THE EFFECT OF SLEEP LATENCY, DURATION, AND QUALITY IN ADOLESCENTS WHEN ALTERING NIGHTTIME SMARTPHONE AND SOCIAL MEDIA USE PDF Free Download
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THE EFFECT OF SLEEP LATENCY, DURATION, AND QUALITY IN
ADOLESCENTS WHEN ALTERING NIGHTTIME SMARTPHONE AND SOCIAL
MEDIA USE
by
Ryan R. May
Liberty University
A Dissertation Proposal Presented in Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Liberty University
June, 2025
ii
THE EFFECT OF SLEEP LATENCY, DURATION, AND QUALITY IN
ADOLESCENTS WHEN ALTERING NIGHTTIME SMARTPHONE AND SOCIAL
MEDIA USE
by
Ryan R. May
Liberty University
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Liberty University
June, 2025
APPROVED BY:
________________________________
Angela Rathkamp, Ph.D., Committee Chair
________________________________
Laura Beiler, Ph.D., Committee Member
iii
ABSTRACT
Smartphones and social media are quickly becoming standard technological tools for
connection and socialization within the modern-day adolescent population. Inversely,
sleep for adolescents/young adults is decreasing while sleep problems are rising. This
research trend is prompting researchers to investigate how restricting or eliminating
smartphone/social media use benefits sleep health. The current study hypothesized that
when adolescents/ young adults aged 18-21 eliminate smartphone/social media usage one
hour before bedtime there would be a significantly lower number of nighttime
awakenings, a reduction in sleep onset, and a higher number of minutes slept for
participants in the experimental group when compared to participants in the control group
with unlimited smartphone/social media access. The study obtained 106 research
participants (n=54 experimental group, n=52 control group) aged 18-21 that engaged in
the weeklong study. The study showed the experimental group fell asleep 11.82 minutes
faster than the control group, sleep latency reductions of 16.15 for the participants in the
experimental group, and the experimental group increased their total number of minutes
slept per night by 16.46 total minutes. Moreover, pre and post PSQI and PSAS scores in
the experimental group revealed a statistically significant large effect thus indicating that
stopping smartphone use one hour before bedtime significantly improved one’s perceived
sleep quality and significantly improved one’s perceived problems falling asleep. Finally,
both the experimental and control groups reported a decrease in nighttime awakenings.
Keywords: smartphone, social media, adolescents, sleep duration, sleep quality,
sleep latency, smartphone use and sleep, social media usage and sleep
iv
© 2024
Ryan R. May
ALL RIGHTS RESERVED
v
Dedication
This dissertation is first and foremost dedicated to my Lord and Savior Jesus Christ. God
has made this work possible through the leadership, sustaining power, and strength given
abundantly through His Holy Spirit. Secondly, I dedicate this project to my family who
have loved, supported, and encouraged me throughout the years.
vi
Acknowledgments
First, I would like to acknowledge my wife. Your steadfast love and motivation made this
dissertation process possible, and I could not have done it without you. The kind words
spoken, the inspiration given, the patience through long hours of study, and the ongoing
prayers have made this work possible. I would also like to thank and acknowledge all my
work colleagues, Liberty University professors, and friends for teaching me through your
many examples, giving me gifts of knowledge, and bestowing upon me words of wisdom
that have transformed me into the student, husband, father, friend, and researcher I am
today.
vii
TABLE OF CONTENTS
ABSTRACT .................................................................................................................... iii
Dedication ........................................................................................................................ v
Acknowledgments ........................................................................................................... vi
List of Tables ................................................................................................................... x
List of Figures ................................................................................................................. xi
CHAPTER 1: INTRODUCTION TO THE STUDY ....................................................... 1
Introduction .......................................................................................................... 1
Background ........................................................................................................... 1
Problem Statement ............................................................................................... 6
Purpose of the Study ............................................................................................. 8
Research Questions and Hypotheses .................................................................... 8
Assumptions and Limitations of the Study ........................................................... 9
Definition of Terms ............................................................................................ 13
Significance of the Study .................................................................................... 16
Summary ............................................................................................................. 17
CHAPTER 2: LITERATURE REVIEW ....................................................................... 19
Overview ............................................................................................................ 19
Description of Research Strategy ....................................................................... 20
Review of Literature .......................................................................................... 22
Biblical Foundations of the Study ...................................................................... 49
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Summary ............................................................................................................ 58
CHAPTER 3: RESEARCH METHOD ......................................................................... 61
Overview ............................................................................................................ 61
Research Questions and Hypotheses ................................................................. 61
Research Design ................................................................................................. 63
Participants ......................................................................................................... 64
Study Procedures ............................................................................................... 65
Instrumentation and Measurement ..................................................................... 67
Operationalization of Variables ......................................................................... 69
Data Analysis ..................................................................................................... 70
Delimitations, Assumptions, and Limitations .................................................... 71
Summary ............................................................................................................. 73
CHAPTER 4: RESULTS ............................................................................................... 75
Overview ............................................................................................................ 75
Descriptive Results ............................................................................................. 77
Study Findings .................................................................................................... 79
Summary ............................................................................................................. 99
CHAPTER 5: DISCUSSION ....................................................................................... 101
Overview .......................................................................................................... 101
Summary of Findings ....................................................................................... 101
Discussion of Findings ..................................................................................... 103
Implications ...................................................................................................... 108
Limitations ....................................................................................................... 109
ix
Recommendations for Future Research ........................................................... 110
Summary .......................................................................................................... 112
REFERENCES ............................................................................................................ 114
APPENDIX A: Information Sheet ............................................................................... 137
APPENDIX B: Demographic Information .................................................................. 140
APPENDIX C: The Pittsburgh Sleep Quality Index (PSQI) ........................................ 141
APPENDIX D: The Pre-sleep Arousal Scale (PSAS) .................................................. 145
x
List of Tables
Table 1 ......................................................................................................................... 147
Table 2 ......................................................................................................................... 148
Table 3 ......................................................................................................................... 150
Table 4 ......................................................................................................................... 151
Table 5 ......................................................................................................................... 152
Table 6 ......................................................................................................................... 153
Table 7 ......................................................................................................................... 154
Table 8 ......................................................................................................................... 155
Table 9 ......................................................................................................................... 156
Table 10 ....................................................................................................................... 157
Table 11 ....................................................................................................................... 158
xi
List of Figures
Figure 1 ........................................................................................................................ 159
Figure 2 ........................................................................................................................ 160
Figure 3 ........................................................................................................................ 161
Figure 4 ........................................................................................................................ 162
Figure 5 ........................................................................................................................ 163
Figure 6 ........................................................................................................................ 164
Figure 7 ........................................................................................................................ 165
Figure 8 ........................................................................................................................ 166
Figure 9 ........................................................................................................................ 167
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CHAPTER 1: INTRODUCTION TO THE STUDY
Introduction
Smartphone/social media use in the adolescent cohort appears to be linked to the
number of times adolescents wake during the night, the length of time it takes to fall
asleep, and the overall number of minutes slept per night (Alotaibi et al., 2022; He et al.,
2020 Höhn et al., 2021; Przepiorka & Blachnio, 2020; Scott et al., 2019; Tandon et al.,
2020; van den Eijnden et al., 2021 van Velthoven et al., 2018). Moreover, research in
late-stage adolescents/young adults has shown sleep duration rates have been drastically
falling in recent years well below the recommended hours of sleep per 24 hours
(Hirshkowitz et al., 2015; Kumar & Pati, 2021; MacCárthaigh et al., 2020; Sung et al.,
2020; Tereshchenko et al., 2021). Research has also highlighted that parents/caregivers
appear to have the greatest direct/ indirect influence over smartphone/social media use
within the adolescent population (Godsell & White, 2019; Hefner et al., 2019; van den
Eijnden et al., 2021). As a result of the increasing negative health impacts associated with
lack of sleep, researchers are beginning to investigate the effects of limiting smart phone
use on sleep prior to bedtime (Alotaibi et al., 2022; Bartel et al., 2018; He et al., 2020;
Harris et al., 2015; Kheirinejad et al., 2022; van den Eijnden et al., 2021).
Background
Smartphones/Social Media
Smartphones have begun to transform the way the adolescent community
interacts, communicates, and socially connects with one another as it is estimated that
over 95 percent of adolescents in the United States have access to a smartphone device
(Gumport et al., 2021; Pirdehghan et al., 2021; Thomée, 2018). Consequently, social
2
media usage also continues to increase with an estimated 3.8 billion users worldwide in
2021, which is predicted to increase to over six billion individual users by the year 2027
due to smartphone accessibility, internet availability, technology advances due to the
COVID-19 pandemic, and the increases in online learning platforms (Campbell et al.,
2021; Chung-Ying et al., 2021; Dixon, 2022; Raudsepp, 2019). Furthermore, smartphone
access is directly contributing to social media transforming into one of the leading social
activities for many late-stage adolescents/young adults with platforms such as Facebook,
Instagram, Snapchat, Twitter (X), WhatsApp, YouTube, and TikTok (Hefner et al., 2019;
Kumar & Pati, 2021; Palmer et al., 2020).
Sleep Recommendations and Concerns
Feldman (2022) has defined sleep as a modified conscious state that leaves one
unaware of one’s surroundings in the physical world, which is essential to human health.
Moreover, researchers have reviewed sleep data and identified the optimal number of
sleep minutes one must obtain to achieve normal functioning and/or alertness
(Hirshkowitz et al., 2015; Paruthi et al., 2016). The National Sleep Foundation (NSF) and
the American Academy of Sleep Medicine (AASM) both purposed the ideal sleep target
for adolescents aged 18-25 should be between seven and nine hours of sleep per night
(Hirshkowitz et al., 2015; Paruthi et al., 2016).
There appears to be growing empirical support displaying a significant decline in
late-stage adolescent/young adult sleep in recent years with studies indicating hours slept
per night to be well below seven hours with some research suggesting adolescent sleep
has declined to below six hours of sleep per night in many instances (Amez et al., 2020;
Hirshkowitz et al., 2015; Kumar & Pati, 2021; Sung et al., 2020; Tereshchenko et al.,
3
2021). Scientists admit there are numerous unknowns related to the rising sleep debt
within the adolescent cohort; however, research has linked a lack of sleep to the
aforementioned increases in adverse effects for late-stage adolescents/young adults
(Hughes & Burke, 2018; Kumar et al., 2019; Li et al., 2019; Palmer et al., 2022; Rafique
et al., 2020).
Adverse Effects of Smartphones/Social Media
The scientific community is also beginning to link smartphone/social media
consumption to sleep disturbances and decreases in sleep duration citing internet over
usage/addiction, fear of missing out (FOMO), overstimulation through multitasking, and
blue light emissions (Akbari et al., 2021; Amez et al., 2020; Azhari et al., 2022; Barry &
Wong, 2020; Bowler & Bourke, 2019; Chaudhury et al., 2019; Godsell & White, 2019;
Kater & Schlarb, 2020; Padilla-Walker et al., 2018; Rozgonjuk et al., 2020; Šmotek et al.,
2020; van der Schuur et al, 2018). Additionally, research also suggests parents continue
to hold the greatest influence on smartphone/social media consumption in the adolescent
cohort, which has direct impacts on the adolescents’ overall sleep habits (Godsell &
White, 2019; Hefner et al., 2019; van den Eijnden et al., 2021).
Previous empirical studies have displayed statistically significant correlations
between adolescent sleep deficits and elevated rates of depressive symptoms, increases in
anxiety, more frequent behavioral problems, increases to suicidal thoughts/risks, lower
cognitive functionality, drops in grade point averages, increases to obesity rates and
diabetes, and substance abuse (Amez et al., 2020; Bersani,et al, 2022; Bozzola et al.,
2022; Chen et al., 2022; Collis et al. 2022; de Sousa et al., 2020; Evers et al., 2020; Flynn
et al., 20221; Fuligni et al., 2017; Giunchiglia, 2018; Glenn et al., 2021; Guerrero et al.,
4
2019; Guinta & John, 2018; Huang & Zhao, 2020; Li et al., 2019; Lin & Zhou, 2022;
Malaeb et al., 2020; Marino et al., 2020; Omede & Akintunde, 2023 Palmer et al., 2018;
Quintana-Orts et al., 2020; Sampasa-Kanyinga et al., 2022; Song et al., 2020; Yang et al.,
2019). Moreover, smartphone/social media usage and adolescent sleep problems have
also been linked to increases in school bullying, cyber bullying behaviors, and decreases
in adolescent self -esteem (Bègue et al., 2022; Kutok et al., 2021; López-Gil et al., 2022;
Peng et al., 2019; Steinsbekk et al., 202; Yang et al., 2020).
Spiritual Implications of Smartphones/Social Media
Smartphones/social media can also act as a source of distraction providing instant
news, connections to friends, alerts, videos, games, and frequent disruptions to one’s
attempts at spiritual connection and scripture reading (Chow, 2022; Omede & Akintunde,
2023; Shim, 2021 Uecker & McClure, 2022). Jesus declared, “The eye is the lamp of the
body. So, if your eye is healthy, your whole body will be full of light, but if your eye is
bad, your whole body will be full of darkness” (English Standard Version Bible,
2001/2016, Matthew 6: 22-23). Therefore, one must guard oneself from what is seen and
consumed as smartphone/social media use creates anonymity, which in turn has led to a
decline in moral and biblically aligned religious attitudes, behaviors, and actions (Chow,
2022; Omede & Akintunde, 2023). Individuals are also becoming addicted to
smartphones/social media, and the Bible instructs Christians to be wary of items that may
control one’s feelings, thoughts, and behaviors for the Bible explains, “All things are
lawful for me, but not all things are helpful. “All things are lawful for me, but I will not
be dominated by anything” (English Standard Version Bible, 2001/2016, 1 Corinthians
6:12; Shim, 2021).
5
Parents are tasked with the responsibility of protecting their children, guiding
them, and instructing them in the ways of God for Proverbs declares for parents to,
“Train up a child in the way he should go; even when he is old he will not depart from it”
(English Standard Version Bible, 2001/2016, Proverbs 22:6; Omede & Akintunde, 2023).
Research appears to show that parents/caregivers who embrace a more authoritative
parenting style increases the chances of passing along one’s belief system to one’s
child/children (Goodman & Dyer, 2019). Nevertheless, Uecker and McClure (2022)
found that increased use of smartphones/social media appeared to be linked to reductions
in religious commitment. Moreover, there was a negative correlation between religion
and social media with the negative correlation stronger for adolescents whose parents
were more religious with social media significantly reducing the adolescents’ reading of
scripture (Uecker & McClure, 2022).
Sleep Latency, Duration, and Disturbances
Empirical research into duration of sleep, sleep disturbances, and sleep latency in
older teenagers continue to be sparse with the overwhelming majority of the studies
relying solely on self-report measures (Chung-Ying et al., 2021; Lee et al., 2017;
Raudsepp,2019; Tereshchenko et al., 2021; van den Eijnden et al., 2021;). Currently,
there are a few research endeavors that have found connections between limiting
smartphone/social media use in the bedroom at night and reductions in sleep latency,
awakenings, and increases to sleep duration (Bartel et al., 2018; He et al., 2020; Hughes
& Burke, 2018; Rafique et al., 2020).
Bartel et al. (2018) conducted quasi-experimental research in South Australia and
discovered that the participants who stopped smartphone operation one hour twenty
6
minutes prior to bedtime decreased sleep onset by 17 minutes when compared to previous
baseline sleep trends. The adolescents also reported an average increase of 21 minutes of
sleep per night during the intervention week, which was a statistically significant increase
compared to the reported baseline sleep numbers (Bartel et al., 2018). He et al. (2020)
expanded on this previous research by conducting a randomized control trial (RCT) with
college undergraduates separating the participants into two groups. First, the researchers
devised a control group with no smartphone limitations prior to bedtime, and then created
an experimental group who were required to stop smartphone use 30 minutes prior to
lights out (He et al., 2020). The researchers discovered significant increases in sleep of 18
minutes per night, an overall decrease in sleep disturbances, and an increase in sleep
onset by 12-minutes in the experimental group when compared to the control group (He
et al., 2020). The experimental group also produced better mood scores than the control
group in these late-stage adolescents/young adults (He et al., 2020).
Problem Statement
The problem is there are significant gaps in the scientific literature investigating
the effects of stopping smartphone/social media usage prior to bedtime on sleep latency,
duration, and awakenings throughout the night. The research has uncovered significant
links between smartphone/social media use and sleep issues due to internet addiction,
FOMO, blue light releases, and increases in brain activity (Akbari et al., 2021; Azhari et
al., 2022; Bowler & Bourke, 2019; Chaudhury et al., 2019; van der Schuur et al, 2018).
Researchers have also linked smartphone/social media use and sleep problems to a wide
range of challenges within the late-stage adolescent/young adult cohort such as increased
problems with emotional/psychological health, physical health, academic scores,
7
cognition, as well as decreases in religious connection and morality (Bersani,et al, 2022;
Bozzola et al., 2022; de Sousa et al., 2020; Evers et al., 2020; Flynn et al., 2021; Fuligni
et al., 2017; Giunchiglia, 2018; Guerrero et al., 2019; Huang & Zhao, 2020; Omede &
Akintunde, 2023; Quintana-Orts et al., 2020; Shim, 2021; Uecker & McClure, 2022;
Yang et al., 2019). Additionally, much of empirical research on smartphone/social media
usage and sleep has relied solely on adolescent self-report surveys without obtaining
verifiable smartphone/social media consumption data (Azhari et al., 2022; Collis et al.,
2022; Lee et al., 2017; Przepiorka & Blachnio, 2020: Rafique et al., 2020). For example,
Lee et al. (2017) uncovered that late-stage adolescent/young adult participants in their
study tended to underrate one’s smartphone/social media consumption in the self-report
data obtained by an estimated 40 % when contrasted with the smartphone/social media
utilization reports.
Research into eliminating smartphone/social media usage prior to bed and its
effects on sleep remains sparse with only three such studies identified (Bartel et al., 2018;
Harris et al., 2015; He et al., 2020). Harris et al. (2015) explored eliminating smartphone
use after 10 o’clock at night in adolescent athletes and uncovered no significant effects on
sleep, athlete production, cognitive ability, or mood. Nevertheless, it is important to note
that the researchers used only self-report data, and admitted the study was conducted with
teenage athletes who may have already had set bedtime structure (Harris et al., 2015). In
addition, Bartel et al. (2018) discovered when teenagers stopped smartphone/social media
consumption 80 minutes prior to their normal bedtime the adolescents sleep latency
decreased by an average of 17 minutes while sleep increased an average of 21 minutes
when compared to standard smartphone/social media consumption. This research
8
endeavor was also dependent on adolescent self-report surveys with no way to verify
actual smartphone/social media use data, no parental participation, and a low sample size
(Bartel et al., 2018). He et al. (2020) orchestrated kindred research on eliminating
smartphone/social media use 30 minutes prior to bedtime in late-stage adolescents/young
adults and found similar results; nonetheless, the participants in this study were college
students in a controlled clinical environment, and this study also had a low sample size.
Purpose of the Study
The purpose of this quantitative quasi-experimental research study will be to
uncover the impacts on sleep when smartphone/social media use is stopped by
participants one hour prior to bedtime for late-stage adolescents/young adults aged 18-21.
Research Question(s) and Hypotheses
RQ1: How will stopping smartphone/social media use one hour prior to bedtime
affect minutes of sleep for late-stage adolescents/young adults aged 18-21?
RQ 2: How will stopping smartphone/social media use one hour prior to bedtime
affect sleep latency for late-stage adolescents/young adults aged 18-21?
RQ 3: How will stopping smartphone/social media use one hour prior to bedtime
affect the number of awakenings during the night for late-stage adolescents/young
adults aged 18-21?
Alternative Hypothesis.
Ha1 = There will be a significantly lower number of awakenings during the night when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
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Ha2 =There will be a significant reduction in the minutes it takes to fall asleep when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
Ha3 =There will be a significantly higher number of minutes slept when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
Null Hypothesis.
Ho 1= There will be no significant differences in the number of recorded awakenings
during the night in late-stage adolescents/young adults when smartphone/social media
usage is stopped one hour before bedtime.
Ho2 =There will be no significant reductions in the minutes it takes to fall asleep in late-
stage adolescents/young adults when smartphone/social media usage is stopped one hour
before bedtime.
Ho 3=There will be no significant increases in minutes slept per night in late-stage
adolescents/young adults when smartphone/social media usage is stopped one hour
before bedtime.
Assumptions and Limitations of the Study
There are numerous potential challenges in conducting the present research study.
For instance, one possible barrier is obtaining enough participants for the study as
participants are expected to consent/engage in the research project for a 7-day period
whereby they are expected to stop smartphone/social media access one hour before
bedtime in the experimental group. Therefore, the age range for the late-stage
adolescents/young adults in this study is 18-21 years of age, which may capture both high
10
school and college students. It is important to note that in Australia many late-stage
adolescents stop school attendance after their sophomore year of high school as Australia
gives students the option to either continue to year 11 and 12 or stop school and obtain a
job, which may also deter some older adolescents from participating in the research study
due to work demands.
