the other hand, the self-

reported data assessment enabled recruitment of athletes from

all over Norway, Sweden, Ireland, and Germany, which often is

problematic if participants have to meet in the laboratory.

Finally, the recruitment during the COVID-19 pandemic, may

somewhat have aﬀected the athletes’ training and eating habits

(Roberts et al., 2020; Shaw et al., 2021; Washif et al., 2021).

The long period of isolation could potentially result in a more

restricted eating behavior and/or excessive training habits for

some athletes, but it is also possible that the pandemic for others

have resulted in reduced training and/or increased food intake.

CONCLUSION

This study conﬁrms that female endurance athletes are at high

risk of LEA (65%) and disordered eating behavior (21%), and

that an association between the two exist. However, athletes

with risk of LEA were also more likely to exhibit symptoms of

Frontiers in Sports and Active Living | www.frontiersin.org 8May 2022 | Volume 4 | Article 869594

Fahrenholtz et al. LEA in Female Endurance Athletes

exercise addiction and a high risk of both primary (10.4%) and

secondary (13%) exercise addiction was found. In a multivariate

analysis, however, symptoms of exercise addiction were not

identiﬁed as predictor of LEA, nor were food intolerances,

whereas lower BMI and symptoms of disordered eating behavior

were. More studies are needed to investigate food intolerances

and the role of exercise addiction with and without disordered

eating behavior and associations with LEA among female

endurance athletes.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this

article will be made available by the authors, without

undue reservation.

ETHICS STATEMENT

The study was approved by the Regional Ethics Committee in

Norway (31640), Sweden (2019-04809), and Norwegian Centre

for Research Data (968634). Because data collection occurred

remotely and included no medical procedures, the study was

considered exempt from additional approval at the other study

sites. Regardless, the study was conducted in full accordance with

the Declaration of Helsinki at all sites. All data were stored and

analyzed in Services for Sensitive Data. The patients/participants

provided their written informed consent to participate in

this study.

AUTHOR CONTRIBUTIONS

ILF, AKM, IG, and MKT were responsible for study design

and conceptualization. AKM and MG were responsible for

the Swedish cohort. ILF, MKT, and IG the Norwegian

cohort. DL and SM for the Irish cohort. PW and KK for

the German cohort. ILF was responsible for coordination

and overall data collection, for data analyzation under the

supervision of AS, and for constructing the article where

everyone reviewed and gave feedback for improvements.

All authors reviewed and approved the ﬁnal version of

the article.

FUNDING

This work was supported by Grants from the University of Agder

and the Norwegian Olympic Sports Center.

ACKNOWLEDGMENTS

We would like to thank master student Ingvild Brattekleiv for

assisting with the recruitment and data collection.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found

online at: https://www.frontiersin.org/articles/10.3389/fspor.

2022.869594/full#supplementary-material

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Frontiers in Sports and Active Living | www.frontiersin.org 11 May 2022 | Volume 4 | Article 869594

Paper II

Effects of a 16-Week Digital Intervention on Sports Nutrition Knowledge and

Behavior in Female Endurance Athletes with Risk of Relative Energy Deficiency

in Sport (REDs)

Fahrenholtz, I. L., Melin, A. K., Garthe, I., Hollekim-Strand, S. M., Ivarsson, A.,

Koehler, K., Logue, D., Lundström, P., Madigan, S., Wasserfurth, P., & Torstveit, M. K.

Published in Nutrients 2023; 15(5): 1082



Nutrients2023,15,1082.https://doi.org/10.3390/nu15051082www.mdpi.com/journal/nutrients

Article

Effectsofa16‐WeekDigitalInterventiononSportsNutrition

KnowledgeandBehaviorinFemaleEnduranceAthleteswith

RiskofRelativeEnergyDeficiencyinSport(REDs)

IdaL.Fahrenholtz

*,AnnaK.Melin

2,†

,InaGarthe

,SiriMarteHollekim‐Strand

,AndreasIvarsson

1,5

,



KarstenKoehler

,DanielleLogue

,PetraLundström

8,9

,SharonMadigan

,PaulinaWasserfurth

and

MonicaK.Torstveit



DepartmentofSportScienceandPhysicalEducation,UniversityofAgder,4630Kristiansand,Norway

DepartmentofSportScience,LinnaeusUniversity,35252Växjö,Sweden

TheNorwegianOlympicandParalympicCommitteeandConfederationofSport,0854Oslo,Norway

DepartmentofNeuromedicineandMovementScience,NorwegianUniversityofScienceandTechnology,

7034Trondheim,Norway

SchoolofHealthandWelfare,HalmstadUniversity,30118Halmstad,Sweden

DepartmentofSportandHealthSciences,TechnicalUniversityofMunich,80809Munich,Germany

SportIrelandInstitute,SportIrelandCampus,Abbotstown,D15PNONDublin,Ireland

DepartmentofHealthScience,KarlstadUniversity,65188Karlstad,Sweden

DepartmentofMolecularMedicineandSurgery,KarolinskaInstitute,17176Stockholm,Sweden

*Correspondence:ida.fahrenholtz@uia.no

†SwedishOlympicCommitteeResearchFellow.

Abstract:Femaleenduranceathletesareconsideredahigh‐riskgroupfordevelopingRelativeEn‐

ergyDeficiencyinSport(REDs).Duetothelackofeducationalandbehavioralinterventionstudies,

targetingandevaluatingtheeffectsofthepracticaldailymanagementofREDs,wedevelopedthe

FoodandnUtritionforEnduranceathletes—aLearning(FUEL)program,consistingof16weekly

onlinelecturesandindividualathlete‐centerednutritioncounselingeveryotherweek.Werecruited

femaleenduranceathletesfromNorway(n=60),Sweden(n=84),Ireland(n=17),andGermany(n

=47).FiftyathleteswithsymptomsofREDsandwithlowriskofeatingdisorders,withnouseof

hormonalcontraceptivesandnochronicdiseases,wereallocatedtoeithertheFUELintervention(n

=32)(FUEL)ora16‐weekcontrolperiod(n=18)(CON).AllbutonecompletedFUEL,while15

completedCON.Wefoundstrongevidenceforimprovementsinsportsnutritionknowledge,as‐

sessedviainterviews,andmoderatetostrongevidenceintheratingsconcerningself‐perceived

sportsnutritionknowledgeinFUELversusCON.Analysesoftheseven‐dayprospectiveweighed

foodrecordandquestionsrelatedtosportsnutritionhabits,suggestedweakevidenceforimprove‐

mentsinFUELversusCON.TheFUELinterventionimprovedsportsnutritionknowledgeandsug‐

gestedweakevidenceforimprovedsportsnutritionbehaviorinfemaleenduranceathleteswith

symptomsofREDs.

Keywords:athleticinjuries;digestion;femaleathletetriad;diettherapy;endurancetraining;

menstruationdisturbances;women’shealth



1.Introduction

Femaleenduranceathletesareathighriskoflowenergyavailability(LEA)andre‐

latedconsequences,includingmenstrualdysfunction,gastrointestinalproblems,andin‐

juries[1,2].Themultiplehealth‐andperformance‐relatedconsequencesofLEAarecol‐

lectivelytermedRelativeEnergyDeficiencyinSport(RED‐s[2,3]withthefemaleathlete

triadhistoricallylayingthefoundationfortheRED‐Smodel[4].

Citation:Fahrenholtz,I.L.;Melin,

A.K.;Garthe,I.;Hollekim‐Strand,

S.M.;Ivarsson,A.;Koehler,K.;

Logue,D.;Lundström,P.;Madigan,

S.;Wasserfurth,P.;etal.Effectsofa

16‐WeekDigitalInterventionon

SportsNutritionKnowledgeand

BehaviorinFemaleEndurance

AthleteswithRiskofRelative

EnergyDeficiencyinSport(REDs).

Nutrients2023,15,1082.

https://doi.org/10.3390/

nu15051082

AcademicEditor:StephenJ.Ives

Received:18January2023

Revised:13February2023

Accepted:16February2023

Published:21February2023



censeeMDPI,Basel,Switzerland.

Thisarticleisanopenaccessarticle

distributedunderthetermsandcon‐

ditionsoftheCreativeCommonsAt‐

tribution(CCBY)license(https://cre‐

ativecommons.org/licenses/by/4.0/).

Nutrients2023,15,10822of21



TherearemanyreasonsforLEArangingfromdisorderedeatingandeatingdisorders

tounintentionalunderfueling[5].OneofthemostprevalentunderlyingcausesofLEA‐

andRED‐S‐relatedsymptomshasbeenreportedtobeunintentional[6,7],duetothesup‐

pressionofappetiteafterhigh‐ andmoderate‐intensitytraining[8,9]and/oralackof

knowledgeofoptimalsportsnutrition,andtheconsequencesofLEAovertime[10–14].

Inaddition,thereseemstobeanormalizationofsomeofthemostfrequentRED‐Scondi‐

tions,preventingfemaleathletesfromseekinghelp,fore.g.,menstrualdysfunction[15].

DietarycharacteristicsassociatedwithLEAandmenstrualdysfunctionincludea

highdietaryfiberintake[16–18],alowfatintake[16,18,19],andalowintakeofcarbohy‐

drates[16].Whiledietaryfiberandproteinintakehavebeenreportedtoexceedgeneral

andsportsnutritionrecommendationsamongfemaleenduranceathletes[16],thecarbo‐

hydrateintakeisgenerallybelowsportsnutritionrecommendations[16,20,21].Though

severalfactorsinfluencefoodchoicesinathletes[22,23],nutritionknowledgeisoneofthe

fewdeterminantsofdietarybehaviorthataremodifiableand,thereforehasthepotential

toimpactathletichealthandperformance[23].Morespecifically,sportsnutrition

knowledgehasbeenpositivelyassociatedwithenergyavailabilityandcarbohydratein‐

takeinfemaleenduranceathletes[24],andeducationalinitiativeshavebeenproposedas

apromisingstrategytopreventandimprovesymptomsofRED‐S[10,25].

ClinicalsymptomsofRED‐Sareconcerning,withpotentialsignificantimpactsonthe

individual’squalityoflifeandrequiringahighlyresourcedemandingtreatment.There‐

fore,earlypreventionandmanagementofRED‐Sinfemaleathletesisofkeyinterest[26],

duetothehighprevalence,rangingfrom22–58%invarioussports[11],andthepotential

irreversiblehealthconsequences,suchasosteopeniaandprematureosteoporosis[27,28].

Giventhatsportsnutritionknowledgehasbeenreportedtobeinadequateinendurance

athletes[29],improvingsportsnutritionknowledgecouldrepresentanimportantstepin

themanagementofRED‐S.

Planninganddeliveringasportsnutritioninterventioninanathlete‐basedpopula‐

tioniscomplex,whenyoucompareitwithdevelopingandproducingpracticalguidelines

forathletesinendurancesportswithalreadyestablishedroutinesandarigidseasonal

competitionplan.Inaddition,thereareotherfactorstoconsider,suchasindividualtaste

preferences,socialinfluences,culturalbackground,time,cost–benefitevaluationsrelated

tofinancialstatus,andpsychologicalfactors[22,30,31].Allthesefactorscaninfluenceath‐

letes’foodchoicesanddietarybehavior,emphasizingtheimportanceofanindividual

approach,andthattheinterventionshouldnotonlyaimforimprovednutrition

knowledge,butalsotoprovidethebasisforskillandcompetenceimprovements,bymo‐

tivating,enablingandsupportingathletes,makingthemabletoimplementtheacquired

knowledgeintheireverydaylives[25,30].

Themodeofdeliveryisanothersignificantfactortoconsiderwheninitiatinginter‐

ventionsforathletes.Digitalinterventionsmayofferadvantagesduetofore.g.,money

andtimesavingfromtransportation,andlowerattritionrates[32].

TherecommendedmanagementofathleteswithRED‐Ssymptomsistoensureade‐

quateenergyintakerelativetoenergyexpenditure[33–35],althoughevidenceontheeffi‐

cacyofthisapproachislimitedandprimarilybasedoncasestudies[36–38],interventions

withoutacontrolgroup[39,40],andinnon‐athleticpopulations[41].

Practice‐orientatedlow‐costRED‐Smanagementstrategieswithsustainablepoten‐

tialwould,therefore,beattractiveforafutureimplementationinreal‐lifesettings.Topro‐

gresssuchefforts,first,programsneedtobedevelopedandtested.Focusingonhigh‐risk

groups,namelyfemaleenduranceathleteswithsymptomsofRED‐S,appearstobeagood

startingpointforinvestigatingsuchstrategies.

Therefore,theaimofthepresentinternationalmulticenterstudywastodevelop,im‐

plementandevaluatea16‐weeknutritionintervention,consistingofonlinesportsnutri‐

tionlecturescombinedwithindividualnutritioncounselingforfemaleenduranceathletes

atriskofRED‐S.Specifically,thegoalofthisanalysiswastoinvestigatewhetherthesports

Nutrients2023,15,10823of21

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nutritionknowledgeanddietaryoutcomeswouldchangedifferentlyfromthebaselineto

post‐interventionintheinterventiongroupcomparedtothecontrolgroup.

2.MaterialsandMethods

IthasbeenproposedthateducationalinitiativestargetingathleteswithRED‐S

shouldunderscorethepositiveaspectsofenergy,namely,thatfoodisfuelandfuelis

neededforperformance[25].Therefore,wecalledtheprojectFoodandnUtritionforEn‐

duranceathletes—aLearningprogram(theFUELproject).Thestudywasregisteredat

www.clinicaltrials.gov(NCT04959565)andwasapprovedbytheregionalethicscommit‐

teeinNorway(31640),Sweden(2019‐04809),andbytheNorwegianCentreforResearch

Data(968634).Originally,thestudywasplannedandapprovedtoincludeawiderange

ofRED‐Srelatedclinicalbiomarkermeasurementsandacontrolgrouppriortoinitiation

oftheintervention.DuetotheCOVID‐19pandemicallphysicalcontactwiththepartici‐

pantswasprohibitedandthefinaldesignandmethodsarestronglyinfluencedbythe

pandemicrestrictions.Sincethefinalresearchplanincludednomedicalprocedures,the

studywasconsideredexemptfromadditionalethicalapprovalattheotherstudysites

(GermanyandIreland).

2.1.StudyDesign

TheFUELprojectwasanon‐randomizedmulticenterstudyincludingfemaleendur‐

anceathletesfromNorway,Sweden,Ireland,andGermany.TheFUELinterventiongroup

receivedweeklylecturesinsportsnutritioncombinedwithfortnightlyindividualathlete‐

centerednutritioncounselingwithanexperiencedsportsnutritionist,whilethecontrol

groupreceivednolecturesorcounseling.Becausemanyfemaleenduranceathletesatboth

theregionalandnationallevelknoweachother,theriskofimitationoftheintervention

wasconsideredtobehigh.Therefore,seasonalallocationofsummerandwintersport

disciplineswasprioritizedoverrandomization,andtheathletesfromsummerandwinter

sportdisciplines,respectively,receivedtheinterventionsimultaneously.

Thestudywasinitiatedwithascreeningphase(Figure1),whereathletescompleted

anonlinesurvey(Part1)viathesafedatacollectiontoolNettskjemathatwasconnected

totheServicesforSensitiveData(TSD)platform(UniversityofOslo),whichcollecteddata

includingbackgroundinformation,trainingvolume(averagehours/month),andin‐

cludedtheLEAinFemalesQuestionnaire(LEAF‐Q)[1]andtheEatingDisorderExami‐

nationQuestionnaire(EDE‐Q)[42].Inthisgroupofathletes,theCronbach’salphacoeffi‐

cientsrangedfrom0.51(menstrualfunction)to0.68(injuries)ontheLEAF‐Qsubscales,

andfrom0.82to0.94ontheEDE‐Qsubscales.DetailedinformationregardingPart1of

thestudyhaspreviouslybeenpublished[7].AthleteswithariskofLEA(LEAF‐Qscore≥

8)[1]andalowriskofdisorderedeatingbehavior(EDE‐Qglobalscore<2.5)[43,44]were

invitedtocompleteanadditionalsurvey,includingquestionsregardingsportsnutrition‐

relatedbehaviorandself‐perceivedsportsnutritionknowledge.Inthesameweekthata

seven‐daydietaryandtrainingrecord(Part2)wasconducted,atelephoneinterviewwith

questionsregardingsportsnutritionknowledgewasperformed.Athleteswhosignedup

duringtheircompetitionseasonandfulfilledinclusioncriteria,wereallocatedtoawaiting

listwithcontrolgroupconditions,beforetheywereofferedtheinterventionwith/without

individualcounseling(datafromthecontrolgroup’sinterventionarenotincludedinthe

presentanalyses).

Studyweek0(baseline)wasfollowedbytheintervention(Part3),duringtheathletes’

off‐seasonora16‐weekcontrolperiod(studyweek1–16).ThedurationfromPart1toPart

2wasapproximatelyfourweeks.Afterthe16‐weekinterventionorcontrolperiod,ath‐

letesonceagaincompletedthetwoonlinesurveys,thetelephoneinterview,andaseven‐

daydietandactivityrecord(Part4,week17).

Nutrients2023,15,10824of21

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Figure1.Overviewofthestudydesignwithmeasuresrelevanttothisanalysis.Athleteswhovol‐

unteeredwereaskedtocompletetheLowEnergyAvailabilityQuestionnaire(LEAF‐Q),theEating

DisorderExaminationQuestionnaire(EDE‐Q),andbackgroundinformation(Part1).Athleteswith

aLEAF‐Qscore≥8andanEDE‐Qglobalscore<2.5wereinvitedtofurtherparticipationandasked

torecordfoodintakeandphysicalactivityincludingtrainingforsevenconsecutivedays,andan‐

swerquestionsrelatedtosportsnutritionbehaviorandself‐perceivedsportsnutritionknowledge.

Atthebeginningofthepre‐interventionweek,athletesreceivedawelcomecallwheretheywere

askedtoreplytostatementsfortheassessmentofsportsnutritionknowledge(Part2).A16‐week

interventionwasconducted(Part3)withweeklysportsnutritionlecturesincombinationwithath‐

lete‐centerednutritioncounselingwithanexperiencedsportsnutritionisteveryotherweekfol‐

lowedbypost‐interventiondatacollection(Part4).Thecontrolgroupwentthroughthesamepre‐

andpost‐assessmentswithoutanylecturesornutritioncounseling.PartelementsfromColourbox.

2.2.InclusionandExclusionCriteria

Participantswerenon‐smoking,femaleenduranceathletes,18–35yearsofage,

trainedaminimumoffivetimesperweek,tier3–4[45],andcompetedinoneofthefol‐

lowingendurancedisciplines:long‐distancerunning,orienteering,cycling,triathlon,

cross‐countryskiing,orbiathlon.Theexclusioncriteriawere:theuseofhormonalcontra‐

ceptives,chronicdiseases(e.g.,Crohn’sdiseaseorhypothyroidism),pregnancy,andmen‐

strualdysfunctionsnotrelatedtoLEA.Athletesusinghormonalcontraceptiveswerein‐

cludediftheydiscontinuedtheuseatleastsixweeksbeforethescreeningphase(Part1).

2.3.RecruitmentandEligibility

AthleteswererecruitedfromNovember2020toSeptember2021viaNorwegian,

Swedish,Irish,andGermancompetitiveendurancesportsclubs,namelytheNorwegian

OlympicSportCentre,theSportIrelandInstitute,theSwedishNationalSportFederation,

Swedishsportfederationswithinendurancesports,theGermanSkiFederation,andthe

GermanOlympicSportsConfederation,andviasocialmediawithalinktotheproject

websiteandthePart1survey[7].Therecruitmenttargetedsummerendurancedisciplines

(runners,orienteers,cyclists,andtriathletes)duringNovember/Decemberwiththeinitia‐

tiontotheinterventioninJanuary,whiletherecruitmenttargetedwinterendurance

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disciplines(biathletesandcross‐countryskiers)inMaywiththeinitiationtotheinterven‐

tioninJune.

Intotal,208participantscompletedPart1ofthestudyandwereassessedforeligibil‐

ityforfurtherparticipation(Figure2).Onehundredandforty‐oneathleteswereexcluded:

n=2maleathletes;n=2<18years;n=1>35years.;n=1badmintonplayer;n=3with

chronicdiseases[n=1:Crohn’sdisease,n=1:Hashimoto’sthyroiditis,n=1:hypothyroid‐

ism];n=55hormonalcontraceptiveusers;n=23withaEDE‐Qglobalscore≥2.5;n=51

withaLEAF‐Qscore<8,andn=3fornotprovidinganycontactinformation.Allexcluded

participantswithavailablecontactinformation(n=138)werecontactedbye‐mailand

giventheopportunitytoreceivethereasonfortheirexclusionviaatelephonecallfrom

theresearchersorinanencryptedfilesentbyemail.AllparticipantswithEDE‐Q≥2.5(n

=43)wereinformedandencouragedtocontacttheirgeneralpractitionerforfurtherex‐

amination,andwereprovidedwithlinkstorelevantwebpages,includingvoluntaryas‐

sociationsthatofferhelptopeoplewithdisorderedeatingbehaviororeatingdisorders.



Figure2.Flowchart.*Severalparticipantshadmorethanonereasonforexclusion,e.g.,hormonal

contraceptivesandahighEDE‐Qglobalscore.Theprimarycauseforexclusionisbasedonthegiven

order.**Athleteswereallocatedtotheinterventionintheiroff‐season.Athleteswhosignedup

duringtheircompetitionseasonwereallocatedtoawaitinglistcontrolgroupcondition.Abbrevia‐

tions:CON:athletesparticipatinginthe16‐weekcontrolperiod;EDE‐Q:EatingDisorderExamina‐

tionQuestionnaire;FUEL:FoodandnUtritionforEnduranceathletes—aLearningprogram;HC:

hormonalcontraceptives;LEAF‐Q:LowEnergyinFemalesQuestionnaire;RED‐S:RelativeEnergy

DeficiencyinSport.

TheLEAF‐Qresponsesofn=67athleteswereanalyzedinmoredetail,andsome

werecontactedtoclarifytheiranswers.Thisresultedinn=7athletesbeingexcludeddue

toasuspectedfalsepositiveidentificationoftheriskofRED‐S.Further,n=4athleteswere

unavailable,n=3respondedtoolateinrelationtointerventionstart‐upandallocationto

sportsnutritionists,andn=3athletesdeclaredsevereillnessaheadofthebaselinemeas‐

urements(e.g.,abdominalsurgeryandCOVID‐19).Intotal,n=18athleteswereallocated

toa16‐weekwaitinglistcontrolconditionofwhichn=15athletescompleted(n=1wanted

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tostartusinghormonalcontraceptives,whilewewereunabletomakecontactwithn=2).

Intotal,n=32athletesweredirectlyallocatedtotheFUELintervention,whilen=1ter‐

minatedparticipationintheprojectinweek13duetoexperiencingtoomuchworkrelated

totheproject.Consequently,n=31(97%)completedtheFUELinterventionandn=15

(83%)completedthecontrolcondition.

2.4.NutritionIntervention

The16‐weekinterventionconsistedofweeklyonlinelecturesinsportsnutritiontar‐

getingfemaleenduranceathleteswithariskofRED‐S,combinedwithindividualathlete‐

centerednutritioncounselingeveryotherweek.Figure3representsanoverviewofthe

FUELintervention.



Figure3.OverviewoftheFUELnutritioninterventionwithweeklylecturesinsportsnutrition

(FUELvideos)andathlete‐centerednutritioncounseling(FUELconsultations)everyotherweek.

PartelementsfromColourbox.

2.4.1.SportsNutritionLectures

Thesixteensportsnutritionlecturesintegratedevidence‐basedsportsnutritionin‐

formationandrecommendations[2,46–48](Figure3).Thelecturesweredevelopedbyfour

researchersandpracticingsportsnutritionists,initiallyinNorwegianandSwedish,in‐

cludingacomprehensivemanuscriptforeachsession,andsubsequentlytranslatedinto

EnglishandGerman.Allsixteenlectureswerecomprehensivelyreviewedandfinallyap‐

provedbyallfourresearchers.Thelectureswererecordedbyexperiencedfemalesports

nutritionresearchersandaveraged25.0±8.4mininduration(range:15–43min,totaldu‐

rationof400min).Everyweekduringtheintervention,participantsreceivedanemail

withalinkandpasswordtothelectureoftheweeklocatedonaclosedonlineplatform.

Participantshadtheopportunitytowatchthelectureswhensuitableduringtheirevery‐

daylivesandtowatchthemrepeatedlyiftheywanted.Inthedevelopmentofthelectures,

emphasiswasplacedonthedisseminationof:sportsnutritionresearchineasilyunder‐

standablelanguage,scientificsportsnutritionrecommendationswithpracticalexamples,

relevantandexplanatorypictures,casestories,aswellasthebenefitsofoptimalnutrition

forhealthandperformance,andthepotentialconsequencesofinadequatefueling.The

lecturesenabledbehaviorchangetechniques,suchas“4.1instructiononhowtoperform

thebehavior”,“4.2informationaboutantecedents”,“5.1informationabouthealthconse‐

quences”,“5.2salienceofconsequences”,and“5.5.anticipatedregret”,accordingtothe

behaviorchangetechniquetaxonomy(v1)definedbyMichieetal.[49].

Thefirstlectureincludedageneralintroductiontosportsnutritionforendurance

athletes(Figure3).Thefollowingthreelecturescoveredthepotentialunderlyingcauses,

thehealth‐related,andperformance‐relatedconsequencesofRED‐S,respectively.Theaim

ofthefifthlecturewastoaddresstypicalmythsinthefieldofsportsnutrition,followed

bylecturesonmacronutrients,mealpatterns,andfoodchoices.Thetenthlecture(nutri‐

tionforperformance)coveredacutefuelingstrategies,i.e.,before,during,andafter

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trainingandcompetition,followedbylecturesaboutperiodization,micronutrients,and

sportnutritionsupplements.Thefourteenthlectureincludedadiscussionontheimpact

ofbodyweightandbodycompositiononhealthandperformance,whilethefifteenthlec‐

turepresentedoptimalnutritionstrategiestoimplementwheninjured.Theintervention

wascompletedwithalecturefocusedontheimportanceofregularmenstruation,andthe

potentialeffectsofthedifferentmenstrualcyclephasesonathleticperformance.

2.4.2.Athlete‐CenteredNutritionCounseling

ThenutritioncounselingwasadministratedviatheteleconferencingplatformZoom,

ZoomVideoCommunication,Inc.(SanJose,California,USA).Thefirstconsultationwas

scheduledtorunfor1.5h,whilethefollowingsevenconsultationswerescheduledtorun

forapproximately1h.Theactualmeandurationforthefirstconsultationwas73±15min,

and55±6minfortheremainingconsultations.