There are also a number of likely limitations to the research that go hand-in-hand
with some of the aforementioned challenges such as obtaining a sufficient sample size.
Moreover, the research sample contains adult participants from rural Australia, which
potentially limits the demographic sample and the generalizability of the research results.
In addition, the dissertation research design is quasi-experimental, which is also a
limiting factor with data being collected only from participants that have knowingly
decided to engage in the smartphone/social media research eliminating adult participants
who may struggle removing devices, lack the desire to limit smartphone/social media
operation, or see no need to increase the sleep duration, shorten sleep latency, or reduce
their nighttime awakenings. Furthermore, when conducting research of this manner there
could be expectation bias from the late-stage adolescents/young adults engaging in the
research to report changes in their sleep that are expected by the researcher.
Theoretical Foundations of the Study
Sleep has been defined as an alternative state of consciousness that is pertinent to
emotional, mental, and physical health, which leaves a person completely oblivious to the
physical world (Chokroverty, 2017; Feldman, 2022). Although essential to human health,
sleep remains a biological mystery to science with no one sleep theory adequately
encompassing and/or explaining the overall purpose of sleep (Chokroverty, 2017;
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Feldman, 2022; Freiberg, 2020; Worley, 2018). Essentially scientists either view and
investigate sleep as a process of repair and restoration, an evolutionary process, a way to
consolidate information, a way to clean up the brain, or a combination of one of these
four ways of conceptualization (Chokroverty, 2017; Feldman, 2022; Freiberg, 2020;
Worley, 2018). This research study is not grounded in one theory on sleep as no one
theory is complete; nevertheless, the theory that best aligns with the research being
conducted is the repair and restoration theory of sleep (Freiberg, 2020).
The repair and restoration theory purposes that sleep gives the brain a chance to
repair, restore, process, and perform clean-up duties to promote heathy individual
functioning (Cao et al., 2020; Freiberg, 2020; Xie et al., 2013). Prolonged periods of time
without sleep can even lead to physical death in humans with scientists hypothesizing
that this occurs due to the brains inability to clear damaged neurons, interstitial proteins,
β-amyloid, and reorganize daytime experiences, memories, experiences, and learning
opportunities obtained while awake (Cao et al., 2020; Freiberg, 2020; Noya et al., 2019;
Xie et al., 2013). Moreover, repair and rescaling take place within the brain while asleep
promoting neuroplasticity with some studies suggesting that during periods of sleep
deprivation synapses rejuvenation can be halted up to 98% (Brüning et al., 2019; Cao et
al., 2020; Noya et al., 2019). Therefore, the repair and restoration theory claim that the
brain has essentially two options: to be awake/alert taking in and exchanging information
with the conscious world, or asleep/unalert in clean-up mode whereby the brain is
processing, culling, filing, and reorganizing experiences obtained throughout periods of
wakefulness (Brüning et al., 2019; Cao et al., 2020; Freiberg, 2020; Noya et al., 2019;
Xie et al., 2013).
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The second theoretical framework used in this research study is called the
bioecological approach, which is also known as the social-ecological model or ecological
systems theory developed by Urie Bronfenbrenner (Crawford, 2020; Feldman, 2022;
Navarro & Tudge, 2022). Bronfenbrenner hypothesized that there are a multitude of
factors across five different levels (individual, interpersonal, institutional, community,
and overarching societal law and policy) that influence and contribute to the various
problems experienced by people (Abo-Zena & Rana, 2020; Crawford, 2020; Feldman,
2022; Navarro & Tudge, 2022). The ecological systems theory suggests that an
individual, for example an adolescent, is influenced by each of these major domains with
the interpersonal relationship domain, such as one’s parents, having the greatest influence
on the individual’s thoughts, feelings, and behaviors (Abo-Zena & Rana, 2020;
Crawford, 2020; Feldman, 2022). Nevertheless, when one level of the ecology impacts an
individual, this impact has implications across other, if not all, domains in the ecological
system (Crawford, 2020; Feldman, 2022; Navarro & Tudge, 2022). For instance, if a late-
stage adolescent/young adult alters or stops smartphone/social media use this act not only
impacts the individual domain (adolescent), but the different domains surrounding the
late-stage adolescent/young adult such as the interpersonal (peers) and institutional
(school) systems to some extent (Abo-Zena & Rana, 2020; Bozzola et al., 2022; Godsell
& White, 2019; Hefner et al., 2019; Feldman, 2022; Navarro & Tudge, 2022; Nur et al.,
2021; Padilla-Walker et al., 2018). Although biological factors are considered in the
social-ecological theory, the theory focuses primarily on the ecological factors at the
multiple levels that simultaneously influence and are influenced by the adolescent’s
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development as these systems are seen as all interconnected (Abo-Zena & Rana, 2020;
Crawford, 2020; Feldman, 2022; Navarro & Tudge, 2022).
Definition of Terms
The following is a list of definitions of terms that are used in this study.
Adolescents – Term one is defined as a stage of human development ranging between
childhood and adulthood, which is defined as 13-24 years of age for this research study
(Bartel et al., 2018; Feldman, 2022; Good and Willoughby, 2008).
Anxiety – Term two is defined as an emotional state that involves feelings of
nervousness, fear, hyper alertness, tension, and even an increased heart rate (Keles et al.,
2020; López-Gil et al., 2022; Malaeb et al., 2020; Werneck et al., 2020).
Authoritative Parenting – Term three is defined as a method of parenting that directs
and guides children/adolescents, which is direct, rational, warm, flexible, receptive,
encourages parent/child discourse, and fosters the autonomy of the child/adolescent in a
developmentally appropriate way facilitating identity formation (Goodman & Dyer,
2019; Lavrič & Naterer, 2020).
Blue Light Emission – Term four is also known as short wavelength light emission, and
is defined as short wave blue light emitted from smartphone, televisions, tablets, and
other light-emitting diode (LED) devices producing wavelengths between 415-455
nanometers (Bowler & Bourke, 2019; Chaudhury et al., 2019; Höhn et al., 2021; Šmotek
et al., 2020).
Cyberbullying – Term five is defined as one individual targeting, harassing, threatening,
or embarrassing another individual through some form of technology including
14
smartphone devices, social media, gaming platforms, and so on (Kutok et al., 2021;
Nagata et al., 2023; Sampasa-Kanyinga et al., 2022).
Depressive Symptoms – Term six is defined as an emotional state producing increased
feelings of hopelessness, sadness, or emptiness, which can produce lower self-esteem,
anger, irritability, insomnia, and/or loss of interest in activities of daily living (Bozzola et
al., 2022; Huang & Zhao, 2020; Li et al., 2019; Malaeb et al., 2020; Marino et al., 2020;
Przepiorka, & Blachnio, 2020; Yang et al., 2019).
Emotional Dysregulation – Term seven is defined as an individual’s inability to control
or mange one’s emotions (Azhari et al., 2022; Bègue et al., 2022; Bersani et al., 2022;
Palmer et al., 2018).
Fear of Missing Out (FOMO) – Term eight refers to extreme anxiety directly linked to
social media overuse where an individual becomes overwhelmed by thoughts connected
to potentially missing messages, videos, or other communications from other members
within one’s social media community (Akbari et al., 2021; Rozgonjuk et al., 2020).
Identity Formation – Term nine refers to the ongoing process whereby individuals
develop who they are as individuals and how they see themselves within society (Sproul,
2018; Halevy & Gross, 2024; Schachter & Ben Hur, 2019).
Internet Use Disorder – Term ten refers to a diagnosis outlined by the American
Psychiatric Association in the Diagnostic and Statistical Manual of Mental Disorders fifth
edition (DSM-5) as an overindulgent, habit-forming, and invasively troublesome
utilization of the internet that could lead to various health problems (Akbari et al., 2021;
Malaeb et al., 2020; Tereshchenko et al., 2021).
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Late-Stage Adolescents/Young Adults – Term eleven is defined as a stage of adolescent
development, which is operationally defined as 18-21 years of age for this research study
(Bartel et al., 2018; Feldman, 2022; Good and Willoughby, 2008).
Nomophobia – Term twelve is defined as fear or anxiety stemming from the lack of
access to a smartphone device (Chung-Ying et al., 2021).
Problematic Smartphone/Social Media Use Disorder – Term thirteen refers to an
excessive, habit-forming, and overly invasive use of smartphones devices and/or social
media that can contribute to health problems (Hughes & Burke, 2018; Kumar & Pati,
2021; Palmer et al., 2018).
Sleep – Term fourteen is defined as an altered state of human awareness making a person
unaware of the material world that is necessarily for overall health and wellbeing, which
has been described as a reversible coma (Chokroverty, 2017; Feldman, 2022; Worley,
2018).
Sleep Diary– Term fifteen is defined as an online platform that is designed to obtain
sleep related information concerning one’s sleep patterns over the two-week research
study (de Alcantara Borba et al, 2020; Short et al., 2017).
Sleep Disturbances – Term sixteen is defined as insomnia, problems falling asleep,
nighttime disturbances, and increased day-time sleepiness (Kater and Schlarb, 2020;
Rafique et al., 2020).
Sleep Duration – Term seventeen is defined as the length of sleep one obtains per night
(He et al., 2020; Hughes & Burke, 2018; Hunt et al., 2018).
Sleep Latency – Term eighteen is defined as the time it takes a person to fall asleep (He
et al., 2020; Hughes & Burke, 2018; Hunt et al., 2018).
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Smartphone – Term nineteen is defined as a handheld mobile device that has intertwined
phone and computer capabilities into one handheld and easy to use device used for phone
calls, text messages, photos, global satellite positioning, typically incorporate touchscreen
technology, internet and social media access, games, music, voice recorders, clock,
calculator, flashlight, and digital wallet (Gumport et al., 2021; Raudsepp, 2019; Ting &
Chen, 2020).
Social Media – Term twenty is defined as a form of communication platform designed
and provided by third-party companies via the internet access enabling social
communication, interactions, and user content sharing and collaboration (Dixon, 2022;
Kumar et al., 2019; Pellegrino et al., 2022).
Suicidal Ideation – Term twenty-one is defined as thoughts or ideas related to the act of
taking one’s own life (Ghaemi, 2020; Kutok et al., 2021; Malaeb et al., 2020; Marino et
al., 2020; Peng et al., 2019; Raudsepp, 2019; Sampasa-Kanyinga et al., 2022).
Significance of the Study
The research has the capability to provide additional evidence supporting previous
research data highlighting the positive effects of limiting smartphone/social media use on
sleep within the adolescent cohort. Moreover, the research study has the possibility to
enhance the scientific community and the adolescents’ understanding of the effects of too
much smartphone/social media use on sleep. Additionally, the study has the promise of
promoting change and even supporting the establishment of smartphone/social media use
warnings, recommendations, and guidelines since Australia is currently exploring social
media restrictions/limits for adolescents. The research also has the ability to promote
smartphone/social media awareness while prompting additional research endeavors into
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both the positive and negative impacts of smartphone/social media use on today’s youth.
Furthermore, the late-stage adolescent/young adult participants may also gain insight into
the implications of their own smartphone/social media usage thus potentially promoting
long-term usage change in this cohort.
Summary
In closing, the current proposed study highlights the recent growth of smart
phone/social media usage in the United States with over 95 percent of American
adolescents having access to smartphones/social media platforms (Dixon, 2022; Feldman,
2022; Giunchiglia et al., 2018; Gumport et al., 2021; Kumar & Pati, 2021; Tandon et al.,
2020; Thomée, 2018). Inversely, the amount of sleep being obtained in the adolescent
cohort is decreasing at an alarming rate leading many in the scientific community to
conclude that these sleep issues within the adolescent population is leading to a public
health crisis (Hughes & Burke, 2018; Kumar & Pati, 2021; Palmer et al., 2018; van den
Eijnden et al., 2021). There is growing evidence linking smartphone/social media
consumption with sleep disturbances that contribute to poor health impacts such as type
two diabetes, obesity, increases in anxiety, depression, loneliness, cyberbullying,
suicidality, and declines in academic performance (Amez et al., 2020; Alotaibi et al.,
2022; Evers et al., 2020; Giunchiglia, 2018; Palmer et al., 2022; Sung et al., 2020).
Current research seeking to access and alter these negative effects of
smartphone/social media operation are scarce with only three known studies that attempt
to eliminate smartphone/social media operation before bedtime in adolescents and late-
stage adolescents/young adults (Bartel et al., 2018; Harris et al., 2015; He et al., 2020).
Therefore, there are numerous gaps in the research pertaining to smartphone/social media
18
use leaving this field completely open to further exploration (Bartel et al., 2018; Harris et
al., 2015; He et al., 2020). This research study attempts to add to the empirical knowledge
surrounding smartphone/social media operation and its effects on sleep length, onset, and
nighttime awakenings in the adolescent population when smartphone/social media use is
stopped one hour prior to bedtime by participants aged 18-21.
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CHAPTER 2: LITERATURE REVIEW
Overview
There are significant gaps in the scientific literature investigating the effects of
stopping smartphone/social media usage prior to bedtime on sleep latency, duration, and
awakenings throughout the night in the late-stage adolescent/young adult population.
Sleep science is an ever changing and evolving domain with scientists continually
attempting to obtain a more thorough understanding of sleep, and what it means to
achieve adequate sleep with many researchers concluding that ample sleep is absolutely
necessary for adolescents of all ages to have healthy development and functioning
(Hirshkowitz et al., 2015; Kumar & Pati, 2021; Marino et al., 2020; Sung et al., 2020;
Tereshchenko et al., 2021; Worley, 2018).
Inversely, insufficient sleep has the potential to contribute to serious health risks
in all adolescents prompting the research community to invest time and resources into
uncovering the potential connections between sleep issues and emotional, physical, and
spiritual health problems (Amez et al., 2020; Evers et al., 2020; Giunchiglia, 2018;
Palmer et al., 2022; Sung et al., 2020; Worley, 2018). Research is starting to link
adolescent smartphone and social media usage to increases in sleep latency, reductions in
the amount of sleep adolescents obtain, and an increase in the number of times
adolescents wake during the night (Hughes & Burke, 2018; Kater & Schlarb, 2020;
Kumar & Pati, 2021; Palmer et al., 2018). Nevertheless, access to smartphones/social
media within the adolescent cohort continues to grow at an unprecedented rate (Campbell
et al., 2021; Chung-Ying et al., 2021; Dixon, 2022; Gumport et al., 2021; Raudsepp,
2019; Thomee, 2018).
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Although researchers are beginning to investigate the potential connections
between smartphone/social media use and increases to sleep issues, investigation into
sleep latency, sleep reduction, and sleep disturbances in the adolescent community
remain alarmingly scant (Chung-Ying et al., 2021; Raudsepp,2019; Tereshchenko et al.,
2021; van den Eijnden et al., 2021). Recent research studies have highlighted a
statistically significant reduction in hours slept by teenagers and late-stage
adolescents/young adults, explored the effects that lack a of sleep have on the entire
adolescent community, and made connections between sleep problems and increases in
smartphone/social media access (Chung-Ying et al., 2021; Hughes & Burke, 2018;
Raudsepp,2019; Tereshchenko et al., 2021; van den Eijnden et al., 2021). However, much
of the data has been obtained using self-report measures without concrete data (Chung-
Ying et al., 2021; Hughes & Burke, 2018; Raudsepp, 2019; Tereshchenko et al., 2021;
van den Eijnden et al., 2021). Nonetheless, issues with falling asleep, sleep deficits, and
sleep disturbances are on the rise globally with many research studies indicating the
adolescent community are sleeping well below the expert’s sleep recommendations
giving rise to reports of barriers in spiritual formation, emotional problems, behavioral
issues, increases in anxiety and depression, more reports of suicidal ideation, falling
academic performance, and even a rise in obesity rates and type two diabetes
(Hirshkowitz et al., 2015; Kumar & Pati, 2021; Paruthi et al., 2016; Sung et al., 2020;
Tereshchenko et al., 2021; Yang et al., 2019).
Description of Search Strategy
The search strategy for the literature review started with keywords such as
adolescents, late-stage adolescents, young adults, smartphones, social media,
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recommended sleep, sleep disturbances, sleep duration, sleep quality, sleep latency, blue
light emissions, depression, anxiety, obesity, cyberbullying, and academics. The search
strategy also included combination searches such as adolescents and smartphones,
adolescents and social media, adolescents and sleep, sleep and smartphone use, sleep
and social media usage, smartphones/social media and anxiety, smartphones/social
media and depression, smartphones/social media and Christianity, and
smartphones/social media and religion.
Data bases explored include Psych Info, CINAHL, EBSCO, google scholar, and
so on accessed online and through the Liberty Online Library. The inclusionary criteria
included articles written or translated into the English language, peer-reviewed journal
articles, relevant topic research, concepts, theories, systemic reviews, research conducted
primarily with adolescents and college-aged students, and all other relevant frameworks.
Additionally, the bulk of the empirical research articles collected (over 80 percent) were
published within the past five years, 2019 to 2024. Exclusionary criteria included non-
peer reviewed articles, blogs, editorials, grey literature, inaccessible full-text documents,
research primarily conducted with adults, and the majority of scientific literature
published before 2019.
The search strategy utilized for the Biblical Foundations section of the literature
review was conducted using a word search for adolescents, children, religion, Bible,
smartphone, social media, faith, faith transmission, spiritual formation, spiritual
development, authoritative parenting, and parenting styles. The Liberty Online Library
and Google Scholar were accessed to find relevant peer-reviewed Bible based journal
articles. Additionally, the websites Open Bible and Bible Gateway were used to search
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aforementioned word topics, related verses, and to cross-reference biblical scriptures
related to the word search topics.
Review of Literature
Smartphone/Social Media Use Trends
Smartphones have been defined as handheld mobile devices that intertwine phone
and computer capabilities into one handheld and easy to use device (Gumport et al.,
2021; Raudsepp, 2019; Ting & Chen, 2020). Smartphones have the ability to make phone
calls, send text messages, take photos, connect to global satellite positioning, typically
incorporate touchscreen technology, provide access to internet and social media sites, and
utilize shortcut applications known as apps (Gumport et al., 2021; Raudsepp, 2019; Ting
& Chen, 2020). Additionally, individuals can access games, music, use the smartphone
devices as a voice recorder, clock, calculator, flashlight, wallet, and smartphones can
even track certain elements of one’s biological health (Gumport et al., 2021; Raudsepp,
2019; Ting & Chen, 2020). These functional elements of smartphones have contributed to
significant increases in usage with almost every teenager and late-stage adolescent/young
adult between 12-21 years of age owning and/or operating a smartphone with an
estimated 95% of American youth having instant access to a smartphone device at home
(Gumport et al., 2021; Ting & Chen, 2020). This smartphone accessibility and usage by
adolescents has exploded in recent years due to the lower costs of smartphones, internet
accessibility, the technology push by the global pandemic, a rise in online peer
communication, and schools adopting online learning and communication (Chung-Ying
et al., 2021; Gumport et al., 2021; Raudsepp, 2019).
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Social media has been described as communication platforms designed and
provided by third-party companies via the internet enabling social communication,
interactions, and user content sharing and collaboration (Dixon, 2022; Kumar et al., 2019;
Pellegrino et al., 2022). Social media has forever changed the way the world interacts
with each other as registered social media users can communicate instantly with other
people right next door or on a global basis by sharing pictures, videos, messages, and
ideas (Dixon, 2022; Kumar et al., 2019; Pellegrino et al., 2022). Consequently, social
media utilization continues to grow and expand at a staggering rate with over 3.8 billion
registered social media users globally in 2021, which is projected to increase to over 6
billion users by 2027 (Dixon, 2022; Pellegrino et al., 2022). Furthermore, smartphone
accessibility coupled with social media popularity has propelled social media sites to
become the dominant social activity for the vast majority of adolescents and late—stage
adolescents/young adults worldwide with platforms such as Facebook, Instagram,
Snapchat, WhatsApp, and TikTok being among the most popular (Hefner et al., 2019;
Kumar et al., 2019; Palmer et al., 2018).
Lee et al. (2017) conducted research and endeavored to collect data on college
student’s smartphone/social media consumption through an application installed on the
participants’ smartphone devices. The researchers obtained 35 college aged participants
for the six-week research study, and the investigation discovered that third-party social
media and instant messenger platforms were the most popular applications accessed by
the participants (Lee et al., 2017). Additionally, the research highlighted major
discrepancies in the actual minutes of smartphone/social media use when compared to the
participants recorded self-report disclosures (Lee et al., 2017). The researchers
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hypothesized that it was entirely possible that theses late-stage adolescents/young adults
accessed the smartphone/social media platforms much more often and for more time than
consciously aware (Lee et al., 2017).
Collis et al. (2022) argued that social media is more than just platforms such as
Twitter, Facebook, Instagram, and Snapchat; rather, social media should be considered
all gaming sites, video sites such as Youtube and Tiktok, and must include vlogs,
messenger apps, and other communication platforms. Collis et al. (2022) in their research
study attempted to restrict smartphone/social media usage with late-stage adolescent/
young adult research participants. The researchers uncovered that when participants
stopped certain social media platform operation the use behavior was not extinguished;
instead, the minutes of social media operation were converted to the same number of
minutes consumed by the late-stage adolescents/young adults in alternative platform
outlets (Collis et al., 2022). Therefore, the researchers highlighted the importance of
including all forms of smartphone/social media when conducting research on
smartphone/social media operation (Collis et al., 2022; Kumar et al., 2019).
Sleep Recommendations
Sleep has been defined as a modified state of awareness that makes a person
oblivious to the material world; yet this state of consciousness has been identified by
researchers to be an essential element of human existence that promotes both mental and
physical health (Chokroverty, 2017; Feldman, 2022; Worley, 2018). Empirical
researchers have continuously explored sleep for numerous years and concluded that
there remains many unknowns concerning this observable state of consciousness that has
been described as a reversable coma (Chokroverty, 2017; Worley, 2018). Throughout the
25
years, there were various research endeavors conducted by the scientific community to
determine the most favorable amount of sleep required for the ideal emotional and
physical health of people (Hirshkowitz et al., 2015; Kumar & Pati, 2021; Paruthi et al.,
2016; Sung et al., 2020; Tereshchenko et al., 2021; Worley, 2018). More specifically, an
individual’s sleep goal was defined as the minimal number of minutes/hours of sleep
necessary to achieve a baseline state of perception and awareness throughout the day,
which was broken down and organized based on age and developmental needs/
milestones of the individuals (Chokroverty, 2017; Hirshkowitz et al., 2015; Kumar &
Pati, 2021; Paruthi et al., 2016; Sung et al., 2020; Tereshchenko et al., 2021; Worley,
2018).
The National Sleep Foundation (NSF) explored and scrutinized numerous sleep
studies and deduced from the research that the optimal number of hours of sleep for late-
stage adolescents/ young adults aged 18-25 was estimated to be between seven to nine
hours of uninterrupted sleep within a 24-hour period of time (Hirshkowitz et al., 2015).
The NSF research analysis included an 18-member team of scientific researchers with
varied professional credentials who were tasked with assessing 312 sleep studies using
the two-round Delphi RAM evaluation system designed to collect and synthesize the
copious amounts of data from the sleep outcomes, which then enabled the NSF to make
the most up to date sleep recommendations for the adolescent community (Hirshkowitz et
al., 2015).
The NSF sleep recommendations for late-stage adolescents/ young adults were
echoed by the American Academy of Sleep Medicine (AASM) who had similar sleep
recommendations for the cohort (Hirshkowitz et al., 2015; Paruthi et al., 2016; Watson et
26
al, 2015). The AASM research committee was comprised of a 13-person group of
recognized sleep experts who reviewed 864 sleep studies over the course of 10 months
(Paruthi et al., 2016; Watson et al, 2015). The AASM concluded that teenagers aged 13-
18 were advised to obtain eight to ten hours of undisturbed sleep within a 24-hour period
for optimal growth and development, while late-stage adolescents/young adults 19-25
were recommended to obtain at least 7-9 hours of sleep per night (Paruthi et al., 2016;
Watson et al, 2015). Although both the NSF and the AASM have investigated and
outlined the most favorable sleep goals for the adolescent community as a whole, recent
metanalysis figures revealed that the global sleep trends for the adolescent cohort have
shown a statistically significant reduction in the hours of sleep being obtained by this
community on a nightly basis (Hirshkowitz et al., 2015; Marino et al., 2020; Paruthi et
al., 2016).
Increased Sleep Concerns
Researchers define a sleep deficit as the difference in minutes/hours between
actual sleep obtained by an individual when compared to the number of minutes/hours of
recommended sleep for a particular age group (Chokroverty, 2017; Hena & Garmy,
2020). The NSF and the AASM both expressed that the promoted sleep guidelines were
established based on research as a holistic guideline for optimal health outcomes;
nevertheless, research has indicated that at least one in four adolescents have sleep
problems, and sleep debt is on the rise globally contributing to concerns for adolescent
health and wellbeing (Chokroverty, 2017; Fuligni et al., 2017; Hena & Garmy, 2020;
Kumar & Pati, 2021; Wolters, 2018). Sleep data from numerous studies suggest that
adolescent sleep trends are drastically declining to below an average of seven hours of
27
sleep per night, and some empirical studies even suggest the number of hours of sleep in
teenagers and late-stage adolescents/young adults achieved per night could be well below
six hours (Hirshkowitz et al., 2015; Kumar & Pati, 2021; Marino et al., 2020; Sung et al.,
2020; Tereshchenko et al., 2021; Worley, 2018). Although there are numerous unknowns
as to why hours slept is trending downwards in the adolescent community at such an
escalated rate, most researchers tend to agree that diverging from well-researched sleep
standards will potentially equate to increased harm within the teenage cohort (Hena &
Garmy, 2020; Hughes & Burke, 2018; Kumar & Pati, 2021; Palmer et al., 2018).
Researchers have theorized there are a vast amount of unknown biological, social,
and environmental components that may be contributing to the alarming sleep emergency
plaguing the adolescent cohort (Hena & Garmy, 2020; Kumar & Pati, 2021; Marino et
al., 2020). Scientific exploration and knowledge continue to grow and expand raising the
overall understanding of brain development and functioning in humans; yet there are no
definitive answers into how much of one’s circadian rhythm or sleep patterns depend on
one’s biological nature or one’s environmental nurture (Feldman, 2022; Garrett &
Hough, 2021; Hena & Garmy, 2020).
The Insufficient Sleep and Smartphone/Social Media Connection
There have been scores of research studies highlighting the effects of
smartphone/social media consumption on sleep latency, length of sleep, insomnia, and
awakenings during the night within the adolescent community, but correlational data
doesn’t prove causation (Hughes & Burke, 2018; Kumar & Pati, 2021; Palmer et al.,
2018). Nevertheless, when viewing the research in its entirely a consensus is being built
within the scientific sector that appears to show smartphone/social media overuse does in
28
fact hinder adequate and sufficient sleep that leads to a plethora of difficulties (Amez et
al., 2020; Evers et al., 2020; Kater & Schlarb, 2020; Hunt et al., 2018; Palmer et al.,
2022; Sung et al., 2020; Worley, 2018). For example, Kater and Schlarb (2020)
conducted research on 201 adolescents aged 16-21 years of age and discovered a strong
correlation between smartphone/social media use while in bed and nighttime sleep
disturbances including insomnia, problems falling asleep, nighttime disturbances, and
increased day-time sleepiness. Additionally, Rafique et al. (2020) orchestrated cross-
sectional research that collected data from 1925 college aged participants. The
researchers found that only 12 percent of the participants in the study did not use
smartphone/social media after their identified bedtime (Rafique et al., 2020). The
researchers were also able to show a significant positive correlation between increased
smartphone/social media operation and nighttime awakenings, daytime drowsiness,
significantly fewer minutes of sleep, and increased sleep onset (Rafique et al., 2020).
Moreover, the study uncovered that even keeping the smartphone in one’s bedroom
decreased one’s ability to fall asleep, increased the number of times a person woke during
the night, and contributed to reductions in daytime alertness (Rafique et al., 2020). The
majority of research studies thus far have focused on late-stage adolescents/young adults;
nonetheless, research endeavors that have focused primarily on other adolescent
populations appeared to have similar outcomes leading researchers to conclude that
alarming linkages exist between global sleep reductions, smartphone/social media
consumption, and the rising emotional, mental, physical, academic, and spiritual health
concerns within the entire adolescent community (Hughes & Burke, 2018; Kumar & Pati,
2021; Palmer et al., 2018; Raudsepp, 2019).
29
Blue Light Emission
There has been no definitive consensus among researchers concerning blue light
emissions as various studies have indicated an absence of correlation between short
wavelength light from smartphones and sleep disturbances (Bowler & Bourke, 2019;
Chaudhury et al., 2019; Šmotek et al., 2020). Nevertheless, some research has linked blue
light or short-wavelength light from smartphone devices to problems falling asleep,
reductions in minutes of sleep obtained, and awakenings throughout the night (Bowler &
Bourke, 2019; Chaudhury et al., 2019; Höhn et al., 2021; Šmotek et al., 2020). For
instance, Höhn et al. (2021) conducted research that uncovered alertness in the morning
was significantly reduced in participants who read at night on their smartphone devices
without using a blue light filter, and participants had increased cortisol levels when
compared to participants who used a blue light filter or read printed material the previous
night. The research also indicated that utilizing a smartphone device at night before
bedtime also affected hormone secretions such as natural melatonin, thermoregulation,
and participants reported increased levels of alertness at night when reading from a
smartphone device in the self-report measures (Höhn et al., 2021).
While the empirical investigation into short-wavelength light from smartphones,
tablets, digital televisions, computers, and other electronic devices have evaded scientific
agreement regarding this technology being a major contributor to sleep problems, the
majority of researchers have come to consensus concerning the direct linkage of
smartphones/social media with increases in sleep issues among the whole adolescent
cohort (Bersani et al., 2022; Chung-Ying et al., 2021; Raudsepp, 2019; Tandon et al.,
2020; van den Eijnden et al., 2021; van der Schuur et al., 2018; van Velthoven et al.,
30
2018). Pirdehghan et al. (2021) conducted cross-sectional research with 576 participants
in grades 10, 11, and 12. The research uncovered a statistically strong linkage between
the amount of time the adolescents utilized smartphones/social media platforms and sleep
problems (Pirdehghan et al., 2021). The more minutes/ hours that were recorded of
smartphone/social media use, the fewer minutes/hours of sleep obtained, the more sleep
disturbances recorded during the night, and the higher the reports of daytime sleepiness
(Pirdehghan et al., 2021).
Internet Use Disorder, FOMO, and Nomophobia
The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) by the
American Psychiatric Association recently added Internet Use Disorder, which the
experts agreed was an overindulgent, habit-forming, and invasively troublesome
utilization of the internet that could contribute to sleep problems (Malaeb et al., 2020).
Since smartphones have easily accessible internet capabilities, this modern device has led
to an increase in Internet Use Disorder diagnoses (Chung-Ying et al., 2021; Malaeb et al.,
2020). Prior to the most recent publication of the DSM-5, experts in the field of
psychology and psychiatry were also considering adding additional diagnoses to the
DSM-5 that included social media and smartphone addition disorders (Akbari et al.,
2021; Alotaibi et al., 2022; Malaeb et al., 2020). Pellegrino et al. (2022) conducted a
bibliometric analysis encompassing the years 2013 to 2021 and found that social media
addiction or problematic social media use were the topic of exploration in 553 articles
during this timeframe. The researchers highlighted in 2013 there were only two
publications on the topic whereas in 2021 there were over 195 peer-reviewed research
31
articles written on the topic, which displayed the growing concern within the research
community (Pellegrino et al., 2022).
Many individuals in the technology field of research advocated for the inclusion
of the diagnosis called fear of missing out (FOMO) that has been defined as extreme
anxiety directly linked to social media overuse where an individual becomes
overwhelmed by thoughts connected to potentially missing messages, videos, or other
communications from other members within one’s social media community (Akbari et
al., 2021; Rozgonjuk et al., 2020). Rozgonjuk et al. (2020) conducted research with 748
participants that uncovered a direct correlation between self-reports of FOMO and
disruptions in one’s home-life and work environment. Various experts within psychiatry
and psychology argue that FOMO was a significant marker indicating a person may be
developing internet use disorder, which in turn has been connected to disruptions in one’s
sleep cycle (Akbari et al., 2021; Alotaibi et al., 2022; Chung-Ying et al., 2021; Malaeb et
al., 2020; Rozgonjuk et al., 2020). Additionally, Chung-Ying et al. (2021) conducted a
longitudinal research study with 1098 students aged 13 to 19. The researchers discovered
a strong relationship between smartphone/social media engagement and a statistically
significant rise in reports of insomnia in this population during the course of their
research (Chung-Ying et al., 2021). The researchers concluded that nomophobia, which is
defined as fear or anxiety stemming from the lack of access to a smartphone device, and
social media addiction were developing problems in the adolescent population that were
contributing to sleep reductions and sleep disturbances throughput the night for this
cohort (Chung-Ying et al., 2021).
32
Although there has been no official DSM-V diagnosis for FOMO, nomophobia,
or problematic smartphone/social media use disorder, adverse health effects have been
identified and documented by the research community with overwhelming evidence
showing direct correlations between increased sleep problems and smartphone/social
media operation in the entire adolescent community (Hughes & Burke, 2018; Kumar &
Pati, 2021; Palmer et al., 2018). Moreover, research appeared to display significant links
between lack of sleep in adolescents with greater displays of depressive symptomology,
reports of anxiety, behavioral problems, suicidality, lowered cognitive functioning,
falling academic scores, increased obesity and type-two diabetes rates, spiritual formation
problems, and addiction issues within the adolescent cohort (Amez et al., 2020; Bozzola
et al., 2022; Evers et al., 2020; Giunchiglia, 2018; Li et al., 2019; Palmer et al., 2022;
Sung et al., 2020; Worley, 2018).
Adverse Impacts from Smartphone/Social Media
Emotional Dysregulation
Researchers identified emotional dysregulation as one mental health concern
connected to inadequate and/or disturbed sleep when one overuses smartphones/social
media (Azhari et al., 2022; Bègue et al., 2022; Bersani et al., 2022; Palmer et al., 2018).
Azhari et al. (2022) argued that many adolescents in their research had various social
media connections without perceived meaningful relationships, which led to significant
reports of loneliness, emotional distress, as well as reductions in sleep. Additional
research in France showed that reductions in sleep contributed to increased psychological
dysregulation that in turn led to significant increases in psychical aggression and fighting
in a sample of 11, 912 8th and 9th grade students who completed an anonymous self-report
33
survey (Bègue et al., 2022). Moreover, cross-sectional research in the United States with
10,148 adolescents aged 13-21 confirmed that there was a statistically significant increase
in reported emotional distress and dysregulation among the participants with reported
sleep issues (Palmer et al., 2018).
Bersani et al. (2022) also explored the connections between lack of sleep and
increased reports of emotional dysregulation/physical aggression among teenagers and
late-stage adolescents/ young adults. The research study obtained 480 participates and
found that sleep deficits were directly connected to the number of minutes/hours of
smartphone/social media operation (Bersani et al., 2022). The researchers were also able
to show direct correlations between reduced sleep in the participants, minutes of
smartphone/social media usage, and increased aggression displayed through reported
irritability, higher impulsivity rates, reported low mood, increased anxiety, higher rates of
perceived negative affect from family, friends, and acquaintances, and a reported overall
inability to self-regulate one’s emotions (Bersani et al., 2022).
Guerrero et al. (2019) collected research data obtained from a parental perspective
and discovered that parents also reported that significant reductions in the sleep of their
children and teenagers were related to increased social media use. The parents in the
cross-sectional study completed the Child Behavior Checklist (CBCL) checklist and
reported lower rates of troublesome conduct such as emotional control issues, impulse
control problems, and aggression in children and adolescents who received more sleep
(Guerrero et al., 2019). Furthermore, the research study appeared to show that increased
social media use and reduced sleep increased both external and internal affect
dysregulation and cognitive processing problems in children and adolescents aged 6-18
34
years of age, which added to the mounting evidence linking smartphone/social media use
and lack of sleep to emotional dysregulation (Azhari et al., 2022; Bègue et al., 2022;
Bersani et al., 2022; Guerrero et al., 2019; Palmer et al., 2018).
Increased Anxiety Levels
Researchers identified anxiety as being another significant mental health concern
associated with smartphone/social media usage and lack of sleep within the total
adolescent population (Bozzola et al., 2022; Yang et al., 2020). Bozzola et al. (2022)
conducted a scoping review of the research literature and found that an adolescent’s
social media usage was directly connected to perceived increases in stress and anxiety.
The research showed that for every hour of smartphone/social media operation reported
stress and anxiety levels significantly increased presenting an overall negative impact
both socially and emotionally (Bozzola et al., 2022). Research studies also identified that
smartphone/social media operation decreased physical activity within the adolescent
population, which in turn contributed to higher rates of anxiety and sleep disturbances
(Keles et al., 2020; López-Gil et al., 2022; Malaeb et al., 2020; Werneck et al., 2020).
Research undertaken by Tandon et al. (2020) pinpointed a significant relationship
between social media operation, increased anxiety levels, and reductions in overall sleep
quality and quantity in 1870 late-stage adolescent/young adult participants. The study
revealed FOMO was the primary contributor to the increased levels of anxiety, which the
researchers related to the excessive communication and information available on social
media platforms, the instant availability of social media through smartphones, and the
constant messaging and video sharing capabilities (Tandon et al., 2020).
35
Yang et al (2020) discovered that 180 minutes or more of social media operation
within a 24-hour period in adolescent females aged 13-18 significantly increased poor
self-image, reduced self-esteem, and escalated reports of anxiety symptoms. The research
showed anxiety specifically related to smartphone/social media usage, as there was no
significant effect on anxiety levels with smartphone use alone (Yang et al., 2020).
Moreover, the research echoed previous research that displayed increased anxiety levels
directly reducing the number of minutes of sleep obtained by these late-stage adolescents/
young adults, especially in relation to visual comparisons made by the adolescent girls
with other females on social media platforms (Keles et al., 2020; López-Gil et al., 2022;
Malaeb et al., 2020; Werneck et al., 2020; Yang et al., 2020). Steinsbekk et al. (2021)
also argued there were significant and notable connections made between social media
consumption and anxiety symptoms, decreased self-esteem, and poorer self-image;
nevertheless, researchers declared more research studies were needed to explore all the
connections between social media and specific anxiety related symptoms for both male
and female adolescents since anxiety can present very differently for each individual
segment of the adolescent population (Keles et al., 2020; López-Gil et al., 2022; Malaeb
et al., 2020; Steinsbekk et al., 2021; Werneck et al., 2020; Yang et al., 2020).
Increased Rates of Depressive Symptoms
Symptoms of depression have also been linked to smartphone/social media usage
within the adolescent and late-stage adolescent/young adult cohort (Bozzola et al., 2022;
Huang & Zhao, 2020; Li et al., 2019; Malaeb et al., 2020; Marino et al., 2020;
Przepiorka, & Blachnio, 2020; Yang et al., 2019). Research has uncovered that sleep
helps to elevate the onset of depressive symptoms with increases in sleep debt linked to
36
increases in depression (Li et al., 2019; Marino et al., 2020; Stiglic & Viner, 2019).
Fuligni et al. (2017) conducted research with 341 teenage freshmen and sophomores in
Los Angeles, California. The research discovered a negative correlation between
increased smartphone/social media consumption and the reported minutes of sleep
acheived within the adolescent participants (Fuligni et al., 2017). Moreover, the research
showed elevated rates of depressive traits for both the male and female students that
appeared to be directly linked to the minutes of smartphone/social media operation and
decline in minutes of sleep obtained by the participants in the study (Fuligni et al., 2017).
Longitudinal research was conducted with 249 adolescents over a two-year
timeframe that displayed increases in self-reported social media consumption correlated
with higher rates of sleep issues (Raudsepp, 2019). Furthermore, Raudsepp (2019)
showed that sleep reductions and higher rates of social media operation within the two-
year study acted as reliable markers forecasting higher rates of reported depression
symptoms in both the female and male research participants (Raudsepp, 2019). There is
growing evidence that appears to connect smartphone/social media consumption to
increases in mental health problems such as depressive symptomology among
adolescents and young adults (Ghaemi, 2020; Raudsepp, 2019; Yang et al., 2019).
Depressive symptomology within the adolescent community in the United States has
risen drastically since the introduction of smartphone/social media with depression now
impacting approximately 22 percent of American teenagers (Ghaemi, 2020; Malaeb et al.,
2020; Marino et al., 2020). Many researchers have concluded that there is a clear and
direct causal relationship between smartphone/social media operation and
signs/symptoms of depression in teenagers with mounting empirical research data
37
indicating that when smartphone/social media usage decreases, depressive symptomology
also decreases (Ghaemi, 2020; Malaeb et al., 2020; Marino et al., 2020; Raudsepp, 2019).