TheFUELcounselingteamconsistedofthreeNorwegian,fourSwedish,twoIrish,

andoneGermanhighlyexperiencedsportsnutritionists.Toimprovestandardization,a

comprehensivemanualwasdeveloped,andthreewebinarswereconductedaheadofthe

intervention,inadditiontoweeklyZoommeetingsbytheFUELcounsellorteamduring

thecourseoftheintervention.Theweeklymeetingswereguidedbytheheadofnutrition

attheNorwegianOlympicandParalympicCommitteeandtheConfederationofSports,

andtheoveralldatacollectioncoordinator.Becauseaself‐determinationtheoryapproach

hasbeenfoundtobeeffectiveinpromotingbehaviorchangethroughinternalsourcesof

motivation[50],autonomyinparticular,butalsocompetenceandrelatedness,wereacore

foundationintheFUELcounseling.Toachievethis,aclient‐based,empathiccommunica‐

tionapproach,inspiredbycoreskillsinmotivationalinterviewing,wasutilized[51].Ath‐

leteswereintroducedtothetranstheoreticalmodelofbehaviorchange[52]inlecture1.

Duringthefirstconsultationwiththeathlete,thesportsnutritioniststrivedtoraisethe

athlete’sawarenessofreadinesstochangethroughempathic,athlete‐centereddialogue.

Ifapplicable,theathletewasaskedtodefineherreadinessforchange,dependingonthe

behavior(s)inquestion,byplacingherselfinthetranstheoreticalmodelofbehavior

change.Theconsultationswerecustomizedfromoneconsultationtothenextdepending

ontheindividualathlete’sreadinessforchange.

Anillustrationofhowtopromoteathlete‐centeredcommunicationispresentedin

Figure4.ThestructureoftheFUELconsultationswasinspiredbytheFourHabitsModel

[53,54]andispresentedinTable1.



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Figure4.Howtopromoteathlete‐centeredcommunicationinFUELbasedon[51–53].Partelement

fromColourbox.

Table1.StructureoftheFUELconsultationsinspiredbytheFourHabitsModel[53].

PreparationBeginningWorkPartConclusionReflection

 Athletetakes

notesandsummarizes

toherselfwhatisim‐

portanttobringup

duringthelecture

 Thecounselor

preparesconversation

asedontemplatefor

conversationsand

previousconversa‐

tionsaswellasonline

lecturesviewedbythe

athlete

 Establishcontact

(specificallyforthefirst

consultation:establish

trust,identifythemoti‐

vationforchange,in‐

formaboutfurther

courseprogression)

 Engage

 Focus

 Setpoint:What

doestheathleteexperi‐

enceasimportant(to‐

day,thenextweeks

andinthefuture)?

 Exploretheathlete’s

perspective(thoughts,

feelings)oftheirpersonal

situationrelatedtoRED‐S



e.g.,potentialsymptoms,

dietaryrestrictions

 Wishandintention

tomakechanges

 Goal,actionplans,

andapproaches

 Supportmastery,

loyalty,andtrustinone’s

ownabilitytoconduct

change/mastery(includ‐

ingexploringpreviousex‐

periences)

 Inform

 Explorethe

athlete’sexperience

oftheconsultation

 Askforaddi‐

tionalquestions

 Summarize

andconclude

 Theathletewrites

personalnotes

 Thecounselor

writesajournal

Maintainalliance∙Expressempathy∙Supportautonomy∙Supportmastery∙Giveinformation

TheFUELnutritioncounsellingmanualincludedadescriptionofthetheoretical

foundationofFUEL,thecounselingapproachescustomizedaccordingtotheperceived

readinessforchange,aframeworkfortheeightconsultations,examplesofambivalence

exploration,andhowtogiveindividualathlete‐targetedinformation.Furthermore,ade‐

cisionflowdiagram,basedontheRED‐SCAT[55],concerningwhenathletesshouldbe

encouragedtoseekanexaminationoutsidetheFUELproject(e.g.,agynecologistrelating

toanundiagnosedmenstrualdysfunction)wasincluded.Anexperiencedpsychiatrist,

specializingineatingdisordersinbothathletesandnon‐athleticpopulations,wasaffili‐

atedasthemedicalprofessionalresponsiblewithintheproject,supportingtheFUEL

counselorsifneeded.Athleteswereencouragedtowritereflectionnotesbetweentheses‐

sions.Inaddition,counselorswereprovidedwiththeathlete’sLEAF‐Qresponseanda

tabletofillintheathlete’sself‐formulatednutritiongoals.Thedate,time,andactualdu‐

rationofthesessionwerenotedforalleightsessions,inadditiontotheathlete’sgoalfor

thenextsession,andanyadditionalcommentsfromthesessioninquestion.Counselors

hadaccesstotheonlinelecturesandaprintoutofthePowerPointpresentationswiththe

manuscript,inorderforthemtoprepareforanyquestionsrelatedtothelatesttopicsin

theFUELlectures.

Becausetheconsultationswerecustomizedaccordingtotheindividualathlete’s

needsandpreferences,differentbehaviorchangetechniqueswereenabledandwere,

amongothers,designedtoinclude,“1.1goalsetting(behavior)”,“1.2problemsolving”,

“1.5reviewbehaviorgoal(s)”,“1.9commitment”,“2.2feedbackonbehavior”,and“3.3

socialsupport(emotional)”,accordingtothebehaviorchangetechniquetaxonomy(v1)

definedbyMichieetal.[49].

2.5.SportsNutritionKnowledge

Althoughquestionnairestoassessnutritionknowledgehavebeenvalidatedamong

enduranceathletes[56],thismethodofmeasurementwasconsideredinappropriatefor

thisstudy,wherephysicalattendancewasnotpossibleandthus,researcherscouldnot

Nutrients2023,15,10829of21

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controlwhetherparticipantssearchedforthecorrectanswersinbooksoronline.Instead,

twentystatementsthatweresuitabletobereadoutduringatelephoneinterviewwere

developedwiththepossibilityofanswering“true”,“false”,or“unsure”.Thenumberof

correctanswerswasassessedatthebaselineandpost‐intervention.Inaddition,partici‐

pantswereaskedtoranktheirsportsnutritionknowledgeonfivestatementsinanonline

surveyonascalefrom1to10(1=totallydisagree,10=fullyagree).Thetwentystatements

usedinthetelephoneinterviewandthefivequestionsconcerningself‐rankedsportsnu‐

tritionknowledgewerepilottestedwithasmallgroupoffemaleenduranceathletesin

termsofrelevanceandreadabilitybeforetheinitiationofthestudy.

2.6.SportsNutrition‐RelatedBehaviorandDietaryIntake

Basedoncurrentsportsnutritionrecommendations[46,47],tenquestionsrelatedto

sportsnutritionbehaviorweredeveloped.Toenableanassessmentofanydevelopments

fromthebaselinetothepost‐intervention/controlperiod,ascoringsystem(seesupple‐

mentaryFileS1)wasdeveloped.Thetotalscorerangedfrom0–30,where30indicatedthe

bestfulfillmentofthesportsnutritionrecommendations.Assomeofthequestionscould

beregardedasirrelevantforsomeathletes,a“notrelevant”boxwasappliedfortwoof

thequestions,andthefinalscorewasthendividedbytheanswersavailable,making3.0

thehighestpossibleglobalscore.Thescoringsystemwasdevelopedinthecontextofwhat

isconsideredtheoptimalapproachforathleteswithsymptomsofLEA.

Dietaryintakewascalculatedfromaseven‐dayweighedfoodrecord.Participants

receivedakitchenscaleviathepostalservice,includingauser’smanualwithinstructions

andademonstrationwithpicturesonhowfoodanddrinkshouldbeweighedandrec‐

orded.Inaddition,allparticipantsreceivedatelephonecalltoensurethattheyunder‐

stoodhowtoconductthedietaryregistrationcorrectly.Allcompleteddietaryrecords

werereviewedbyoneoftheprojectmemberswhoaskedtheathletesforin‐depthanswers

whenneeded,fore.g.,whenunder‐oroverreportingwassuspected.TheNorwegianand

SwedishparticipantsregisteredallfoodandbeveragesintheDietistNetMatdagbok(Kost

ochNäringsdataAB,Bromma,Sweden),inwhichtheparticipantswereunabletosee

anynutritionalcontentwhenregisteringthefoodandbeverages.Thedataweresubse‐

quentlyanalyzedusingDietistNetPro[57].TheIrishandGermanparticipantsregistered

foodandbeveragesinpaperformbeforearesearcherenteredthedataintoNutritics(2019,

ResearchEditionv5.09,Dublin,Germany)andEBISpro[58](2016,UniversityofHohen‐

heim,Stuttgart,Germany)[59],respectively.

Foreachday,carbohydrateintake(g/kgbodymass)wascomparedtocurrentcarbo‐

hydraterecommendationsforenduranceathletes[46,47,60,61].Thus,thedailycarbohy‐

drateintakewasassessedrelativetothetrainingvolumeoftheday.Thefollowingcriteria

formeetingcarbohydrateintakerecommendationswereused:training<0h/day:mini‐

mum4g/kg;training0.5–1.5h/day:minimum6g/kg;training1.6–3.9h/day:min.7g/kg;

training≥4h/day:min.9g/kg.

2.7.PhysicalActivityandTraining

Participantswereinstructedtouseachest‐wornheartratemonitorduringalltrain‐

ingsessionsandtoregisteralltrainingintheBestronlinetrainingdiary(www.bestr.no,

),inasmuchdetailaspossibleduringthesamesevendaysastheweighedfoodrecord.

Athletes,whowithinthelatestyear,hadperformedamaximalheartratetestinalabora‐

torysetting(n=13),enteredtheirmaximalheartrate(HRmax)manuallyintoBestr,whereas

theremainingparticipantshadtheirHRmaxestimatedinBestrviatheequationHRmax=208

−0.7×age.ThetimeinfiveintensityzoneswascalculatedinBestr:I1:60–72%ofHRmax;

I2:60–72%ofHRmax;72–82%ofHRmax;I3:82–87%ofHRmax;I4:87–92%ofHRmax;I5:92–

97%ofHRmax.Actigraphy(ActiGraphGT3X®,Pensacola,FL,USA)andthedataanalysis

softwareActiLife5(ActiGraph)wereusedfortheassessmentofnon‐exerciseactivityther‐

mogenesis.Subjectswereinstructedtowearanaccelerometerontheirhipfromgetting

upinthemorninguntilbedtime,andonlytakeitoffduringshoweringandtraining.

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2.8.Statistics

DataanalyseswereconductedusingJASP(version0.16.3.0).Allanalyseswerecon‐

ductedwithintheBayesianstatisticalframework[62].Incomparisontoclassicalstatistics,

Bayesianstatisticsislesssensitivetomultipletesting,reducingtheriskoftypeIerrors,

andisalsolesssensitivetosmallsamplesizes[62,63].

Descriptivestatisticswereexpressedasfrequencieswithpercentagesforbinaryand

categoricaldata,andasmeans±standarddeviation(SD)forcontinuousdata.Groupcom‐

parisonsofthebaselinecharacteristicswereconductedusingtheBayesianindependent

samplest‐testfornormallydistributeddata,andtheMann–Whitneytestfornon‐normally

distributeddata.Groupcomparisonsfromweek0toweek17wereconductedusing

Bayesianmixedfactoranalysisofvariance(ANOVA)withdefaultpriorsandcompared

tothenullmodel.Agroupxtimeinteractioneffectwashypothesized,i.e.,thatthetwo

groups’nutritionknowledgewouldchangedifferentlyovertime(alternativehypothesis).

TocalculatetheBayesfactor(BF)fortheinteractioneffect,onlyinclusionprobabilitiesfor

matchedmodelswereconsidered[64].Bayesfactorsbetween1and3wereconsideredto

indicateweakevidenceofthealternativehypothesis,BFsbetween3and10wereconsid‐

eredmoderateevidenceofthealternativehypothesis,whileBFsgreaterthan10werecon‐

sideredstrongevidenceofthealternativehypothesis[65].

3.Results

Themeanageoftheincludedathleteswas24.9±4.7years,withanaveragetraining

volumeof47.1±17.0hpermonth(Table2).Themajorityoftheathletes(82%)werestud‐

yingorworkingalongsidetheirendurancesportparticipation,whiletheremaining18%

reportedbeingfull‐timeathletes.Thirty‐threepercenthadattendedsecondaryschoolas

theirhighestlevelofeducation,39%hadattendeduniversity/collegeforlessthan4years,

while28%hadattendeduniversity/collegefor4yearsormore.Seventypercentcompeted

atclublevel,18%wereatthenationalteamlevel,8%wereprofessionals,while4%classi‐

fiedthemselvesas“others”(e.g.,competinginanendurancesportbutnotaffiliatedwith

aclub).Consequently,themajorityoftheparticipantswereclassifiedasTier3athletes

[45].Therewasnoevidenceofstatisticaldifferenceswhencomparingthetwogroups’

baselinecharacteristics(BFs<1).

Table2.Participantcharacteristicsaredividedbyintervention(FUEL)andcontrol(CON)groups.

FUEL

(n=31)

CON

(n=15)

Age(years)24.1±4.725.3±4.8

Height(cm)169.3±6.2171.2±7.1

Bodyweight(kg)59.5±7.059.3±5.0

BMI(kg/m2)20.8±2.1 20.3±1.7

Trainingvolume(h/month)46.3±16.748.6±17.7

Full‐timeathlete(%)16.120.0

Occupation1 

Full‐timejob(%)45.233.3

Part‐timejob(%)0.00.0

Studying(%)38.740.0

Other(%)0.06.6

Levelofcompetition  

Club(%)64.586.7

Nationalteam(%)19.46.7

Professional(%)9.76.7

Other(%)6.50.0

Levelofeducation 

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Primaryschool(%)0.00.0

Secondaryschool(%)25.846.7

University/college<4years(%)48.420.0

University/college≥4years(%)25.833.3

Continuousdataarepresentedasmean±SDandcategoricaldataasapercentage.1Athleteswho

respondedthattheywerenotfull‐timeathleteswereaskedabouttheiroccupationbeyondtheir

endurancesport.Abbreviations:BMI:bodymassindex;CON:controlgroup;FUEL:theFUELin‐

terventiongroup.

3.1.SportsNutritionKnowledge

Figure5illustratesthenumberofcorrectanswersfromthetelephoneinterview.Each

statementandthedistributionofcorrectanswerscanbefoundinTableS1.Wefound

strongevidenceofthealternativehypothesis,i.e.,aninteractioneffectbetweenthegroups

andmeasurementtimepointwaspresent(BFincl=216.93).



Figure5.Correctanswersfromthetelephoneinterviewonsportsnutritionknowledge.Dataare

presentedasmeanand95%credibleintervals.Abbreviations:CON:controlgroup;FUEL:FUEL

interventiongroup.

Similarly,wefoundmoderatetostrongsupportforthealternativehypothesis,indi‐

catinganinteractioneffectbetweengroupandmeasurementtimepoint,forfourofthe

fivequestionsrelatedtoself‐perceivedsportsnutritionknowledge(Table3).

Table3.Self‐perceivedsportsnutritionknowledgedividedbyintervention(FUEL)andcontrol

(CON)group.

FUELCON 

StatementsRatedonaScalefrom1to10

(1=TotallyDisagree,10=FullyAgree)Week0Week17

Within

Group

Difference

Week0Week17

Within

GroupDif‐

ference

BFincl

Ihavegreatknowledgeinthefieldof

sportsnutrition5.8±1.98.1±1.52.2±1.67.0±1.56.7±1.9−0.3±1.6592.02

Ihavefollowedallsportsnutritionrec‐

ommendationsIcan5.3±1.87.6±1.72.2±2.05.8±1.95.3±1.8−0.4±2.268.88

TherehasbeenagreementbetweenhowI

haveeatenandmytheoreticalknowledge

ofsportsnutrition

6.4±1.67.8±1.41.4±1.94.5±1.44.8±2.20.3±2.80.94

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Ihaveconfidenceinmynutritionroutines 6.0±1.67.8±1.41.8±2.25.8±2.05.8±2.10.1±2.89.38

IhaveknownwhereIshouldgatherre‐

search‐basedinformationaboutsports

nutrition

5.3±2.9 8.7±1.63.5±2.86.0±2.96.8±2.70.8±3.18.70

Dataarepresentedasmean±SD.FortheFUELinterventiongroup,postdataforself‐perceived

sportsnutritionknowledgeweremissingforn=1participants,whilepostdataweremissingforn

=3participantsinthecontrolgroup.Abbreviations:BFincl=Bayesfactorforinclusionofgroup *

timeinteraction;CON:controlgroup;FUEL:theFUELinterventiongroup.

3.2.SportsNutrition‐RelatedBehavior,OverallDietaryIntake,andPhysicalActivity

Forthesportsnutritionglobalscore,wefoundweakevidence(BFincl=2.75)forthe

interactioneffectbetweengroupandmeasurementtimepoint(FUEL:1.7±0.5atweek0

and2.2±0.4atweek17;CON:1.9±0.4atweek0and2.0±0.4atweek17).Similarly,we

foundweaksupportforaninteractioneffectbetweengroupandmeasurementtimepoint

fortotalenergyintake,andcarbohydrates(g/dayandg/kg/day),protein(g/dayand

g/kg/day),andfat(E%)intake(Table4).

AmongathletesintheFUELinterventiongroup,74%increasedtheircarbohydrate

intakecomparedto44%inthecontrolgroup(BFincl=1.266),while61%intheFUELinter‐

ventiongroupincreasedtheirtotalenergyintakecomparedto56%inthecontrolgroup

(BFincl=0.341).

Wheneachparticipant’scarbohydrateintakewascomparedtothecurrentguide‐

lines,theFUELinterventiongroupmetthecarbohydraterecommendations1.2±1.1

days/weekatweek0and2.6±1.8days/weekatweek17,whilethecontrolgroupmetthe

carbohydraterecommendations1.4±1.2atweek0and1.8±1.8atweek17(BFincl=1.204

forthegroupxtimeinteractioneffect).

Onaverage,athletestrained12.3±4.2h/weekatweek0anddecreasedtheirtotal

trainingvolumeduringthestudyto10.6±5.1hperweek(BFincl=10.92),timeinintensity

zone1(BFincl=30.31)and2(BFincl=45.47)butshowednosupportforaninteractioneffect

betweengroupandmeasurementtimepoint.Timeinintensityzone3,4,and5andnon‐

exerciseactivitythermogenesisdidnotchangefromthebaselinetoweek17inanyofthe

groups.AmoredetaileddescriptionoftheactivitycharacteristicscanbefoundinTable

S2.

Table4.Dietarycharacteristicsfortheintervention(FUEL)andcontrol(CON)group.

FUELCON

BFincl

DietaryIntakeWeek0Week17

within

GroupDif‐

ference

Week0Week17

within

GroupDif‐

ference

Energyintake(kcal/day)2588±5282726±547138±4532455±4822300±449−155±3961.03

Carbohydrates(g/day)

Carbohydrates(g/kg/day)

Carbohydrates(E%)

290±68326±8836±74285±65280±74−6±621.09

4.8±1.05.5±1.40.6±1.34.8±1.04.7±1.2−0.1±1.01.04

47±850±82.7±9.449±551±62.2±5.40.38

Dietaryfibers(g/day)37.5±12.536.7±12.7−1±1036±937±151±120.37

Dietaryfibers(g/1000kcal)14.4±3.813.3±3.1−1.1±3.214.9±3.615.9±5.21.0±4.70.88

Protein(g/day)107±30115±318±2295±1988±24−7±171.06

Protein(g/kg/day)1.8±0.51.9±0.50.1±0.41.6±0.31.5±0.4−0.1±0.31.12

Protein(E%) 17±418±43±317±416±4−1±20.42

Fat(g/day)106±3596±26−10±3497±2488±23−9±230.36

Fat(g/kg/day)1.8±0.61.6±0.4−0.2±0.61.6±0.31.4±0.4−0.2±0.40.38

Fat(E%)37±932±6−5±935±534±7−1±71.43

Dataarepresentedasmean±SD.Atotalofn=6athletesinthecontrolgrouphadmissingorin‐

completedietaryrecords.Analyseswerethereforeavailableforn=31athletesintheintervention

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groupandn=9athletesinthecontrolgroup.Abbreviations:BFincl=Bayesfactorforinclusionof

groupxtimeinteraction;CON:controlgroup;FUEL:theFUELinterventiongroup.

4.Discussion

Thisisthefirstsportsnutritioninterventionstudyonnutritionknowledgeanddie‐

tarybehaviorchangesinfemaleenduranceathleteswithsymptomsofRED‐S,categorized

byaLEAF‐Qscore≥8.Weconductedaninternationalmulticenterstudywithatheoretical

frameworkandstandardizedprocedurescombiningweeklyonlinelecturesandindivid‐

ualconsultationseveryotherweek.Themainfindingwasstrongevidenceofimproved

sportsnutritionknowledge,butweakevidenceofanimprovementindietarybehavior

afterthesixteenweeksofintervention.

TheFUELinterventiongroupshoweda28%improvementintheirsportsnutrition

knowledgeduringthe16‐weekinterventionperiod.Thisishigherthanthemeanincrease

of16%basedontheresultsof32studiesreviewedbyTamandcolleagues,investigating

theeffectivenessofeducationalinterventionsdesignedtoimprovenutritionknowledge

inathletes[32].Overall,theincludedstudiesintheirsystematicreviewusedinterventions

thatwereprimarily deliveredinaface‐to‐facegroupsetting,wereshort‐term(typically<

4weekswithatotalcontacttimeof<300min),andhadavarietyofsessionfrequencies

(fromasinglesessiontoyearlysessions)anddurationsforeachsession(usually≤1h).

Studiesusingonlinecontenthadalowerattritionrange,whiletheknowledgescoresin‐

creasedcomparedtonon‐technology‐basededucationprotocols.Inthepresentstudy,the

longerdurationoftheintervention,thehigheroverallcontacttime(558min),andtheuse

ofanonlineapproachmayinpartexplaintheimprovementinnutritionknowledgecom‐

paredtothefindingsinthesystematicreviewbyTametal.[32].Becausetier3–4athletes

oftenhavesignificanttimechallengesduetohightrainingloadincombinationwitheither

workorstudies,onlinelecturesthatcanbeseenwheneverissuitableintheathletes’eve‐

rydaylivesmayofferasustainablemeansofsupportingathleteeducation[32].Addition‐

ally,astheindividualconsultationsinthepresentstudyalsowereonline,athletescould

avoidtransportationtimespenttomeettheirnutritioncounselor,whichmayimprove

retentionrates,eveninnon‐pandemictimes.

Theadditionofindividualathlete‐centeredconsultationstothelecturesmayalsobe

anadvantageforimprovingnutritionknowledge,sincethisgivesathletestheopportuni‐

tiestoaskquestionsaboutthelectures,anddiscussindividualchallengesandhowtoim‐

plementoptimizedstrategiesintotheireverydaylives,therebystimulatingprocedural

knowledge,notonlydeclarativeknowledge[66].Althougheducationprogramsare

neededwithinthefieldofRED‐S[25],andarefrequentlyusedbehavioralstrategiesto

promotedietarybehaviorchangeinathletes[67],theassociationbetweennutrition

knowledgeanddietaryintakewasfoundtobemodest(r<0.44)inasystematicreview

[68].Despiteapositiverelationship,severalotherfactorsinfluencefoodintakeinathletes,

includingsocialandeconomicfactors,[22,23]anditisoftenarguedthatnutrition

knowledgeisanecessarybutnotasufficientfactorforchangingbehavior[31,66,68],

whichisalsoimpliedbytheresultsofthepresentstudy.

Whilewefoundstrongevidenceofimprovednutritionknowledgeintheinterven‐

tiongroup,wefoundlimitedevidenceofchangesintheirdietaryintake.Thisdiscrepancy

wassupportedbytherankingofthestatement“Therehasbeenagreementbetweenhow

Ihaveeatenandmytheoreticalknowledgeofsportsnutrition”,whichwastheonlyone

offivesubjectiverankingswithnogroupxtimeinteractioneffect,reflectingthecomplex‐

ityofchanginghabits,despiteimprovedknowledge[52,66].WhensplittingtheFUELin‐

terventiongroupintothosewiththehighestversusthelowestchangeinsportsnutrition

knowledgescore,andintothosewiththehighestversusthelowestsportsnutrition

knowledgescorepost‐intervention,wedidnotfindevidenceofdifferencesinchangesin

anydietaryoutcomes,supportingtheabovementionedincompleteassociationbetween

nutritionknowledgeandbehavior.Itcouldbearguedthatathletes’initialreadinessto

changecouldinfluencetheeffectoftheintervention.BasedonTheTranstheoretical

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Model,themajority(56%)oftheathletesplacedthemselvesinthepreparationphase,while

22%placedthemselvesinthecontemplationphase,andtheremaining22%placedthem‐

selvesintheactionphase.Wedidnot,however,findevidenceofanygroup×timeinterac‐

tioneffectsforthedietaryoutcomescomparingthesethreegroups.

Improvedsportsnutritionknowledgewithoutacorrespondingimprovementindi‐

etaryintakehasalsobeenreportedinearlierinterventionstudiesofenduranceathletes.

Three90mineducationsessionswithandwithoutamobilephoneapplicationimproved

nutritionknowledge,butnotdietaryintake,among79maleandfemaleenduranceath‐

letesagedbetween16–20[69].Likewise,DickeyandNoltereportedimprovednutrition

knowledgeamongsevenfemalerowersaftereightindividualsportsnutritioncounseling

sessionsandeightco‐activelifecoachingsessions,butdietaryintakedidnotimprove,and

energyavailabilitywasstillloworreducedforallsevenparticipantsaftertheintervention

[70].Contrarily,inasystematicreviewbyBentleyetal.[67]investigatingbehavioralstrat‐

egiesusedtopromotedietarybehaviorchangeinathletes,moststudiesreportedchanges

inathletes’dietarybehaviorpost‐intervention.Theparticipantsintheincludedstudies

were,however,mostlyfromballgamesportsandwerenotspecificallyidentifiedasbeing

atriskofRED‐Sliketheathletesinthepresentstudy.Inaddition,manyofthestudiesin

theaforementionedreviewhavebeencriticizedforusingfewoftheavailablebehavior

changetechniques,andfornotreportingwhichtheorygroundsareusedasthebasisfor

theintervention[67].Eventhoughweusedanevidence‐basedtheoreticalframeworkin

thepresentstudy,weonlyfoundweakevidenceofanimprovementindietaryintake

comparedtothecontrolgroup.We,therefore,speculatewhetheritmaybeextrachalleng‐

ingforfemaleenduranceathleteswithsymptomsofRED‐Stoimprovetheirdietarybe‐

havior,fore.g.,increasetheirenergyintake,duetotheirfocusonmaintainingalower

bodymass[16],alimitedtimetoeatbecauseofabusytrainingscheduleorduetosup‐

pressionofappetiteaftertraining[8,9].Beinganon‐professionalcompetitiveathletewith

studiesand/orworkinadditiontoabusytrainingschedulemayaddextrachallengesto

behaviorchangeandmeetingenergyrequirements.Itcouldalsobespeculatedasto

whetherathletesatdifferentagesreceivedandrespondeddifferentlytotheFUELinter‐

vention,asa35‐year‐oldathletemayhavemoreingrainedhabitscomparedtoan18‐year‐

oldathlete.Indeed,asub‐analysisrevealedweakevidenceofagreaterimprovement

(BFincl=1.575)incarbohydrateintake(g/kg/day)amongFUELathletes≥26years(4.8±1.1

atweek0and5.9±1.3atweek17)ofage(themedian)comparedtoathletes<26yearsof

age(4.9±1.0atweek0and5.2±1.3atweek17).Anotherpossibilityfordifferentialrecep‐

tivitytotheinterventioncouldbeduetosportmodality,becauseperceivedsocialnorms

forhealthyeatingmaybemorepowerfulinsomesportscomparedtoothers;wehave

previouslyreportedahigherriskofLEAamongrunnerscomparedtotriathletes,cyclists,

biathletes,andcross‐countryskiers[7].Wedidnot,however,findevidenceofdifferences

inchangeaccordingtosportmodalityintheoutcomesrelevanttothisanalysis.

InlinewithpreviousresearchonfemaleenduranceathleteswithsymptomsofRED‐

S[16–19],participantsinthepresentstudyhadacarbohydrateintakebelowtherecom‐

mendedminimumrequirementof6g/kg/day[61]andadietaryfiberintakeabovethe

recommended25–35g/day[71]atbothtimepoints.Alowintakeofcarbohydrate‐rich

foodsandahighintakeofdietaryfibershavebeenassociatedwithsymptomsofRED‐S

infemaleenduranceathletes[16],andwerethereforeimportanttopicsinthenutrition

lecturesandtheindividualconsultationsintheFUELintervention.Although74%ofthe

athletesintheFUELinterventiongroupincreasedtheircarbohydrateintakefromthe

baselinetopost‐intervention(12%increaseingcarbohydrate/dayand15%increaseing

carbohydrate/kg/day),ourresultssuggestthatthisgroupofathletesonlymetthecarbo‐

hydraterecommendationsafewdaysaweek,evenafterthenutritionintervention.Since

theathletesintheFUELinterventiongroupdecreasedtheirtrainingvolumefromthe

baselinetopost‐interventionandincreasedtheirdailyenergyintakeby5%itwouldhave

beeninterestingtomeasurethepotentialchangesinenergyavailability.Unfortunately,

assessmentoftheleanbodymasswasnotpossibleinthepresentstudyduetotheCOVID‐

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19restrictionsprohibitingtheathletesfromtravelingtothelabtoparticipateindual‐en‐

ergyX‐rayabsorptiometryscans.Whencomparingthosewhohadthegreatestreduction

intrainingvolumewiththosewithaminorreductionintrainingvolumeintheinterven‐

tiongroup(mediansplit),wedidnotfindevidenceofdifferencesinchangesinenergy

intakeorcarbohydrateintakefromthebaselinetopost‐intervention.Łagowskaetal.

(2014)reportedimprovedenergyintake(+234kcal/day)andenergyavailability(+7.5

kcal/kgFFM/day)among45femaleathleteswithmenstrualdysfunctionafterathree‐

monthintervention,whereathleteshadbeeninformedofnutritionalmistakesandwere

givenanindividualdiet[40].Afterextendingthestudytoninemonths,theresearchers

reportedamoreprofoundincreaseinenergyintakeandenergyavailability[72].That

studyis,however,limitedbythelackofacontrolgroupanddoesnotreportwhichtheory

andbehaviorchangemethod(s)theyhaveusedasabasisfortheirintervention,butcan

neverthelessindicatethatalongerinterventiondurationmaybeneededforfemaleath‐

letestochangetheirdietarybehavior.Amongnon‐athletes,ithasbeensuggestedthat

long‐termeducationprograms(>5months)havethebestefficacyonimprovingnutrition

behaviors[73].ItispossiblethatalongerinterventionintheFUELstudycouldhaveena‐

bledfurthermotivationforchangeandsupportedmoreathletesduringtheactionphase.

4.1.StrengthsandLimitations

Anovelaspectofourstudy,whichalsorepresentsanunderlyingstrength,isthe

combinationofonlinelecturesandindividualconsultations,whichwereathlete‐centered

andaimedatinducingbehavioralchange.Weusedbehaviorchangetheoriesandap‐

proachestopromotestandardizationintheinterventionbetweenthecountriesandsports

nutritionists,whichwereallhighlyexperiencedinworkingwithathleteswithRED‐S.

However,sincetheconsultationswerenotrecorded,wearenotabletoevaluatetowhat

degreetheattendedtheoriesandapproacheswereused.Recordingtheconsultations,on

theotherhand,mayhavepreventedtheathletesfrombeingopenabouttheirfeelingsand

behaviors.

Incontrasttootherinterventionstudiesinvolvingfemaleathleteswithsymptomsof

RED‐S[74],itisastrengththatthepresentstudyincludedacontrolgroup.Itwouldbe

interestingtoincludeadditionalgroups(agroupreceivingFUELlecturesonlyanda

groupreceivingFUELconsultationsonly),forthepurposeofidentifyingtheactivecom‐

ponentsofdifferentoutcomes.Anotherstrengthwastheexclusionofathleteswitharisk

ofdisorderedeating,thoseusinghormonalcontraceptivesandathleteswithpreviously

diagnosedmenstrualdysfunctionsnotrelatedtoRED‐S,wherebythelikelihoodofinclud‐

ingfalsepositiveRED‐Scaseswasdecreased.

Althoughwerecruitedathletesfromfourdifferentcountries,thestrictinclusioncri‐

teriaresultedinarelativelylownumberofparticipants.Aslaboratoryassessmentswere

canceled,athletes’onlymotivationforparticipatinginthestudymayhavebeentheinter‐

ventionitself.Thismayexplaintheparticularlylownumberofcontrols.Althoughcon‐

trolswereofferedduringtheFUELinterventionafterthe16‐weekcontrolperiod,thismay

havebeentoolongaperiodtokeeptheminthestudy.

Educationalstudieshavebeencriticizedforusingawiderangeofknowledgeassess‐

menttoolswithlimitedvalidation,whichmakesitdifficulttocompareresultsacrossstud‐

ies[32,75].Inthepresentstudy,wedevelopedtwentystatementsspecificallyrelevantfor

femaleenduranceathleteswithariskofRED‐Sthatweresuitableforashorttelephone

interview,sothatparticipantswerenotabletofindtheanswersinbooksorontheinternet

whichtheycouldhavedonewithanonlinedistributedquestionnaire.Ourknowledge

toolisanabbreviatedversionofwhathasbeenusedinpreviousstudies[32,69,75].Con‐

sequently,scoresmaynotbefullyrepresentativeofsportsnutritionknowledgeanddirect

comparisonswithotherstudiesarenotpossible.Despitethesementionedlimitations,it

wasthedevelopmentfromthebaselinetopost‐interventioncomparedtothedevelop‐

mentfromthebaselinetopost‐testforthecontrolconditionthatwasinfocus,thereby

makingtheappliedmethodsuitableforthepresentstudy.

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Finally,itisalimitationthatthedataassessmentwasconductedatdifferentphases

oftheathleticseasonfortheinterventiongroupandthecontrolgroup.Thisreducesthe

comparabilitybetweenthetwogroupsconsideringtheperiodizationoftraining,anden‐

ergyintakeandcarbohydrateintake.However,anobservationalstudyreportedpersis‐

tentLEAthroughouttheseasonamongtriathletes[76],suggestingthatenduranceathletes

donotnecessarilyperiodizetheirdietaryintake.Inaddition,datafromthepresentstudy

showsthesamereductionintrainingvolumeinbothgroups.Nevertheless,itisimportant

toacknowledgethenumerousexistinginterferenceswithathletes’nutritionalbehaviors,

beyondtheathleticseason,includingbutnotlimitedtointerpersonalfactors,influences

fromsocialmediaandmarketing,theweather,andlifesituationsthatmayaffecttheir

mood,whichareoutsidetheinfluenceofthisstudy[31].



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4.2.FutureDirections

Giventhecomplexityofbehaviorchange,futurestudiesshouldstartearlyandaim

forprimaryprevention,therebyincludingjuniorathletes.Inaddition,duetoincreased

knowledgeofRED‐Salsoamongmaleathletes[2],futurestudiesshouldalsoincludemale

participants.Further,coachesfromendurancesportswhodictatetheenergydemandsof

trainingandareinaprimepositiontoobservechangesinathletes’healthandperfor‐

mance,arenotwellenougheducatedaboutRED‐S[77].Culturalrevolutionsandchanges

insocialnormsareneeded[25],andtherefore,futurestudiesshouldnotonlyincludein‐

dividualathletes,butalsotheircoaches,healthprofessionals,entireteams,clubs,and

sportsorganizations.Futurestudiesareencouragedtouseasystematicneedsassessment

intheplanningphaseusingaframework(e.g.,usingworksheetsfromthebehavior

changewheel[78])anduserengagement.

4.3.PracticalImplications

Therecommendedtreatmentforimprovingenergyintakeseemsstraightforwardat

firstglancebutiscomplexinpractice.Providinginformationaboutoptimalsportsnutri‐

tionstrategies,withtheaimofcorrectingdietarybehavior,maybecommonplacewhen

RED‐Sissuspected,atleastamongeliteathleteswithaccesstoaprofessionalsportsnu‐

tritionteam.Practitionersshould,however,beawarethatimprovingsportsnutrition

knowledgeisnotnecessarilysufficientforbehaviorchangeinfemaleenduranceathletes

withariskofRED‐S[33],whichisalsoindicatedbytheresultsofthepresentstudy.Even

withthecombinationofeducationallectures,specificallydesignedforthetargetgroup,

andindividualconsultationsincludingbehaviorchangetechniquessuchasgoalsetting

andsocialsupport,thepresentstudyimpliesthatadditionalinitiativesshouldbeimple‐

mented.Ofimportance,however,therecommendedtreatmenttargetsforLEAshouldbe

gradualandaccomplishedoverseveralmonths[35],andtherefore,aninterventionperiod

of16weeksmaybeinsufficient.

5.Conclusions

OurresultsprovidestrongevidencethattheFUELinterventionimprovedsportsnu‐

tritionknowledgeamongfemaleenduranceathleteswithsymptomsofRED‐S.Although

improvementsindietarybehaviorwereonlymodest,theFUELinterventionshowsprom‐

iseasafoundationforbehaviorchangeinfemaleenduranceathletesatriskofREDs.

SupplementaryMaterials:Thefollowingsupportinginformationcanbedownloadedat:

https://www.mdpi.com/article/10.3390/nu15051082/s1,FileS1:Scoringkeyforsportsnutrition‐re‐

latedbehavior,TableS1:Sportnutritionknowledge;TableS2:Trainingandactivitycharacteristics.

AuthorContributions:M.K.T.wastheoverallprojectowner.I.L.F.wasresponsiblefortheoverall

datacollectionandstandardizationbetweenthecountries.Thestudydesignandthesixteensports

nutritionlecturesweredevelopedbyI.L.F.,A.K.M.,I.G.andM.K.T.TheNorwegianlectureswere

recordedbyI.G.andM.K.T.,theSwedishlectureswererecordedbyA.K.M.,theIrishlectureswere

recordedbyS.M.,andtheGermanlectureswererecordedbyP.W.andS.M.H.‐S.developedthe

manualforthecounsellorsandwasresponsibleforthewebinarsaheadoftheinterventionandwas

togetherwithP.W.,S.M.,D.L.andP.L.apartofthecounsellorteam.I.G.andI.L.F.headedthe

weeklymeetingswiththecounsellorteam.I.L.F.andM.K.T.wereresponsiblefortheNorwegian

cohort,A.K.M.fortheSwedishcohort,P.W.andK.K.fortheGermancohort,andS.M.andD.L.for

theIrishcohort.I.L.F.conductedthestatisticalanalysisunderthesupervisionofA.I.andI.L.F.con‐

structedthepaperafterwhichtheotherauthorsreviewedandgavefeedbackforimprovements.All

authorshavereadandagreedtothepublishedversionofthemanuscript.

Funding:ThisworkwassupportedbyGrantsfromtheUniversityofAgderinNorwayandthe

NorwegianOlympicSportsCenter.

InstitutionalReviewBoardStatement:ThestudywasconductedinaccordancewiththeDeclara‐

tionofHelsinkiandapprovedbytheregionalethicscommitteeinNorway(31640),Sweden(2019‐

04809),andbytheNorwegianCentreforResearchData(968634).

Nutrients2023,15,108218of21



InformedConsentStatement:Informedconsentwasobtainedfromallsubjectsinvolvedinthe

study.

DataAvailabilityStatement:Therawdatasupportingtheconclusionsofthisarticlewillbemade

availablebytheauthors,withoutunduereservation.

Acknowledgments:TheauthorsthankprofessorandpsychiatristFinnSkåderudforbeingthemed‐

icalprofessionalresponsibleintheproject,MariaGräfnings,IngvildBrattekleiv,andMonaSaller

forassistingwiththerecruitmentanddatacollection,andMiriamMyhrenBouchlehforassisting

withtheaccelerometeranalyses.Theauthorsgreatlyappreciatetheathletes’contributiontothe

project.

ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.

Abbreviations

ListofAbbreviations

ANOVAAnalysisofvariance

BFBayesfactor

BFinclBayesfactorforinclusionofgroupxtimeinteraction

BMIBodymassindex

CONControlgroup

EDE‐QEatingDisorderExaminationQuestionnaire

E%Energypercentage

FUELFoodandnUtritionforEnduranceathletes—aLearningprogram

ggram

HCHormonalcontraceptives

kcalKilocalorie

kgKilogram

LEALowenergyavailability

LEAF‐QLowEnergyAvailabilityinFemalesQuestionnaire

RED‐SRelativeEnergyDeficiencyinSport

SDStandarddeviation

TSDServicesforSensitiveData

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Disclaimer/Publisher’sNote:Thestatements,opinionsanddatacontainedinallpublicationsaresolelythoseoftheindividual

author(s)andcontributor(s)andnotofMDPIand/ortheeditor(s).MDPIand/ortheeditor(s)disclaimresponsibilityforanyinjury

topeopleorpropertyresultingfromanyideas,methods,instructionsorproductsreferredtointhecontent.

Paper III

Short-term effects and long-term changes of FUEL – a digital sports nutrition

intervention on REDs related symptoms in female athletes

Fahrenholtz, I. L., Melin, A. K., Garthe, I., Wasserfurth, P., Ivarsson, A., Hollekim-

Strand, S. M., Koehler, K., Logue, D., Madigan, S., Gräfnings, M., & Torstveit, M. K.

Published in Frontiers in Sports and Active Living 2023; 5: 1254210

EDITED BY

Boye Welde,

UiT The Arctic University of Norway, Norway

REVIEWED BY

Luca Paolo Ardigò,

NLA University College, Norway

Evgen Benedik,

University of Ljubljana, Slovenia

Eva Peklaj

University Rehabilitation Institute, Slovenia, in

collaboration with reviewer EB

*CORRESPONDENCE

Ida Lysdahl Fahrenholtz

ida.fahrenholtz@uia.no

RECEIVED 06 July 2023

ACCEPTED 24 November 2023

PUBLISHED 18 December 2023

CITATION

Fahrenholtz IL, Melin AK, Garthe I,

Wasserfurth P, Ivarsson A, Hollekim-Strand SM,

Koehler K, Logue D, Madigan S, Gräfnings M

and Torstveit MK (2023) Short-term effects and

long-term changes of FUEL—a digital sports

nutrition intervention on REDs related

symptoms in female athletes.

Front. Sports Act. Living 5:1254210.

doi: 10.3389/fspor.2023.1254210

Wasserfurth, Ivarsson, Hollekim-Strand,

Koehler, Logue, Madigan, Gräfnings and

Torstveit. This is an open-access article

distributed under the terms of the Creative

Commons Attribution License (CC BY). The use,

distribution or reproduction in other forums is

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No use, distribution or reproduction is

permitted which does not comply with these

terms.

Short-term effects and long-term

changes of FUEL—a digital sports

nutrition intervention on REDs

related symptoms in female

athletes

Ida Lysdahl Fahrenholtz1*, Anna Katarina Melin2, Ina Garthe3,

Paulina Wasserfurth4, Andreas Ivarsson1,5,

Siri Marte Hollekim-Strand6, Karsten Koehler4, Danielle Logue7,

Sharon Madigan7, Maria Gräfnings8and Monica K. Torstveit1

Department of Sport Science and Physical Education, University of Agder, Kristiansand, Norway,

Department of Sport Science, Linnaeus University, Växjö/Kalmar, Sweden,

The Norwegian Olympic and

Paralympic Committee and Confederation of Sport, Oslo, Norway,

Department Health and Sport

Sciences, School of Medicine and Health, Technical University of Munich, Munich, Germany,

School of

Health and Welfare, Halmstad University, Halmstad, Sweden,

Department of Neuromedicine and

Movement Science, Norwegian University of Science and Technology, Trondheim, Norway,

Sport Ireland

Institute, National Sports Campus, Dublin, Ireland,

Department of Medical Science, Dalarna University,

Falun, Sweden

Female endurance athletes are at high risk for developing Relative Energy

Deﬁciency in Sport (REDs), resulting in symptoms such as menstrual dysfunction

and gastrointestinal (GI) problems. The primary aim of this study was to

investigate effects of the FUEL (Food and nUtrition for Endurance athletes—a

Learning program) intervention consisting of weekly online lectures combined

with individual athlete-centered nutrition counseling every other week for

sixteen weeks on REDs related symptoms in female endurance athletes at risk of

low energy availability [Low Energy Availability in Females Questionnaire

(LEAF-Q) score ≥8]. Female endurance athletes from Norway (n= 60), Sweden

(n= 84), Ireland (n= 17), and Germany (n= 47) were recruited. Fifty athletes with

risk of REDs (LEAF-Q score ≥8) and with low risk of eating disorders [Eating

Disorder Examination Questionnaire (EDE-Q) global score <2.5], with no use of

hormonal contraceptives and no chronic diseases, were allocated to either the

FUEL intervention (n= 32) (FUEL) or a sixteen-week control period (n= 18)

(CON). All but one completed FUEL and n= 15 completed CON. While no

evidence for difference in change in LEAF-Q total or subscale scores between

groups was detected post-intervention (BF

incl

< 1), the 6- and 12-months

follow-up revealed strong evidence for improved LEAF-Q total (BF

incl

= 123) and

menstrual score (BF

incl

= 840) and weak evidence for improved GI-score (BF

incl

= 2.3) among FUEL athletes. In addition, differences in change between groups

was found for EDE-Q global score post-intervention (BF

incl

= 1.9). The reduction

in EDE-Q score remained at 6- and 12- months follow-up among FUEL

athletes. Therefore, the FUEL intervention may improve REDs related symptoms

in female endurance athletes.

Clinical Trial Registration: www.clinicaltrials.gov (NCT04959565).

KEYWORDS

sports injuries, menstrual disturbances, low energy availability, endurance exercise,

women’s health

TYPE Clinical Trial

PUBLISHED 18 December 2023

DOI 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 01 frontiersin.org

1. Introduction

Sustainable and low-cost management of symptoms related to the

syndrome Relative Energy Deﬁciency in Sport (REDs) is of key interest

in female endurance athletes (1–3) due to the high reported prevalence

of symptoms, ranging from 31% to 80% (4–8). The frequent occurrence

of negative health and performance related consequences, including

menstrual dysfunction with associated low bone mineral density and

overuse injuries (3) calls for action. Insufﬁcient energy intake relative

to exercise energy expenditure, often denoted low energy availability

(LEA), is the underlying etiological factor for REDs (3). The

recommended treatment is therefore to increase energy intake,

reduce exercise energy expenditure or a combination of both (9,10).

However, the evidence of intervention efﬁcacy for the managements

of REDs symptoms is limited and primarily based on case studies

(11–13) and interventions without a control group (14,15), or in

non-competitive females (16). Successful nutrition interventions have

suggested that future studies implementing strategies to provide more

personalized dietary interventions accounting for food preferences,

dietary patterns across the day, timing of food intake and

macronutrient composition may have the potential to be more

effective (16).

The menstrual cycle is an energy demanding process, involving

hormonal synthesis and follicular development, and eumenorrhea is

recognized as an important health indicator for female athletes (17).

Therefore, menstrual function is a frequently used marker when

screening for LEA and REDs and assessing the safety of female

athletes’sports participation (4,10,18). In fact, it has been

suggested that assessing self-reported symptoms of LEA, including

menstrual function, provides a better assessment of the overall

health status of an athlete compared to a snapshot of current

energy availability, where assessment of dietary intake and exercise

energy expenditure is susceptible to several sources of errors

(6,19,20). The Low Energy Availability in Females Questionnaire

(LEAF-Q) (4), where the main emphasis is laid on menstrual

function, is one of the most frequently used screening tools for

detecting female athletes at risk of LEA and REDs (20). The

LEAF-Q is validated in endurance athletes (4) and this is also

where one of the highest prevalence of menstrual dysfunction is

reported in sports ranging from 0% to 20% for primary

amenorrhea (late menarche), 0%–56% for secondary amenorrhea

(no bleeding for minimum of three consecutive menstrual cycles),

and 0%–39% for oligomenorrhea (<9 menstrual bleedings per

year), depending on the diagnostic method used (21). Although

the infertility associated with menstrual dysfunction in athletes

may be transient (22), prolonged or severe LEA and the multiple

metabolic and endocrine alteration associated with menstrual

disturbances e.g., elevated cortisol and lowered estradiol, insulin

and T3 levels can have serious negative impact on bone health via

an estrogen-dependent and estrogen-independent pathway, which

may be irreversible (23,24). Low bone mineral density constitutes

an increased risk for bone stress injuries, resulting in long

absences from sport participation (25,26), emphasizing the need

for prevention at all levels (27).

Negative gastrointestinal tract function has also been associated

with LEA and REDs (3). More speciﬁcally, persistent LEA can

result in mucosal atrophy characterized by diminished intestinal

function and morphological changes including decreased villous

height, crypt depth, surface area, and epithelial cell numbers

(28). In addition, LEA and REDs have been associated with an

excessive dietary ﬁber intake among female endurance athletes

(29). The gastrointestinal problems may appear as delayed gastric

emptying, bloating, constipation, and increased intestinal transit

time (3). Gastrointestinal problems, commonly reported by

endurance athletes, may not only be detrimental to health and

quality of life, but also to athletic performance (30,31).

As formulated by Ackerman et al. (32)“It is time for a drastic

paradigm change in women’s sport, coupled with education at all

levels to improve the long-term health and athletic achievement of

female athletes”[(32), p.1 line 15–19]. One of the proposed steps

in the management of REDs is to raise awareness of the negative

effects of LEA so athletes can make wise decisions for their own

long-term health (32). In essence, inadequate knowledge of

optimal sports nutrition and the negative health and performance

consequences of LEA, coupled with a normalization of REDs

symptoms, e.g., menstrual dysfunction, appears to be frequent

underlying causes of LEA (33,34). Though, adequate nutrition

knowledge is necessary for optimal nutrition habits, it may not be

asufﬁcient factor for ensuring a true change in nutritional

behavior in athletes (35). Furthermore, motivation, enabling, and

supporting athletes have been identiﬁed as additional components

necessary for changes in nutritional behavior (33). We have

previously reported strong evidence for improved sports nutrition

knowledge along with weaker evidence for increased energy intake

in female endurance athletes with risk of REDs after a 16-week

sports nutrition intervention, consisting of online sports nutrition

lectures combined with individual athlete-centered nutrition

counseling (the FUEL study) (36).

Parallel to measure physiological symptoms that may be

affected by a nutrition intervention, it is important to monitor

psychological symptoms associated with LEA. This includes

eating disorder symptoms. Nutrition education and counseling

have been reported to increase eating disorder symptoms in

young ballet dancers (37,38). Hence, although LEA and REDs

occurs frequently without disordered eating behavior or eating

disorders (39–41), one may fear that a nutrition intervention

aiming at increasing energy intake may pose a risk for the

development of eating disorders in an already high-risk group

(42). Furthermore, there is a reported association between

symptoms of eating disorders and exercise addiction (43) and

REDs may therefore be associated with exercise addiction in

endurance athletes (39,44).

Therefore, the primary aim of the present analysis was to

investigate immediate effects (pre-post intervention) and long-

term changes (6- and 12-months follow-up) of the FUEL

intervention study, on common symptoms associated with REDs;

menstrual and gastrointestinal function and injuries. Secondary,

the aim was to investigate any symptoms related to the risk of

eating disorders and exercise addiction. Speciﬁcally, the goal of

this analysis was to investigate whether the LEAF-Q, Eating

Disorder Examination (EDE-Q), and Exercise Addiction

Inventory (EAI) scores change differently from the pre- to

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 02 frontiersin.org

postintervention in the intervention group compared to the control

group, and to investigate how the symptoms develop up to 12-

months follow-up in the intervention group.

2. Methods

The study design, recruitment process, and intervention

content have been described in detail elsewhere (36). The study

was approved by the regional ethics committee in Norway

(31,640), Sweden (2019-04809), and by the Norwegian Centre for

Research Data (968,634) and registered at www.clinicaltrials.gov

(NCT04959565). Originally, the study was planned and approved

to include a wide range of REDs related clinical biomarker

measurements and a control group prior to initiation of the

intervention. Due to the COVID-19 pandemic the ﬁrst round of

recruitment had to be cancelled. Further, since all physical

contact with the participants was prohibited, the ﬁnal design and

measures are strongly inﬂuenced by the pandemic restrictions.

Consequently, all medical procedures were excluded in the ﬁnal

research plan, thus, the study did not need an additional ethical

approval at the other study sites (Germany and Ireland).

2.1. Study design

This was a multicenter study recruiting female endurance athletes

from Norway, Sweden, Ireland, and Germany. Athletes were

seasonally allocated to the FUEL intervention (FUEL) or a control

condition (CON). The intervention group received weekly online

lectures in sports nutrition combined with individual athlete-

centered nutrition counseling with an experienced sports

nutritionist for sixteen weeks. The control group received no

lectures or counseling. The study was initiated with a screening

phase, where athletes completed an online survey via the data

collection tool Nettskjema that was connected to the safe Services

for Sensitive Data (TSD) platform (University of Oslo). In this part

of the study, athletes provided background information and

completed the LEAF-Q (4), EDE-Q (45), and the Exercise

Addiction Inventory (46). Athletes with risk of LEA, deﬁned as a

LEAF-Q score ≥8(4), and low risk of disordered eating behavior,

deﬁned as an EDE-Q global score <2.5 (47), were invited to

participate in the study. Athletes completed the same questionnaires

after the 16-week FUEL/CON condition. In addition, the FUEL

intervention group completed a 6- and 12-months follow-up

answering the LEAF-Q, the EDE-Q, and the EAI. Other

assessments included in the study, including diet and training log,

has previously been described and analyzed (36).

2.2. Eligibility criteria

Eligibility criteria for the study were (1) competitive female

endurance athlete, (2) 18–35 years of age, (3) training ≥5 times/

week, (4) no use of hormonal contraceptives for at least six

weeks prior to the study, (5) no chronic disease (e.g., Crohn’s

disease or hypothyroidism) or diagnosed menstrual dysfunctions

not related to LEA (e.g., polycystic ovarian syndrome or

endometriosis), (6) non-smoker, (7) not pregnant or planning a

pregnancy, (7) speaking/understanding Norwegian, Swedish,

English, or German.