Hunt et al. (2018) conducted an RCT that appeared to show direct evidence that
reduced smartphone/social media operation led to significant reductions in depression
and anxiety in late-stage adolescents/ young adults. The RCT was conducted over a four-
week timeframe, obtained 143 participants, and in the experimental group the researchers
restricted individual social media consumption to only 10 minutes of use per day for each
social media site membership (Hunt et al., 2018). On average, this intervention reduced
the experimental participants overall social media operation by 150 minutes each day;
however, the control group had no such social media limitations (Hunt et al., 2018). The
researchers compared their RCT outcomes from restricting social media usage with other
RCTs testing the effects of antidepressants on depressive symptoms; surprisingly, the
results from the experimental group showed such significant reductions in depressive
symptomology that the effects appeared to equal the effects of taking a prescribed
antidepressant medication (Hunt et al., 2018).
Cyberbullying
Research shows that smartphone/social media have become frequently utilized
tools that have contributed to a rise in cyberbullying, which also increases self-reported
rates of depressive symptoms in both male and female adolescents and late-stage
adolescents/young adults (Guinta & John, 2018 Quintana-Orts et al., 2020; Sampasa-
Kanyinga et al., 2022). A randomized control trial (RCT) uncovered that approximately
60 percent of adolescents aged 13-17 in the United States reported some type of
cyberbullying behavior within the past year that included being called derogatory names,
38
being the subject of rumors, receiving unsolicited graphic sexual images and content,
other people sharing one’s private/personal photos, and encountering physical threats of
violence (Kutok et al., 2021).
Nagata et al. (2023) conducted research with 9,443 children and adolescents aged
10-14 that connected cyberbullying to self-reported and parental reported sleep
disturbances, both falling and staying asleep. The researchers also found that the reported
sleep problems fell when smartphone/social media access and minutes of use were
limited by parents (Nagata et al., 2023). Further research discovered that cyberbullying
not only impacts sleep for the victims of the online abuse, but also for the perpetrators of
the cyberbullying (Sampasa-Kanyinga et al., 2022). The cross-sectional research data was
collected in Ontario, Canada on 6834 teenagers ranging in age from 11-21 with reduced
sleep defined as acquiring less than the minimal AASM recommended 8-10 hours of
sleep for one’s age (Sampasa-Kanyinga et al., 2022). Furthermore, the research revealed
the more incidents of reported cyberbullying, either as the victim or the perpetrator, the
greater the chance of reported sleep problems and sleep deficits in the participants
(Sampasa-Kanyinga et al., 2022).
Increased Loneliness and Suicidal Ideation
Smartphone/social media usage have been used to increase social connection;
nonetheless, loneliness and suicidal ideation have been on the rise within the whole
adolescent community, which has also been linked to difficulties with obtaining ample
sleep (Azhari et al., 2022; Glenn et al., 2021; Huang et al., 2022). Glenn et al. (2021)
conducted research with 48 adolescents who had depressive symptoms and were admitted
to a psychiatric hospital. The research project transpired over a 28-day period post
39
discharge, and the researchers discovered that increased sleep latency, decrease sleep
duration, and increased sleep disturbances coincided with higher reports of suicidal
ideation in the adolescents (Glenn et al., 2021). Moreover, self-reported rumination and
nightmares, which is thought to be linked to the sleep latency and nighttime awakenings,
were also a significant marker that precited increased suicidal thinking the next day
(Glenn et al., 2021).
Research also appears to display evidence for a significant overlap between
smartphone/social media use, depression symptomology, cyberbullying, and suicidal
ideation in the entire adolescent community (Ghaemi, 2020; Kutok et al., 2021; Malaeb et
al., 2020; Marino et al., 2020; Peng et al., 2019; Raudsepp, 2019; Sampasa-Kanyinga et
al., 2022). Research showed that individuals that encountered cyberbullying reported
higher levels of distress and suicidal ideation when interviewed by professional working
in the mental health field (Kutok et al., 2021; Peng et al., 2019). In addition, these
adolescents disclosed feelings of excessive loneliness and helplessness, and thoughts of
unworthiness and of being less than human with these feelings and thoughts persisting
throughout the day while sometimes persisting into the night (Kutok et al., 2021; Peng et
al., 2019). Furthermore, the teenagers reported that the cyberbullying caused severe
social distress, which contributed to a negative self-image, unwanted negative emotions,
problems with thought processing, concentration issues, reductions in sleep, increased
depressive symptoms, and increased suicidal thoughts (Kutok et al., 2021; Peng et al.,
2019; Quintana-Orts et al., 2020). Macrynikola et al. (2021) conducted a systematic
review of the empirical research and uncovered that there appeared to be a consistent
connection between increased suicidal ideation and problematic social media use. Huang
40
et al. (2022) conducted research with 489 college students with the median age of 18 and
found that excessive smartphone use of more than 5 hours per day was associated with
significant increased depressive symptoms, suicidal ideation, lack of physical activity,
and poorer physical health.
Nevertheless, adolescents and late-stage adolescents/young adults from numerous
studies reported persisting heightened emotional states and pervasive thoughts connected
to smartphone/social media use, which the teenagers and late-stage adolescents/young
adults felt were unescapable due to smartphone/social media accessibility, peer pressure,
and social expectations (Azhari et al., 2022; Glenn et al., 2021; Huang et al., 2022; Kutok
et al., 2021; Macrynikola et al., 2021; Peng et al., 2019; Quintana-Orts et al., 2020). The
social norms related to smartphone/social media access and availability have created a
virtual prison for many teenagers and late-stage adolescents/young adults who have been
unable to disconnect or escape the social pressures carried in one’s pocket or purse
(Azhari et al., 2022; Glenn et al., 2021; Huang et al., 2022; Kutok et al., 2021;
Macrynikola et al., 2021; Peng et al., 2019; Quintana-Orts et al., 2020). Empirical
research has shown that constant access has created higher rates of emotional
dysregulation, depressive symptoms, cyberbullying, suicidal ideation, violence, and even
death in the entire adolescent community (Azhari et al., 2022; Glenn et al., 2021; Huang
et al., 2022; Kutok et al., 2021; Macrynikola et al., 2021; Peng et al., 2019; Quintana-Orts
et al., 2020).
Increased Obesity and Type-two Diabetes
Obesity rates have been on the rise within the whole adolescent population
prompting investigation through empirical research (Alotaibi et al., 2022; Bartosiewicz et
41
al., 2020; Kim et al., 2015; Ryu et al., 2022). Smartphone/social media use has been
connected to poor eating habits in adolescents and late-stage adolescents/young adults,
which has been linked to increased obesity rates and risks associated with developing
type-two diabetes (Alotaibi et al., 2022; Bartosiewicz et al., 2020; Kim et al., 2015; Ryu
et al., 2022). Researchers conducted cross-sectional research obtaining data on 54,601
adolescents aged 12-18, and uncovered that smartphone use greater than 300 minutes per
day correlated to reductions in fruit, vegetable, and breakfast intake while sugary drink
consumption and fast-food consumption were significantly increased (Ryu et al., 2022).
Bartosiewicz et al., 2020 investigated Body Mass Index (BMI) scores,
smartphone/social media use rates, and length of sleep with 376 children and adolescents
ranging in age from 6 to 15. The research discovered a significant positive correlation in
BMI scores and smartphone/social media use, and a negative correlation between hours
slept per night by the participants and BMI data that showed as sleep decreased in the
cohort weight increased. Moreover, research showed that obesity was directly connected
to lower than recommended sleep durations, increased sleep disturbances, and chronic
reductions in sleep over long stents of time for adolescents (Alotaibi et al., 2022;
Bartosiewicz et al., 2020; Kim et al., 2015). Kim et al. (2015) conducted research with
110 late-stage adolescent/young adult international students and discovered that study
participants with higher minutes of smartphone/social media operation had a significant
reduction in steps walked within a 24-hour period. Moreover, the researchers were able to
show that increased smartphone/social media use positively correlated with increased
number of calories ingested, fast food uptake, spikes in BMI, and reductions in muscle
mass (Kim et al., 2015). Although there was no direct evidence displaying increased
42
smartphone/social media use contributing to the development of diabetes, the researchers
highlighted the potential dangers between smartphone/social media overuse, significant
weight gain, and the overall negative health risks that can contribute to the onset of type-
two diabetes (Kim et al., 2015).
Decreased Academic Performance
Decreased academic performance, increased smartphone/social operation, and
problematic sleep have been another area of interest for research scientists in recent years
(Alotaibi et al., 2022; Evers et al., 2020; Giunchiglia et al., 2019; Lin & Zhou, 2022;
Rathakrishnan et al., 2021). Evers et al. (2020) directed a research project made up of
2462 participants from 8th and 9th grade from 93 participating schools. The longitudinal
data that was collected, which included the student’s grade point averages and self-report
data on smartphone/social media operation, showed smartphone/social media use to be
significantly connected to sleep problems within the adolescent cohort (Evers et al.,
2020). The researchers highlighted that sleep quantity (minutes of sleep) of the
adolescents were directly linked to reported academic fatigue, which in turn was
expressed by the lower grade point averages in the students with the largest amount of
time spent in smartphone/social media use. Moreover, the research showed the
smartphone/social media consumption not only reduced the amount of sleep obtained by
the students, but it also decreased the quality of sleep by increasing sleep latency and
disturbances, which also contributed to lower academic scores and higher reports of
educational burnout in the participants (Evers et al., 2020). Research from Ontario
Canada acquired self-report information from 14,000 adolescents enrolled in primary and
secondary educational institutions, which uncovered similar links between
43
smartphone/social media overuse and reductions in grade point averages (Sampasa-
Kanyinga et al., 2022). This research endeavor showed that smartphone/social media
operation greater than five hours a day was directly associated with significant reductions
in academic performance regardless of one’s gender (Sampasa-Kanyinga et al., 2022).
Further research studies conducted with enrolled university students displayed
direct links between smartphone/social media overuse and overall reductions in academic
performance (Alotaibi et al., 2022; Lin et al., 2021; Rathakrishnan et al., 2021). One such
study conducted by Lin et al. (2021) exposed that those students operating
smartphone/social media in class, during lectures, or while studying had the greatest
reductions in academic performance. Additionally, news applications and educational
portals provided by the university had no impact on sleep or academic performance while
social media related applications including gaming applications increased
smartphone/social media overuse and problems obtaining adequate sleep (Lin et al.,
2021).
Adverse Spiritual Impacts
Smartphones/social media have become time consuming instruments, which
create an environment full of distraction (Chow, 2022; Omede & Akintunde, 2023; Shim,
2021 Uecker & McClure, 2022). Smartphones/social media have become a primary
source for news coverage and alerts, a source for social connection, video entertainment,
a platform to share one’s beliefs, values, and opinions, gaming centers, and a virtual
space for online groups and meetings (Dixon, 2022; Kumar et al., 2019; Pellegrino et al.,
2022). Consequentially, smartphone/social media operation has the tendency to disrupt
one’s ability to connect to God through prayer and scripture reading due to constant
44
engagement and distraction (Chow, 2022; Omede & Akintunde, 2023; Shim, 2021
Uecker & McClure, 2022).
Smartphones/social media have been deemed to be morally neutral devices;
however, smartphones/social media have the ability to provide access to unethical and
immoral material such as pornographic photos and videos, inappropriate emotional and
physical relationships, unnatural sexual partnerships, fraudulent activities, and so on
(Bingaman, 2023; Mendrofa et al., 2023). The Bible instructs Christians to be wary of
what one watches and sees for the Bible proclaims, “The eye is the lamp of the body. So,
if your eye is healthy, your whole body will be full of light, but if your eye is bad, your
whole body will be full of darkness” (English Standard Version Bible, 2001/2016,
Matthew 6: 22-23). Therefore, smartphones/social media consumption can become a
resource that contributes and leads an individual into sinfulness and spiritual disharmony
with God (Chow, 2022; Mendrofa et al., 2023; Omede & Akintunde, 2023). Mendrofa et
al. (2023) defined sin as thoughts and behaviors displayed by humans that are in direct
opposition to God and His divine nature, which the researcher argued was the only thing
that God hates about humanity. Christian researchers highlighted that smartphone/social
media operation had the ability to create seemingly undetectable ad untraceable online
activity, which appeared to be contributing to the moral decline (sexual immorality,
impurity, passion, evil desire, and covetousness) in today’s society leading to reductions
in overall Christian faith, emotional responses, and behavioral displays both online and in
everyday life (Chow, 2022; English Standard Version Bible, 2001/2016, Colossians 3:5;
Mendrofa et al., 2023; Omede & Akintunde, 2023).
45
Bingaman (2023) argued that smartphones/social media have been designed to
occupy a person’s time even referencing a former president of Facebook who boldly
admitted the social media giant’s goal was to consume one’s attention and keep people
coming back for more as this guaranteed marketing revenue. As a result, Shim (2021)
explained that more and more Christian teenagers and late-stage adolescents/young adults
were experiencing smartphones/social media addiction, which created a need for Christin
12-step/ recovery programs designed to help break addiction, focus or refocus an
individual on God, gain a more comprehensive understanding of sin, and connect
adolescents with adult mentors for guidance. Although smartphones/social media did not
exist during biblical times, the Bible warns the Christian community about addiction
(Mendrofa et al., 2023; Omede & Akintunde, 2023; Shim, 2021). Paul expressed, “All
things are lawful for me, but not all things are helpful. “All things are lawful for me, but I
will not be dominated by anything” (English Standard Version Bible, 2001/2016, 1
Corinthians 6:12). Additionally, the Bible instructs believers to “Put to death therefore
what is earthly in you: sexual immorality, impurity, passion, evil desire, and
covetousness, which is idolatry” (English Standard Version Bible, 2001/2016, Colossians
3:5). Therefore, smartphone/social media operation has the potential to become not only
an addiction, but a form of idol worship for individuals thus further damaging spiritual
formation, connection, and relationship with God (Chow, 2022; Mendrofa et al., 2023;
Omede & Akintunde, 2023; Shim, 2021 Uecker & McClure, 2022).
Sleep Latency, Duration, and Disturbances
The majority of correlational research data appears to show statistically strong
links between smartphone/social media consumption and sleep problems such as sleep
46
latency, a reduction in minutes of sleep per night, and sleep disturbances such as
nighttime awakenings in the entire adolescent community (Akbari et al., 2021; Amez et
al., 2020; Evers et al., 2020; He et al, 2020; Hefner et al., 2019; Hughes & Burke, 2018;
Hunt et al., 2018; Kater & Schlarb, 2020; Kumar & Pati, 2021; Palmer et al., 2022;
Pirdehghan et al., 2021; van den Eijnden et al., 2021; Palmer et al., 2018; Sung et al.,
2020; Worley, 2018). Although there may be numerous aspects of smartphones/social
media operation that have contributed to the overall sleep crisis in the whole adolescent
community, scientific exploration has begun to investigate whether the aforementioned
adverse effects of smartphone/social media use can be altered in any statistically
meaningful way (He et al, 2020; Hefner et al., 2019; Hughes & Burke, 2018; Pirdehghan
et al., 2021; van den Eijnden et al., 2021).
Research appears to have uncovered that limiting or stopping smartphone/social
media use at night helps improve one’s overall sleep quality (He et al., 2020; Hughes &
Burke, 2018; Hunt et al., 2018; Kheirinejad et al., 2022; Lin & Zhou, 2022;
MacCárthaigh et al., 2020). Moreover, several research endeavors discovered that parents
had the most direct and indirect influence over smartphone/social media operation with
the whole adolescent community through limit setting and behavior modeling (Bozzola et
al., 2022; Godsell & White, 2019; Hefner et al., 2019; Nur et al., 2021; Padilla-Walker et
al., 2018). Hefner et al. (2019) completed a qualitative research study whereby the
researchers conducted more than 500 face-to-face interviews with adolescents and
parents. The researchers found that the majority of parents/caregivers did not set concrete
rules or structure regarding smartphone/social media use until the parents/caregivers
viewed the adolescent smartphone/social media operation to be excessive, intrusive, and/
47
or problematic in some way (Hefner et al., 2019). The study also concluded from the
interviews, both from the parents/caregivers and the adolescent points of view, that the
parents/ caregivers had the greatest influence and control over the smartphone/social
media usage (Hefner et al., 2019). Furthermore, the research highlighted that rule
conformity surrounding smartphone/social media operation was most effective with the
adolescents when the parents/caregivers modeled rule adherence concerning
smartphone/social media operation within the home (Bozzola et al., 2022; Godsell &
White, 2019; Hefner et al., 2019; Nur et al., 2021; Padilla-Walker et al., 2018).
Recent research studies have also begun to investigate if the presumed adverse
impacts of smartphone/social media consumption on the quality and quantity of sleep in
adolescents and late-stage adolescents/young adults could be managed, transformed, or
even reversed in any meaningful way (Bartel et al., 2018; He et al, 2020; Hefner et al.,
2019). Bartel et al. (2018) performed quasi-experimental research with adolescents aged
14-18 in secondary schools located in South Australia. The participants were recruited for
a week-long smartphone/social media research study to investigate the impacts on
adolescent sleep latency, minutes of sleep per night, and sleep disturbances when
smartphone/social media use was halted one hour before bedtime (Bartel et al., 2018).
The research initially recruited 243 participants; however, only 63 adolescents engaged in
the project, and there was no parental/caregiver oversight or participation (Bartel et al.,
2018). Moreover, only 17 out of the 63 participants completed all the requested data sets,
which given the small sample size diminished the statistical power of the research
endeavor (Bartel et al., 2018). Nevertheless, the research study indicated that the
teenagers who stopped smartphone use 60 minutes prior to bedtime had reduced sleep
48
latency by an average of 17 minutes per night when compared to their normal sleep
patterns (Bartel et al., 2018). Moreover, the research displayed that the adolescents who
stopped smartphone/social media use one hour before bedtime increased their minutes of
sleep by 21 minutes per night, which was considered to be clinically significant by the
researchers (Bartel et al., 2018).
He et al. (2020) built on the previous research by conducting an RCT whereby 38
late-stage adolescents/young adults from a Chinese University were randomly separated
into a control group and an experimental group. The researchers placed the participants in
a controlled institutional environment located on the university campus where they
observed and recorded data on both the control group and experimental group
participants (He et al., 2020). The researchers obtained data on the participants for a four-
week period measuring the student’s sleep latency, the amount of sleep achieved, sleep
disturbances, working memory, and mood (He et al., 2020). The researchers stopped the
participants’ smartphone/social media operation 30 minutes prior to bedtime in the
experimental group and uncovered that by eliminating smartphone/social media use the
experimental participants acquired significantly improved changes to one’s reported
quality of sleep, minutes of sleep, mood, cognitive abilities, and sleep onset (He et al.,
2020). The experimental group compared to the control group recorded an increase of
approximately 18 minutes of sleep per night, a significant reduction in awakenings
throughout the night, and on average fell asleep 12-minutes faster during the four-week
research trial (He et al., 2020). Furthermore, the research provided strong evidence that
appeared to support that stopping smartphone/social media at least 30 minutes before
49
bedtime improved one’s overall mood and increased one’s ability to access working
memory (He et al., 2020).
Biblical Foundations of the Study
When humans enter the adolescent phase of development one normally acquires
an increased ability to contemplate abstract concepts, which in turn leads to questioning
authority figures, the purpose of societal rules and laws, relationship value, and even
one’s spiritual connections making this stage of development crucial for identity
formation (Feldman, 2022; Francis, 2020; Good & Willoughby, 2008; Keskintürk, 2021).
Moreover, adolescence can also be marked by increased egocentric thinking,
transformation and exploration of one’s identity, formation of new social connections,
and increased responsibilities at home, school, and in the community (Feldman, 2022;
Francis, 2020; Good & Willoughby, 2008; Keskintürk, 2021). For many adolescents in
western societies, this includes the added responsibility of owning and navigating
smartphones/social media platforms (Campbell et al., 2021; Chung-Ying et al., 2021;
Dixon, 2022; Gumport et al., 2021; Pirdehghan et al., 2021; Raudsepp, 2019; Thomée,
2018). Nevertheless, Christian parents have a God given mandate and responsibility to
raise children in accordance with scripture for the Bible commands believers to “Train up
a child in the way he should go; even when he is old, he will not depart from it” (English
Standard Version Bible, 2001/2016, Proverbs 22:6).
Theoretical Foundations and the Bible
The Bible doesn’t have much to say about the exact purpose of sleep; yet, the
Bible does give general references to sleep proclaiming that sleep is from God, it is for
rest, it is good, and that people should not over indulge in sleep (English Standard
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Version Bible, 2001/2016, Proverbs 6:9-11; 19:15; 20:13; Ecclesiastes 5:12; Psalm 127:2;
Matthew 26:45). Therefore, one can conclude from scripture that sleep is a normal human
function designed by God, and a gift from God to humanity for the Bible says, “It is in
vain that you rise up early and go late to rest, eating the bread of anxious toil for he(God)
gives to his beloved sleep” (Ancoli-Israel, 2001; English Standard Version Bible,
2001/2016, Psalm 127:2). Moreover, Ancoli-Israel (2001) declares that scripture supports
the repair and restoration theory as sleep is seen biblically as restorative, healing, and
necessary for proper human functioning. Furthermore, God created both day and night
designing our human bodies to excrete hormones at night that enable sleep thus
reaffirming God’s grand design concerning sleep having a major purpose in the lives and
health of humans (Ancoli-Israel, 2001).