2.3. Recruitment

Athletes were recruited from November 2020 to September

2021 via Norwegian, Swedish, Irish, and German competitive

endurance sports clubs, coaches in endurance sports at the

Olympic sports center in Norway and via social media with a

link to the project website. The recruitment targeted summer

endurance disciplines (runners, orienteers, cyclists, and

triathletes) during November/December with the initiation to the

intervention in January, while the recruitment targeted winter

endurance disciplines (biathletes and cross-country skiers) in

May with the initiation to the intervention in June.

In total, 208 participants signed up for the study. Of these, 141 were

excluded: n=2maleathletes;n= 2 < 18 years; n= 1 > 35 years.; n=1

badminton player; n= 3 with chronic diseases (n=1: Crohn’s

disease, n=1: Hashimoto’s thyroiditis, n= 1: hypothyroidism); n=

55 hormonal contraceptive users; n=23 with a EDE-Q global score

≥2.5; n= 51 with a LEAF-Q score <8, and n= 3 for not providing

any contact information. The LEAF-Q responses of n= 67 athletes

were analyzed in more detail, and some were contacted to clarify

their answers. This resulted in n= 7 athletes being excluded due to a

suspected false positive identiﬁcation of the risk of LEA. Further, n=

4 athletes were unavailable, n= 3 responded too late in relation to

intervention start-up and allocation to sports nutritionists, and n=3

athletes declared severe illness ahead of the baseline measurements

(i.e., abdominal surgery and COVID-19). In total, n= 18 athletes,

who had signed up during their competition season, were allocated

to a 16-week waiting control condition (CON) of which n=15

athletes completed (n= 1 wanted to start using hormonal

contraceptives, while we were unable to contact n= 2). In total,

n= 32 athletes were directly allocated to the FUEL intervention,

while n= 1 terminated participation in the project in week 13 due to

experiencing too much work related to the project. Consequently,

n= 31 (97%) completed the FUEL intervention and n= 15 (83%)

completed the CON condition.

2.3.1. Final inclusion of participants in the analyses

One athlete in FUEL missed the postintervention survey

relevant for this paper (but completed the other measurements),

while all participants in CON completed the survey with the

LEAF-Q, EDE-Q, and EAI pre- and postintervention.

Consequently, n= 30 and n= 15 athletes were included in the

analyses comparing pre- and post-measurements for FUEL and

CON, respectively. Twenty-six of the 30 FUEL athletes completed

the 6-months follow-up. In terms of the LEAF-Q analysis, n=3

had started using hormonal contraceptives, n= 1 reported

pregnancy/breastfeeding and was therefore excluded from the

6-months follow-up. Twenty-three FUEL athletes completed the

12-months follow-up. Additional n= 2 had started using

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 03 frontiersin.org

hormonal contraceptives and n= 1 reported pregnancy/

breastfeeding and was therefore excluded in the 12-months

follow-up for the LEAF-Q analyses.

2.4. Nutrition intervention

The 16-week intervention consisted of weekly online lectures in

sports nutrition targeting female endurance athletes with risk of

REDs, combined with individual athlete-centered nutrition

counseling every other week.

The sixteen sports nutrition lectures integrated evidence-based

sports nutrition information and recommendations. They were

developed by four researchers and practicing sports nutritionists,

initially in Norway and Sweden, including a comprehensive

manuscript for each session, and subsequently translated into

English and German. All sixteen lectures were comprehensively

reviewed and ﬁnallyapprovedbyallfour researchers/sports

nutritionists. The recorded lectures had a mean duration time of

25.0 ± 8.4 min. Key topics were information about REDs, the

importance of the menstrual cycle for health and performance,

macronutrient recommendations for endurance athletes, and

nutritional periodization. Every week during the intervention,

participants received an e-mail with a link and password to the

lecture of the week located on a closed online platform. Participants

had the opportunity to watch the lectures when suitable during

their everyday lives and to watch them repeatedly if they wanted.

The nutrition counseling was administrated via the

teleconferencing platform Zoom, Zoom Video Communication,

Inc. (California, USA). The ﬁrst consultation was scheduled to run

for 1.5 h (actual duration: 73 ± 15 min), while the following seven

consultations were scheduled to run for approximately 1 h (actual

duration: 55 ± 6 min). The team of counsellors, consisted of three

Norwegian, four Swedish, two Irish, and one German highly

experienced sports nutritionists, who work with Elite athletes on a

daily basis. Self-determination theory was chosen as a core

foundation for the FUEL counseling, since this approach has been

found to be effective in promoting behavior change (48). An

athlete-centered, empathic communication approach, inspired by

core skills in motivational interviewing (49) was utilized.

2.5. Measures and instruments

2.5.1. Low energy availability in females

questionnaire

The validated screening tool LEAF-Q (4) was used to assess

self-reported symptoms of LEA; injury frequency the past year,

current gastrointestinal function, and current and past

reproductive function. The LEAF-Q is validated in female

endurance athletes and has a total of 9–25 questions depending

on the respondent’s answer, including those related to hormonal

contraceptive use. A total score ≥8 was considered at risk of LEA

(4). Athletes completed the LEAF-Q at pre- and

postintervention/control period, and also at 6- and 12-months

follow-up for the FUEL group. Because a LEAF-Q score ≥8 was

used as an inclusion criterion, all athletes in the present study

had a LEAF-Q score ≥8 at pretest. Since the LEAF-Q assesses

injuries the past year, the injury score was considered less

important at postintervention measurement. Similarly, it is not

possible to change the answer to some of the questions related to

menstrual function during a 16-week period [“How old were

when you had your ﬁrst period?”and “Did your ﬁrst

menstruation come naturally (by itself)?”]. Therefore, it was of

interest to look at possible changes on single questions related to

menstrual function, namely: “Do you have normal menstruation?”

and “Do you experience that your menstruation changes when

you increase your exercise intensity, frequency or duration?”

Minor clariﬁcations from the original LEAF-Q were added and

has been described previously (39).

2.5.2. Eating disorder examination questionnaire

The EDE-Q was used to measure behavioral and cognitive

symptoms of eating disorders the past 28 days (45). It has been

validated in an athletic population (50) and is a frequently used

screening tool for disordered eating and LEA/REDs (20). The

EDE-Q consists of 28 items which can be divided into four

subscales (restraint, eating concern, shape concern, and weight

concern) and a global score averaging the subscales, used as

cut-off for eating disorder pathology. In the present study, a

global EDE-Q score ≥2.5 was used to classify athletes with

disordered eating behavior (39,47,51). Because an EDE-Q

global score <2.5 was used as an inclusion criterion, all included

athletes had an EDE-Q global score <2.5 at pretest.

2.5.3. Exercise addiction inventory

The EAI was used to assess symptoms of exercise addiction

with a score ≥24 considering participants at risk of exercise

addiction (46,52). The EAI consists of six general components

describing the degree of addiction rated on a ﬁve-point Likert

scale: salience (exercise is the most important thing in life),

conﬂicts (e.g., interpersonal conﬂicts due to the exercise

behavior), mood modiﬁcation (a coping strategy to regulate

emotions), tolerance (increasing amounts of exercise is needed to

achieve effect), withdrawal symptoms (e.g., irritability when an

exercise session is missed), and relapse (reversions to earlier

patterns). Originally, the EAI was validated in recreational

exercisers but has later been validated in elite athletes (53).

2.6. Statistics

Data analyses were conducted using JASP (version 0.17.1.0).

All analyses were conducted within the Bayesian statistical

framework (54,55). Descriptive statistics were expressed as

frequencies with percentage for binary and categorical data and

as means ± standard deviation (SD) for continuous data. Group

comparisons for baseline characteristics were conducted using

Bayesian Independent Samples t-test for normally distributed

data and Mann–Whitney test for non-normally distributed data.

Bayesian contingency table tests were used to compare groups for

categorical data. Within-group differences from pre- to

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 04 frontiersin.org

postintervention were investigated using Bayesian Paired Samples

t-test for normally distributed data and with Wilcoxon Signed-

Rank test for non-normally distributed data. Group comparisons

from pre- to postintervention were conducted using a Bayesian

repeated measures analysis of variance (ANOVA) with default

priors and compared to the null model. Non-normally

distributed data were transformed using SPSS [version 28.0.1.1

(14)] but did not change the interpretations of the results

compared to analyzing the non-transformed data. A group × time

interaction effect was hypothesized, i.e., that the FUEL and CON

group’s LEAF-Q scores would change differently over time

(alternative hypothesis). To calculate the Bayes Factor (BF) for

the interaction effect only inclusion probabilities for matched

models were considered (55). BFs between 1 and 3 were

considered to indicate weak evidence for the alternative

hypothesis, BFs between 3 and 10 were considered moderate

evidence for the alternative hypothesis, while BFs greater than 10

were considered as strong evidence for the alternative hypothesis

(56). Menstrual function for individual questions in the LEAF-Q

was analyzed in a descriptive manner due to insufﬁcient number

of participants. Within FUEL group comparisons for LEAF-Q,

EDE-Q, and EAI scores for the four measurement time points

(pre-, postintervention, 6- and 12-months follow-up), were

conducted using a Bayesian repeated measures ANOVA.

Menstrual function for individual questions in the LEAF-Q was

analyzed in a descriptive manner.

3. Results

Endurance athletes from Norway (n= 11), Sweden (n= 17),

Ireland (n= 5), and Germany (n= 12) were included from the

following endurance disciplines: running (n= 14), orienteering (n

= 7), triathlon (n= 12), cycling (n= 5), cross country skiing (n=

1), and biathlon (n= 6). Participant characteristics are presented

in Table 1. There was no evidence of statistical differences when

comparing the two groups’baseline characteristics (BFs < 1).

3.1. Symptoms of low energy availability

3.1.1. Comparing pre- and postintervention group

differences

The FUEL athletes reduced the LEAF-Q total score from

12.0 ± 2.8 to 9.8 ± 4.3 (BF

= 20.92) compared to CON athletes

reducing the LEAF-Q total score from 11.0 ± 3.0 to 10.3 ± 2.5

(BF

= 0.79) with no evidence for difference in change between

groups (Table 2). Nor did any of the changes in the LEAF-Q

subscale scores differ between groups as indicated by the lack of

an interaction effect (BF

incl

< 1). At posttest, total LEAF-Q score

was <8 for n= 11 (37%) of the FUEL athletes and n= 2 (13%) of

the CON athletes (BF

= 1.267).

The number of participants that reported eumenorrhea

increased among FUEL athletes from 30% (n=9 athletes) at

pretest to 67% (n= 20 athletes) at posttest and decreased among

CON athletes from 73% (n= 11) to 53% (n=8) (Figure 1). Five

of the 14 (36%) FUEL athletes, who reported menstrual

dysfunction at pretest, reported eumenorrhea at posttest. Of the

FUEL athletes who reported menstrual dysfunction at pretest and

eumenorrhea at posttest, all reported their latest menstruation

within the last 0–3 month at pretest. Three FUEL athletes and one

CON athlete reported secondary amenorrhea at pretest. None of

them improved their menstrual function form pre to posttest.

Seven (23%) FUEL athletes and three (20%) CON athletes were

unaware whether they had normal menstruation at pretest. All

FUEL athletes were able to deﬁne whether they had normal

menstruation or not at posttest, while the number was

unchanged among CON athletes. The number of athletes who

reported reduced or absence of menstrual bleedings with

increased training load decreased from n= 21 (70%) to n=14

(47%) among FUEL athletes while the number was unchanged

among CON athletes (n= 14/73%) (Figure 2). Twelve (40%)

FUEL athletes and four (27%) CON athletes reported late

menarche a (menarche after 15 years of age).

3.1.2. Six- and 12-months follow-up

Six- and 12-months follow-up revealed strong evidence for

improvement in LEAF-Q total score for FUEL athletes comparing

the three (BF

incl

= 441) and four (BF

incl

= 123) measurement

points, respectively (Figure 3A). This was explained by

improvements in the menstrual score (6-months: BF

incl

= 4,486,

12-months: BF

incl

=840) (Figure 3B) and the gastrointestinal score

(6-months: BF

incl

= 9.5, 12-months: BF

incl

=2.3) (Figure 3C). We

found weak evidence for an improvement in the gastrointestinal

score from 6- to 12-months follow-up (BF

= 1.2) while no

evidence for improvement in LEAF-Q total score, menstrual score

or injury score when comparing 6- and 12-months follow-up

(BF

<1) (Figure 3D).

At 6-months follow-up, 45% of FUEL athletes had a total

LEAF-Q score <8, and 21% at 12-months follow-up. The two

FUEL athletes with secondary amenorrhea at pretest, who was

eligible for long-term follow-up, still had not improved menstrual

function at 6 months follow-up, but one reported eumenorrhea

at 12-months follow-up.

TABLE 1 Participant characteristics divided by intervention (FUEL) and

control (CON) groups.

FUEL (n= 30) CON (n= 15)

Age (years) 25.2 ± 4.09 24.1 ± 4.7

Height (cm) 169.5 ± 6.3 171.2 ± 7.1

Body weight (kg) 59.6 ± 7.1 59.3 ± 5.0

Body Mass Index (kg/m

) 20.7 ± 2.1 20.3 ± 1.7

Training volume (h/month) 45.2 ± 16.5 47.0 ± 18.9

Full-time athlete (%) 16.7 20.0

Level of competition

Club (%) 63.3 86.7

National team (%) 20.0 6.7

Professional (%) 10.0 6.7

Others (%)

6.7 0.0

FUEL, the FUEL intervention group; CON, the control group.

Continuous data are presented as mean ± SD and categorical data as percentage.

Athletes who did not identify themselves as competing at club-, national team-, or

professional level, e.g., competing in one of the endurance disciplines but not

afﬁliating within a club.

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 05 frontiersin.org

3.2. Symptoms of disordered eating

behavior

3.2.1. Comparing pre- and postintervention group

differences

The EDE-Q global score decreased from 1.03 ± 0.73 to

0.72 ± 0.69 (BF

= 11.84) among FUEL athletes and was

unchanged among CON athletes (0.80 ± 0.74 at pretest and

0.96 ± 0.85 at posttest, BF

= 0.41) with weak evidence for a

difference in change between groups as indicated by the

interaction effect of BF

incl =

1.858 (Table 3). The largest

within-group difference among FUEL athletes for the EDE-Q

subscales was detected for the restraint subscale score (BF

14.87). In contrast, weak evidence for an increase in the

EDE-Q subscale weight concern was found among controls

(BF

= 1.68).

The EDE-Q global score increased above the 2.5 threshold

post-intervention for two (7%) FUEL athletes

(pre-intervention EDE-Q global scores of 0.4 and 2.4,

respectively) and one (7%) CON athlete (pre-intervention

EDE-Q global score 2.4) to EDE-Q global scores 2.8, 3.0,

and 2.5, respectively.

3.2.2. Six- and 12-months follow-up

Long-term follow-up revealed moderate evidence (BF

incl

= 5.18)

forreducedEDE-QglobalscoreforFUELathletescomparingall

four measuring points (Figure 4). The largest reduction in the EDE-

Q subscale scores was seen in the restraint subscale (BF

incl

= 16.45).

The two FUEL athletes with EDE-Q global scores ≥2.5 at

postintervention, had EDE-Q global scores of 0.0 and 0.3,

respectively, at 6-months follow-up and 0.3 and 0.8, respectively,

at 12-months follow-up.

3.3. Exercise addiction inventory

3.3.1. Comparing pre- and postintervention group

differences

Within group analyses revealed no evidence for changes from

pre- to posttest in EAI total or the six item scores among FUEL

nor CON athletes (BF

< 1). Nor did we ﬁnd evidence for

difference in change between groups for the EAI total score

(FUEL pre: 20.7 ± 3.0, FUEL post: 20.8 ± 2.7 vs. CON pre:

20.6 ± 3.0, CON post: 21.1 ± 2.9) or any of the six item scores

(BF

incl

< 1).

FIGURE 1

Self-reported eumenorrhea from the low energy availability questionnaire pre-and postintervention. Data are presented as percentages. CON, the control

group; FUEL, the FUEL intervention group.

TABLE 2 Low energy availability in females questionnaire scores pre- and postintervention.

FUEL (n= 30) CON (n= 15) Difference in change

between groups,

incl

Pre Post Difference

pre-post

Pre Post Difference

pre-post

LEAF-Q total score 12.0 ± 2.8 9.8 ± 4.3 −2.2 ± 3.6 11.0 ± 3.0 10.3 ± 2.5 −0.7 ± 1.7 0.850

Injury score 3.2 ± 2.3 2.8 ± 2.3 −0.3 ± 2.0 3.7 ± 2.0 3.5 ± 2.1 −0.3 ± 1.4 0.320

Gastro-intestinal score 2.3 ± 2.1 1.7 ± 1.5 −0.6 ± 1.6 2.3 ± 1.7 2.1 ± 1.5 −0.1 ± 1.6 0.544

Menstrual score 6.6 ± 2.5 5.3 ± 3.0 −1.2 ± 1.8 5.1 ± 2.7 4.7 ± 2.3 −0.4 ± 2.2 0.729

Data are presented as mean ± SD.

incl

, bayes factor for inclusion of group × time interaction; CON, control group; FUEL, the FUEL intervention group, LEAF-Q, low energy availability in females

questionnaire.

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 06 frontiersin.org

FIGURE 2

Reduced, or absence of, menstrual bleedings with increased training load from the low energy availability questionnaire pre-and postintervention. Data

are presented as percentages. CON, the control group; FUEL, the FUEL intervention group.

FIGURE 3

Changes in LEAF-Q (A) total score, (B) menstrual score, (C) gastrointestinal score, and (D) injury score for the FUEL athletes at pre- and postintervention,

and at 6- and 12-months follow-up. Data are presented as mean and 95% credible intervals. BF

incl

, bayes factor for inclusion of time interaction, LEAF-Q,

low energy availability in females questionnaire.

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 07 frontiersin.org

3.3.2. Six- and 12-months follow-up

Six- and 12-months follow-up revealed strong evidence

(BF

incl

= 31.50) for reduced EAI total score for FUEL athletes

comparing the four measuring points (Figure 5).

4. Discussion

To our knowledge, this is the ﬁrst study to explore changes on

several REDs related symptoms in female endurance athletes with

risk of REDs after a nutrition intervention using validated

screening tools, comparison with a control group, and inclusion

of long-term follow-up. More speciﬁcally the current study

explored changes in menstrual and gastrointestinal function,

injuries, eating disorder and exercise addiction symptoms. The

FUEL study was an international multicenter study with weekly

online sports nutrition lectures combined with individual

consultations every other week. The lectures were speciﬁcally

designed for female endurance athletes with risk of REDs.

Although no evidence for difference in change between FUEL

and CON athletes pre- to postintervention were found in

LEAF-Q scores, long-term follow-up revealed strong evidence for

reduced LEAF-Q total and menstrual scores among FUEL

athletes. Importantly, the nutrition intervention did not result in

negative effects related to eating disorder or exercise addiction

symptoms. Rather, there was evidence for improved EDE-Q

scores after the FUEL intervention. The reduction in eating

disorder symptoms for FUEL athletes remained at 6- and 12-

month follow-up.

In this study, athletes were categorized with risk of REDs using

the LEAF-Q (4). The LEAF-Q has been validated in female

endurance athletes, 18–39 years of age, training ≥5 times/week

with Cronbach’s Alpha 0.61–0.79, and an acceptable sensitivity

(78%) and speciﬁcity (90%) (4), making it a good alternative to

assess symptoms of LEA in this group of athletes. The LEAF-Q

has subsequently been validated in a mixed sport-cohort (n= 75,

18–32 years), which demonstrated high sensitivity for the

detection of low bone mineral density and menstrual

dysfunction, suggesting that injury and menstrual function cutoff

score also may be appropriate in mixed-sport cohort (57). The

researchers concluded that LEAF-Q total score <8 can be used to

determine females at low risk of LEA related conditions given

the high negative predictive values identiﬁed in this study (57).

In the present study we examined all LEAF-Q responses in detail

and excluded athletes who had been diagnosed with menstrual

dysfunction not related to LEA and others where false positive

identiﬁcation of problematic LEA was expected (e.g., athletes who

had been involved in a bicycle crash and menstrual dysfunction

TABLE 3 Eating disorder examination questionnaire scores pre- and postintervention among FUEL (n= 30) and CON athletes (n= 15).

FUEL (n= 30) CON (n= 15) Difference in change

between

groups, BF

incl

Pre Post Difference

pre-post

Pre Post Difference

pre-post

EDE-Q global 1.03 ± 0.73 0.72 ± 0.69 −0.31 ± 0.76 0.80 ± 0.74 0.96 ± 0.85 0.16 ± 0.56 1.858

EDE-Q restraint 0.79 ± 0.94 0.38 ± 0.62 −0.41 ± 0.98 0.96 ± 1.21 0.83 ± 1.08 −0.16 ± 0.51 0.498

EDE-Q eating concern 0.72 ± 0.75 0.46 ± 0.62 −0.25 ± 0.64 0.39 ± 0.39 0.51 ± 0.45 0.12 ± 0.30 2.006

EDE-Q weight concern 1.18 ± 0.85 0.97 ± 0.90 −0.21 ± 0.96 0.92 ± 1.08 1.4 ± 1.3 0.46 ± 1.08 1.768

EDE-Q shape

concern

1.42 ± 0.94 1.05 ± 0.95 −0.37 ± 1.02 0.98 ± 0.65 1.16 ± 1.11 0.18 ± 0.83 1.230

Data are presented as mean ± SD.

incl

, bayes factor for inclusion of group × time interaction; CON, the control group; FUEL, the FUEL intervention group; EDE-Q, eating disorder examination

questionnaire.

FIGURE 4

Eating disorder examination questionnaire global score for the FUEL

athletes at pre- and postintervention, and at 6- and 12-months

follow-up. Data are presented as mean and 95% credible intervals.

incl

, bayes factor for inclusion of time interaction; EDE-Q, eating

disorder examination questionnaire.

FIGURE 5

Exercise addiction inventory total score for the FUEL athletes at pre-

and postintervention, and at 6- and 12-months follow-up. Data are

presented as mean and 95% credible intervals. BF

incl

, bayes factor for

inclusion of time interaction; EAI, exercise addiction inventory.

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 08 frontiersin.org

in the past resulting in a LEAF-Q total score ≥8). Nevertheless,

using screening tools as inclusion and exclusion criteria contains

a risk of including false positive cases (e.g., high LEAF-Q total

score due to acute injuries), including false negative cases (e.g.,

athletes reporting eumenorrhea while undetected subclinical

menstrual dysfunction (23,58), as well as excluding false

negative cases (e.g., high menstrual function score due to

polycystic ovarian syndrome while coexisting symptoms of LEA).

In our study, the decline in LEAF-Q total score was 18%

among FUEL and 6% among CON athletes. Although there was

a lower risk rate of LEA among FUEL (64%) compared to CON

(87%) athletes at posttest, we did not detect between group

difference from pre- to post intervention when comparing LEAF-

Q total score. The 16-week intervention period may have been

too short to detect differences in the measured symptoms.

Especially regarding the LEAF-Q injury score, which is related to

the previous year and associated with low bone mineral density

(4,57), where an improvement cannot be expected within the

time frame of the study period (59). But the absence of

intervention effect may also be attributed to the time required to

restore normal menstrual function and the complexity of

changing eating habits.

All FUEL athletes who reported menstrual dysfunction at

pretest but eumenorrhea at posttest, had reported a recent

bleeding at pretest, while none of the three FUEL athletes

reporting long-term absence of bleeding at pretest, had improved

menstrual function at posttest. The two FUEL athletes with long-

term absence of bleeding, who were eligible for long-term follow-

up, still had not improved menstrual function at 6-months

follow-up, but one reported eumenorrhea at 12-months follow-

up, suggesting that recovery time from more severe menstrual

disorders may be longer. Previous studies have reported mean

time to restoration of menstruation to be as high as 16 ± 3

months among college athletes with nonpharmacologic therapies

(60) and researchers have suggested that the time required to

resume menstruation depends to a large extend on the starting

point, including the duration of the menstrual dysfunction (14,

15). Unfortunately, the maximum duration of menstrual

dysfunction assessed in the present study was ≥6 months

(corresponding to the response option when answering “no”to

“do you have normal menstruation”). In the present study six

athletes, all with menstrual dysfunction, reported bone stress

injuries during the last year at pretest, indicating long-term

exposure of LEA.

Indeed, habits may take more than sixteen weeks to change

(61) and we have previously emphasized the complexity of

improving eating habits in this group of athletes (36). Since the

increase in energy intake was modest among FUEL athletes

(138 ± 453 kcal/day, corresponding to an increase of only 5%)

(36), it may have been insufﬁcient for improving REDs related

symptoms in some of the athletes. The ﬁve FUEL athletes who

reported menstrual dysfunction at pretest and eumenorrhea at

posttest had a slightly higher increase in energy intake compared

to the nine FUEL athletes who reported menstrual dysfunction

both at pre- and posttest (7% vs. 1%). Previous studies with

athletes and active females have reported increase in energy

intake of 17% (14) and 18% (15,16) after nutrition interventions

of 6, 9, and 12 months, respectively. In these studies, 88% (14)

and 23% (15) of the athletes restored regular menstruation after

the intervention, while De Souza et al. reported improved

menstrual function in 64% in a group of active females (16).

Although we recognized the complexity of habitual changes in

the study planning phase and implemented individual athlete-

centered nutrition counseling, sixteen weeks may be too short for

changing eating habits that can result in improvement of REDs

symptoms. Since energy availability is energy intake relative to

exercise energy expenditure, changes in LEAF-Q score could also

be attributed to changes in training volume. We have previously

reported decreased training volume among FUEL and CON

athletes from pre to posttest with no difference in change

between groups (36). Hence, training load was reduced

independent of group and athletic season. Although training

adjustment was not a part of the FUEL intervention, it is

possible that some athletes deliberately have reduced their

training volume to improve REDs symptoms. While the relative

increase in energy intake may be crucial, it has been suggested

that increase in body fat mass is an important predictor of

restoration of menstrual function in athletes and active females

(15,16). Unfortunately, body composition was not possible to

measure in the present study due to the COVID-19 pandemic (39).

At pretest ∼20% of the participants in the present study were

unable to deﬁne their menstrual status while all FUEL athletes

could deﬁne their menstrual status at posttest, with unchanged

results for CON athletes, suggesting that the FUEL intervention

succeeded in increasing the awareness of the menstrual cycle. Being

aware of one’s menstrual cycle, and the importance of having a

regular menstrual cycle, seems like an obvious ﬁrst step in the

prevention of problematic LEA and REDs for female athletes.

Especially since menstrual dysfunction is associated with low bone

mineral density reported in 17%–45% of female endurance athletes

(6,40,62–64) and an increased risk for bone stress injuries,

resulting in long absences from sport participation (25,26).

Among FUEL athletes the 6- and 12-months follow-up

revealed strong evidence for improvement of LEAF-Q total score

explained by improvements in the gastrointestinal score and in

particular the menstrual score. Although positive changes in

LEAF-Q total and subscale scores were observed, menstrual-,

injury-, and total scores were all above the suggested cut-offs [≥2

for injuries, ≥2 for gastrointestinal symptoms, ≥4 for menstrual

function, and ≥8 for total score (4)] at all four measuring points.

At 6-months follow-up, 45% had a LEAF-Q score <8, while only

21% at 12-months follow-up. These ﬁndings may indicate that

these female athletes need continuous nutritional support (e.g.,

individual follow-up sessions).

Importantly, no adverse effects on eating disorder or exercise

addiction symptoms were found after participating in the FUEL

intervention. Rather, we found evidence for a difference in

change between groups for the EDE-Q global score, while long-

term follow-up for FUEL athletes suggested persistent reduction

in EDE-Q global and a reduction in EAI score. A recent

systematic review of eating psychopathology interventions

delivered to athletes (65,66) found that less than half of the

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 09 frontiersin.org

included studies reported sustained reductions in eating

psychopathology, while two studies on ballet dancers reported an

increase in eating psychopathology symptoms following the

interventions. Importantly, our study differentiates from the

studies in the systematic review by excluding athletes with risk of

eating disorders, since these athletes are recommended an

interdisciplinary treatment including psychiatric treatment (66).

Interestingly, the authors of the review conclude that future

interventions should investigate other modes of delivery beyond

face-to-face group sessions, including digital approaches, which

makes intervention retention more ﬂexible for the participants

but also serve to overcome stigma (65). This may in part explain

the positive development in EDE-Q scores among FUEL athletes

in the present study, but the explanation may also be found in

the principles of the FUEL intervention reﬂected in the teaching

videos and the individual consultations: Focus away from body

weight and more towards food as fuel and that there are no

“good”or “bad”foods. Two FUEL athletes had increased EDE-Q

global score above the 2.5 cut-off from pre- to posttest which

reduced well-below the 2.5 at long-term follow-up indicating that

some athletes may have transient changes in eating disorder

symptoms during the athletic seasons, and that regular screening

and follow up assessments are needed.

Among FUEL athletes, post-hoc tests found weak to moderate

evidence for change comparing the preintervention EAI total score

with 6- and 12-months follow-up, respectively. Although no

difference in changes in EAI scores between FUEL and CON

athletes were detected postintervention, it is interesting that

FUEL athletes reduced their score at long-term follow-up. This

should be seen in light of the reduced LEA and eating disorder

symptoms at 6- and 12-months follow-up, symptoms that have

been reported to be associated with symptoms of exercise

addiction (39,43,44). However, both changes in athletic season

(67) and the COVID-19 pandemic (68) may also be explanatory

factors to the changes in exercise addiction symptoms. Further,

FUEL athletes with risk of primary exercise addiction

preintervention, reduced and increased LEAF-Q total score,

respectively, suggesting a complex symptom picture and the

potential interaction between exercise addiction and risk of REDs.

4.1. Strengths and limitations

A strength of the present study is the combined intervention

design, including both online lectures and individual consultations,

which were athlete-centered and aimed at inducing long-term

behavioral change by enabling female athletes to actively formulate

nutritional and behavioral goals to support their own long-term

health, as researchers have requested (32). As previously

recommended (16), this type of intervention opens for a more

individual-centered approach compared to previous studies aiming at

improving REDs related symptoms in females (16). The knowledge

and tools acquired by the athletes presumably enables a longer-

lasting behavior change compared to studies where the participants

are given nutritional supplements only (14). Other strengths of the

present study are the use of validated screening tools, long-term

follow-up, and inclusion of a control group, which have been lacking

in previous studies (14,15). In addition, hormonal contraceptive

users were excluded, in order to get the true picture of menstrual

function. This, however, complicates the recruitment of the

participants since the prevalence of hormonal contraceptive users

among endurance athletes have been reported to be as high as 68%

(69). Although the participant information material described that

hormonal contraception users couldnotparticipate,wehadto

exclude 28% of the athletes who had signed up for the study due to

the use of hormonal contraceptives. Unfortunately, the exclusion of

hormonal contraceptive users may have prevented potential REDs

cases to participate, e.g., since hormonal contraceptives may mask

underlying menstrual dysfunctions.

A limitation of the present study is the lack of long-term

follow-up in the CON athletes. Consequently, the long-term

effects of the FUEL intervention can only be speculative. By

having long-term follow-up of the CON condition, athletes

would have to wait an additional year before being offered the

FUEL intervention, thereby increasing the risk of a higher drop-

out rate in this group. There may also be ethical considerations,

since these athletes all have REDs related symptoms, early

intervention is important. While prioritizing the intervention in

athletes’off-season, it is a limitation that data assessment was

conducted at different phases of the athletic season for the

intervention group and the control group, which may reduce

comparability between the two groups.

The low number of participants in the CON group is also a

limitation. Based on an initial analysis during the recruitment phase

with an expected improvement in LEAF-Q score of 3 and type I

and Type II error of 5% and 20% respectively, the power calculation

suggested 28 subjects in each group, suggesting an insufﬁcient

number of CON athletes. In addition, the expected improvement in

LEAF-Q score of 3 is theoretically founded based on a project group

discussion, without any previous studies to lean on.

Although it would have been interesting to collect data via

physical laboratory tests, e.g., including body composition and

female sex hormones to verify menstrual status, this study and its

measurement methods more closely reﬂect what is practically

possible for most athlete-based centers where time and resources

are a critical constraint. The intervention and methods used may

therefore more easily be implemented in real life settings.

Despite that this study included a combination of online

lectures and individual consultations using behavior change

theories and approaches, intervening athletes alone may be

insufﬁcient for behavior change and thus changes in REDs

related symptoms. As previously addressed (36), cultural

revolutions and changes in social norms are needed, which

involves inclusion of coaches, health professionals, entire teams/

clubs, and relatives of the athletes. Hence, future research should

aim for also including the athletes’entourage.

5. Conclusion

In this group of endurance athletes, participating in the FUEL

intervention implies long-term improvement of REDs related

Fahrenholtz et al. 10.3389/fspor.2023.1254210

Frontiers in Sports and Active Living 10 frontiersin.org

symptoms, including menstrual function. In addition, short and

long-term follow-up suggest no adverse effects on eating disorder

symptoms. The lack of long-term follow-up for the CON

condition indicates, however, that the results should be

interpreted with caution. Nevertheless, the FUEL intervention

seems promising as a part of management of REDs related

symptoms in female endurance athletes.

Data availability statement

The raw data supporting the conclusions of this article will be

made available by the authors, without undue reservation.

Ethics statement

The study was approved by the regional ethics committee in

Norway (31,640), Sweden (2019-04809), and by the Norwegian

Centre for Research Data (968,634) and registered at www.

clinicaltrials.gov (NCT04959565).

Author contributions

IF: Conceptualization, Data curation, Formal Analysis,

Investigation, Methodology, Visualization, Writing –original

draft, Writing –review & editing. AM: Conceptualization,

Investigation, Methodology, Supervision, Writing –review &

editing. IG: Conceptualization, Methodology, Supervision,

Writing –review & editing. PW: Investigation, Writing –review

& editing, Data curation, Methodology, Resources. AI: Formal

Analysis, Methodology, Supervision, Writing –review & editing.

S-MH-S: Methodology, Resources, Writing –review & editing,

Conceptualization, Supervision. KK: Methodology, Resources,

Writing –review & editing, Investigation. DL: Investigation,

Resources, Writing –review & editing. SM: Investigation,

Resources, Writing –review & editing. MG: Investigation,

Writing –review & editing. MT: Conceptualization, Data

curation, Investigation, Methodology, Project administration,

Resources, Supervision, Visualization, Writing –review & editing.

Funding

This work was supported by Grants from the University of

Agder and the Norwegian Olympic Sports Center.

Acknowledgments

We greatly appreciate the athletes’contribution to the

FUEL project. We would also like to thank professor and

psychiatrist Finn Skåderud for being the medical responsible

and the master students Ingvild Brattekleiv, Mona Saller, and

Miriam Myhren Bouchleh for assisting with the recruitment

and data collection.

Conﬂict of interest

The authors declare that the research was conducted in the

absence of any commercial or ﬁnancial relationships that could

be construed as a potential conﬂict of interest.

Publisher’s note

All claims expressed in this article are solely those of the

authors and do not necessarily represent those of their

afﬁliated organizations, or those of the publisher, the

editors and the reviewers. Any product that may be

evaluated in this article, or claim that may be made by

its manufacturer, is not guaranteed or endorsed by the

publisher.

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Frontiers in Sports and Active Living 13 frontiersin.org

Paper IV

Participant Evaluations of the FUEL Intervention Designed for Female Endurance

Athletes at Risk of REDs: A Mixed Methods Approach

Solstad, B. E., Fahrenholtz, I.L., Melin, A.K., Garthe, I., Torstveit, M.K.

Submitted November 2024 to Sports Psychiatry – Journal of Sports and Exercise Psychiatry

NB! This manuscript has not yet been accepted for publication

Sports Psychiatry

Participant Evaluationsof the FUEL Intervention Designed for Female Endurance

Athletes at Risk of REDs: A Mixed Methods Approach

--Manuscript Draft--

Manuscript Number:

Full Title: Participant Evaluationsof the FUEL Intervention Designed for Female Endurance

Athletes at Risk of REDs: A Mixed Methods Approach

Short Title: The FUEL Sports Nutrition Intervention

Article Type: Original article

Section/Category: Sports Psychiatry

Keywords: Behaviour change; Individual sports nutrition counselling; Interventional evaluation;

Low energy availability; Digital sports nutrition lectures

Corresponding Author: Bård Erlend Solstad, Ph.D.

University of Agder: Universitetet i Agder

Kristiansand, NORWAY

Corresponding Author Secondary

Information:

Corresponding Author's Institution: University of Agder: Universitetet i Agder

Corresponding Author's Secondary

Institution:

First Author: Bård Erlend Solstad, Ph.D.

First Author Secondary Information:

Order of Authors: Bård Erlend Solstad, Ph.D.

Ida Lysdahl Fahrenholtz

Anna Melin, PhD

Ina Garthe, PhD

Monica Klungland Torstveit, PhD

Order of Authors Secondary Information:

Abstract: Based on a mixed-methods design, we investigated the experiences and evaluations

of female endurance athletes at risk of REDs who participated in the FUEL sports

nutrition intervention. Eighty-two percent of the FUEL participants filled out an

evaluation questionnaire, and ten participated in a semi-structured interview. The

quantitative data were analysed using a Bayesian Mann-Whitney test, while a critical

realist approach was used to analyse the qualitative data. The overall satisfaction rate

(from 1-10) was 9.1 ± 1.1, with weak evidence for a higher satisfaction level among the

FUELcombined (16 digital lectures and 8 individual consultations) compared to

FUELlectures (16 digital lectures) participants (9.3 ± 0.9 versus 8.0 ± 1.5, BF10=1.00).

Most of the participants found the duration and frequency of the consultations

appropriate and did not find the digital format limiting. These results were supported by

the qualitative findings showing that the participants expressed positive educative

experiences. They also appreciated the 16-week intervention duration providing the

opportunity to absorb new knowledge, implement it in their daily routines, become

more body conscious, and reflect on their new positive bodily experiences. The

participants in this multicenter study were satisfied with the FUEL intervention

expressing their intentions to recommend it to other athletes and their entourage. They

also highlighted the time given to acquire new knowledge, being able to reflect on their

positive bodily experiences, and positively change their mindsets. Our findings may be

of value for future studies developing highly needed sports nutrition interventions for

the prevention of REDs at all levels.

Additional Information:

Question Response

Are there any conflicts of interest you or

any of your co-authors need to disclose

regarding the present submission?

Participant Evaluations of the FUEL Intervention Designed for

Female Endurance Athletes at Risk of REDs: A Mixed Methods

Approach

Word count: 7939

Manuscript Click here to access/download;Manuscript;FUEL evaluation

FINAL VERSION 241124.docx

Abstract

Based on a mixed-methods design, we investigated the experiences and evaluations of female

endurance athletes at risk of REDs who participated in the FUEL sports nutrition intervention.

Eighty-two percent of the FUEL participants filled out an evaluation questionnaire, and ten

participated in a semi-structured interview. The quantitative data were analysed using a

Bayesian Mann-Whitney test, while a critical realist approach was used to analyse the

qualitative data. The overall satisfaction rate (from 1-10) was 9.1 ± 1.1, with weak evidence for

a higher satisfaction level among the FUELcombined (16 digital lectures and 8 individual

consultations) compared to FUELlectures (16 digital lectures) participants (9.3 ± 0.9 versus 8.0 ±

1.5, BF10=1.00). Most of the participants found the duration and frequency of the consultations

appropriate and did not find the digital format limiting. These results were supported by the

qualitative findings showing that the participants expressed positive educative experiences.

They also appreciated the 16-week intervention duration providing the opportunity to absorb

new knowledge, implement it in their daily routines, become more body conscious, and reflect

on their new positive bodily experiences. The participants in this multicenter study were

satisfied with the FUEL intervention expressing their intentions to recommend it to other

athletes and their entourage. They also highlighted the time given to acquire new knowledge,

being able to reflect on their positive bodily experiences, and positively change their mindsets.

Our findings may be of value for future studies developing highly needed sports nutrition

interventions for the prevention of REDs at all levels.

Keywords: Behaviour change; Individual sports nutrition counselling; Interventional

evaluation; Low energy availability; Digital sports nutrition lectures

Introduction

Insufficient energy intake relative to energy expenditure can cause the syndrome Relative

Energy Deficiency in Sport (REDs) [1]. REDs predispose to a wide range of physiological

complications in female athletes and physically active women, including menstrual

dysfunction, impaired bone health, and reduced athletic performance [1,2]. Psychological

factors can precede REDs, but long-term or severe insufficient energy intake (i.e., problematic

low energy availability (LEA)) may also result in psychological distress [1,3]. Moreover, while

disordered eating behaviours and eating disorders are common among female athletes [4] which

may lead to REDs [5], REDs is also frequently observed among athletes who are at low risk for

eating disorders [6,7]. Factors such as insufficient knowledge of optimal sports nutrition and

the consequences of REDs [8,9], lack of time and difficulties in meal preparation [10], and

suppressed appetite [11,12] may therefore contribute to the development of REDs.

Female endurance athletes are considered a high-risk group for REDs due to a high total

energy expenditure [13], insufficient energy intake [14], and focus on “healthy eating” and

thinness [15]. Hence, research-based preventive and treatment initiatives (i.e., primary,

secondary, and tertiary prevention), such as educational interventions, are warranted in female

endurance sports [1,16]. A few intervention studies providing increased energy intake in female

athletes with menstrual dysfunction have been conducted [17–21]. The FUEL intervention is a

16-week educational intervention program consisting of weekly digital lectures in sports

nutrition combined with individual consultations with a sports nutritionist every-other-week

designed for female endurance athletes with REDs symptoms [22]. In a subsequent set of

studies incorporating the FUEL intervention [22,23], we found strong evidence for improved

sports nutrition knowledge and modest improved dietary intake [22], reduced eating disorder

symptoms, and improved menstrual and gastrointestinal function at long-term follow-up [23].

To implement effective sports nutrition interventions in practice, a key element is to

evaluate the experiences of the participants [24]. Indeed, participants’ experiences and

evaluations are highly valuable sources to make changes and adjustments to improve the

intervention content, but also to contribute to the understanding of the results [24]. Since the

statistical evidence of the treatment effects and the actual experiences of participants

undergoing a treatment may differ, it has been argued that participants’ treatment experiences

should be considered as a crucial component of the clinical evidence [25]. To our knowledge,

however, none of the previous intervention studies aiming at improving REDs symptoms in

female athletes [17–20,26] have conducted a thorough evaluation of the participants’

experiences and perspectives of taking part in these interventions.

During the last decade, it has become common in sport science research to explore how

synergistic combinations of both quantitative and qualitative methods may offer a more

nuanced understanding of a given phenomenon [27]. The mixing of quantitative and qualitative

methods is referred to as mixed methods research [28] and is often used in interventional

evaluation studies [29]. Specifically, Fetters and Molina-Azorin (2020) argued, “the default

expectation for conducting interventional studies should be a combination of qualitative and

quantitative components as an integrated mixed methods interventional evaluation” (p. 131).

Moreover, knowing that the FUEL intervention addressed female athletes contextualized in

endurance sports [22,23] and acknowledging that sports nutrition theory-based interventions

cannot offer a complete view of a phenomenon [28,30,31], the present study used critical

realism as an evaluative or underlabouring philosophy to investigate female endurance athletes’

experiences and evaluations from participating in the FUEL intervention. In doing so, we

clearly recognise the theory-generating and generalisability of our results and their context-

based nature [28]. Thus, the overall aim of the present study was to investigate female

endurance athletes’ experiences and evaluations (i.e., their mental perspectives involving

different viewpoints and diverse voices) from participating in the FUEL intervention. As an

inherent part of our aim, we have worked systematically to facilitate integration across different

forms of knowledge to develop an explanatory synthesis on a specific sports nutrition education

intervention (i.e., the FUEL intervention) in female endurance athletes at risk of REDs.

Methods

A critical realist methodology

Our research investigated: What are the causal mechanisms contributing to beneficial

outcomes among female endurance athletes who participated in a sports nutrition education

intervention? Given the importance of sports nutrition knowledge and improved nutritional

behaviours in relation to prevention and treatment of REDs [1,16], we aimed to understand how

female endurance athletes experienced participating in the FUEL intervention. More

specifically, we explored which aspects of the intervention content facilitated educative

learning processes and sustained interest and motivation for changing their sports nutrition

behaviour. We also aimed at understanding the structures, contexts, and mechanisms that could

explain these educative and motivational experiences [28,31]. Our approach to answering these

questions was therefore grounded in the critical realist philosophy of social science [31–33],

thereby combining a realist ontology with a constructivist epistemology [32]. Indeed, while

critical realists accept that the social world is real and independent of our knowledge, they also

embrace the idea of multiple legitimate perspectives on social reality [32]. Thus, we have used

various methods and their associated epistemologies to explore the mechanisms that

underpinned participation among female endurance athletes in the FUEL intervention. In doing

so, our research evaluated how the FUEL intervention worked in this group of athletes and how

100

the various parts constituting the intervention might be manipulated and improved in future

101

intervention studies [33,34].

102

Study design

103

The present study used a mixed methods approach, which is conducive when conducting

104

interventional evaluation studies [29]. Shannon-Baker (2016) argued, “critical realism utilizes

105

the compatibility thesis of worldviews, supporting the point that quantitative and qualitative

106

research can work together to address the other’s limitations” (p. 329). Moreover, given that

107

critical realists believe that theories are inherently impartial representations of reality [28], our

108

aim was to use both quantitative and qualitative data to discuss how our findings might inform

109

and potentially adjust existing theories in the context of sports nutrition for female endurance

110

athletes. The FUEL intervention study, however, was conducted using a quasi-experimental

111

study design [22], aiming at comparing the FUEL intervention with a control condition.

112

The FUEL intervention: sports nutrition lectures and individual consultations

113

The FUEL intervention and applied theoretical foundation have previously been

114

described in detail [22]. In brief, every week during the 16-week intervention period,

115

participants received an email with a link and password to the lecture of the week located on a

116

closed digital platform. Participants had the opportunity to watch the lectures when suitable

117

during their everyday lives and to watch them repeatedly if they wanted. The lectures averaged

118

25.0 ± 8.4 min in duration (range: 15–43 min, total duration of 400 min). The themes of the

119

lectures were targeted towards female endurance athletes at risk of REDs and included

120

information about REDs, micro- and macronutrient recommendations for endurance athletes,

121

and the menstrual cycle and athletic performance [22].

122

The sports nutrition counselling using client‐based, empathic communication inspired

123

by Motivational Interview [35] was administrated via the teleconferencing platform Zoom,

124

Zoom Video Communication, Inc. (San Jose, California, USA). Autonomy [36] was a core

125

value in the FUEL consultations, which were customized according to the individual

126

participant’s needs and preferences. The first consultation was scheduled to run for 1.5 hours,

127

while the following seven consultations were scheduled to run for approximately 1 hour. The

128

FUEL counselling team consisted of one German, two Irish, three Norwegian, and four Swedish

129

highly experienced sports nutritionists. To improve standardisation, a comprehensive manual

130

was developed, and three webinars were conducted ahead of the intervention, in addition to

131

weekly Zoom meetings for the FUEL counsellor team during the intervention period [22].

132

Participants

133

Participants were female endurance athletes, 18-35 years of age, training ≥ 5 times per

134

week, competing in one of the following endurance disciplines: long‐distance running,

135

orienteering, cycling, triathlon, cross‐country skiing, or biathlon, and recruited in Germany,

136

Ireland, Norway, and Sweden. Only athletes identified at risk of REDs [defined as a LEA in

137

Females Questionnaire Score ≥ 8] [37] but with low risk of disordered eating [defined as a

138

global score <2.5 using the Eating Disorder Examination Questionnaire (EDE-Q)] [38] were

139

offered the FUEL intervention or control condition with subsequent FUEL intervention.

140

Athletes were excluded if they used hormonal contraceptives, had a chronic disease (e.g.,

141

Crohn’s disease or hypothyroidism), were pregnant or were planning pregnancy, or had a

142

menstrual dysfunction not related to LEA.

143

In total, 33 participants completed the FUEL intervention with digital sports nutrition

144

lectures combined with individual consultations (FUELcombined), while 11 participants

145

completed the FUEL intervention with digital lectures only (FUELlectures).

146

Data Collection

147

Evaluation Questionnaire

148

One week after the post-intervention measurements, athletes were invited to fill out a

149

questionnaire evaluating their experiences of participating in the FUEL intervention. The

150

evaluation questionnaire consisted of seventeen closed questions and twelve open-ended

151

questions. Of the 33 athletes who completed FUELcombined, 29 athletes (88%) filled out the

152

evaluation questionnaire, while seven of the 11 athletes (64%) who completed FUELlectures filled

153

out the evaluation questionnaire.

154

Qualitative Interviews

155

Three weeks after completing the FUEL intervention, participants were invited to

156

participate in a semi-structured qualitative interview to elaborate on their experiences with the

157

FUEL intervention. Note, however, that the interviews were conducted one to three months

158

after the end of the intervention period. Nine participants from FUELcombined and one participant

159

from FUELlectures agreed to participate in the qualitative interview. Based on recent research

160

explaining how to phrase critical realist interview questions, it is important to note that

161

interview questions represent the link between a researcher’s ontological perception of reality

162

and realist findings as mechanism-based theories [39]. In the current study, we were interested

163

in understanding the mechanisms contributing to increased motivation and educative

164

experiences while participating in the FUEL intervention. As such, it was important for us to

165

explore how certain conditions influenced participants’ reflections, which, in turn, resulted in

166

various forms of thoughts, emotions, and behaviours [39]. The interview questions were

167

therefore centered around six key topics: (a) why they chose to participate in the FUEL

168

intervention program; (b) what they thought about the content of the digital lectures; (c) how

169

they experienced the sports nutrition counselling sessions; (d) what they thought about the

170

combination of digital lectures and individual sports nutrition counselling sessions; (e) whether

171

they thought the content of the FUEL intervention was sufficient to facilitate behavioural

172

change during the 16-week intervention period; and (f) what changes they would like to see in

173

the content of the FUEL intervention (the content of the interview guide and specific framing

174

of the interview questions can be found in Appendix 1). Indeed, we deemed these interview

175

questions appropriate in terms of the philosophical foundations of critical realism, thereby

176

acknowledging through the interview questions that the social world is driven by mechanisms

177

and the idea of multiple legitimate perspectives on reality [32].

178

Data Analyses

179

Quantitative data analyses were conducted using JASP (version 0.16.3.0) and are

180

presented as mean ± standard deviation. To investigate potential differences between

181

FUELcombined and FUELlectures, Bayesian Mann-Whitney tests were applied. Bayes Factors (BFs)

182

between 1 and 3 were considered to indicate weak evidence for the alternative hypothesis, BFs

183

between 3 and 10 were considered to indicate moderate evidence for the alternative hypothesis,

184

while BFs greater than 10 were considered to indicate strong evidence for the alternative

185

hypothesis [40].

186

The qualitative data analysis consisted of two parts. First, responses to the open-ended

187

evaluation questions were analysed using qualitative content analysis [41]. The analysis was

188

guided by our overall aim to understand participants’ experiences and evaluations of taking part

189

in the FUEL intervention. Hence, the open-ended questions were coded and categorized by two

190

of the authors (BES and MKT), which were further discussed using an iterative process until a

191

consensus was reached. Themes were generated based on participants’ responses to the open-

192

ended evaluation questions and their content applicability to produce causal knowledge about

193

the experiences and evaluations of participating in the FUEL intervention. However, to

194

accumulate a comprehensive basis of knowledge pertaining to participants’ experiences and

195

evaluations of the FUEL intervention, the iterative data analysis process aimed to retain as many

196

relevant responses as possible (not only showing a selection of responses). Thus, the application

197

of qualitative content analysis using the lenses of critical realism provided us with the

198

opportunity to learn about and, thus, illuminating the potential effects of the FUEL intervention

199

in the context of female endurance sports [41].

200

Second, and based on the philosophical framework of critical realism [30,41], the

201

qualitative data analysis of the semi-structured interviews used the following steps. First,

202

interviews were transcribed by one of the authors (ILF) and a research assistant using intelligent

203

verbatim transcription. In total, 112 pages of single-spaced transcripts for the semi-structured

204

interviews were generated. Transcripts were read repeatedly by two researchers (i.e., BES and

205

a research assistant), who were both experienced in qualitative methodology and methods.

206

Second, in the data analysis process, we used a primarily deductive yet flexible coding process

207

that drew on existing theory and literature [1,42–45]. During the coding process, the two

208

researchers independently and systematically coded the transcripts, and then discussed the

209

codes to verify their interpretations of the transcribed data material. After the coding process,

210

the next step was the process of abduction [46]. Specifically, our aim was to combine the

211

strengths of both deductive and inductive inquiry by reasoning from the transcribed data

212

material and, thus, use the data material to extend, refine, or refute existing theories or

213

propositions [46]. The final step in the data analysis was retroduction, which focused on causal

214

mechanisms and conditions [30,47]. It has been argued that “the goal of retroduction is to

215

identify the necessary contextual conditions for a particular causal mechanism to take effect

216

and to result in the empirical trends observed” [48]. In the data analysis process, retroduction

217

was used to investigate the causal mechanisms and conditions influencing female endurance

218

athletes’ experiences and reflections while participating in the 16-week FUEL intervention.

219

Thus, a key factor was to investigate the psychological mechanism of female endurance

220

athletes’ perceptions and interpretations of the FUEL intervention content, thereby

221

acknowledging that a psychological mechanism will not always produce the same outcome

222

[47].

223

Ethical considerations and data security

224

The study was approved by the regional ethics committees in Norway (31640) and

225

Sweden (2019‐04809), and by the Norwegian Centre for Research Data (968634). Since the

226

final research plan included no medical procedures, the study was considered exempt from

227

additional ethical approval at the other study sites (Germany and Ireland). The safe data

228

collection tool Nettskjema connected to the Services for Sensitive Data (TSD) platform

229

(University of Oslo) was used to collect data from questionnaires and interviews. All data were

230

safely stored in TSD.