The ecological systems theory is not explicitly outlined or alluded to within a
biblical context; nevertheless, the Bible affirms that parents, which are found in
Bronfenbrenner’s interpersonal relationship domain, have the most impact on a child’s
development (English Standard Version Bible, 2001/2016, Deuteronomy 6:6-7; Exodus
20:12; Proverbs 22:6; 29:17; Psalm 127:3-5; Ephesians 6:1-4). The Bible instructs
parents to teach about God and his ways within the home setting the example for their
children in obedience to God (English Standard Version Bible, 2001/2016, Deuteronomy
6:6-7; Exodus 20:12; Proverbs 1: 8-9; 13:24; Psalm 127:3-5; Ephesians 6:1-4; 2 Timothy
3:14-15). Moreover, children were expected to attend synagogues and learn from local
Rabbis thus displaying the institutional, and community systems level of influence from a
Bible perspective, which was even supported by Jesus as he rebuked his disciples are
trying to turn away children from coming to him (English Standard Version Bible,
51
2001/2016, Matthew 19:13-15; Luke 18:15-17). Abo-Zena & Rana (2020) argue that
religion and spirituality are seen across all of Bronfenbrenner’s ecological domains
impacting child/adolescent development across the lifespan eventually shaping society at
the macro level impacting laws, overarching social frameworks, and dictating supports
and constraints.
Adolescence and Spiritual Development
Good and Willoughby (2008) argued that the adolescent stage of development
(12-24) was a highly pivotal phase for spiritual formation as adolescents were prone to
engage in more religious related ventures, contemplate foundational beliefs and values,
and embrace a comprehensive spiritual formulation with long-term life impacts.
Goodman and Dyer (2019) referenced the National Study of Youth and Religion
(NSYR), which showed at least 90 percent of adolescents in the United States had some
sort of belief in God, 80% held the view that an individual’s spiritual beliefs and faith
were of high importance, and the data uncovered over 65% of American youth pray
weekly, 60% pray daily, and at least 60% go to some church related event each month.
Unfortunately, there remains a void within the scientific community exploring
spirituality and religiosity in adolescent development, even though religious and spiritual
formulation is referenced by theorists as being a foundational element of adolescent
development (Feldman, 2022; Francis, 2020; Good & Willoughby, 2008; Goodman &
Dyer, 2019; Keskintürk, 2021). There have been a few research studies that appear to
show that parents play one, if not the most vital role in adolescent religious development,
which is linked to modeled behavior, the personality traits of the teenagers, the level of
consistency in instruction, and the type of parenting style embraced by the caregivers
52
(Francis, 2020; Good & Willoughby, 2008; Goodman & Dyer, 2019). Goodman & Dyer
(2019) found in their research that parents with higher rates of religious attendance and
practice within the home had a statistically significant impact on adolescent religious
development when compared to parents with minimal religious involvement within the
home. Francis (2020) also conducted research with 7,059 adolescents aged 13 to 15 and
discovered that parents had a greater influence on church attendance than the adolescents’
peers. This display of family values, spiritual commitment, and active religious practice
aligns with the Bible when James instructed believers in the following manner:
“…be doers of the word, and not hearers only, deceiving yourselves. For if
anyone is a hearer of the word and not a doer, he is like a man who looks intently
at his natural face in a mirror. For he looks at himself and goes away and at once
forgets what he was like. But the one who looks into the perfect law, the law of
liberty, and perseveres, being no hearer who forgets but a doer who acts, he will
be blessed in his doing” (English Standard Version Bible, 2001/2016, James 1:22-
25).
Furthermore, it is important to highlight that both research studies appeared to display
that integration of parental religious beliefs by teenagers/ young adults were higher if the
parents embraced church attendance and incorporated their daily faith activities within
the home environment holding to a true consistency of belief and values in both public
and private settings (Francis, 2020; Goodman & Dyer, 2019).
Adolescent Identity Formation
Children and adolescents do not have the ability to orchestrate or hand-pick one’s
family or culture of origination, instead humans are propelled into a pre-formed
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community with its own values, perspectives, and standards (Sproul, 2018; Halevy &
Gross, 2024). The fallen state of humankind has contributed to ongoing sin that continues
to impact all aspects of God’s grand design including the family and one’s identity
formation (Sproul, 2018; Halevy & Gross, 2024). Although identity development is an
area of social sociology that is fast-growing, investigation into religion’s role into
forming one’s identity is limited (Halevy & Gross, 2024). Nevertheless, Goodman and
Dyer (2019) argue that the parents as well as one’s Christian community must seize the
opportunity to invest in an adolescent’s religious development, which can have
tremendous long-term impacts on one’s overall identity helping to formulate one’s
overall identity structure (Schachter & Ben Hur, 2019).
Religious belief and participation have the potential to deliver foundational
answers to a variety of questions related to one’s life purpose connecting
adolescents/young adults to a broader historical narrative influencing goals, choices,
personal characteristics, interpretation of life events, and overall purpose (Halevy &
Gross, 2024; Schachter & Ben Hur, 2019). The Bible explains that “children are a
heritage from the Lord” and instructs parents in ways to help influence child/adolescent
identity formation (English Standard Version Bible, 2001/2016, Proverbs 1:8-9; 13:24;
Psalms 127:3; Mark 9:37; Ephesians 6:1-4; Colossians 3:20). For instance, the Bible
proclaims that parents should guide and instruct children in the ways of God “…these
words that I command you today shall be on your heart. You shall teach them diligently
to your children and shall talk of them when you sit in your house, and when you walk by
the way, and when you lie down, and when you rise” (English Standard Version Bible,
2001/2016, Deuteronomy 6:6-7). Additionally, if parents are grounding and teaching
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children/adolescents the ways of God, the Bible gives specific instructions for children on
how they are to respect, engage, and interact with one’s parents and God thus further
influencing one’s identity (English Standard Version Bible, 2001/2016, Exodus 20:12;
Proverbs 20:11; Isaiah 54:13; Ephesians 6:1-2; Colossians 3:30; Halevy & Gross, 2024;
Schachter & Ben Hur, 2019).
Layton et al. (2012) conducted a qualitative study investigating religion’s role in
identity formation in American adolescents and late-stage adolescents/young adults. The
researchers found that adolescent connections with close family members, friends,
schoolteachers, religious community members, and church youth workers provided living
role models for the adolescents exposing the teenagers to various religious worldviews,
practices, and ideological frameworks that contributed to one’s nuanced identity
formation (Layton et al., 2012). Nevertheless, the researchers point out that one’s
spiritual growth and development could be hindered by these social relationships if
autonomy and openness were not embraced since free will and choice are essential to
healthy spiritual development thus reaffirming the value of an authoritative parenting
style for healthy attachment and development (Good & Willoughby, 2008; Goodman &
Dyer, 2019; Layton et al., 2012; Lavrič & Naterer, 2020; Naudé & Capitano, 2020; Nur
et al., 2021). Naudé and Capitano (2020) also found that teenagers valued choice and
authenticity when contemplating and/or integrating spirituality or religious practice into
one’s life. The researchers concluded that true identity development was only possible
through unhindered investigation by adolescents/ young adults, which was identified as
an ongoing life-long process involving religious knowledge, personal social relationships,
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and life experiences impacting thoughts, feelings, and one’s behaviors (Layton et al.,
2012; Naudé & Capitano, 2020).
Authoritative Parenting Practices
Lavrič and Naterer (2020) referred to Diana Baumrind as the first person to coin
the term authoritative parenting in 1971. The authoritative parenting style has been
described as a method of directing and guiding children/adolescents that is direct,
rational, warm, flexible, receptive, encourages parent/child discourse, and fosters the
autonomy of the child/adolescent in a developmentally appropriate way facilitating
identity formation (Goodman & Dyer, 2019; Lavrič & Naterer, 2020). Cross-sectional
research was conducted on survey data collected from individuals aged 14-29 across 10
European counties and found life satisfaction was highest among participants who rated
parents as having an authoritative parenting style (Lavrič & Naterer, 2020). Moreover,
parents that embraced and practiced more authoritative parenting had a greater chance of
passing along individual faith practices (Goodman & Dyer, 2019).
Even though there remain numerous social and societal components that
contribute to adolescent/young adult spiritual and religious maturation, parents that
engaged authoritative parenting, rather than authoritarian (strict, inflexible and directive)
or permissive (uninvolved, anything allowed) parenting, had overall greater influence on
spiritual formation and life satisfaction (Good & Willoughby, 2008; Goodman & Dyer,
2019; Lavrič & Naterer, 2020). Moreover, teenagers and late-stage adolescents/young
adults of authoritative parents were theorized to be more securely attached, indicated
greater expressions of love and affection from parents, experienced more emotional,
behavioral, and psychical support, and had significantly fewer reports of anxiety or
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depression (Good & Willoughby, 2008; Goodman & Dyer, 2019; Lavrič & Naterer,
2020; Nur et al., 2021). Furthermore, Nur et al. (2021) conducted research on 225 late-
stage adolescents/young adults and found that smartphone/social media addiction and
overuse had significantly higher associations with permissive and authoritarian parenting
styles in the study; however, there was no significant effect with the authoritative
parenting group in the study showing authoritative parenting appears to help reduce
smartphone/social media addiction and overuse.
Self-Control Practices
Paul proclaims that “All things are lawful for me, but not all things are helpful.
“All things are lawful for me, but I will not be dominated by anything” (English Standard
Version Bible, 2001/2016, 1 Corinthians 6:12). Therefore, from a biblical perspective, it
is important to exercise self-control with parents being instructed biblically to teach,
discipline, and help teenagers achieve self-control for the Bible says “do not provoke
your children to anger, but bring them up in the discipline and instruction of the Lord”
and “a man without self-control is like a city broken into and left without walls” (English
Standard Version Bible, 2001/2016, Ephesians 6:1-4; Proverbs 22:6; 25:28). Adolescents
don’t always understand what is positive or detrimental to healthy development;
however, parents play a vital role in modeling and influencing behavior including various
aspects of self-control (Bozzola et al., 2022; Godsell & White, 2019; Hefner et al., 2019;
Li et al., 2024; Nur et al., 2021; Padilla-Walker et al., 2018).
Wei et al. (2020) conducted research with 459 students in junior high school and
found that conflict between parents was positively correlated to significant increases in
internet addiction within the entire adolescent cohort. The research discovered the more
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prevalent the interparental conflict, the greater the chance of internet addiction for the
adolescents in the study (Wei et al., 2020). Moreover, the research showed that
interparental conflict also contributed to insecure attachments between the adolescents
and parents, which also helped predict internet addiction (Wei et al., 2020). Furthermore,
interparental conflict appeared to decease adolescent self-control practices that in turn
increased internet addiction within the cohort (Wei et al., 2020). Inversely, adolescents
with higher reports of self-control appeared to be living in families with lower perceived
parental conflict (Wei et al., 2020).
Additionally, Bolger et al. (2022) conducted a 15-year longitudinal study and
uncovered evidence that indicated lower maternal self-control practices appeared to
directly contribute to lower adolescent self-control at age 15 for the 1,526 families that
participated in the National Institute of Child Health and Human Development’s
(NICHD) Study of Early Child Care and Youth Development (SECCYD) from 1991 to
2007. Li et al. (2024) conducted research with 426 families who had adolescents in grade
7 that appeared to support the previous research findings. The researchers explored the
effects of perceived parental self-control on an adolescent’s psychological adjustment and
self-control (Li et al., 2024). The researchers uncovered that the greater the scores on the
parental self-control scale, the more positive the effect on an adolescent’s psychological
health and stability (Li et al., 2024). The research also showed that an adolescent’s
propensity for psychological flexibility was especially evident when father’s reported
lower levels of stress and mothers reported more mindful parenting practices aligned with
an authoritative parenting style, which displayed a direct correlation with improvements
in adolescent wellbeing and self-control practices (Goodman & Dyer, 2019; Lavrič &
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Naterer, 2020; Li et al., 2024). Moreover, self-control by the parents appeared to help
convey and model effective self-control practices to the adolescents, which in turn
reduced the teenagers stress levels (Li et al., 2024). The research also highlighted that a
father’s inability to manage stress or exert self-control had a greater negative influence on
overall family health than a mother’s self-control practices thus exerting greater direct
influence on the negative impacts experienced by the adolescent’s concerning
psychological wellbeing and the ability of the teenagers to self-regulate (Li et al., 2024).
Summary
In closing, the literature review displayed a plethora of research regarding smart
phone/social media consumption within the whole adolescent community and how these
modern technological tools continue to grow in use and popularity (Alosaimi et al.,2016;
Dixon, 2022; Feldman, 2022; Giunchiglia et al., 2018; Gumport et al., 2021; Kumar &
Pati, 2021; Tandon et al., 2020; Thomée, 2018). In the United States alone, over 95
percent of American teenagers and late-stage adolescents/young adults have access to
smartphone devices and social media platforms daily (Alosaimi et al.,2016; Dixon, 2022;
Feldman, 2022; Giunchiglia et al., 2018; Gumport et al., 2021; Kumar & Pati, 2021;
Tandon et al., 2020; Thomée, 2018). Thus, a distinct correlation is being made among
researchers connecting the rise in smartphone/social media consumption to statically
significant reductions in minutes of sleep obtained per night, sleep onset problems, and
sleep disturbances throughout the night for many within the entire adolescent community
(Hughes & Burke, 2018; Kumar & Pati, 2021; van den Eijnden et al., 2021).
The number of hours of sleep per night has dropped well below the recommended
7-9 hours for countless late-stage adolescents/ young adults, which appears to be creating
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a major public health calamity (Hughes & Burke, 2018; Kumar & Pati, 2021; Palmer et
al., 2018). There are numerous advantages to smartphone/social media technology;
however, empirical evidence is beginning to show that the problems associated with
smartphone/social media operation may indeed outweigh the positives (Applequist et al.,
2020; D’Souza et al., 2021; Kumar & Pati, 2021). Consequently, the psychiatric
community recently added Internet Addition Disorder to the DSM-5, and many within
the field of psychology and research have requested additional disorders be added to the
DSM including FOMO and social media disorder (Alosaimi et al., 2016; Alotaibi et al.,
2022; Franchina et al., 2018; Malaeb et al., 2020; Rozgonjuk et al., 2020). Additionally,
smartphones/social media have been directly linked to growing reports of emotional
dysregulation, symptoms of anxiety and depression, reports of loneliness, cyberbullying,
increases in suicidal ideation, and a decline in academic success (Amez et al., 2020;
Evers et al., 2020; Giunchiglia, 2018; Palmer et al., 2022; Worley, 2018; Sung et al.,
2020). Moreover, collective research evidence is starting to link smartphone/social media
operation and sleep issues to a rise in obesity and type two diabetes (Alosaimi et al.,2016;
Alotaibi et al., 2022; Kim et al., 2015; Palmer et al., 2022; Worley, 2018). Furthermore,
research is starting to connect smartphone/social media use to increased problems with
spiritual formation and connection within the entire adolescent cohort (Chow, 2022;
Omede & Akintunde, 2023; Shim, 2021 Uecker & McClure, 2022).
Empirical research exploring ways to alter the negative impacts of
smartphone/social media operation are in short supply with only three known research
endeavors specifically designed to alter smartphone/social media consumption before
bedtime (Bartel et al., 2018; Harris et al., 2015; He et al., 2020). Nevertheless, research
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continues to show that parents have the most influence over smartphone/social media
consumption, sleep habits, and spiritual development within the whole adolescent
community (Bozzola et al., 2022; Godsell & White, 2019; Hefner et al., 2019; Nur et al.,
2021; Padilla-Walker et al., 2018). There remain numerous gaps in the research exploring
how altering or stopping smartphone/social media before bedtime may impact sleep
latency, minutes of sleep per night, and awakenings throughout the night creating a vast
area for future investigation (Bartel et al., 2018; Harris et al., 2015; He et al., 2020).
The proposed research study endeavors to add empirical insights into the effects
on sleep when smartphone/social media use is stopped one hour before bedtime in late-
stage adolescents/young adults. Furthermore, this research study hopes to show
quantifiable support through the research affirming one’s ability to make positive
changes to sleep problems by limiting/stopping nighttime smartphone/social media
consumption.
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CHAPTER 3: RESEARCH METHOD
Overview
The purpose of this quasi-experimental research study will be to examine the
effects of stopping smartphone/social media usage prior to bedtime on sleep latency,
duration, and awakenings throughout the night in late-stage adolescents/young adults
aged 18-21. This research study will randomly separate research participants into a
control group that will have no limitations on adolescent smartphone/social media usage
before bedtime, and an experimental group that will require the late-stage
adolescent/young adult participants to stop smartphone/social media use one hour prior to
bedtime. This chapter will provide an overall outline of the research design, which
defines the research questions, hypothesis, design of the study, describes the participants
in the study, the research procedures, and the instruments utilized. Furthermore, this
section will define the operational variables, highlight the data analysis procedure, and
discuss the delimitations, assumptions, and limitations of the study.
Research Questions and Hypotheses
The purpose of this quantitative quasi-experimental research study will be to
uncover the impacts on sleep when smartphone/social media use is stopped by
participants one hour prior to bedtime for late-stage adolescents/young adults aged 18-21.
Research Question(s) and Hypotheses
RQ1: How will stopping smartphone/social media use one hour prior to bedtime
affect minutes of sleep for late-stage adolescents/young adults aged 18-21?
RQ 2: How will stopping smartphone/social media use one hour prior to bedtime
affect sleep latency for late-stage adolescents/young adults aged 18-21?
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RQ 3: How will stopping smartphone/social media use one hour prior to bedtime
affect the number of awakenings during the night for late-stage adolescents/young
adults aged 18-21?
Alternative Hypothesis.
Ha1 = There will be a significantly lower number of awakenings during the night when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
Ha2 =There will be a significant reduction in the minutes it takes to fall asleep when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
Ha3 =There will be a significantly higher number of minutes sleep when
smartphone/social media operation is stopped one hour before bedtime when compared
with no smartphone/social media restrictions before bedtime.
Null Hypothesis.
Ho 1= There will be no significant differences in the number of recorded awakenings
during the night in late-stage adolescents/young adults when smartphone/social media
usage is stopped one hour before bedtime.
Ho2 =There will be no significant reductions in the minutes it takes to fall asleep in late-
stage adolescents/young adults when smartphone/social media usage is stopped one hour
before bedtime.
Ho 3=There will be no significant increases in minutes slept per night in late-stage
adolescents/young adults when smartphone/social media usage is stopped one hour
before bedtime.
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Research Design
This research study will utilize a quasi-experimental research design with
convenience sampling whereby research participants knowingly agree to participate in
research regarding their smartphone/social media usage. The quasi-experimental research
design will be used since the late-stage adolescent/young adult participants must already
own and operate a smartphone device and have full access to social media platforms.
Consequently, there may be unaccounted and uncontrolled differences between the
control and experimental groups since convenience sampling will be used, which will
also make the quasi-experimental design the best approach for this study (Carter et al.,
2024; Miller et al., 2020).
This research design will be advantageous for this study as it will evaluate the
links between an intervention (limiting smartphone use one hour before bedtime), and the
outcomes (number of awakenings, sleep latency, and sleep duration) while incorporating
random selection of participants into the control and experimental groups using an
Internet based randomizing tool called the Research Randomizer (Research Randomizer,
1997-2024). Research scientists have indicated that a quasi-experimental research design
is much like an RCT when using random assignment, which will provide a way to
establish stronger causation (Carter et al., 2024; Maciejewski, 2020; Miller et al., 2020).
Convenience sampling has been used in a vast majority of social science research
as it is considered to be the most cost effective, easily obtained, and quickly gathered
approaches when compared to other sampling methods (Scholtz, 2021; Winton & Sabol,
2021). Therefore, convenience sampling will be utilized in this research study to avoid
barriers to obtaining research participants, to overcome time constraints, and since it is
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cost effective. In addition, convenience sampling will allow the research to broaden the
research footprint by accessing participants outside the geographical area via social
media recruitment through Facebook, Twitter (X), and Instagram. Once research
participants have met inclusion criteria, they will be randomly dispersed into two separate
groups using the research randomizer website (Research Randomizer, 1997-2024). The
first group, the control group, will have no limitations placed on nighttime
smartphone/social media use. The second group, the experimental group, will have
smartphone/social media restrictions for a duration of seven days, whereby the
participants will stop their smartphone/social media use one hour prior to the reported
bedtimes.
Participants
This research study will be comprised of late-stage adolescents/young adults
ranging in age from 18 to 21. The research participants will be recruited through
convenience sampling techniques such as local flyers placed throughout Dubbo, New
South Wales in Australia, social media platforms such as Facebook, Twitter (X), and
Instagram, and a local newspaper advertisement.