231

232

Results

233

Athletes from the FUEL study participating in the quantitative and qualitative parts of the

234

evaluation (quantitative evaluation: n=36; qualitative evaluation: n=10) were on average 24.6

235

± 4.8 (25.1 ± 5.8) years of age with a training volume of 47.1 ± 17.7 (42.0 ± 16.5) hours/month

236

and competing in the following endurance disciplines: cycling: n = 6 (n=2), running: n = 14

237

(n=4), orienteering: n = 5 (n=1), triathlon: n = 7 (n=2), and biathlon: n = 4 (n=1). Most athletes

238

were competing at club level (Tier 2) and were working and/or studying beyond their endurance

239

training, while 14% identified themselves as full-time athletes (Tier 2-4) (see Table 1).

240

INSERT TABLE 1

241

Although the FUEL intervention was conducted and evaluated in four European

242

countries, no cross-cultural differences in participants’ experiences and evaluations of the

243

FUEL intervention were found. Thus, the quantitative and qualitative findings are presented as

244

a whole, demonstrating the collective experiences of taking part in the intervention program.

245

Quantitative results

246

The overall satisfaction with participating in the FUEL intervention program was high (9.1 ±

247

1.1, min: 5 – max: 10), with weak evidence for a higher satisfaction level among athletes

248

participating in FUELcombined compared to athletes participating in FUELlectures (9.3 ± 0.9, min:

249

7 – max: 10 versus 8.0 ± 1.5, min: 5 – max: 10, BF10=1.00).

250

There was no difference in participants’ motivation for watching the lectures when

251

comparing the two groups (FUELcombined: 7.7 ± 1.9 versus FUELlectures: 7.3 ± 1.3, BF10 = 0.41).

252

Also, most participants found the duration of the intervention period, and the duration and

253

difficulty level of the 16 lectures appropriate (see Table 2).

254

INSERT TABLE 2

255

Participants reported more positive than negative experiences during the FUEL

256

intervention (see Table 3). In total, 61% reported feeling more energised and having an

257

improved mood, 58% reported improved self-confidence and improved enjoyment of training,

258

while 44% reported improved body satisfaction. In addition, 38% reported improved food

259

pleasure and 36% reported improved ability to cope with everyday stress. A few negative

260

experiences were reported by five athletes in a question with set answer options (e.g., reduced

261

enjoyment of training and feeling less energised). These negative experiences were reported in

262

combination with several positive experiences and an overall satisfaction level of 10.

263

INSERT TABLE 3

264

The satisfaction level and self-reported motivation for participating in the individual

265

nutrition consultation was high and the duration and frequency of the consultations were in

266

general found to be appropriate (see Table 4). Twelve (41%) athletes did not find it limiting at

267

all (reported a score of 1) that the consultations were digital instead of physically, while one

268

athlete found it limiting (reported a score of 8 out of 10). On average, participants found the

269

digital format of the consultations to be minimally limiting (2.4 ± 1.7).

270

INSERT TABLE 4

271

In addition, based on the 16 different lectures, the participants found the lectures (a)

272

Menstruation and performance; (b) Performance consequences of REDs; and (c) Carbohydrates

273

as the most interesting or educative lectures. Three participants suggested more information

274

about weight loss and one participant about gastrointestinal health.

275

Qualitative findings

276

Open-ended questions in the evaluation questionnaire

277

Based on the qualitative content analysis, participants’ responses to the open-ended evaluation

278

questions were distributed between personal experiences of taking part in the FUEL

279

intervention and personal evaluations of the FUEL intervention content. More specifically,

280

participants’ responses pertaining to their personal experiences were generated into the

281

following themes: (a) Increased knowledge (e.g., “I have gained much more knowledge about

282

what I need to eat to feel good and be able to perform”); (b) Bodily experiences (e.g., “better

283

body awareness”); and (c) Changed mindset (e.g., “I have become much more confident”).

284

Also, in terms of participants’ personal evaluations of the FUEL intervention content, the

285

responses were generated into the following themes: (a) Positive feedback regarding the FUEL

286

intervention content (e.g., “tailored to suit the needs of the individual and not a one-size-fits-all

287

approach”); (b) Feedback regarding the duration of the FUEL intervention (e.g., “nice to have

288

some time to make changes when you learned things that were relevant to you”); and (c)

289

Improving the FUEL intervention content (e.g., “I wish it was possible to make requests for

290

topics regarding the digital video content, such as gut health and nutrition related to this”). More

291

specific details regarding the responses to the open-ended evaluation questions can be found in

292

Tables 5-6.

293

INSERT TABLE 5

294

INSERT TABLE 6

295

Semi-structured interviews

296

Three overarching themes were generated to illustrate the breadth and depth of the participants’

297

experiences: (a) the personal experiences of participating, (b) the personal benefits of

298

participating, and (c) suggestions for further improvements of the FUEL intervention.

299

Topic 1: The personal experiences of participating

300

Although the participants had different personal experiences prior to the study (e.g., menstrual

301

function or dysfunction, little or none experience with sports nutrition counselling) and different

302

motivation for participating in the FUEL intervention (e.g., diverse bodily experiences, limited

303

sports nutrition knowledge), all of them perceived the intervention content to be personally

304

relevant, educational, and helpful in their daily life as endurance athletes. Accordingly, several

305

participants highlighted the value of weekly digital lectures, “It was structured super well”

306

(Participant 2; P2). Another point highlighted was that even though some of the content varied

307

in perceived degree of difficulty, based on the participants’ age, background, and education

308

level, several expressed the advantage of being able to view the digital lectures repeatedly.

309

Moreover, a key factor, which facilitated learning among the participants, was that the structure

310

of the digital lecture content and topics was presented in an orderly manner. As expressed by

311

one participant, “If it is precise and right to the point, you are more likely to take experience

312

from it” (P1). However, it is important to note that the participants expressed some individual

313

preferences regarding the length of the digital lectures. For example, one participant expressed,

314

“I would have liked to go deeper and have longer lectures” (P4) while another participant

315

expressed, “Manageable, but rather short than long; 20-30 minutes would be suitable” (P5). As

316

a final point, all participants highlighted the value of the digital lecture content in terms of

317

facilitating consultation with the sports nutritionist, “The experience was good; complementary

318

and relevant learning” (P2).

319

The second part of the FUEL intervention consisted of sports nutrition counselling with

320

specialised sports nutrition counsellors working with elite athletes daily. All participants were

321

very pleased with the sports nutrition counselling, “My experience was 10 out of 10 to be

322

honest. She was brilliant. I cannot give her enough credit” (P1). Another point that is closely

323

linked to the previous one was the participants’ willingness to talk more freely about their

324

previous experiences concerning sports nutrition, “I’ve never liked to talk about it, but I found

325

it getting easier and easier” (P7). As such, the participants put emphasis on the counsellors’

326

ability to individualise the counselling sessions and, thus, pressing the right buttons. As one

327

participant explained in more detail,

328

I really felt that I received a very personal follow-up in the counselling sessions. In the

329

beginning, I sort of felt that she wanted to get to know me and how I was feeling,

330

what I did, what I liked, and that kind of things. I didn’t really feel that she had a

331

question standard that she followed, but I felt that it was personalised according to

332

what we talked about at the last session and what we should work on next. (P9)

333

Along with their outlining of the nutrition counselling sessions, the participants talked more

334

about their experiences of these digital sessions. Due to time constraints with their busy

335

schedules consisting of various activities (e.g., multiple training sessions, work/education

336

requirements), several participants expressed that the digital counselling sessions made the

337

consultation more available and, thus, more personal because the counselling sessions were

338

adapted to their personal schedules. As expressed by one participant, “Very good with Zoom

339

since I’m busy” (P3). However, it should be noted that several participants stated that it would

340

have been preferable to have an in-person meeting, especially in the beginning of the

341

intervention period, “The digital counselling sessions worked, but I would have appreciated

342

some in-person sessions, especially in the beginning” (P6). Another participant further nuanced

343

it by arguing, “If I had been a full-time athlete, I would have liked to have an in-person meeting,

344

but for me, Zoom was absolutely top notch” (P3). Additionally, although the first consultation

345

was scheduled to run for 1.5 hours and the following consultations were scheduled to run for

346

approximately 1 hour, there was some variation in terms of the length of the various counselling

347

sessions. One participant expressed, “I thought the counselling sessions lasted too long (2-3

348

hours). 1 hour would have been more desirable” (P4) while another participant expressed, “The

349

counselling sessions could have been more than 60 minutes in the beginning, but 30-60 minutes

350

eventually was suitable” (P9). The participants also talked about the frequency of the

351

counselling sessions during the intervention period. While most participants expressed that they

352

were really satisfied with every-other-week, one participant expressed, “Every-two-week was

353

appropriate, but not more frequently. Every-three-week had also worked, but not every-four-

354

week” (P5).

355

In correspondence to the combination of digital lectures and sports nutrition counselling

356

sessions, the participants were in full agreement that the digital lectures gave them more in-

357

depth knowledge about sports nutrition and the counselling sessions provided them with goals

358

and direction in their daily life. This point was further explained by one of the participants,

359

Oh yeah, 100%. Because if you just had the digital lectures, you wouldn’t be able

360

to, if you didn’t understand something you are on your own. Whereas if you only

361

had the counselling sessions, you might not go into so much depth, and it’s probably,

362

it would go over your head because you wouldn’t have time to talk about stuff and go

363

to the PowerPoint-presentation as well. So, I think the two together is a good

364

balance and both equally important. (P2)

365

Although there was some degree of variation among the participants in terms of their personal

366

experience in taking part in the FUEL intervention, they were generally satisfied with the digital

367

lectures and sports nutrition counselling that made up the content of the FUEL intervention.

368

Thus, the next section will expand on the personal benefits of participating in the intervention.

369

Topic 2: The personal benefits of participating

370

The duration of the FUEL intervention was a key factor contributing to the participants’

371

personal benefits of participating in the intervention. Indeed, the participants were close to

372

unanimous in saying that a 16-week intervention period was an appropriate length of time,

373

thereby considering the duration of the FUEL intervention to be sustainable to build new habits

374

in relation to sports nutrition. As expressed by one participant, “Fantastic, because you got to

375

incorporate it into your lifestyle” (P2). This was a personal experience that was multifaceted

376

due to the newly acquired knowledge, adaptations of daily life routines, and positive bodily

377

changes throughout the intervention period. Accordingly, the week-to-week interplay between

378

digital lectures, sports nutrition counselling sessions, and testing of the newly acquired

379

knowledge maintained the participants’ motivation and engagement, which, in turn, led the

380

participants to experience positive bodily changes of the newly acquired knowledge related to

381

sports nutrition and make changes in their daily lives. While one participant expressed, “I’ve

382

become even more conscious of what I already knew” (P6), another participant elaborated by

383

saying, “It has made me somewhat less stressed in relation to dietary intake and helped me with

384

meal timing” (P8). Additionally, one participant went as far as expressing, “My patterns have

385

changed completely since the FUEL intervention” (P1). In correspondence to the personal

386

benefits of the intervention content, the participants specifically pointed out that they were

387

shocked by the amount of carbohydrates they needed to eat during a regular week. As expressed

388

by one participant, “I used to fear carbohydrates, but now I like them” (P4). Similarly, another

389

participant elaborated more on her personal benefits of participating in the FUEL intervention,

390

I would say that I’ve learned a lot, and it has given me a lot and I have become much

391

more confident about what is good to eat and how much is good to eat. And it has been

392

very good that there have been academic and knowledgeable people who have used

393

research to support the claims they have made. (P9)

394

Moreover, the participants also talked about which athletes would benefit the most from

395

participating in the FUEL intervention. As expressed by one of the participants,

396

You can certainly manage to get an athlete on the right path, I think, but I think an

397

athlete who really struggles and has challenges with REDs and who eats too little,

398

maybe even up to the eating disorder level, I suspect needs to have follow-up over a

399

longer period. But if it’s likely that you eat relatively okay, but it goes a bit up and

400

down, and you don’t have huge challenges, then I think this intervention can be enough

401

to stabilise the athlete. (P7)

402

Our findings show that all participants experienced personal benefits by participating in the

403

FUEL intervention. Indeed, it seems crucial that the participants experience positive bodily

404

changes because of participating instead of only being told about the possible effects of taking

405

part in a particular intervention. However, knowing that all interventions have room for

406

improvements, the next section will expand on the participants’ own suggestions for further

407

improvements of the FUEL intervention.

408

Topic 3: Suggestions for further improvements of the FUEL intervention

409

Although most of the participants were really satisfied with the intervention content, a few

410

participants had some suggestions for further improvements of the FUEL intervention. More

411

specifically, some of the participants argued that the FUEL intervention should have focused

412

more on a good and healthy body image while athletes try to adjust their own body weight and

413

body composition. For instance, one of the participants expressed,

414

Performing in training and in everyday life is demanding for me, and at the same time

415

manage to lose body weight because I’ve been too heavy and manage to maintain a

416

good and healthy body image at the same time as I lose weight, is quite difficult. It has

417

a lot to do with one’s body image. (P8)

418

Future adjustments and improvements of the FUEL intervention could therefore put emphasis

419

on the participants’ body image concerns. This suggestion is further supported by the fact that

420

several endurance sports are focused on body weight, particularly since coaches tend to be

421

preoccupied with athletes’ body weight. Notably, one participant put it as follows: “I’ve been

422

reluctant to talk about it since cycling has a weight focus” (P7).

423

Consequently, another suggestion that was put forward by some of the participants was

424

to involve the participants’ entourage (e.g., coaches, parents, athlete health and performance

425

team) in future revisions of the FUEL intervention. Individuals in the athlete’s entourage might

426

have a decisive role in the participants’ everyday life and can, thus, be either a promoting or

427

inhibiting factor regarding positive experiences and consequences of participating in a sports

428

nutrition intervention. Indeed, one of the participants expressed,

429

I would assume that spreading the information also to coaches, as well as other people

430

surrounding the participant could be very appropriate, and for my part, it would be

431

nice if my family could have some information material regarding food type, etc.

432

(P8)

433

When the participants were talking about their coaches, the participants had mixed experiences.

434

For instance, one participant expressed,

435

My coach, the coach at the university, he was the one who encouraged me to join the

436

intervention. Even though I couldn’t have so much training with him during Covid-

437

19, he is very aware of sports nutrition as well as enhancing performance and recovery.

438

(P2)

439

While another participant put it,

440

My coach doesn’t talk much about it, but I think maybe if I had asked him, he would

441

have knowledge that could help me. But he doesn’t mention it himself. (P6)

442

These findings indicate that there are room for some improvements of the FUEL intervention

443

content. Given the body image challenges, which some of the athletes experienced, it seems

444

relevant to incorporate educational strategies contributing to healthy body image among the

445

participants in future revisions of the FUEL intervention. Additionally, to reassure that future

446

participants experience feelings of mastery and empowerment during the intervention period,

447

and after the end of the intervention, it would be preferable to include and involve the

448

participants’ entourage in future revised FUEL interventions. This, in turn, would most likely

449

help and support the participants to better implement and apply the new acquired knowledge

450

about sports nutrition in their daily life routines and training practices.

451

452

Discussion

453

The overall aim of the present study was to investigate the experiences and evaluations of

454

female endurance athletes at risk of REDs who participated in the FUEL sports nutrition

455

intervention. As part of our goal, we aimed to integrate various forms of knowledge to develop

456

a comprehensive explanatory synthesis on the FUEL intervention program. The quantitative

457

results revealed that the athletes were satisfied with their participation in the FUEL intervention

458

and reported high motivation for watching the digital lectures and participating in the

459

counselling sessions. Additionally, they found the intervention’s duration, the lectures’

460

difficulty level, and the consultation sessions’ frequency and duration to be appropriate.

461

Notably, the participants did not find the digital format of the intervention as a weakness but

462

rather as an advantage in their busy daily lives. These findings were supported by the qualitative

463

findings, where participants elaborated on their positive educational experiences and how the

464

FUEL intervention had given them the opportunity to integrate their newly acquired knowledge

465

into their daily life routines and training practices. However, although it was an unintended

466

implication, a few participants mentioned some general body image concerns during the

467

intervention period. Hence, future interventions should pay more attention to a healthy body

468

image. Additional suggestions for improvements included more information on gastrointestinal

469

health, improved quality of the digital lectures, and involving the athletes’ entourage in future

470

revisions of the FUEL intervention program. A more detailed discussion of the findings is

471

provided in the subsequent paragraphs.

472

Personal experiences and evaluations of participating

473

A key aspect in the FUEL intervention program was the 16-week intervention period providing

474

the participants with adequate time to absorb, reflect, and apply the new sports nutrition

475

knowledge in their daily life routines and training practices. This, in turn, is crucial when

476

considering that behaviour change often is easy in the short run but can be difficult to sustain

477

over time [49]. Indeed, it has been argued that “methods used to create behaviour change

478

(including extinction, counterconditioning, punishment, reinforcement of alternative

479

behaviour, and abstinence reinforcement) tend to inhibit, rather than erase, the original

480

behaviour” [49]. Hence, it seems imperative that participants who take part in interventions

481

acknowledge and understand the new acquired knowledge, consistently repeat the new acquired

482

knowledge, build strong attitudes in relation to the new acquired knowledge, effortful monitor

483

themselves while applying the new acquired knowledge, and over time internalize the new

484

acquired knowledge in terms of maintaining the behavioural change in contexts where strong

485

cues to their original behaviour still remain in place [50]. Therefore, it seems important to

486

highlight that the FUEL intervention program found no effects on physiological REDs

487

symptoms at posttest, assessed with the LEA in Female Questionnaire (LEAF-Q), but strong

488

evidence for long-term improvements of menstrual function were found in addition to weak

489

evidence for improved gastrointestinal function. Moreover, the FUEL study provided moderate

490

evidence for positive effects on eating disorder symptoms through reduced EDE-Q global score,

491

which remained reduced at long-term follow-up. Consequently, these findings support the long-

492

term improvements of the FUEL intervention program in this group of female athletes from

493

endurance sports [23]. Hence, it is of interest to look at the correspondence between the reported

494

long-term improvements and the participants’ responses in the evaluation questions. More

495

specifically, the participants reported more positive than negative experiences during the FUEL

496

intervention period, involving feeling more energised as well as reporting improved mood, self-

497

confidence, enjoyment of training, body satisfaction, ability to cope with stress, and food

498

pleasure. Importantly, only a few participants reported reduced enjoyment of training and

499

feeling less energised, but these negative experiences were reported in combination with several

500

positive experiences. Moreover, given that a recent scoping review has indicated a relatively

501

consistent concurrence of the valence of self-talk and affective responses [51], it seems

502

reasonable to suggest that the participants’ positive experiences during the intervention period

503

might have influenced their valence of self-talk. Indeed, a recent synthesis of the self-talk

504

literature in general psychology has linked self-talk to beneficial educational reflection

505

processes [52].

506

Another aspect positively contributing to the participants’ experiences was the

507

combination of nutrition education and nutritional counselling in the FUEL intervention. While

508

most participants completed the FUEL intervention with digital lectures combined with

509

individual sports nutrition consultations (FUELcombined), a smaller group of participants

510

completed the FUEL intervention with digital lectures only. From the semi-structured

511

interviews and the open-ended evaluation questions, the FUELcombined participants highlighted

512

the synergistic interactions between the digital lecture content and the digital consultations with

513

their sports nutritionist, which, in turn, provided complementary and relevant learning. A recent

514

systematic review supports these experiences by showing evidence of a positive behavioural

515

impact when applying nutritional counselling to athletes, with positive effects also in athletes

516

with eating disorders [43].

517

An important point to note is that nutrition education alone, without nutritional

518

counselling, may be insufficient to induce behavior change among athletes. [43]. Our results

519

revealed weak evidence for a higher satisfaction level among participants who received digital

520

lectures in combination with individual sports nutrition counselling compared to those who only

521

received digital lectures. Nevertheless, our findings showed that there was no difference in

522

participants’ motivation for watching the lectures when comparing the two groups. Also, and

523

regardless of group affiliation, our findings showed that the digital lectures were comprehensive

524

in scope, provided useful information and covered topics from an endurance-athlete

525

perspective, and were tailored to suit the needs of the individual. However, it would have been

526

interesting to compare the dietary intake of the two groups to get additional information on

527

whether they were equally successful in applying the knowledge in practice. Taken together,

528

our findings provide initial support to further develop and adapt the digital lectures to other

529

groups of female athletes, with intentions of drawing more on principles of user centered design

530

and, thus, develop a fully digital solution that is more focused on user engagement and

531

sustainable health and performance outcomes in the long term [53].

532

Furthermore, our quantitative results showed that the female endurance athletes who

533

participated in the FUEL intervention found the lecture on carbohydrates, along with the

534

lectures on menstruation and performance and performance consequences of REDs, as the most

535

interesting or educative digital lectures. More specifically, the qualitative findings showed that

536

the participants were surprised by the daily amount of carbohydrates they needed to eat during

537

a regular training week. These findings clearly show that female endurance athletes need more

538

evidence-based knowledge related to their intake of energy and carbohydrates, and by

539

implementing this knowledge in the daily routines of female endurance athletes, it might have

540

beneficial effects on both health and performance outcomes in this group of athletes. Indeed,

541

findings in a recent narrative review highlight that female athletes remain underrepresented in

542

sports science research and that current intake of carbohydrates recommendations and strategies

543

may fail to consider female-specific adaptations and hormone responses [54]. It is therefore

544

important that female (endurance) athletes have access to the latest evidence-based sports

545

nutrition recommendations, which, based on future research, may become more female-

546

specific. Digital solutions offer an opportunity to distribute updated recommendations relatively

547

quickly and to a wide range of athletes.

548

Unintended implications of participating

549

A recent review [55] highlights that there are some sports in which there are pressure and

550

unwritten expectations to look a certain way or have a certain body type, arguing that “athletes

551

who compete in leanness-focused sports tend to have higher rates of disordered eating

552

compared to athletes competing in sports that do not have weight class, body shape, or body

553

weight expectations and degrees of subjectivity” [55]. More specifically, the female athletes

554

who participated in the FUEL intervention competed in long‐distance running, orienteering,

555

cycling, triathlon, cross‐country skiing, or biathlon. Hence, before taking part in the

556

intervention, some of the athletes might have believed that being small statured, petite, lean, or

557

‘light’ (in the context of body weight) is crucial for physical performance. This, in turn, may

558

also have been reinforced using social media and feedback from coaches or other members of

559

the athletes’ entourage. A key element in the FUEL program was to present food as fuel rather

560

than focusing on body weight, e.g., lecture 14 included a critical view of the “perfect body

561

composition.” Still, a few participants mentioned that it was demanding for them to maintain a

562

good and healthy body image during the intervention period. They argued that their sport had

563

a weight focus and mentioned difficulty in discussing this with their coaches. However, it

564

should also be mentioned that 44% of the participants reported improved body satisfaction

565

during the intervention period. Thus, it seems reasonable to suggest that future revisions of the

566

FUEL intervention program should incorporate more specific information about body image

567

and body satisfaction to mitigate the risk of unwanted or unintended consequences of

568

participating in the FUEL intervention.

569

Informing and adjusting existing sports nutrition interventions for preventing REDs

570

among female endurance athletes

571

The current study was grounded in the critical realist philosophy of social science [31–33],

572

investigating the causal mechanisms contributing to beneficial outcomes among female

573

endurance athletes who participated in the FUEL intervention program. This interventional

574

evaluation study is an important contribution to further improvements and implementation of

575

the intervention program [22,23], as well as an important contribution to development of future

576

interventions aiming at preventing REDs among athletes in a variety of sport contexts [1,16,29].

577

Regardless of group affiliation, the participants were highly satisfied with their participation in

578

the FUEL intervention, reporting more positive than negative experiences during the

579

intervention period. The chosen methods and their associated epistemologies, however,

580

provided us with a more nuanced understanding of the various mechanisms that underpinned

581

the participants’ experiences of participating in the intervention program. Furthermore, our

582

findings highlight the importance of the participants’ everyday context and the necessity that

583

future revisions of the FUEL intervention also include the participants’ entourage, which

584

corresponds to a recent review, arguing that “the creation of new habits requires attention to

585

those features that lead to cue-contingent automaticity – namely, consistent repetition in a

586

stable, friction-minimizing context” [50]. This recommendation is further supported by a

587

subgroup of the IOC consensus statement on REDs, which argued that future research initiatives

588

should “include a multipronged approach targeting the athlete health and performance team,

589

the athlete entourage, and sport organizations, who all need to ensure a supportive and safe

590

sporting environment, have sufficient REDs knowledge and remain observant for the early

591

signs and symptoms of REDs” [16].

592

Strengths, limitations, and future directions

593

The FUEL intervention program was a multicenter study, including female endurance athletes

594

from Germany, Ireland, Norway, and Sweden. The findings revealed that there were no cross-

595

cultural differences in participants’ experiences and evaluations of the FUEL intervention.

596

Thus, one might argue that the FUEL intervention has had a positive impact across national

597

borders and diverse sports cultures. Furthermore, the present study used a mixed methods study

598

design, which has been highlighted as the default expectation for conducting interventional

599

evaluation studies [29]. This study also used a critical realist perspective, supporting the notion

600

that quantitative and qualitative data can address the other’s limitations [28]. Moreover,

601

although there was a small difference in response rate between the two groups (i.e.,

602

FUELcombined vs. FUELlectures), it is worth noting that most of the participating athletes filled out

603

the evaluation questionnaire. However, the present study is not without limitations, and these

604

issues should be considered when the findings are interpreted. Given the total number of

605

participants taking part in the FUEL intervention, one might question the relatively low number

606

of participants (n=10) who chose to take part in the semi-structured interviews. One viable

607

explanation could be the time between the post-testing and the time of the scheduled interviews

608

(from one to three months after the intervention period). Another limitation is that the present

609

study relied on self-report data, which is related to the potential of common method biases [56].

610

Finally, the risk of respondent biases must be acknowledged. It should therefore be considered

611

whether the participants have felt an obligation or need to respond more favorable than pertinent

612

to please the project members. To remedy this potential problem, the developers of the FUEL

613

intervention program did not conduct the qualitative interviews or were a part of the nutrition

614

counsellor team.

615

In terms of future research and given the positive experiences and evaluations from the

616

current FUEL intervention program, it seems reasonable to extend the intervention program to

617

other groups of athletes, including female athletes in other sport contexts, and potentially male

618

athletes and adolescents. Moreover, it is important to be aware of the ways humans (e.g., girls

619

and women) inhabit their bodies [57]. Indeed, given that the development of REDs involves a

620

range of negative bodily experiences along with a recognition that humans are relational beings

621

situated in time and different contexts [1,38], we encourage future sports nutrition interventions

622

to recognise that embodiment matters in knowledge building [58]. As a final note, we also

623

suggest that future research should involve the participants’ entourage (e.g., coaches, spouse,

624

family members, and/or health and performance team members) since these individuals play

625

decisive roles in athletes’ daily life routines and training practices.

626

Conclusion

627

The participants seemed to be pleased with the FUEL intervention and its content, reporting

628

mainly positive experiences as well as expressing their intentions to recommend the

629

intervention to other potential participants, such as athletes and other members of their

630

entourage. Moreover, the participants enjoyed the digital lectures, which provided them the

631

flexibility to watch the lectures whenever it suited them, and they could revisit the lecture

632

content multiple times if needed. Additionally, participants who completed the FUEL

633

intervention with digital lectures in combination with digital individual consultations had the

634

opportunity to discuss the lecture content during their individual consultations with the sports

635

nutritionist. Finally, and perhaps most importantly, the participants highlighted the time given

636

(16 weeks) to acquire new sports nutrition knowledge, reflect on their positive bodily

637

experiences regarding recovery and performance, and change mindsets regarding sports

638

nutrition in their daily life routines and training practices.

639

Perspectives

640

The FUEL intervention program offers tertiary prevention to limit health consequences of

641

REDs. Indeed, the overall aim of tertiary prevention is to identify the source of and treat the

642

underlying cause of problematic LEA, which can be reversed by increasing energy intake,

643

decreasing exercise energy expenditure or a combination of both [16]. Thus, the subgroup of

644

the IOC consensus statement on REDs recommends an interdisciplinary team to achieve a

645

comprehensive treatment [16]. The recovery timeline from REDs, however, varies and is

646

dependent on a range of factors, such as the REDs outcomes affected, the severity of REDs, the

647

presence of comorbidities, and the underlying cause of LEA [16]. These factors, along with the

648

relationship between attitudes, habits, and behaviour change [50], should be considered when

649

revising, planning, and conducting future revisions of the FUEL intervention program. More

650

specifically, it has been argued that previous research has not taken the nature and operation of

651

habits into account. This is important because habits form boundary conditions for attitude-

652

directed interventions [50]. Future interventions are therefore encouraged to integrate research

653

on attitudes and habits, which, in turn, might enable researchers to identify when and how

654

behaviour change strategies will be most effective [50]. Thus, given the specific REDs

655

condition, which eventually will determine the recovery timeline from REDs, it might be

656

beneficial to move sports nutrition interventions, and their related behaviour change efforts,

657

from an attitude change strategy to a habit change strategy [42,50]. This suggestion would

658

require researchers to make certain adjustments in future sports nutrition prevention studies.

659

First, they must acknowledge and understand that behaviour change is an inherently unstable

660

and unsteady process. Second, they must be aware of the specific REDs condition due to the

661

risk of habit formation and the recovery timeline from the specific REDs outcome. Third, they

662

must think about their procedure related to sample characteristics, with additional awareness of

663

context-dependent operation of habits. Lastly, a greater awareness of the already mentioned

664

factors, including awareness related to designs in real world settings, would eventually

665

strengthen the context-specific and context-generic findings in future sports nutrition

666

prevention studies [50]. As a final note, we hereby suggest an even stronger collaboration

667

between sports nutrition and sports psychology in relation to future sports nutrition

668

interventions, since an interdisciplinary approach can achieve outcomes that cannot be achieved

669

within each discipline alone [59]. Based on previous research within sports medicine [60], we

670

therefore introduce the term nutrition psychology in future research and practical work related

671

to prevention of REDs.

672

673

Acknowledgements

674

The authors thank Runar Furre, Maria Gräfnings, Andreas Ivarsson, Karsten Koehler, Danielle

675

Logue, Sharon Madigan, Siri Marte Hollekim-Strand, and Paulina Wasserfurth, for their

676

valuable cooperation in this research project. In addition, the authors greatly appreciate the

677

athletes’ and the sports nutritionists’ contribution to the FUEL project.

678

679

Disclosure statement

680

No potential conflict of interest was reported by the authors.

681

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682

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880

Table 1. Participant characteristics.

Evaluation questionnaire

n = 36

Qualitative interview

n = 10

Age (years)

24.6 ± 4.8

25.1 ± 5.8

Training volume (h/month)

47.1 ± 17.7

42.0 ± 16.5

Full‐time athlete (%)

14%

10%

Level of competition

Club (%)

National team (%)

Professional (%)

Other (%)

Continuous data are presented as mean ± SD and categorical data as a percentage.

Tables Click here to access/download;Table;Fuel_evaluation_Tables_Final_241124.docx

Table 2. Participants’ experiences with the intervention duration and the duration and difficulty level of the lectures based on closed-ended

questions from the questionnaire.

FUELcombined

n = 29

FUELlectures

n = 7

What do you think about the duration of the FUEL

intervention (16 weeks)?

Too short: 0

Appropriate: 28

Too long: 1

Too short: 0

Appropriate: 6

Too long: 1

In general, what do you think of the duration of the

lectures?

Too short: 1

Appropriate: 26

Too long: 2

Too short: 0

Appropriate: 7

Too long: 0

How did you find the difficulty level of the

lectures?

Very low: 0

Low: 4

Appropriate: 21

High: 4

Very high: 0

Very low: 1

Low: 1

Appropriate: 5

High: 0

Very high: 0

Abbreviations: FUELlectures: athletes participating in the group receiving weekly online lectures in sports nutrition, FUELcombined: athletes participating in the group receiving

weekly online lectures in sports nutrition combined with individual consultations every other week.

Table 3. Participants’ experiences during the FUEL intervention based on closed-ended questions from the questionnaire.

During the FUEL intervention, did you experience any

of the following (it is possible to select several options)

FUELcombined

n = 29

FUELlectures

n = 7

More energized

20 (69%)

2 (29%)

Improved mood

19 (66%)

3 (43%)

Improved self-confidence

17 (59%)

4 (14%)

Improved enjoyment of training

14 (48%)

3 (43%)

Improved body satisfaction

13 (45%)

3 (43%)

Improved ability to cope with everyday stress

13 (45%)

0 (0%)

Improved food pleasure

11 (38%)

3 (43%)

Less energized

1 (3%)

0 (0%)

Decreased ability to cope with everyday stress

1 (3%)

0 (0%)

Reduced food pleasure

1 (3%)

0 (0%)

Reduced body satisfaction

1 (3%)

0 (0%)

Reduced enjoyment of training

1 (3%)

0 (0%)

Poorer mood

0 (0%)

Reduced self-esteem / self-confidence

0 (0%)

None of the above

1 (3%)

2 (29%)

Abbreviations: FUELlectures: athletes participating in the group receiving weekly online lectures in sports nutrition, FUELcombined: athletes participating in the group receiving

weekly online lectures in sports nutrition combined with individual consultations every other week.

Table 4. Participants’ experiences with the individual sports nutrition consultations based on closed-ended questions from the questionnaire.

FUELcombined

n = 29

How satisfied were you with the individual nutritional consultations in the FUEL

project? (1 = Very dissatisfied, 10 = Very satisfied)

9.4 ± 1.2

(min: 7 max: 10)

Did you find it limiting that the consultations took place digitally (via Zoom) instead

of physically? (1=Not limiting at all, 10=Very limiting)

2.4 ± 1.7

(min: 1 max: 8)

How did you experience your motivation for participating in the individual

consultations? (1 = Not motivated at all, 10 = Always motivated)

8.8 ± 1.3

(min: 5 – max: 10)

Did you find that your motivation to participate in the individual consultations

changed during the FUEL intervention?

Decreased motivation: 1

Unchanged motivation: 17

Increased motivation: 11

In general, what do you think of the duration of the individual consultations?

Too short: 3

Appropriate: 25

Too long: 1

What do you think about the frequency of the individual consultations (every other

week)?

Too frequent: 3

Appropriate: 26

Too infrequent: 0

Table 5. Personal experiences based on the open-ended evaluation questions in the questionnaire.

Increased knowledge

Bodily experiences

Changed mindset

I am very happy because I have learned a lot of

exciting and interesting things about sports

nutrition, the female body, sports performance

and health. The combination of close follow-up

of nutritional physiology and learning videos has

made me much more confident in sports nutrition

and given me many good reasons why it is so

important to get enough energy. There is an

incredible amount of information about the topic,

and many people who have opinions. Therefore, I

have very much appreciated learning research-

based facts in the project, from people I have

been confident have knowledge about the topic. It

has made me much more confident about

nutrition, menstruation, and generally how the

female body develops, and that it is a natural

process.

Fewer bad workouts, slightly better recovery.

Less often really bad daily shape.

I have experienced change in that I have become

much more confident in the food I eat, and I am

no longer as afraid of eating ‘too much’ or

gaining weight because I have received good

follow-up and guidance on how much I need to

eat from a sports nutritionist. In addition, I have

changed my mindset and the relationship I have

with the amount of food. Instead of thinking that

it is good or healthy to eat little, I have rather

started to think that I am good if I eat well with

food, and for example bring sports drinks and

energy bars with me on hard/long workouts.

It has been incredibly valuable for me to

participate. I have gained much more knowledge

about what I need to eat to feel good and be able

to perform. I have also felt clear results.

Got my period back and found the balance.

What FUEL has helped me with, is a deeper

understanding of what nutrition has for both

health and performance. I have realized that one

is extremely vulnerable to impressions and

dietary advice from sources that are not aimed at

athletic women. Knowing that will make it easier

in the future to get distracted when I hear, see,

become subject to - advice, well-intentioned tips

and opinions about food and nutrition. But not

only that, I have also received help to incorporate

what we have learned in practice, in everyday

life. Seemingly small changes in not only food

choices, but also the timing of them, have proven

to be valuable.

Grateful to have had the chance to learn more

about both sports nutrition and myself.

No feeling of hunger during training.

It takes time to change meal routines.

I have gotten a lot more out of being involved

than I thought. You have managed to hit the mark

with the teaching of sports nutrition in a way that

[…] cannot manage.

Reduced gut issues that affected my everyday

life.

At times, my appetite decreased. Then I ate on a

routine.

A lot of lessons learned and good follow-up.

During parts of the study, my weight fluctuated

and that made me feel body conscious at times

but feeling better now.

Better relationship with food, much easier for me

to treat myself to something ‘unhealthy’ now

compared to before. I also feel that I get more out

of the workouts as I have continued with juice

during sessions and become even more careful

about recovery meals.

Learned so much from the videos.

Better body awareness.

It has helped me change my lifestyle to

something more sustainable and healthier.

Better knowledge and feeling of not being alone

in the situation.

More conscious of what I was eating and when,

better concentration when studying.

A lot more motivation for both training but above

all to plan food intake.

Knowledge about sports nutrition.

Really. It has helped me, and it has been so

interesting. It is hard to understand without really

using yourself and your habits as an example.

Better relationship with food.

Increased understanding and knowledge of diet

and nutrition that I have been able to use when

planning my intake of energy.

It helped me a lot to have energy for a longer

period of time.

Positive change, I think more about my eating

habits, and it feels like I have much more

knowledge about how I can benefit from my

training better.

More knowledge and security.

Security in nutrition after conversation with a

sports nutritionist.

I was in a very positive mood, because before I

often thought about whether it was too much

what I was eating and through the videos I

became encouraged that eating enough and

energy intake is the most important thing to

perform well.

I thought it was interesting to see how many

consequences low energy availability can have

there and then, but also in retrospect. I also

thought it was interesting to learn about how

menstruation affects exercise, and why it is

important not to lose your period.

For me, it has been a good amount of time

because I have been through a few phases in my

training during this period. So, I was able to

apply my knowledge to each training phase.

No, except that you think more about food.

Shortly after certain videos, more attention was

paid to the intake of carbohydrates.

I had little knowledge from before about how

important it was to have food during and

immediately after training.

It has brought me a lot and I have the feeling that

it has also brought positive changes to my

performance.

It has helped me so much in my own

development going forward.

It was interesting to find out what actually

happens when you eat too little food and the

importance of eating right.

This has been very educational, and I have been

able to absorb a lot to improve my training.

Gained a greater understanding of how the body

works and what is important to think about.

I highly benefited from taking part and gained

knowledge and confidence in my nutrition

strategies that I will be able to implement over

the course of my sporting career which will

improve my performance over time.

Educational and necessary especially for young

athletes. If you want to be good, you also have to

be good at sports nutrition.

Knowledge is very important, and everyone who

struggles with this, I think can benefit greatly.

Even those who do not struggle, because diet and

large amounts of exercise can be a difficult

balancing act and gaining knowledge can help

them not to end up in a situation where they

cannot do what they like best, namely exercise.

I have learned a lot of useful things that I will

take with me in the future, I think this is

something all women in endurance sports should

participate in.

I have got so much useful stuff with me, thank

you.

Because it was educational, and I think many

people would benefit from learning this (not just

women).

Super good to educate yourself when diet is so

crucial for performance.

Absolute. I want everyone to take part in the

knowledge I have learned through this project.

Table 6. Evaluations from the following open-ended question in the questionnaire: “Do you have any suggestions for improving the FUEL

program?”.

Positive feedback regarding the FUEL intervention content

Feedback regarding the

duration of the FUEL

intervention

Improving the FUEL

intervention content

General feedback

Specific feedback on either

videos or counseling sessions

Would definitely recommend the

program to other female endurance

athletes. I have talked a lot of positive

things about the project to training

friends from orienteering.

I thought all the lectures were good.

However, some were a bit difficult to

grasp while others were more

concrete. For me, it was easier to

absorb the lectures when there were

many concrete examples.

I think it was a decent length.

Because there are some

presentations we have to see, and

then it is good to take little by

little. Because if it gets too often,

you often do not have time to see.

But if it gets too long, it can also

be boring.

The quality of the videos could

have been improved and made a

little more engaging for the

viewer.

A lot of useful lessons that you do not

learn in primary school, from coaches

or otherwise in everyday life.

Good, raised many points that I was

hesitant about, as well as been able to

discuss it with my sports nutritionist

who has helped me to individualize it

to myself.

Quite appropriate, nice to have

some time to make changes when

you learned things that were

relevant to you. How much I noted

from the videos varies greatly.

I wish it was possible to make

requests for topics regarding the

digital video content, such as gut

health and nutrition related to this.

Instructive and important to spread

this topic in sports.

It was really comprehensive. The

weekly lectures gave a lot of useful

information and covered topics from

an endurance-athlete perspective,

which was really useful. The meetings

with the sports nutritionist were also

extremely beneficial as it gave me an

opportunity to ask questions on things

that were brought up in the lectures.

I have really enjoyed being part of

the project. I think the distribution

of information evenly over a long

period of time has been good.

Since I have learned a lot of new

things, I think it has been good that

the project has lasted at least 16

weeks so that I have been able to

gradually make changes in my diet

and absorb what I have learned.

Therefore, I think that the project

should last for at least 16 weeks so

that you can see the effects of the

It would also have been possible to

merge some of the videos as they

were about the same thing, for

example, the video about dietary

supplements was somewhat

repetitive from things that had

previously been covered in the

videos.

learning and the information the

participants have received.

It has been incredibly rewarding and

fun to be part of the project. I am

passionate about the subject and hope

to be able to contribute to sharing

what I have learned in the study with

other running girls, mentees that I

coach, and so on. Looking forward to

reading the study when it is finished.

Super happy with the lectures and the

contact I got with mu nutritionist.

Dietary advice is also beneficial.

Between moderate and ‘too long’ -

maybe the last 2-3 weeks at the

dietary counselling felt that there

were some of the same discussions

about me.

Very rewarding lectures and

conversations. However, the

lectures could perhaps have

contained more concrete tips on

how you as an athlete should think

to put together the best possible

diet instead of going into details

about what happens in the body.

So good and important.

Tailored to suit the needs of the

individual and not a one size fits all

approach. […] was kind and

understanding of my situation over

the course. Videos were an

appropriate length while informative.

I would have liked to have more

time to test and change diet plans

with the help of a nutritionist, but

at the same time it is a strain to

have ‘musts’ to do, for example,

watch lectures, fill in an extra

exercise diary for a longer period

of time.

Learned a lot and the check-ins

were really helpful more so than

the videos which I found a little bit

dull due to the format of them.

This programme was really

comprehensive - having the

educational videos as well as the

nutritionist meetings. Compared to a

lot of research studies, I think the

participants in this programme got a

lot out of participating. I also think

that there is a lot of misinformation

(or just lack of information) relating

to nutrition for female endurance

athletes, so this programme provided

really useful information and

guidance in an area that I think is

really lacking. There are many

athletes who would benefit from a

I found the carbohydrate lecture very

helpful because it quantified the

amount of carbohydrate needed in

‘real food’ terms i.e. giving examples

of what foods = 100g CHO. The

REDs (part 2) lecture I found

interesting because it explained the

energy availability part versus body

weight/body fat. I found the Meal

Pattern lecture interesting because as

athletes we are often relying on

nutrition information that is targeted

at a normal (or overweight)

population - outlining how much an

Lots of info to cover... a shorter

duration and it could have been

information overload, and it would

not be as effective.

It was ok, but an individual

concrete consultation intervention

would be useful.1

This participant only received the videos.

programme like this being made

available.

endurance athlete has to eat and the

timings was really interesting.

Stuff every female athlete should

know.

Good information from the lectures

and very much appreciated with the

sports nutritionist meetings.

Appropriate with one lecture a

week. I think all the lectures were

relevant and good, so would not

shorten the length of the project.

For some videos, I would have

liked to see more concrete

examples. What a diet can look

like.

Without a doubt. Useful.

My sports nutritionist took very good

care of me, and I was able to learn a

lot.

It should not be any longer, as

there is a risk of not watching the

videos towards the end. Change

takes time.

Especially some of the content of the

videos is so important to know.

The lectures were great, it was easy to

absorb information and nice to know

that what was brought up is based on

good scientific sources.

Enough time for good follow-up

by a nutritionist in addition to

needing some time to adjust and

get a better relationship with food

and change habits related to this.

For many of my acquaintances, the

instructional videos would be helpful.

I really liked the lectures that dealt

with menstruation and the

consequences of lack of menstruation.

Some repetition of this through

several lectures was only helpful.

This is a long enough intervention

period to be able to make

adjustments and notice their

impact.

I have gotten a lot out of being

involved.

I was too late to get my own guidance

from a nutritionist, my fault. That

would give a full score.

You had enough time to really

change something in your diet.

Helpful to me and I see a lot of other

riders with disordered eating.

Good help from a sports nutritionist

and has been instructive with the

lectures.

I think it was a good length

because it was long enough to

actually enact some changes,

without an ‘intervention effect’.

Some of the things became

routine, rather than just doing them

because you are part of a study.

Several good lectures, but perhaps

most useful of all was the

conversation with a sports nutritionist.

The time was long enough to have

time to learn while I did not get

tired. The difficult thing was to

keep up with the rest of my

everyday life.

The sessions with the sports

nutritionist were very helpful.

The videos helped me and found

that all topics were covered to an

appropriate extent. In the 16

weeks, the content was able to

settle and sink in and I had the

feeling that it was the right

duration to implement and try out

certain things in practice.

Would not have wanted to be without

the lectures.

The videos are very scientifically

sound, but also understandable.

Very interesting video footage.

Important topic and instructive with

all the lectures.

Great that they were recorded, could

watch in my own time.

Interview guide

Click here to access/download

Electronic supplementary material

Interview_guide_english_version_FUEL_study.docx

Author contributions

Click here to access/download

Electronic supplementary material

Author_Contributions_241124.docx

Appendix I-X

Appendix I

Ethical approvals

Alle skriftlige henvendelser om saken må sendes via REK-portalen

Du finner informasjon om REK på våre hjemmesider rekportalen.no

Region:

REK sør-øst C

Saksbehandler:

Anders Strand Telefon: Vår dato:

14.01.2020

Vår referanse:

31640

Deres referanse:

Monica Klungland Torstveit

31640 The FUEL program

Forskningsansvarlig: Universitetet i Agder

Søker: Monica Klungland Torstveit

Søkers beskrivelse av formål:

Utilstrekkelig energiinntak, i forhold til energiforbruk, er en vanlig utfordring blant

kvinnelige utholdenhetsutøvere, og manifesteres ofte som menstruasjonsforstyrrelser på

grunn av lave østrogennivåer. Dette kan med tiden medføre dårlig beinhelse og

beinskjørhet. Andre konsekvenser av RED-S kan være forringet stoffskifte, dårlig

psykososial helse, redusert idrettslig prestasjon og hormonell ubalanse.

Forskningsprosjektet ønsker å undersøke om undervisningsprogrammet FUEL (Forstå

Utholdenhetsidretts Ernæring – et Læringsprogram) kan forbedre disse symptomene blant

kvinnelige utholdenhetsutøvere. FUEL består av et nettbasert undervisningsprogram

kombinert med individuelle kostholdsveiledninger over 6 måneder. Prosjektet vil bli

evaluert ved hjelp av laboratorietester, spørreskjemaer, kostholds- og

aktivitetsregistreringer.

REKs vurdering

Komiteen viser til tilbakemelding fra prosjektleder av 09.12.2019, hvor komiteens

merknader anført i utsatt vedtak av 23.10.2019 besvares. Videre opplyser prosjektleder at

prosjektet nå skal inkludere samarbeid med forskere i München og Liverpool.

Tilbakemeldingen klargjør at alle deltagere, inkludert de i kontrollgruppen i Sverige, skal

motta FUEL programmet. Kontrollgruppen tilbys dette etter en 6 måneders

Alle skriftlige henvendelser om saken må sendes via REK-portalen

Du finner informasjon om REK på våre hjemmesider rekportalen.no

observasjonsperiode, og dette er begrunnet ved at prosjektet antar at programmet vil ha

positiv effekt og derfor bør tilbys alle deltagere. Komiteen sier seg enig i at dette er en

hensiktsmessig løsning.

Samtykkeskrivene er nå revidert med mer utfyllende informasjon om studiens design, og

komiteen har nå mottatt etterspurte samtykkeskriv for alle deltagere/kohorter.

Komiteen etterspurte en mer utførlig begrunnelse for ønsket om å oppbevare

prosjektopplysninger i 15 år etter prosjektslutt. Søkers svar på dette var som følger:

«Vi ønsker å ha mulighet for å følge opp disse kohortene for å undersøke hvorvidt FUEL

programmet kan ha hatt innflytelse på fysisk og psykososial helse på lang sikt. Vi forventer

å finne en høy andel av deltakere med menstruasjonsforstyrrelser og andre symptomer på

RED-S, som er blitt rapportert å kunne ha alvorlige langsiktige konsekvenser på beinhelse

(Braam et al., 2003; Keen & Drinkwater, 1997). Ingen studier har imidlertid undersøkt

mulige langtidskonsekvenser av RED-S på utfallsvariabler som hjerte-karsykdommer,

psykososial helse og fertilitet. En femårs oppfølging vil da være for kort tid, mens vi anser

at 15 års oppfølging vil være tilstrekkelig. Å få fulgt opp disse deltakerne vil være av

relevans både for den enkelte deltaker (mulighet for å identifisere/forebygge/håndtere

mulige konsekvenser av RED-S) og i et samfunnsperspektiv (blant annet gjennom å øke

kunnskapen om disse fenomener til helsepersonell). Vi ønsker imidlertid å imøtekomme

komiteens ønske om å redusere oppbevaringstiden og ber derfor komiteen vurdere

forlenget oppbevaring av personopplysninger inntil 10 år grunnet årsaker beskrevet over.»

Komiteen vurderer en slik langtidsoppfølging som potensielt meget nyttig, og mener det

derfor foreligger gode grunner for å legge til rette for utvidet oppbevaring av

prosjektopplysninger. På den annen side er oppbevaring av prosjektopplysninger etter

prosjektslutt begrunnet ved kontrollhensyn, jfr. helseforskningsloven §38. Siden en

eventuell langtidsoppfølging, og vurdering av behovet og potensialet for å gjennomføre en

slik, ikke er å anse som kontrollhensyn, så har komiteen i stedet vurdert en forlengelse av

omsøkt prosjektperiode. Komiteen setter derfor ny sluttdato for prosjektet til 31.12.2028,

og godkjenner følgelig oppbevaring av prosjektopplysninger til 31.12.2033. Dersom en

eventuell langtidsoppfølging vil strekke seg utover her godkjent sluttdato, så kan

prosjektleder på et senere tidspunkt søke om ytterligere konkret begrunnede forlengelser

ved endringsmelding til REK.

Når det gjelder utvidelsen av prosjektet til også å inkludere kohorter og forskere i

München og Liverpool, så legger komiteen til grunn at dette ikke vil medføre endringer i

studiens design (med unntak av mindre justeringer av fysiske tester, slik at de blir like for

alle teststeder) og dermed for de vurderinger som allerede er lagt til grunn. Komiteen har

derfor ingen forskningsetiske innvendinger til denne utvidelsen av prosjektet.

På denne bakgrunn finner komiteen grunnlag for å godkjenne prosjektet slik det nå

foreligger.

Alle skriftlige henvendelser om saken må sendes via REK-portalen

Du finner informasjon om REK på våre hjemmesider rekportalen.no

Vedtak

Godkjent

Komiteen har gjort en helhetlig forskningsetisk vurdering av alle prosjektets sider.

Prosjektet godkjennes, med hjemmel i helseforskningsloven § 10.

Komiteen gjør samtidig oppmerksom på at etter ny personopplysningslov må det også

foreligge et behandlingsgrunnlag etter personvernforordningen. Det må forankres i egen

institusjon.

Tillatelsen er gitt under forutsetning av at prosjektet gjennomføres slik det er beskrevet i

søknaden og protokollen, og de bestemmelser som følger av helseforskningsloven med

forskrifter.

Tillatelsen gjelder til 31.12.2028. Av dokumentasjons- og oppfølgingshensyn skal

opplysningene likevel bevares inntil 31.12.2033. Opplysningene skal lagres avidentifisert,

dvs. atskilt i en nøkkel-og en opplysningsfil. Opplysningene skal deretter slettes eller

anonymiseres, senest innen et halvt år fra denne dato.

Komiteens avgjørelse var enstemmig.

Med vennlig hilsen

Britt Ingjerd Nesheim

Prof. Dr.med

Komiteleder, REK sør-øst C

Anders Strand

Rådgiver

Kopi til: post@uia.no

Sluttmelding

Søker skal sende sluttmelding til REK sør-øst C på eget skjema senest seks måneder etter

godkjenningsperioden er utløpt, jf. hfl. § 12.

Søknad om å foreta vesentlige endringer

Dersom man ønsker å foreta vesentlige endringer i forhold til formål, metode, tidsløp eller

organisering, skal søknad sendes til den regionale komiteen for medisinsk og helsefaglig

Alle skriftlige henvendelser om saken må sendes via REK-portalen

Du finner informasjon om REK på våre hjemmesider rekportalen.no

forskningsetikk som har gitt forhåndsgodkjenning. Søknaden skal beskrive hvilke

endringer som ønskes foretatt og begrunnelsen for disse, jf. hfl. § 11.