There has been limited research exploring sleep duration, latency, and awakenings
when smartphone/social media use is stopped prior to bedtime, and only one with a
control group and experimental group (Bartel et al., 2018; He et al, 2020; Harris et al.,
2015). The only three studies that have been conducted in this area were comprised of 63
adolescent participants (Bartel et al., 2018), 36 college aged participants in a laboratory
based RCT (He et al, 2020), and a study that eliminated electronics after 10pm that was
comprised of 84 adolescent student athletes (Harris et al., 2015). Therefore, this research
65
study will utilize the a-priori method to determine the minimal sample size needed when
the effect size is set at 0.5 (medium effect), the desired statistical power level is 0.8, and
the probability level is 0.05. The results indicate that a minimum sample size of 102
participants, or 51 per group (control and experimental), are needed for the current
research study to obtain more precise results.
Study Procedures
Research approval will be requested and received from Liberty University
Institutional Review Board (IRB) prior to conducting any research for this study.
Following IRB approval, the data collection for this study will commence. The late-stage
adolescents/young adults aged 18-21 will be recruited through social media platforms
such as Facebook, Twitter (X), and Instagram. Additionally, flyers will be posted
throughout Dubbo, NSW in Australia, and a local advertisement for the study will be
published in the Dubbo Photo News. The recruitment outlets (social media platforms,
advertisements, flyers) will all include a quick response (QR) code that will link to a
questionnaire through Survey Monkey.
Once the participants access Survey Monkey via the QR code, participants will
complete an initial questionnaire. The initial questionnaire will contain five yes or no
questions concerning the qualifying criteria for the study. The late-stage
adolescent/young adult participants will have to answer yes to the first three questions
and answer no to questions four and five to qualify and proceed to the next steps of the 7-
day research study. The questions will be as follows:
Q1: Are you between the ages of 18-21?
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Q2: Do you have access to your own smartphone device with at least one active
social media account?
Q3: Are you willing to stop your smartphone device use one hour before bedtime
for the 7-day research period?
Q4: Do you have a current or former mental health or medical diagnosis such as
depression, anxiety, psychotic/mood disorder, sleep disorder, and/or
neurological/neurodevelopmental disorder?
Q5: Do you take medication to treat a mental health and/or medical diagnosis?
The participants that do not qualify for the research study will be thanked for their
time and asked to leave and/or close out the questionnaire. The participants that qualify
for the 7-day research study will automatically continue to the research information page
where they will be required to read the information form (Appendix A), check yes to the
study, and provide the date. The participants will then be directed to fill out the
following: The Demographic Questionnaire (Appendix B), The Pittsburgh Sleep Quality
Index (PSQI) (Appendix C), and The Pre-Sleep Arousal Scale (PSAS) (Appendix D).
Next, once a minimum of 102 qualified late-stage adolescent/ young adult
participants are identified for the 7-day research study, participants will be assigned a
pseudonym to protect anonymity. The participants will be randomly dispersed into two
separate groups throughout the research gathering process with at least 51 participants in
each group (control and experimental group) using the research randomizer website
(Research Randomizer, 1997-2024). If selected for the control group, the participants will
receive an email after completing the initial survey with their identification number that
will be needed to complete the post research Pittsburgh Sleep Quality Index (PSQI) and
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Pre-Sleep Arousal Scale (PSAS), detailed instructions for the experiment, and the QR
code for the post research surveys that they can access on day 8 of the study.
Additionally, if the participants are selected to be in the experimental group, the
participants will receive an email instructing them to remove the smartphone device one
hour before bedtime each day for the 7-day research period. The email will also provide
the experimental participants with their identification number that will be needed to
complete the post research Pittsburgh Sleep Quality Index (PSQI) and Pre-Sleep Arousal
Scale (PSAS), detailed instructions for the experiment, and the QR code for the post
surveys that they can access on day 8 of the study. Furthermore, if the goal of 102
participants is not reached after the first two months of the research study, the
snowballing technique, also known as referral or chain sampling, will be utilized to try
and obtain additional research participants (Ting et al., 2025). The snowballing research
technique refers to researchers asking initial research participants who completed the
research study to recruit additional research participants by accessing and sharing the
research study with the participants own family, friends, and other known acquaintances
in their social networks (Ting et al., 2025). However, the snowballing technique will only
be utilized if necessary to achieve a greater number of research participants thus
increasing research reliability.
Instrumentation and Measurement
Demographic Information
Demographics will be obtained through the initial survey completed before the
experiment. The late-stage adolescent/young adult participants will be required to provide
their basic demographic information on this consent form, which will include their age,
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sex, and ethnicity. Additionally, the late-stage adolescent/young adult participants will be
required to answer the following question: Over the past week, how many times a night
did you wake (none; 1-2; 3-4; 5 or more)?
Pittsburgh Sleep Quality Index
The Pittsburgh Sleep Quality Index (PSQI) will be used by the participants to
measure sleep quality. The PSQI will provide an overall score from 0 to 21 with higher
scores indicating greater sleep problems (Buysse et al., 1989; Huang & Zhao, 2020). The
PSQI has been utilized for decades and is embraced by the research community as a
highly reliable and effective self-report measure designed to obtain a rating for overall
sleep quality (Buysse et al., 1989; He et al., 2020). This questionnaire has been used to
identify participants who perceive their sleep as either good (score of 0 to 4) to poor (5 to
21) (Buysse et al., 1989; He et al., 2020). The PSQI is described as an instrument
designed to gather sleep information from seven domains including perceived sleep
quality, sleep onset, length of sleep, sleep habits, the effectiveness of sleep, sleep
interruptions, and daytime sleepiness (Buysse et al., 1989; Huang & Zhao, 2020; Sancho-
Domingo et al, 2024). There have been numerous studies conducted to analyze the
psychometric properties of the PSQI (Sancho-Domingo et al, 2024; Scialpi et al., 2022;
Wang et al., 2022); however, the designers initial evaluation uncovered an internal
reliability of a = .83, a test–retest reliability of .85 for the global scale, a sensitivity of
89.6%, and a specificity of 86.5% (Buysse et al., 1989).
Pre-Sleep Arousal Scale
The Pre-Sleep Arousal Scale (PSAS) is a 16-question measure designed to obtain
a pre-sleep arousal rating derived from one’s perceived difficulty in falling asleep at night
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(He et al., 2020; Nicassio et al., 1985; Scott et al., 2019). Late-stage adolescent
participants will be required to rate themselves on the 16 questions from one to five, with
1 being “not at all”, 2 being “slightly,” 3 being “moderately,” 4 being “a lot,” and 5 being
“extremely” (Nicassio et al., 1985). The PSAS questions will be scored and tallied to
garner a total score between 16 to 80 with higher scores indicating greater pre-sleep
arousal (Nicassio et al., 1985; Scott et al., 2019). The questions will be organized into
two separate categories that have 8 questions, with each domain designed to measure
either somatic or cognitive symptoms (He et al., 2020; Nicassio et al., 1985; Scott et al.,
2019). The PSAS has been reported to display significant evidence of validity and
reliability with internal reliability of a = .89, a test–retest reliability of .87 (Nicassio et al.,
1985).
Operationalization of Variables
Variable One – will be the independent variable (IV) in the current research study,
which will be the late-stage adolescents’/young adults’ smartphone/social media
restriction. The IV will be operationally defined for the control group as the participants
having unlimited access to their smartphone/social media before bedtime for a 7-day
period. The IV will be operationally defined for the experimental group as the
participants’ restricting their smartphone/social media access, which will be stopped one
hour prior to bedtime for the 7-day research study.
Variable Two – will be a dependent variable (DV) called sleep duration. This DV in the
study will be operationally defined as the minutes of sleep obtained by the late-stage
adolescent/young adult participants throughout the night, which will be measured by
using a pre and post PSQI.
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Variable Three – will be a DV called sleep latency or sleep onset. This DV in the study
will be operationally defined as the number of minutes it takes for the late-stage
adolescent/young adult to fall asleep each night for the 7-day research study, which will
be measured by using a pre and post PSAS.
Variable Four – will be a DV called sleep disturbances or nighttime awakenings. This
DV in the study will be operationally defined as the number of awakenings throughout
the night, which will be obtained through the nighttime awakening question gathered in
conjunction to the pre and post PSQI and the PSAS.
Data Analysis
The statistical analysis will be conducted using the most recent SPSS Statistics
software, which is version 29. Demographic information such as age will be calculated in
this study with mean averages provided in the results section of this study. Moreover, sex
and ethnicity data of the participants will be recorded, and percentage rates will be shared
in the final analysis. The research study will be testing the group baseline differences and
post research differences using the unpaired t-test in both the control group and the
experimental group on the PSQI and the PSAS. Moreover, the between-subject analyses
of variance, with one between-group factor (intervention vs control group) and one
within-subjects/repeated measures factor (baseline vs follow-up), will be conducted to
ascertain if there will be any impact by the intervention on the participants scores in the
minutes of sleep acquired, sleep onset, and sleep awakenings. The main effects on the
length of sleep and the interaction effects will be assessed and provided in the research
studies results section. If the interaction effects that will be gathered are considered
statistically significant a simple effect analysis will then be included in the results section.
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Delimitations, Assumptions, and Limitations
Delimitations
The participant population in the research study will consist of late-stage
adolescent/young adult participants aged 18-21. This population will be targeted because
there has been a lack of research accessing the impacts of restricting smartphone/social
media use within the late-stage adolescent/young adult population. Therefore, this
research study will seek to explore how the removal of smartphone/social media for one
hour prior to bedtime in this specific adolescent population impacts minutes of sleep,
sleep onset, and awakenings throughout the night. Secondly, this adolescent age grouping
of 18-21 will be used because it reflects the average ages of many college students, and
this adolescent age group is comprised of consenting adults. Third, the age range of 18-21
will be used because it is essential for the adolescents to own and operate their own
smartphone device and belong to at least one social media platform, which is more
common for older adolescents/ young adults. Another delimitation in the research study
will be the limiting of the smartphone/social media use one hour prior to bedtime. This
boundary will be put in place to study how nighttime smartphone/social use behavior
impacts the sleep habits of the 18-21-year-old late-stage adolescent/young adult
participants since the hours of sleep for the entire adolescent population has been
declining drastically in conjunction with the dramatic rise of smartphone/social media use
in recent decades.
Assumptions
There will be six main assumptions in this research study. First, it will be assumed
that smartphone/social media use is impacting sleep duration in late-stage
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adolescents/young adults aged 18-21. Secondly, it will be assumed that smartphone/social
media use is also impacting the length of time it takes the late-stage adolescents/young
adults to fall asleep at night. Third, it will be assumed that smartphone/social media use is
also impacting the quality of sleep in the form of nighttime awakenings for late-stage
adolescents/young adults aged 18-21. Fourthly, it will be assumed that sleep duration,
onset, and awakenings all have negative emotional, psychological, physical, spiritual, and
academic implications for late-stage adolescents/young adults aged 18-21. Lastly, it will
be assumed that by altering one’s smartphone/social media use at night before bedtime
that late-stage adolescents/young adults aged 18-21 could improve overall sleep quantity
and quality, which could in turn improve overall health.
Limitations
There will also be numerous limitations in the current research study. First, the
study will embrace a convenience sampling method for collecting participants, which will
restrict the range of participants gathered for the research study. Moreover, since
participants will be collected through social media, advertisements, and local flyers this
too will narrow the selection of late-stage adolescent/young adult participants perhaps
limiting the range of diversity being collected by the data. Another limitation will include
the research being conducted in the home environment with no definitive way of ensuring
the participants will adhere to restricting the use of their smartphone device in the
experimental group, and that both the control and experimental group participants will
consistently restrict their use of other forms of technology including tablets, computers,
televisions, and gaming devices for the week-long research study. In addition, the
research will rely heavily on self-report data on the late-stage adolescents’/young adults’
73
sleep duration, latency, and awakenings, which will have the potential to skew the results.
Furthermore, there will be no hard-physical evidence from a polysomnographic device
confirming the actual sleep reports form the questionnaires.
Summary
In closing, the overall purpose of this quasi-experimental research study will be to
obtain a minimum of 102 late-stage adolescent/young adult participants aged 18-21. The
late-stage adolescent/young adult participants will be recruited through convenience
sampling in order to explore how stopping their smartphone/social media use one hour
prior to bedtime effects minutes of sleep, sleep latency, and the number of awakening
throughout the night for these late-stage adolescents/ young adults. The research study
will be set up to randomly separate the acquired late-stage adolescent/young adult
research participants that meet inclusion criteria into a control group that have no
smartphone/social media limitations, and an experimental group, which will be
comprised of late-stage adolescent/young adult participants required to stop
smartphone/social media use one hour prior to bedtime. The research study will utilize
pre and post PSQI scores and pre and post PSAS scores accessed through Survey
Monkey to obtain the research data over a 7-day period.
The analysis of the data will be conducted using SPSS Statistics Software Version
29. A between subjects and within-subject t-tests will be conducted to determine if any
statistically significant data is observed when the smartphone/social media use is altered
by the adolescents. Moreover, the study will incorporate specific research parameters
including age restrictions for the late-stage adolescent/young adult participants, which
will be utilized due to a lack of research in the adolescent population, the increasing sleep
74
concerns with the adolescent population, and the increasing access to smartphone/social
media platforms. The research study will also hold various assumptions such as
smartphones/social media having some impact on the length of sleep, the onset of sleep,
and the number of awakenings a night for late-stage adolescents/young adults aged 18-
21, and that these various sleep issues impact numerous aspects of one’s health in these
adolescents. Finally, numerous limitations will exist in the research study including the
use of convenience sampling, reliance on the participants aged 18-21 completing
questionnaires on their sleep habits, and a lack of physical data from smartphone/social
media tracking applications to verify smartphone/social media usage or
polysomnographic data to verify the reports on the late-stage adolescents’/young adults’
sleep. Nevertheless, the research project will be a valuable step in the right direction as
there remains an enormous lack of research into how altering smartphone usage
positively or negatively affects the sleep health of late-stage adolescents/young adults.
75
CHAPTER 4: RESULTS
Overview
The purpose of this quasi-experimental research study was to investigate the
effects of stopping smartphone/social media usage prior to bedtime on sleep latency,
duration, and awakenings throughout the night in late-stage adolescents/young adults
aged 18-21. There were three main research questions that guided the study:
RQ1: How will stopping smartphone/social media use one hour prior to bedtime
affect minutes of sleep for late-stage adolescents/young adults aged 18-21?
RQ 2: How will stopping smartphone/social media use one hour prior to bedtime
affect sleep latency for late-stage adolescents/young adults aged 18-21?
RQ 3: How will stopping smartphone/social media use one hour prior to bedtime
affect the number of awakenings during the night for late-stage adolescents/young
adults aged 18-21?
To answer the research questions the study incorporated an initial questionnaire,
which was accessed via QR Code and directed the potential participants to the Survey
Monkey website. The initial screening questionnaire included five yes or no qualifying
criteria questions. To advance to the research portion of the study, the participants had to
answer yes to the first three questions and answer no to questions four and five. The
questions were as follows:
Q1: Are you between the ages of 18-21?
Q2: Do you have access to your own smartphone device with at least one active
social media account?
76
Q3: Are you willing to stop your smartphone device use one hour before bedtime
for the 7-day research period?
Q4: Do you have a current or former mental health or medical diagnosis such as
depression, anxiety, psychotic/mood disorder, sleep disorder, and/or
neurological/neurodevelopmental disorder?
Q5: Do you take medication to treat a mental health and/or medical diagnosis?
Once the participants qualified for the seven day research study, the participants were
automatically directed to the information sheet, asked to provide a valid email address,
completed demographic information which included age, gender (male, female, other, or
prefer not to answer), and ethnicity (Australian, Aboriginal, American, African
American, or Other), answered a question regarding average nighttime awakenings per
night over the past month (none, 1-2, 3-4, or 5 or more), and completed the initial PSQI
and PSAS questionnaires.
Next, the research participants were randomly separated into a control group that
had no limitations on adolescent smartphone/social media usage before bedtime, and an
experimental group that required the late-stage adolescent/young adult participants to
stop smartphone/social media use one hour prior to bedtime. The participants were
assigned a pseudonym to protect anonymity and emailed to inform them as to what group
they had been assigned (control or experimental) then given explicit instruction to either
not make any changes to current smartphone/social media use (control group) or
instructed to stop smartphone/social media use (experimental group) one hour before
bedtime for the next seven days. Additionally, a QR code was provided in the email that
linked them to Survey Monkey, which asked the follow-up question regarding average
77
nighttime awakenings per night over the research week (none, 1-2, 3-4, or 5 or more),
and provided them with the post-PSQI, and post-PSAS questionnaires. The participants
were asked to complete the research week and then complete the post-research surveys
on or after day eight of the research study.
This chapter will provide a comprehensive review of the results gathered from the
106 research participants obtained through random sampling and the snowballing
research technique. These outcomes are comprised of the descriptive results including
age, gender, and ethnicity results and the study findings from the pre- and post- questions
regarding one’s nighttime awakenings, the PSQI, and the PSAS questionnaires. Finally,
an overall summary of the results section is provided before advancing to the final
chapter of the study.
Descriptive Results
For this quasi-experimental research study, the a-priori method was utilized to
determine minimum sample size, which yielded a result of a minimum of 102 participants
that were needed for the current research study to ensure the reliability of results. The
research study obtained a total of 222 participants who took the initial questionnaire
accessed through the QR Code that was provided on social media, email, flyers, and
advertisements. There were 91 potential participants that were completely disqualified
from participating in the current research with 47.25% (n=43) disqualified for taking
prescription medications, 45.05% (n=41) disqualified for having a formal mental health
diagnosis, .06% (n=6) having both a mental health diagnosis and taking medications, and
.01% (n=1) of participants were disqualified from the study for being unwilling to stop
smartphone/social media use one hour prior to bedtime. Additionally, there were 25
78
research participants who did not complete either the initial or the follow-up survey
leaving a total of 106 research participants in the study. Since the researcher randomly
dispersed participants into two separate groups using the research randomizer website
throughout the 3-month research gathering process, the experimental group concluded
with a total of 54 (n=54) participants and the control group concluded with a total of 52
(n=52) participants.
Demographic information was also gathered on the 106 research participants
including age, ethnicity, and gender for both the experimental (n=54) and control groups
(n=52). There were no significant differences between the experimental and control
groups identified through the data analysis concerning age, gender, and ethnicity.
Adolescents/young adults who were 18 years of age at the time of the study represented
the greatest number of participants, which was 42.6% in the experimental group and
32.7% in the control group. Moreover, the demographic information revealed two
surprising results. Although most of the flyers and advertisements for this study were
distributed throughout New South Wales, Australia, over 46% of research participants in
both the experimental and control groups identified ethnically as American. Furthermore,
there were more males than females that participated in the research study with 57 male
participants and 49 female participants.
Table 1
Participants
N
%
Experimental Group
54
50.0%
Control Group
52
48.1%
Missing
System
2
1.9%
79
Table 1 Continued
Age
Participants
N
%
.
Missing
System
2
100.0%
Experimental
Group
18
23
42.6%
19
11
20.4%
20
14
25.9%
21
6
11.1%
Control Group
18
17
32.7%
19
14
26.9%
20
10
19.2%
21
11
21.2%
Ethnicity
Participants
N
%
.
Missing
System
2
100.0%
Experimental
Group
Australian
18
33.3%
Aboriginal
5
9.3%
American
25
46.3%
Other
6
11.1%
Control Group
Australian
20
38.5%
Aboriginal
4
7.7%
American
24
46.2%
Other
4
7.7%
Gender
Participants
N
%
.
Missing
System
2
100.0%
Experimental
Group
Male
26
48.1%
Female
28
51.9%
Control Group
Male
31
59.6%
Female
21
40.4%
Study Findings
Data was analyzed using IBM SPSS Statistics Version 29 for this research study.
Baseline statistics were calculated using an unpaired or independent t-test to test
80
experimental and control group differences. There was no significant baseline differences
identified between the control and experimental groups.