Region:

REK sør-øst C

Saksbehandler:

Anders Strand Telefon: Vår dato:

26.04.2021

Vår referanse:

31640

Deres referanse:

REK sør-øst C

: Gullhaugveien 1-3, 0484 OsloBesøksadresse

:22 84 55 11 | :Telefon E-post rek-sorost@medisin.uio.no

:Web https://rekportalen.no

Monica Klungland Torstveit

31640 The FUEL program

Forskningsansvarlig: Universitetet i Agder

Søker: Monica Klungland Torstveit

REKs vurdering

REK viser til endringsmelding mottatt 20.04.2021, for prosjekt 31640 «The FUEL

program». Komiteleder for REK sør-øst C har vurdert meldingen på fullmakt fra REK

sør-øst C, med hjemmel i helseforskningsloven §11.

De omsøkte endringene er begrunnet ved behov for dels omfattende justeringer og

tilpasninger som følge av den pågående Covid-19 situasjonen. Endringene er godt

oppsummert i endringsmeldingen, og inkluderer:

Bård Erlend Solstad (UiA), Finn Skårderud (UiA) og Siri-Marte Hollekim Strand

(NTNU) inkluderes som prosjektmedarbeidere. Komiteen har ingen forskningsetiske

innvendinger til dette.

Endringer av selve FUEL programmet, slik at dette kan gjennomføres under de

gjeldende smittevernsrestriksjoner. Dette innebærer blant annet at flere

laboratorieundersøkelser utgår. Oversikt over endringene fremgår av

endringsmeldingen med vedlegg.

I lys av den pågående Covid-19 situasjonen vurderer komiteen endringene som

hensiktsmessige og forsvarlige, og godkjenner derfor disse.

Videre viser komiteen til sitt svar på fremleggingsvurdering 267365, av 26.04.2021, hvor

en kvalitativ delstudie, som ble omsøkt i en tidligere versjon av endringsmeldingen,

vurderes til å falle utenfor helseforskningslovens virkeområde.

Vedtak

Godkjent

Komitéen har vurdert endringsmeldingen og godkjenner prosjektet slik det nå foreligger

med hjemmel i helseforskningslovens § 11.

Tillatelsen er gitt under forutsetning av at prosjektendringen gjennomføres slik det er

beskrevet i prosjektendringsmeldingen og endringsprotokoll, og de bestemmelser som

følger av helseforskningsloven med forskrifter.

Vennligst oppgi vårt referansenummer i korrespondanse.

Med vennlig hilsen,

Britt Ingjerd Nesheim

Prof. Dr.med

Komiteleder, REK sør-øst C

Anders Strand

Seniorrådgiver, REK sør-øst C

Klageadgang

Du kan klage på komiteens vedtak, jf. forvaltningsloven § 28 flg. Klagen sendes til REK

sør-øst C. Klagefristen er tre uker fra du mottar dette brevet. Dersom vedtaket

opprettholdes av REK sør-øst C, sendes klagen videre til Den nasjonale forskningsetiske

komité for medisin og helsefag (NEM) for endelig vurdering.

Region:

REK sør-øst C

Saksbehandler:

Anders Strand Telefon: Vår dato:

26.04.2021

Vår referanse:

267365

Deres referanse:

REK sør-øst C

: Gullhaugveien 1-3, 0484 OsloBesøksadresse

:22 84 55 11 | :Telefon E-post rek-sorost@medisin.uio.no

:Web https://rekportalen.no

Monica Klungland Torstveit

267365 The FUEL program, qualitative part

Forskningsansvarlig: Universitetet i Agder

Søker: Monica Klungland Torstveit

Søkers beskrivelse av formål:

Dette er et delprosjekt til det allerede pågående "The FUEL program" med

prosjektnummer 31640. Dette prosjektet har tidligere fått godkjennelse av REK sør-øst C.

Grunnet korona kunne ingen planlagte fysiologiske målinger gjøres i laboratoriet og hele

datainnsamlingen og intervensjonen ble først utsatt og deretter gjort "korona vennlig" ved

at forsøkspersonene kunne delta uten å møte i laboriatoriet. FUEL prosjektet er primært et

doktorgradsprosjekt og da stipendiaten ikke kunne innhente planlagte fysiologiske

målinger av deltakerne, ble det vurdert å legge til en kvalitativ del for å ha et større fokus

på evaluering av denne korona-vennlige intervensjonen.

Dette betyr at vi nå ønsker å inkludere en kvalitativ del i FUEL prosjektet, samt å legge til

et kortfattet spørreskjema som undersøker deltagernes og ernæringsveiledernes erfaringer

med prosjektet. Prosjektbeskrivelsen inneholder nå informasjon om evaluering av

prosjektet inkludert kvalitative intervjuer, dette er markert med gult. Det er denne

kvalitative delen det nå ønskes en vurdering knyttet til.

De nye forskningsspørsmålene er følgende:

• How do the participants experience to be part of the FUEL recovery program?

• How do the nutritional counselors experience the feasibility of the FUEL recovery

program?

• How feasible is the FUEL program based on the RE-AIM framework?

REKs vurdering

REK viser til innsendt fremleggingsvurdering for prosjekt 267365 «The FUEL program,

qualitative part», mottatt 20.04.2021. Komiteleder for REK sør-øst C har nå vurdert

henvendelsen, med tilhørende dokumentasjon.

Studien er en delstudie knyttet til prosjekt 31640 «The FUEL program», og skal undersøke

deltagernes og ernæringsveiledernes erfaringer med prosjektdeltagelsen.

Forskningsspørsmålene er som følger:

• How do the participants experience to be part of the FUEL recovery program?

• How do the nutritional counselors experience the feasibility of the FUEL recovery

program?

• How feasible is the FUEL program based on the RE-AIM framework?

REK mener at denne kvalitative delstudien ikke har som formål å etablere ny kunnskap om

sykdom og helse, slik dette forstås i helseforskningslovens § 4. Prosjektet fremstår derfor

ikke som fremleggelsespliktig, jf. helseforskningslovens §§ 2 og 4.

Delstudien kan således gjennomføres uten REK-godkjenning.

REK antar for øvrig at prosjektet kommer inn under de interne regler for behandling av

opplysninger som gjelder ved ansvarlig virksomhet. Søker bør derfor ta kontakt med enten

forskerstøtteavdeling eller personvernombud for å avklare hvilke retningslinjer som er

gjeldende.

Vedtak

Ikke fremleggspliktig

Vi gjør oppmerksom på at avgjørelsen av spørsmålet om fremlegging er å anse som

veiledende jfr. forvaltningsloven § 11.

Med vennlig hilsen

Britt Ingjerd Nesheim

Prof. Dr.med

Komiteleder, REK sør-øst C

Anders Strand

Seniorrådgiver, REK sør-øst C

Appendix II

Approval from Norwegian Agency for Shared Services in Education and Research,

previous The Norwegian Centre for Research Data (Norwegian: Norsk senter for

forskningsdata, NSD)

Referansenummer

968634

Vurderingstype

Standard

Dato

17.02.2020

Tittel

Effects of a practice-oriented recovery program for female endurance athletes with relative energy deficiency. The FUEL recovery

program

Behandlingsansvarlig institusjon

Universitetet i Agder / Fakultet for helse- og idrettsvitenskap / Institutt for ernæring og folkehelse

Prosjektansvarlig

Monica Torstveit

Prosjektperiode

02.03.2020 - 31.12.2028

Kategorier personopplysninger

Alminnelige

Særlige

Lovlig grunnlag

Samtykke (Personvernforordningen art. 6 nr. 1 bokstav a)

Uttrykkelig samtykke (Personvernforordningen art. 9 nr. 2 bokstav a)

Behandlingen av personopplysningene er lovlig så fremt den gjennomføres som oppgitt i meldeskjemaet. Det lovlige grunnlaget gjelder

til 31.12.2033.

Meldeskjema

Kommentar

BAKGRUNN

Prosjektet er vurdert og godkjent av Regionale komiteer for medisinsk og helsefaglig forskningsetikk (REK) etter helseforskningsloven

(hfl.) § 10 (REK sin ref: REK sør-øst C 31640 ).

Det er NSD sin vurdering at behandlingen også vil være i samsvar med personvernlovgivningen, så fremt den gjennomføres i tråd med

det som er dokumentert i meldeskjemaet datert 17.02.2020 med vedlegg, samt i meldingsdialogen mellom innmelder og NSD.

Behandlingen kan starte.

MELD VESENTLIGE ENDRINGER

Dersom det skjer vesentlige endringer i behandlingen av personopplysninger, kan det være nødvendig å melde dette til NSD ved å

oppdatere meldeskjemaet. Før du melder inn en endring, oppfordrer vi deg til å lese om hvilke type endringer det er nødvendig å

melde: https://nsd.no/personvernombud/meld_prosjekt/meld_endringer.html

Du må vente på svar fra NSD før endringen gjennomføres.

TYPE OPPLYSNINGER OG VARIGHET

Prosjektet vil behandle særlige kategorier av personopplysninger om helseopplysninger og alminnelige kategorier av

personopplysninger frem til 31.12.2028. Data med personopplysninger oppbevares deretter internt ved behandlingsansvarlig institusjon

frem til 31.12.2033, dette grunnet dokumentasjonshensyn.

LOVLIG GRUNNLAG

Prosjektet vil innhente samtykke fra de registrerte til behandlingen av personopplysninger. Vår vurdering er at prosjektet legger opp til

et samtykke i samsvar med kravene i art. 4 nr. 11 og art. 7, ved at det er en frivillig, spesifikk, informert og utvetydig bekreftelse, som kan

dokumenteres, og som den registrerte kan trekke tilbake.

Lovlig grunnlag for behandlingen vil dermed være den registrertes uttrykkelige samtykke, jf. personvernforordningen art. 6 nr. 1 bokstav

a, jf. art. 9 nr. 2 bokstav a, jf. personopplysningsloven § 10, jf. § 9 (2).

PERSONVERNPRINSIPPER

NSD vurderer at den planlagte behandlingen av personopplysninger vil følge prinsippene i personvernforordningen om:

Vurdering av behandling av personopplysninger

06.08.2024, 08:50

Vurdering av behandling av personopplysninger - Ref. 968634

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- lovlighet, rettferdighet og åpenhet (art. 5.1 a), ved at de registrerte får tilfredsstillende informasjon om og samtykker til behandlingen

- formålsbegrensning (art. 5.1 b), ved at personopplysninger samles inn for spesifikke, uttrykkelig angitte og berettigede formål, og ikke

viderebehandles til nye uforenlige formål

- dataminimering (art. 5.1 c), ved at det kun behandles opplysninger som er adekvate, relevante og nødvendige for formålet med

prosjektet

- lagringsbegrensning (art. 5.1 e), ved at personopplysningene ikke lagres lengre enn nødvendig for å oppfylle formålet

DE REGISTRERTES RETTIGHETER

Så lenge de registrerte kan identifiseres i datamaterialet vil de ha følgende rettigheter: åpenhet (art. 12), informasjon (art. 13), innsyn (art.

15), retting (art. 16), sletting (art. 17), begrensning (art. 18), underretning (art. 19).

Unntak fra retten til sletting etter helseforskningsloven § 16 tredje ledd, og personvernforordningen art. 17 nr 3 bokstav d:

I utgangspunktet har alle som registreres i forskningsprosjektet rett til å få slettet opplysninger som er registrert om dem. Etter

helseforskningsloven § 16 tredje ledd vil imidlertid adgangen til å kreve sletting av sine helseopplysninger ikke gjelde dersom materialet

eller opplysningene er anonymisert, dersom materialet etter bearbeidelse inngår i et annet biologisk produkt, eller dersom

opplysningene allerede er inngått i utførte analyser. Regelen henviser til at sletting i slike situasjoner vil være svært vanskelig og/eller

ødeleggende for forskningen, og dermed forhindre at formålet med forskningen oppnås.

Etter personvernforordningen art 17 nr. 3 d kan man unnta fra retten til sletting dersom behandlingen er nødvendig for formål knyttet til

vitenskapelig eller historisk forskning eller for statistiske formål i samsvar med artikkel 89 nr. 1 i den grad sletting sannsynligvis vil gjøre

det umulig eller i alvorlig grad vil hindre at målene med nevnte behandling nås.

NSD vurderer dermed at det kan gjøres unntak fra retten til sletting av helseopplysninger etter helseforskningslovens § 16 tredje ledd og

personvernforordningen art 17 nr. 3 d, når materialet er bearbeidet slik at det inngår i et annet biologisk produkt, eller dersom

opplysningene allerede er inngått i utførte analyser.

Vi presiserer at helseopplysninger inngår i utførte analyser dersom de er sammenstilt eller koblet med andre opplysninger eller

prøvesvar. Vi gjør oppmerksom på at øvrige opplysninger må slettes og det kan ikke innhentes ytterligere opplysninger fra deltakeren.

NSD vurderer at informasjonen som de registrerte vil motta oppfyller lovens krav til form og innhold, jf. art. 12.1 og art. 13.

Vi minner om at hvis en registrert tar kontakt om sine rettigheter, har behandlingsansvarlig institusjon plikt til å svare innen en måned.

FØLG DIN INSTITUSJONS RETNINGSLINJER

NSD legger til grunn at behandlingen oppfyller kravene i personvernforordningen om riktighet (art. 5.1 d), integritet og konfidensialitet

(art. 5.1. f) og sikkerhet (art. 32).

For å forsikre dere om at kravene oppfylles, må dere følge interne retningslinjer og eventuelt rådføre dere med behandlingsansvarlig

institusjon.

OPPFØLGING AV PROSJEKTET

NSD vil følge opp underveis (hvert annet år) og ved planlagt avslutning for å avklare om behandlingen av personopplysningene er

avsluttet/pågår i tråd med den behandlingen som er dokumentert.

Lykke til med prosjektet!

Kontaktperson hos NSD: Mathilde Hansen

Tlf. Personverntjenester: 55 58 21 17 (tast 1)

06.08.2024, 08:50

Vurdering av behandling av personopplysninger - Ref. 968634

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Appendix III

Participant informations

Appendix IV

Declaration of confidentiality for work related to the FUEL project

Declaration of confidentiality for work related to the

FUEL project

I understand

• that during this project I will come across conditions that unauthorized persons should not be

aware of

• that I work with very sensitive data

I commit to:

• to maintain absolute silence towards unauthorized persons about matters that I become aware

of during my work in this project. This applies in particular to knowledge of the project's

participants, partners and internal affairs. The duty of confidentiality does not include

professional experiences from the work

• to act with the utmost care for matters regarding the project's correspondence, contracts,

agreements, etc., so that unauthorized persons do not become aware of the content.

• to act with the utmost care and always keep documents in such a way that no unauthorized

persons has access to or knowledge of them.

I am aware of:

• that violation of the declaration of confidentiality may provide grounds for

resignation/dismissal from the project. Furthermore, data collected in this project may not

be used for master's theses/other publications.

• that violation of the declaration of confidentiality may provide grounds for data collected in

connection with the project not to be used for master's theses/other publications.

• that violation of the declaration of confidentiality, with the mentioned restrictions, also

applies after the project is ended.

Reference is otherwise made to sections §§ 28 and 29 of the Marketing Control Act and sections §§

207 and 208 of the Criminal Law.

Place and date:___________________________________________

Signature:___________________________________________

Corres pon da n ce

: Monica K Torstveit, professor, faculty of Health and Sport Science;

monica.k.torstveit@uia.no. Tlf: 3814 183

Appendix V

Athlete’s journal

FUEL ID:

Age: Height: Weight: BMI:

Food intolerance /allergies:

Current sport:

Level of competition:

Training hours/month:

Dietary supplements:

Injuries

Have you had absences from your training, or participation in competitions during the last year due to injuries?

No, not at all Yes, once or twice Yes, three or four times Yes, five times or more

If yes, for how many days absence from training or participation in competition due to injuries have you had in the last year?

1-7 days

8-14 days

15-

ays

If yes, what kind of injuries have you had in the last year?

Comments or further information regarding injuries:

General information and LEAF-Q summary

Gastro-intestinal function

Do you feel gaseous or bloated in the abdomen, also when you do not have your period?

Yes, several times a day Yes, several times a week

Yes, once or twice a week or more seldom

Rarely or never

Do you get cramps or stomachache which cannot be related to your menstruation?

Yes, several times a day Yes, several times a week

Yes, once, or twice a week or more seldom

Rarely or never

How often do you have bowel movements on average?

Several times a day Once a day Every second day Twice a week

Once a week or more rarely

How would you describe your normal stool?

Normal (soft)

Diarrhoea-like (watery)

Hard and dry

Comments regarding gastrointestinal function:

Menstrual function

How old were when you had your first period?

11 years or younger 12-14 years 15 years or older I don’t remember

I have never menstruated

Did your first menstruation come naturally (by itself)?

Yes No I don’t remember

If no, what kind of treatment was used to start your menstrual cycle?

Hormonal treatment Weight gain Reduced amount of exercise Other

Do you have normal menstruation?

Yes

I don’t know

If yes, when was your last period?

0-4 weeks ago

nth

ago

3-4 months ago

nths

o o

yes

you

egula

(

Eve

ay)

Yes, most of the time No, mostly not

If yes, for how many days do you normally bleed?

3-4 days

7-8 days

9 days or more

If yes, have you ever had problems with heavy menstrual bleeding?

Yes No

any

have

you

ing

yea

12 or more 9-11 6-8 3-5 0-2

When did you have your last period?

0-4 weeks ago 2

nth

o 6 months ago

I am pregnant/I am breastfeeding and therefore do not menstruate I am using hormonal contraceptives

Have your periods ever stopped for 3 consecutive months or longer (besides pregnancy/breastfeeding)?

No, never Yes, it has happened before Yes, that’s the situation now

Do you experience that your menstruation changes when you increase your exercise intensity, frequency or duration?

Yes No

How?

I bleed less I bleed fewer days My menstruation stops I bleed more I bleed more days

ATHLETE GUIDELINE TARGETS

Overall goal:

Athlete practices

Action 1

Action 2

Action 3

FUEL

consultation

Date

and

time

Actual duration of

the consultation

Athlete goal(s) for

next consultation

E.g. recovery meal after

training sessions, include

sports drink during training

or plan one or more actions

according to the FUEL

decision flow diagram (see

document A)

Comments

Appendix VI

FUEL decision tree

Appendix VII

Statements used to assess sports nutrition knowledge

Statements used to assess sports nutrition knowledge (correct

answers) from the telephone interview and results from FUEL

and CON expressed as % of correct answers

FUEL

CON

Statements (correct answer)

Week 0

Week 17

Week 0

Week 17

I should eat the same types of food and amounts every day (false)

83%

100%

My vitamin- and mineral needs are so high that it is hard to fulfill

through ordinary food (false)

72%

93%

70%

80%

The absence of menstruation for longer or short periods is due to a

high training load per se (false)

31%

59%

20%

An athlete who is amenorrheic have the potential to regain her period

through eating more food (true)

83%

100%

50%

80%

It is smart for me to train fasted (false)

76%

97%

80%

90%

Protein supplements contribute to a better protein synthesis than

protein from ordinary food (false)

93%

100%

90%

80%

Athletes who are amenorrheic often have a lower resting metabolic

rate than those who are eumenorrheic (true)

31%

83%

20%

30%

I should avoid eating saturated fat (false)

83%

86%

30%

10%

I should avoid eating food and drink with added sugar like cakes and

other treats (false)

83%

100%

20%

Endurance training does not increase my daily need for protein (false)

83%

90%

I should choose low fat foods to maintain a low weight (false)

93%

100%

90%

100%

I should supplement with vitamins and minerals to make sure I get

enough (false)

41%

79%

80%

60%

I should consume carbohydrates on training sessions and

competitions lasting >1,5 hours (true)

100%

For me there is no need to eat a recovery meal (false)

97%

100%

90%

100%

It is difficult to get enough protein from ordinary food (false)

86%

100%

60%

80%

Whole wheat products are what I should choose as my main

carbohydrate source (false)

35%

83%

20%

10%

I need more iron than other female athletes who are not endurance

athletes (true)

76%

79%

60%

70%

A high dietary fiber content may be the reason for an insufficient

energy intake (true)

41%

90%

40%

50%

As a female endurance athlete, I should maintain the same body

weight throughout the year (false)

86%

97%

80%

Fruit and vegetables are sufficient snacking meals for me (false)

48%

97%c

20%

10%

Total number of correct answers

14.3 ± 2.6

18.3 ± 1.5

12.1 ± 2.6

12.6 ± 2.2

For the FUEL intervention group, post data for the telephone interview were missing for two

participants. For the control group, predata for the telephone interview were missing for two

participants and pre- and postdata were missing for three participants. Abbreviations: FUEL: the

FUEL intervention group, CON: control group.

Appendix VIII

Questions concerning sports nutrition related behavior

Questions and scoring for sports nutrition related behavior

1) How often have you consumed carbohydrate containing drink/food during training

sessions lasting more than 1 hour?

Never = 0

Sometimes = 1

Often = 2

Always = 3

2) Have you eaten breakfast before an early morning session?

Never = 0

Sometimes = 1

Often = 2

Always = 3

3) How often have you eaten a snack or meal right after a training session (within 30

min)?

Never = 0

Sometimes = 1

Often = 2

Always = 3

4) How often have you eaten carbohydrate-rich foods (for example bread, rice, pasta,

potatoes, or cereal/oats)?

<1 time per day = 0

1 time per day = 1

2-3 times per day = 2

4-5 times per day = 3

≥ 6 times per day = 4

5) Have you practiced nutrition strategies before an important competition? (e.g. do

you test your pre-race meal and mid-race fluids)?

Never = 0

Sometimes = 1

Often = 2

Always = 3

Not relevant = missing

6) Have you had routines that allows you to increase your carbohydrate stores before

long-lasting competitions (> 90 min)?

Never = 0

Sometimes = 1

Often = 2

Always = 3

Not relevant = missing

7) Have you changed your energy intake when you have increased your training load?

When I have trained more, I have eaten less = 0

I have eaten the same amounts regardless of how much I have trained = 0

When I have trained more, I have eaten more = 3

Before an important training session or competition, I have tried to…

8) increase my carbohydrate intake = 3

9) limit my fiber intake = 2

10) increase my fluid intake = 3

The total score ranged from 0–30, where 30 indicated the best fulfillment of the sports

nutrition recommendations. As some of the questions could be regarded as irrelevant

for some athletes, a “not relevant” box was applied for two of the questions, and the

final score was then divided by the answers available, making 3.0 the highest possible

global score. The scoring system was developed in the context of what is considered

the optimal approach for athletes with symptoms of LEA.

Appendix IX

Semi-structured interview guide

Interview guide, FUEL participants

The questions in the following interview guide will not be handed to the participants

beforehand but the participants will in the information letter get a written overview of the

topics being addressed. The main questions are asked to all participants. Supplementary

questions are asked when needed.

********************************************************************

INTRODUCTION: Welcome. I would like to hear about your experiences in the FUEL project,

before, during, and after your participation in the FUEL project.

• First, what made you participate in the FUEL project?

o How old are you?

o For how long have you been a competitive athlete?

o What are your ambitions as a competitive athlete?

o What experiences did you have with sports nutrition before your participation in the

FUEL project?

▪ Have you ever tried to adjust your energy availability?

• Now we have talked about your ambitions and your previous experiences with sports

nutrition. Can you please describe you experiences with your participation in the FUEL

intervention?

• You got access to 16 digital lectures, how were your experiences with these lectures?

o What do you think about the content of the digital lectures?

▪ How would you describe the level of difficulty of the content in the digital

lectures?

▪ Did you experience the course as a natural build-up during the 16 weeks?

o Were there any videos you liked better/learned more from than others?

▪ (X)

RED-S

▪ (X) Macro nutrients

▪ (X) Performance nutrition

▪ (X) Body composition / menstruation

o What do you think about the frequency of the digital lectures (every week) and

the duration of the digital lectures (20-40 minutes)?

o Do you think there was anything lacking in the digital lectures?

Questions / keywords that start with (X) should not be used during the interview. They are only a control for

the interviewer.

o Do you want to add anything else regarding your experiences from the digital

lectures?

• You were also offered 8 individual conversations with a sports nutritionist. How did

you experience these conversations?

o How many conversations were conducted between you and your sports

nutritionist?

o How would you describe these conversations?

▪ How would you describe the content of these conversations?

▪ How would you describe the quality of the conversations between you

and your sport nutritionist?

o How would you describe your relationship with your sports nutritionist?

o How did you experience having your personal sports nutritionist?

o What do you think of the duration of the conversations (every-other-week) and

the duration of each conversation (30-60 minutes)?

o What do you think about the conversations being held on Zoom instead of

physical meetings?

o Do you think anything was lacking in the sports nutrition conversations?

o Do you want to add anything else regarding your experiences from the sports

nutrition conversations?

• What do you think about the combination of 16 digital lectures and 8 individual

conversations in the FUEL intervention?

o Do you consider both parts as equally important?

o What do you think about the length of the entire FUEL intervention (16 weeks)?

o How did you experience your own motivation to participate in the FUEL

intervention during the 16 weeks?

o Did your motivation change during the FUEL intervention?

▪ (X): want to, wish, should, must, do not want to

o Did you experience that there was someone in your environment/context

who has been facilitative with respect to your learning experience in the FUEL

project?

▪ Did you experience that there was someone in your environment/context

who has been debilitative with respect to your learning experience in the

FUEL project?

▪ (X) environment = training

▪ (X) Context = family/friends/school/job etc.

• Do you think that the content of the FUEL intervention is sufficient for changing

energy availability (with respect to behavioral change)?

▪ (X) Energy expenditure (training and other physical activity)

▪ (X) Energy intake

• Have you made any specific changes to your diet or exercise since you joined the

FUEL project?

If you had the opportunity, is there anything you would like to change (i.e., content) in the

FUEL intervention?

(X): Is there any intervention other than teaching and personal conversations, which

would be more useful for changing energy availability in athletes?

(X): Change of physical environment in the sports club, training of coaches or other

type of intervention?

Is there anything else you would like to tell me?

If there is anything you come up with after the interview, you are welcome to contact me.

Finally, let us take a summary of what you have said, so I can check that I have understood

you correctly.

Appendix X

Test protocol for the FUEL study - Planned before the COVID-19 pandemic

Test protocol for the FUEL study

- Planned before the COVID-19 pandemic

Participants will arrive at the laboratory at 7 am in a fasted state. This will be followed by a four-day dietary and activity record. Abbreviations:

DXA: dual-energy x-ray absorptiometry; RMR: resting metabolic rate.

RMR will be measured according to the protocol described by Torstveit et al. (2018). Within-day Energy Deficiency and Metabolic Perturbation

in Male Endurance Athletes. Int. J. Sport Nutr. Exerc. Metab. 1–28. Performance tests will be conducted according to the protocol described by

Tornberg et al (2017): Reduced Neuromuscular Performance in Amenorrheic Elite Endurance Athletes. Med. Sci. Sports Exerc. 49, 2478–2485.

Pregnancy test,

height, body

weight, and

DXA scan

30 min RMR

measurement

Blood pressure

and blood

sampling

Standardized

meal

Instructions

and

questionnaires

4 min time trial

Reaction time

(BodyLab)

Knee muscular

strentgh, knee

muscular endurance

(Biodex)

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Development and Evaluation of the FUEL Program PDF Free Download

Development and Evaluation of the FUEL Program PDF free Download. Think more deeply and widely.

Uploaded by campos_shelley on 4/18/2026

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