Table 2
Baseline Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Pre-Nighttime
Awakenings
Experimental
Group
54
1.96
.751
.102
Control Group
52
1.87
.687
.095
Pre-Total Minutes of
Sleep (min)
Experimental
Group
54
435.39
72.998
9.934
Control Group
52
435.04
52.884
7.334
Pre-Time it Takes to
Fall Asleep (min)
Experimental
Group
54
35.87
24.592
3.347
Control Group
52
32.13
17.755
2.462
Pre-PSQI
Experimental
Group
54
8.80
4.218
.574
Control Group
52
8.06
4.513
.626
Pre-PSAS
Experimental
Group
54
33.26
10.387
1.414
Control Group
52
31.98
10.457
1.450
81
Table 2 Continued
Baseline Independent Samples Test
Levene's Test for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pre-
Nighttime
Awakenings
Equal
variances
assumed
.118
.732
.697
104
.244
.487
.098
.140
-.180
.375
Equal
variances
not
assumed
.698
103.723
.243
.487
.098
.140
-.180
.375
Pre-Total
Minutes of
Sleep (min)
Equal
variances
assumed
4.054
.047
.028
104
.489
.978
.350
12.421
-
24.281
24.982
Equal
variances
not
assumed
.028
96.673
.489
.977
.350
12.348
-
24.157
24.858
Pre-Time it
Takes to Fall
Asleep (min)
Equal
variances
assumed
1.684
.197
.894
104
.187
.374
3.736
4.180
-4.553
12.024
Equal
variances
not
assumed
.899
96.520
.185
.371
3.736
4.155
-4.511
11.982
Pre-PSQI
Equal
variances
assumed
1.111
.294
.871
104
.193
.386
.739
.848
-.943
2.420
Equal
variances
not
assumed
.870
102.852
.193
.386
.739
.849
-.946
2.423
Pre-PSAS
Equal
variances
assumed
.015
.904
.631
104
.265
.529
1.278
2.025
-2.737
5.294
Equal
variances
not
assumed
.631
103.792
.265
.529
1.278
2.025
-2.737
5.294
82
Research Question 1
The first research question was designed to investigate if there were any
significant effect on minutes of sleep for adolescents/young adults aged 18-21 when
smartphone/social media use was stopped one hour prior to bedtime. The Pittsburgh
Sleep Quality Index (PSQI) was used in the research study to measure sleep quality of
participants. An independent t-test was conducted and found a statistically significant
difference in reports of sleep quality between participants who stopped smartphone/social
media use one hour prior to bedtime (experimental group) when compared to the
participants who did not stop smartphone/social media use one hour prior to bedtime
(control group). The adolescents/young adults in the experimental group reported a
statistically significant difference in sleep quality (M=3.54, SD=3.289) when compared
with the control group (M=7.25, SD=4.715) with no pre-sleep conditions, t (104) = -
4.777, p < .001., 𝑟2=0.18, 95% CI [ -5.274, -2.152] (two-tailed), thus rejecting the null
hypothesis. There was a moderate effect on PSQI scores as anything between 0.09-0.25 is
considered a medium effect. Moreover, an additional independent t-test was conducted
on the difference of total minutes of sleep achieved by the experimental group verses the
control group following the intervention week. Although the experimental group
achieved an average of 17.43 more minutes of sleep per night on average, the results of
the test were considered statistically insignificant.
83
Table 3
Post PSQI and Total Minutes of Sleep Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Post-PSQI
Experimental
Group
54
3.54
3.289
.448
Control Group
52
7.25
4.715
.654
Post-Total Minutes of Sleep
(min)
Experimental
Group
54
451.85
61.555
8.377
Control Group
52
434.42
60.380
8.373
Post PSQI Independent Samples T-Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-
PSQI
Equal
variances
assumed
11.961
<.001
-
4.717
104
<.001
<.001
-3.713
.787
-5.274
-2.152
Equal
variances
not
assumed
-
4.686
90.812
<.001
<.001
-3.713
.792
-5.287
-2.139
Post-
Total
Minutes
of Sleep
(min)
Equal
variances
assumed
.381
.539
1.471
104
.072
.144
17.429
11.848
-6.067
40.924
Equal
variances
not
assumed
1.472
103.963
.072
.144
17.429
11.844
-6.058
40.916
84
Figure 1
Post PSQI Scores
Additionally, a paired samples t-test, also known as a repeated samples t-test, was
conducted to compare sleep quality before and after the experimental week in both the
experimental and control groups. There were no significant differences found in the pre-
PSQI and post-PSQI scores in the control group. However, the test found a statistically
significant difference in pre-PSQI and post-PSQI scores in the experimental group. The
adolescents/young adults in the experimental group reported a statistically significant
difference in sleep quality on the pre-PSQI (M=8.80, SD=4.218) when compared with the
post-PSQI (M=3.54, SD=3.289) following the research week, t (54) = 12.918, p < .001.,
85
𝑟2=0.75, 95% CI [ 4.443, 6.076] (two-tailed), thus rejecting the null hypothesis. There
was a large effect on pre- and post-PSQI scores in the experimental group as anything
above 0.26 is considered a large effect, and in the experimental group this number was
0.75.
Table 4
Experimental Group PSQI Paired Samples Statistics
N
Correlation
Significance
One-
Sided
p
Two-
Sided
p
Pair
1
Pre-PSQI &
Post-PSQI
54
.708
<.001
<.001
Experimental Group PSQI Paired Samples T-Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std. Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-PSQI
- Post-
PSQI
5.259
2.992
.407
4.443
6.076
12.918
53
<.001
<.001
86
Figure 2
Experimental Group Pre-and Post PSQI Scores
Furthermore, a paired samples t-test was conducted to compare overall total
minutes of sleep obtained by participants before and after the experimental week in both
the experimental and control groups. There were no differences found in the number of
minutes slept before and after the experimental week in the control group. However, in
the experimental group the correlational test found a statistically significant difference in
the minutes of sleep obtained after the research week when compared to the minutes of
sleep obtained before the research week in the experimental group. The
adolescents/young adults in the experimental group reported a statistically significant
lower number of minutes slept obtained in the month leading up to the research study
(M=435.39, SD=72.998) when compared to the week of the research study (M=451.85,
87
SD=61.555), t (54) = -2.859, p < .006,
𝑟2 =0.13, 95% CI [ -28.011, -4.915] (two-tailed),
thus rejecting the null hypothesis. There was a medium effect observed in the
experimental group when comparing the average minutes of sleep in the experimental
group before and after the study as anything between 0.09-0.25 is considered a medium
effect. On average, the experimental group increased their nightly sleep by 16.46 total
minutes.
Table 5
Experimental Group Total Minutes of Sleep Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair 1
Pre-Total Minutes of
Sleep (min)
435.39
54
72.998
9.934
Post-Total Minutes of
Sleep (min)
451.85
54
61.555
8.377
Experimental Group Total Minutes of Sleep Paired Samples T-Test
Paired Differences
t
df
Significance
Mean
Std.
Deviatio
n
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided p
Lower
Upper
Pair
1
Pre-Total
Minutes of Sleep
(min) - Post-
Total Minutes of
Sleep (min)
-
16.46
3
42.308
5.757
-28.011
-4.915
-2.859
53
.003
.006
88
Figure 3
Experimental Group Pre-and Post Total Minutes of Sleep
Research Question 2
The second research question investigated whether there was any significant
effect on sleep latency in adolescents/young adults aged 18-21 when smartphone/social
media use was stopped one hour prior to bedtime. The Pre-Sleep Arousal Scale (PSAS)
was used in the research study to measure the sleep latency of participants. An
independent t-test was conducted and found a statistically significant difference in reports
of sleep latency between adolescents who stopped smartphone/social media use one hour
prior to bedtime (experimental group) when compared to the adolescents/young adults
who did not stop smartphone/social media use one hour prior to bedtime (control group).
The participants in the experimental group reported a statistically significant difference in
89
sleep latency (M=23.72, SD=5.731) when compared with the control group (M=31.37,
SD=11.561) with no pre-sleep conditions, t (104) = - 4.337, p < .001., 𝑟2=0.15, 95% CI [
-11.138, -4.148] (two-tailed), thus rejecting the null hypothesis. The results show there
was a moderate effect on one’s sleep latency when stopping one’s smartphone/social
media use one hour before bedtime.
Table 6
Post PSAS Group Statistics
Participants
N
Mean
Std. Deviation
Std. Error Mean
Post-PSAS
Experimental Group
54
23.72
5.731
.780
Control Group
52
31.37
11.561
1.603
Post PSAS Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-
PSAS
Equal
variances
assumed
32.811
<.001
-
4.337
104
<.001
<.001
-7.643
1.762
-
11.138
-4.148
Equal
variances
not assumed
-
4.287
74.007
<.001
<.001
-7.643
1.783
-
11.195
-4.091
90
Figure 4
Post PSAS scores
Moreover, an additional independent t-test was conducted on the difference of
minutes it takes to fall asleep by the experimental group verses the control group
following the intervention week. The adolescents/young adults in the experimental group
reported a statistically significant difference in actual minutes it took to fall asleep
(M=19.72, SD=8.322) when compared with the control group (M=31.54, SD=15.034)
with no pre-sleep conditions, t (104) = - 5.031, p < .001., 𝑟2=0.19, 95% CI [ -16.474, -
7.159] (two-tailed), thus rejecting the null hypothesis. This independent t-test further
validates the results from the PSAS on sleep latency as this independent t- test on minutes
of sleep it takes to fall asleep also shows that stopping smartphone use one hour prior to
bedtime had a moderate effect on the minutes it took the experimental group to fall
91
asleep. The experimental group fell asleep an average of 11.82 minutes faster on average
when compared to the control group in the research study.
Table 7
Post-Sleep Latency Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Post-Time it Takes to Fall
Asleep (min)
Experimental
Group
54
19.72
8.322
1.133
Control Group
52
31.54
15.034
2.085
Post Sleep Latency Independent Samples Test
Levene's Test
for Equality
of Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-Time
it Takes to
Fall Asleep
(min)
Equal
variances
assumed
20.512
<.001
-
5.031
104
<.001
<.001
-11.816
2.349
-
16.474
-
7.159
Equal
variances
not
assumed
-
4.980
78.927
<.001
<.001
-11.816
2.373
-
16.539
-
7.094
92
Figure 5
Post Sleep Latency in Minutes
Additionally, a paired samples t-test was conducted to compare sleep latency
before and after the experimental week in both the experimental and control groups.
There were no significant differences found in the pre-PSAS and post-PSAS scores in the
control group. However, the test found a statistically significant difference in pre-PSAS
and post-PSAS scores in the experimental group. The adolescents/young adults in the
experimental group reported a statistically significant difference in sleep latency on the
pre-PSAS (M=33.26, SD=10.387) when compared with the post-PSAS (M=23.72,
SD=5.731) following the research week, t (54) = 11.221, p < .001., 𝑟2=0.70, 95% CI [
7.832, 11.242] (two-tailed), thus rejecting the null hypothesis. There was a large effect on
pre- and post-PSAS scores in the experimental group displaying a high correction
93
between stopping smartphone/social media use one hour before bedtime and a reduction
in problems falling asleep.
Table 8
Experimental Group PSAS Paired Samples Statistics
Mean
N
Std. Deviation
Std. Error Mean
Pair 1
Pre-PSAS
33.26
54
10.387
1.414
Post-PSAS
23.72
54
5.731
.780
Experimental Group PSAS Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided p
Two-
Sided p
Lower
Upper
Pair
1
Pre-PSAS -
Post-PSAS
9.537
6.246
.850
7.832
11.242
11.221
53
<.001
<.001
Figure 6
Experimental Group Pre-and Post PSAS Scores
94
Moreover, a paired samples t-test was conducted to compare the minutes it took to
fall asleep by adolescents before and after the experimental week for both the
experimental and control groups. There were no differences found in the minutes it took
to fall asleep before and after the experimental week in the control group. Nevertheless,
in the experimental group the repeated measures t-test found a statistically significant
reduction in the minutes it took to fall asleep after the research week when compared to
the minutes it took to fall asleep before the research week in the experimental group. The
adolescents/young adults in the experimental group reported a statistically significant
higher number of minutes in the month leading up to the research study to fall asleep
(M=35.87, SD=24.592) when compared to the week of the research study (M=19.72,
SD=8.322), t (54) = 5.643, p < .001, 𝑟2=0.37, 95% CI [ -28.011, -4.915] (two-tailed),
thus rejecting the null hypothesis. There was a large effect observed in the experimental
group when comparing the minutes it took to fall asleep in the experimental group before
and after the study. On average, the experimental group decreased their sleep latency by
16.15 minutes.
Table 9
Experimental Group Sleep Latency Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair 1
Pre-Time it Takes to Fall
Asleep (min)
35.87
54
24.592
3.347
Post-Time it Takes to Fall
Asleep (min)
19.72
54
8.322
1.133
95
Table 9 Continued
Experimental Group Sleep Latency Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided p
Lower
Upper
Pair
1
Pre-Time it
Takes to Fall
Asleep (min) -
Post-Time it
Takes to Fall
Asleep (min)
16.148
21.029
2.862
10.408
21.888
5.643
53
<.001
<.001
Figure 7
Experimental Group Pre-and Post Sleep Latency Scores
Research Question 3
The third research question investigated whether there were any significant effects
on nighttime awakenings in adolescents/young adults aged 18-21 when
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smartphone/social media use was stopped one hour prior to bedtime. To determine
nighttime awakenings, a sleep Likert scaling question was used to determine how many
times a night adolescents wake during the night on average with one representing zero,
two representing one to two times per night, three representing three to four times a night,
and four representing five or more times a night. An independent t-test was conducted
and found no statistically significant difference in reports of nighttime awakenings
between adolescents/young adults who stopped smartphone/social media use one hour
prior to bedtime (experimental group) when compared to the adolescents who did not
stop smartphone/social media use one hour prior to bedtime (control group). However,
both the experimental group and the control group displayed a statistically significant
change in the number of awakenings during the night when a paired samples t-test was
conducted on the number of awakenings reported for each group before and after the
research week.
The adolescents/young adults in the experimental group reported a statistically
significant reduction in the number of nighttime awakenings on the scaling question prior
to research week (M=1.96, SD=.751) when compared to the post-research week reports
(M=1.48, SD=.637), t (54) = 5.836, p < .001., 𝑟2=0.39, 95% CI [ .316, .647] (two-tailed),
thus rejecting the null hypothesis. There was a large effect on pre- and post-nighttime
awakenings in the experimental group.
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Table 10
Experimental Group Pre-and Post Nighttime Awakenings Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error Mean
Pair 1
Pre-Nighttime
Awakenings
1.96
54
.751
.102
Post-Nighttime
Awakenings
1.48
54
.637
.087
Experimental Group Pre-and Post Nighttime Awakenings Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair 1
Pre-Nighttime
Awakenings -
Post-Nighttime
Awakenings
.481
.606
.083
.316
.647
5.836
53
<.001
<.001
Figure 8
Experimental Group Pre- and Post Nighttime Awakenings
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In addition, the adolescents/young adults in the control group reported a
statistically significant reduction in the number of nighttime awakenings on the scaling
question prior to research week (M=1.87, SD=.687) when compared to the post-research
week reports (M=1.65, SD=.590), t (52) = 3.699, p < .001., 𝑟2=0.21, 95% CI [ .097, .326]
(two-tailed), thus rejecting the null hypothesis. Therefore, there appeared to be a
moderate effect on pre- and post-nighttime awakenings in the control group.
Table 11
Control Group Pre-and Post Nighttime Awakenings Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair 1
Pre-Nighttime
Awakenings
1.87
52
.687
.095
Post-Nighttime
Awakenings
1.65
52
.590
.082
Control Group Pre-and Post Nighttime Awakenings Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-
Nighttime
Awakenings -
Post-
Nighttime
Awakenings
.212
.412
.057
.097
.326
3.699
51
<.001
<.001
99
Figure 9
Control Group Pre-and Post Nighttime Awakenings
Summary
In conclusion, the results of this research study revealed a vast amount of
correlational data that further supports earlier research endeavors such as the finding by
Bartel et al. (2018) and He et al. (2020) whereby smartphone devices were stopped in the
adolescent/young adult population prior to bedtime and found quantifiable positive
changes in sleep duration and/or latency. Although demographic information and pre-
testing numbers were statistically insignificant at baseline between the experimental and
control groups, this study found a significant moderate effect between the experimental
group and the control group in post PSQI and PSAS scores with the experimental group
reporting better sleep quality and fewer problems falling asleep. Additionally, there was a
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moderate effect observed in the number of minutes it took for the adolescents to fall
asleep between the groups with the experimental group falling asleep 11.82 minutes
faster than the control group.
Moreover, when comparing pre and post PSQI and PSAS scores in the
experimental group the correlational data revealed a statistically significant large effect
thus revealing that stopping smartphone use one hour before bedtime significantly
improved one’s perceived sleep quality and significantly improved one’s perceived
problems falling asleep. Moreover, the minutes it took the experimental group to fall
asleep decreased on average by 16.15 minutes, which was also a significant large effect
observed through the research. Additionally, the experimental group increased their total
number of minutes slept per night by an average of 16.46 total minutes, which was a
statistically significant moderate effect. Finally, although there were no differences in
nighttime awakenings observed between the experimental and control group, both groups
interestingly reported a decrease in nighttime awakenings during the research week with
the experimental group’s data indicating a large effect on the number of nighttime
awakenings reported and the control data signifying a moderate effect on nighttime
awakenings reported. The following chapter will provide a more detailed discussion of
these research outcomes, the study’s implications, limitations, and further research
recommendations.
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CHAPTER 5: DISCUSSION
Overview
This chapter discusses the results associated with this quasi-experimental research
study that investigated the effects of stopping smartphone/social media usage prior to
bedtime on sleep latency, duration, and awakenings throughout the night in late-stage
adolescents/young adults aged 18-21. The chapter reviews and considers the study’s
outcomes by providing an overall summary of findings, provides an in-depth discussion
of the findings compared to previous research, the implications of the research findings,
the limitations of the study, and provides suggestions for future research endeavors.
Summary of Findings
The results of this research study revealed a significant moderate effect between
the experimental group and the control group in post PSQI sores as the participants in the
experimental group reported a statistically significant difference in sleep quality
(M=3.54, SD=3.289) when compared with the control group (M=7.25, SD=4.715) with
no pre-sleep conditions, t (104) = - 4.777, p < .001., 𝑟2=0.18, 95% CI [ -5.274, -2.152]
(two-tailed), thus rejecting the null hypothesis. Additionally, the PSAS scores revealed a
similar effect between the experimental and control group with the experimental group
reporting fewer problems falling asleep. The participants in the experimental group
reported a significant difference in sleep latency (M=23.72, SD=5.731) when compared
with the control group (M=31.37, SD=11.561) with no pre-sleep conditions, t (104) = -
4.337, p < .001., 𝑟2=0.15, 95% CI [ -11.138, -4.148] (two-tailed), thus rejecting the null
hypothesis. Moreover, there was an additional moderate effect observed in the number of
minutes it took for the adolescents to fall asleep. The adolescents in the experimental
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group reported a statistically significant difference in actual minutes it took to fall asleep
(M=19.72, SD=8.322) when compared with the control group (M=31.54, SD=15.034)
with no pre-sleep conditions, t (104) = - 5.031, p < .001., 𝑟2=0.19, 95% CI [ -16.474, -
7.159] (two-tailed), thus rejecting the null hypothesis. This equated to the experimental
group falling asleep 11.82 minutes faster on average when compared with the control
group.
Moreover, when examining the pre and post PSQI scores in the experimental
group a statistically significant difference was reported in sleep quality on the pre-PSQI
(M=8.80, SD=4.218) when compared with the post-PSQI (M=3.54, SD=3.289) following
the research week, t (54) = 12.918, p < .001., 𝑟2=0.75, 95% CI [ 4.443, 6.076] (two-
tailed), thus rejecting the null hypothesis. There was a large effect on pre- and post-PSQI
scores in the experimental group as anything above 0.26 is considered a large effect, and
the experimental group recorded an effect size of 0.75. In addition, the experimental
group also reported a statistically significant large effect with the PSAS scores. The
participants in the experimental group reported a statistically significant difference in
sleep latency on the pre-PSAS (M=33.26, SD=10.387) when compared with the post-
PSAS (M=23.72, SD=5.731) following the research week, t (54) = 11.221, p < .001.,
𝑟2=0.70, 95% CI [ 7.832, 11.242] (two-tailed), thus rejecting the null hypothesis. These
results highlight a strong correction between stopping smartphone/social media use one
hour before bedtime and a reduction in problems falling asleep. The number of minutes it
took the experimental group to fall asleep decreased on average by 16.15 minutes, which
was also a significant large effect observed through the research. The experimental group
reported it took more minutes to fall asleep in the month prior to the week of research
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(M=35.87, SD=24.592) when compared to the week of the research study (M=19.72,
SD=8.322), t (54) = 5.643, p < .001, 𝑟2=0.37, 95% CI [ -28.011, -4.915] (two-tailed),
thus rejecting the null hypothesis. Furthermore, the participants in the experimental group
reported a lower number of minutes slept in the month leading up to the research study
(M=435.39, SD=72.998) when compared to the week of the research study (M=451.85,
SD=61.555), t (54) = -2.859, p < .006, 𝑟2=0.13, 95% CI [ -28.011, -4.915] (two-tailed),
thus rejecting the null hypothesis. This data equated to the experimental group reportedly
increasing the total number of minutes of sleep per night by an average of 16.46, which
was a statistically significant moderate effect.
Finally, both the experimental and control groups reported a decrease in nighttime
awakenings during the research week. The participants in the experimental group
reported a statistically significant reduction in the number of nighttime awakenings on the
scaling question prior to research week (M=1.96, SD=.751) when compared to the post-
research week reports (M=1.48, SD=.637), t (54) = 5.836, p < .001., 𝑟2=0.39, 95% CI [
.316, .647] (two-tailed), thus rejecting the null hypothesis. This data represented a large
effect on pre- and post-nighttime awakenings in the experimental group at 0.39.
Nevertheless, the participants in the control group also reported a reduction in the number
of nighttime awakenings on the scaling question prior to research week (M=1.87,
SD=.687) when compared to the post-research week reports (M=1.65, SD=.590), t (52) =
3.699, p < .001., 𝑟2=0.21, 95% CI [ .097, .326] (two-tailed), thus rejecting the null
hypothesis and indicating a moderate effect on pre- and post-nighttime awakenings in the
control group.
Discussion of Findings
104
In previous research studies, recruiting research participants had been a huge
challenge leading to low recruitment numbers, lower statistical power, and diminished
reliability of the results (Bartel et al., 2018; He et al, 2020). For instance, Bartel et al.
(2018) conducted a weeklong quasi-experimental research study whereby adolescents
aged 14-18 were asked to stop smartphone use one hour before bedtime and use a sleep
diary to record the results. The research study started with 243 participants but concluded
with a mere 17 participants completing the full weeklong study along with the pre-and
post- sleep diary. He et al. (2020) conducted research that built off this initial research
study by Bartel et al. (2018) whereby the Chinese researchers conducted an RCT in a
controlled environment on a university campus. The researchers initially screened 72
students for the research study; however, only 38 students qualified to participate in the
research (He et al, 2020). In comparison to these previous two research studies, the
present study started with 222 participants and concluded with a total of 106 research
participants ensuring stronger statistical power from a greater number of research
participants engaging in the study within the research participants own natural home
environment.
Nevertheless, the research findings of this study do have numerous similarities in
data outcomes from both Bartel et al. (2018) and He et al. (2020) whereby smartphone
devices were stopped in the adolescent/young adult population prior to bedtime. For
example, in the research study conducted by Bartel et al. (2018) the research study was
designed for adolescents aged 14-18 to stop smartphone use 60 minutes before bedtime.
The results of Bartel et al.’s (2018) study showed that sleep latency was reduced by an
average of 17 minutes per night, which is on par with the current research study that
105
showed sleep latency reductions of 16.15 minutes on average for the participants in the
experimental group. Although He et al. (2020) only required smartphone stoppage 30
minutes prior to bedtime in their experimental group for their study, the researchers also
measured sleep latency numbers and found participants in the experimental group fell
asleep 12 minutes faster than the control group, which correlates with the current research
study that found the experimental group fell asleep 11.82 minutes faster than the control
group. These findings in this current research study appear to display further evidence
supporting the stoppage of smartphone use before bedtime is directly correlated to
statistically significant reductions in sleep onset.
Moreover, Bartel et al. (2018) discovered that the adolescents who stopped
smartphone use one hour before bedtime increased their minutes of sleep by 21 minutes
per night, while He et al. (2020) found an increase of 18 minutes of sleep per night in
their experimental group when compared to the control group. In comparison, the current
study found similar results as the experimental group increased their total number of
minutes slept per night by an average of 16.46 total minutes. While the actual number of
minutes gained in the current study was not as significant as the numbers found in the
previous two research studies the increase in minutes of sleep was considered statistically
significant. This indicated a (strong/moderate) correlation between stopping one’s
smartphone/social media use before bedtime and increases in minutes of sleep per night.
Furthermore, He et al. (2020) through their research displayed a significant
difference in PSQI scores in the between-groups post-research t-test (experimental verses
control groups) and a significant difference in the repeated measures t-test for the
experimental group in the PSQI scores. Post research analysis of the current research
106
study showed similar results for the post-PSQI scores in the between-groups t-test with a
moderate effect and a significant difference in the experimental group repeated measures
t-test with a large effect. Additionally, He et al. (2020) uncovered a significant difference
in PSAS scores in the between-groups post-research t-test (experimental verses control
groups) and a significant difference in the repeated measures t-test for the experimental
group in the PSAS scores. Post research analysis of the current research study discovered
similar results for the post-PSAS in the between-groups t-test with a moderate effect
observed and a significant difference in the experimental group repeated measures t-test
with a large effect observed through the research.
Biblical Integration of Research
Sleep is also a God given gift that is used for restoration and healing (Ancoli-
Israel, 2001; English Standard Version Bible, 2001/2016, Proverbs 6:9-11; 19:15; 20:13;
Ecclesiastes 5:12; Matthew 26:45). The Bible explains that “It is in vain that you rise up
early and go late to rest, eating the bread of anxious toil; for he gives to his beloved
sleep” (English Standard Version Bible, 2001/2016, Psalm 127:2). The current research
displays clear supporting correlational connections between nighttime smartphone/social
media use and reductions in sleep quality, sleep onset, and overall sleep duration, which
may positively impact one’s overall health and spiritual health (Chow, 2022; Dubar et al,
2024; Omede & Akintunde, 2023; Shim, 2021 Uecker & McClure, 2022). Therefore, if
the findings in this study are embraced by Christian community, it could further
strengthen previous research displaying evidence linking religious practice such as
spiritual discipline to increased mental health functioning, decreased behavioral
107
problems, increased physical health, and reductions in remuneration positively impacting
one’s sleep (Dubar et al, 2024).
Scripture supports the idea that sleep restores, heals, and promotes normal human
functioning; yet sleep for adolescents/young adults has been drastically reduced in recent
years causing numerous negative health outcomes (Ancoli-Israel, 2001; Hirshkowitz et
al., 2015; Kumar & Pati, 2021; Paruthi et al., 2016; Sung et al., 2020; Tereshchenko et
al., 2021; Yang et al., 2019). Moreover, the Bible also argues that “All things are lawful,
but not all things are helpful. All things are lawful, but not all things build up” (English
Standard Version Bible, 2001/2016, 1 Corinthians 10:23). While smartphone/social
media use is a morally neutral activity, smartphone/social media use has been connected
to spiritual distraction, reductions in prayer, and reductions in scripture reading thus
negatively impacting one’s connection to God (Bingaman, 2023; Chow, 2022; Mendrofa
et al., 2023 Omede & Akintunde, 2023; Shim, 2021 Uecker & McClure, 2022).
Nevertheless, the present research clearly shows how introducing smartphone/social
media limitations/discipline into one’s daily life can positively impact one’s overall sleep
thus creating opportunity for more rest and less distraction, which could lead to greater
spiritual health.
Moreover, previous research has shown that parents influence both
adolescent/young adult religious development and smartphone/social media use patterns
through modeled behavior and parenting consistency (Bozzola et al., 2022; Francis, 2020;
Godsell & White, 2019; Good & Willoughby, 2008; Goodman & Dyer, 2019; Hefner et
al., 2019; Nur et al., 2021; Padilla-Walker et al., 2018;). Therefore, one can argue that
implementing boundaries around smartphone/social media use before bedtime and/or
108
modelling overall smartphone/social media use could have positive impacts on the
smartphone/social media habits of adolescents/young adults. In turn, these boundaries
could positively impact the adolescent/young adult community’s overall sleep health
decreasing sleep latency, increasing overall minutes of sleep, and decreasing nighttime
awakenings.
Implications
The growing correlational evidence (Bartel et al., 2018; He et al, 2020), along
with the findings in this study, provide further support linking nighttime
smartphone/social media use to reductions in the amount of sleep obtained and the
quality of sleep achieved. Reducing smartphone/social media use before bedtime could
help reduce the effects of blue light emission and reduce arousal from smartphone/social
media content (Bowler & Bourke, 2019; Chaudhury et al., 2019; Pellegrino et al., 2022;
Rozgonjuk et al., 2020; Šmotek et al., 2020). The data helps show how technology is
negatively impacting sleep patterns in adolescents/ young adults; nevertheless, these
research findings along with previous studies show that these negative effects on sleep
can be altered and/or decreased if boundaries are incorporated by smartphone/social
media users (Bartel et al., 2018; He et al, 2020). The current research has direct
implications in the scientific community as it shows the need for further investigation
into ways negative effects of smartphone/social media use on sleep can be altered or
reversed. These current research findings help provide hope that documented negative
sleep trends in the adolescent/young adult community can be positively changed and may
even be reversed with the right interventions.
109
Moreover, these research results could be used as a tool by relatives, friends,
schools, churches, and the community to help adolescent’s/young adult’s shape their
worldview and values concerning smartphone/social media use (Godsell & White, 2019;
Good & Willoughby, 2008; Goodman & Dyer, 2019; Layton et al., 2012). Nevertheless,
it is important to remember that a rational, warm, flexible, and receptive approach when
presenting the data to adolescents/ young adults helps build trust, encourage autonomy,
and enhances informed choice (Goodman & Dyer, 2019; Lavrič & Naterer, 2020).
Paul expressed, “All things are lawful for me, but not all things are helpful. All things are
lawful for me, but I will not be dominated by anything,” (English Standard Version
Bible, 2001/2016, 1 Corinthians 6:12). Although there may not be anything explicitly or
morally wrong with smartphone/social media use (Bingaman, 2023; Mendrofa et al.,
2023), this study adds to the growing evidence that adopting a few nighttime boundaries
around smartphone/ social media use could have major positive impacts on one’s God
given gift of sleep by increasing actual minutes of sleep per night achieved, decreasing
the time it takes to fall asleep, and also by decreasing nighttime awakenings thus
increasing overall sleep quality.
Limitations
There are numerous limitations to the current research study. First, the
adolescent/young adult participants were gathered using convenience sampling and the
snowballing technique, which reduces the generalizability and external validity of the
research findings as these individuals were not randomly selected to participate in the
study even though they were randomly selected to be in the experimental or control
groups. Moreover, the quasi-experimental research design also limited the
110
generalizability of the findings as the data was collected only from participants that
knowingly and willingly made the decision to engage in the smartphone/social media
research thus excluding adolescent/ young adult participants who may have a
smartphone/social media use problem, lacked the desire to limit or stop
smartphone/social media nighttime use, and/or did not believe they had sleep problems or
any negative effects from smartphone/social media use. Moreover, the study eliminated
numerous potential participants who were taking medications and/ or had a mental health
diagnosis, which further limited the generalizability of the results to this segment of the
population.
Additionally, the PSQI and the PSAS rely solely on self-report measures, which
could impact on the overall results of the findings due to the potential for expectation bias
from the late-stage adolescents/young adults. In other words, the research participants
could have responded to the surveys in a way that altered the results as they could have
answered the surveys in a manner they thought was expected by the researcher.
Moreover, the measure used to analyze nighttime awakenings was insufficient as it was
comprised of one self-report question instead of an actual evidenced based instrument to
measure nighttime awakenings in the participants. Furthermore, there was no way to
verify that the experimental group stopped the smartphone use one hour before bedtime
in this study as smartphone/social media concrete data was unavailable, and there was no
hard objective data, such as data derived from an actigraphy device, to verify the sleep
information provided by participants including the actual minutes of sleep, the minutes it
took to fall asleep, and the number of nighttime awakenings.
Recommendations for Future Research
111
Recommendations for future research include the need to expand the research to
include adolescents/young adults that may have a mental health diagnosis. By doing so,
one could explore if altering smartphone/social media use could directly improve
depression and anxiety symptoms in this population. Moreover, expanding the research to
older adults could increase the understanding of how smartphone/social media use effects
this population. For instance, correlational studies could be conducted with adults and
adolescents/ young adults residing at home to explore if adult smartphone/social media
use can be correlated to adolescent smartphone/social media use.
Moreover, future research endeavors could incorporate third party reports of
adolescent/young adult smartphone/social media usage from parents or caregivers to
further verify the adolescent/young adult self-reports. Additionally, future studies could
gather concrete data using smartphone technology apps that gather real time
smartphone/social media use data or data from applications such as Rescue Time
software, which can be downloaded to research participants smartphone devices (Collis et
al., 2022). The Rescue Time application could be used to track research participants
smartphone use in 5-minute intervals (Collis et al., 2022). In addition, actigraphy devices
such as the Dreem2 Headband (Arnal et al., 2020) can be worn nightly in the home
environment to measure actual brain and muscle activity, eye movement, exact time one
falls asleep, the number of nighttime awakenings, precise wake time, monitor one’s
breathing/airflow, pulse, and blood oxygen levels. This modern technology device,
Dreem2 Headband, could increase the scientific communities’ insight into how stopping
or limiting smartphone/social media use before bedtime physically impacts the research
112
participants on a more wholistic level using tangible real time information verses self-
report data alone (Arnal et al., 2020).
Summary
In summary, the current research provided stronger statistical power from a
greater number of research participants (n=106) completing the study when compared to
previous research endeavors (Bartel et al., 2018; He et al, 2020). Moreover, the current
research supported earlier research findings by He et al. (2020), which displayed a
significant moderate effect between the experimental group and the control group in post
PSQI and PSAS scores. Moreover, the research showed a moderate effect in the number
of minutes it took for the participants to fall asleep in the experimental group when
compared to the control group with 11.82 minutes gained, which further supports the data
from the RCT conducted by He and his colleagues (He et al, 2020). The current research
study also displayed sleep latency reductions of 16.15 minutes on average in the
experimental group, which aligns with the previous findings by both Bartel et al. (2018)
and He et al. (2020). Furthermore, the research displayed clear correlations between
stopping nighttime smartphone/social media use one hour before bedtime, and overall
self-reported gains in sleep quality, reduction in time it took to fall asleep, and significant
increases to one’s sleep duration, which may all positively affect personal physical and
spiritual health (Chow, 2022; Omede & Akintunde, 2023; Shim, 2021 Uecker &
McClure, 2022).
These observed research outcomes provide further evidence that negative effects
on sleep can be altered, and positive sleep alterations can be incorporated through small
changes in smartphone/social media use by adolescents/ young adults (Bartel et al., 2018;
113
He et al, 2020). Nevertheless, there were numerous limitations to the study including the
use of convenience sampling, generalizability issues, and the data relied solely upon self-
report data without concrete smartphone/social media use or sleep data. However, this
also highlighted the potential for future research endeavors to incorporate more research
tools such as the Rescue Time smartphone tracking application that could be used to
verify smartphone/social media use data, and the Dreem2 Headband, which could be used
to objectively measure sleep outcomes from research participants. In essence, these
research outcomes clearly show the need for further research into the effects of altering
smartphone/social media use, and it also shows how simple and practical interventions
can have significant impacts on overall sleep quality, onset, and duration in the
adolescent/young adult community.
114
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137
APPENDIX A: Information Sheet
Title of the Project: THE EFFECTS ON SLEEP LATENCY, DURATION, AND
QUALITY IN ADOLESCENTS WHEN ALTERING NIGHTTIME SMARTPHONE
AND SOCIAL MEDIA USAGE
Principal Investigator: Ryan R. May, MA, LPC, PhD Candidate, Liberty University
School of Behavioral Science
Key Information about the Research Study
You are invited to participate in a research study. To participate:
• you must be between the ages of 18-21
• you must have access to your own smartphone device with at least one active
social media account
• you must be willing to stop the smartphone/ social media device use one hour
before bedtime for the 7-day research period
• you must not have a current or former mental health or medical diagnosis such as
depression, anxiety, psychotic/mood disorder, sleep disorder, and/or
neurological/neurodevelopmental disorder
• you must not be taking medication(s) to treat a mental health and/or medical
diagnosis
Things you should know:
• The purpose of the study is to uncover the impacts on sleep when
smartphone/social media use is stopped one hour prior to bedtime for participants
aged 18-21.
• Participants will be asked to complete a questionnaire, the Pittsburgh Sleep
Quality Index, and the Pre-Sleep Arousal Scale at the beginning of and at the
conclusion of the research week. These assessments should take 10-20 minutes to
complete in each instance. Participants assigned to the experimental group will be
asked to stop their smartphone/social media device use 1 hour before bedtime
each day of the research week.
• Subjects are not expected to receive direct benefits from the study.
• Taking part in this research project is voluntary. You do not have to participate,
and you can stop at any time.
Please read this entire form and ask questions before deciding whether to participate in
this research.
What is the study about and why is it being done?
The purpose of this quantitative quasi-experimental research study will be to uncover the
impacts on sleep when smartphone/social media use is stopped by participants one hour
prior to bedtime for late-stage adolescents aged 18-21.
What will happen if you take part in this study?
138
If you agree to be in this study, I will ask you to do the following:
1. All participants will complete the demographic information, the initial 10 item
PSQI questionnaire, and the 16 item PSAS questionnaire. This will
approximately take 10-20 minutes.
2. Participants in the experimental group will stop their smartphone/social media
device use one hour before bedtime each night for the 7-day research period.
Participants in the control group will not.
3. All participants will complete one questionnaire regarding their night-time
awakenings over the past week of research and complete the post PSQI and
PSAS on day 8 of the study. These follow-up questionnaires will take
approximately 10-20 minutes to complete all together.
How could you or others benefit from this study?
Participants should not expect a direct benefit from participating in this study.
Benefits to society include furthering the understanding of how limiting
smartphones/social media use before bedtime impacts sleep in the late-stage adolescent
population. Additionally, the study has the promise of promoting change, and even
supporting the establishment of smartphone/social media use warnings,
recommendations, and guidelines since Australia is currently exploring social media
restrictions/limits for adolescents. The research also can promote smartphone/social
media awareness while prompting additional research endeavors into both the positive
and negative impacts of smartphone/social media use.
What risks might you experience from being in this study?
The expected risks from participating in this study are minimal, which means they are
equal to the risks you would encounter in everyday life.
How will personal information be protected?
The records of this study will be kept private. Research records will be stored securely,
and only the researcher and his faculty chair will have access to the records.
• Participant responses will be kept confidential by replacing names with numbers.
• Data will be stored on a password-locked computer. The researcher his faculty
chair will have access to the data. After three years all electronic records will be
deleted.
How will you be compensated for being part of the study?
All participants will be entered into a drawing using the number picking wheel website
(https://pickerwheel.com). The participants will be entered into the drawing using their
anonymous identification number, and one winner will be selected. The prize will be a
new iPhone 16. The iPhone 16 winner will be emailed to inform the participant that he or
139
she has won using the email address that was provided by the participant at the beginning
of the research study. Once the winner has been randomly selected and informed, the
participant will need to provide a mailing address to which the phone can be sent.
Is study participation voluntary?
Participation in this study is voluntary. Your decision whether to participate will not
affect your current or future relations with Liberty University.
What should you do if you decide to withdraw from the study?
If you choose to withdraw from the study, please exit the survey and close your internet
browser. Your responses will not be recorded or included in the study.
Whom do you contact if you have questions or concerns about the study?
The researcher conducting this study is Ryan May. You may ask any questions you have
now. If you have questions later, you are encouraged to contact him at
rrmay@liberty.edu. You may also contact the researcher’s faculty sponsor, Dr. Angela
Rathkamp, at arathkamp@liberty.edu.
Whom do you contact if you have questions about your rights as a research
participant?
If you have any questions or concerns regarding this study and want to talk to someone
other than the researcher, you are encouraged to contact the IRB. Our physical address
is Institutional Review Board, 1971 University Blvd., Green Hall Ste. 2845, Lynchburg,
VA, 24515; our phone number is 434-592-5530, and our email address is
irb@liberty.edu.
Disclaimer: The Institutional Review Board (IRB) ensures that human subjects research
will be conducted ethically as defined and required by federal regulations. The topics
covered and viewpoints expressed or alluded to by student and faculty researchers are
those of the researchers and do not necessarily reflect the official policies or positions of
Liberty University.
140
APPENDIX B: DEMOGRAPHIC INFORMATION
Please provide the following background information about yourself and your adolescent.
Your responses are completely anonymous and cannot be connected to you directly.
1. How old are you? _________________
2. What is your ethnicity?
a. Australian
b. Aboriginal
c. American
d. African American
e. Other
3. What is your gender?
a. Male
b. Female
c. Other
d. Prefer Not to Answer
4. Over the past week on average, how many times per night did you wake up?
a. None
b. 1-2
c. 3-4
d. 5 or more
5. Please provide an email address: ______________
141
APPENDIX C: THE PITTSBURGH SLEEP QUALITY INDEX (PSQI)
INSTRUCTIONS: The following questions relate to your usual sleep habits during the
past month only. Your answers should indicate the most accurate reply for the majority of
days and nights in the past month.
Please answer all questions.
1. During the past month, what time have you usually gone to bed at night?
BEDTIME ___________________
2. During the past month, how long (in minutes) has it usually taken you to fall
asleep each night?
NUMBER OF MINUTES_________
3. During the past month, what time have you usually gotten up in the morning?
GETTING UP TIME______________
4. During the past month, how many hours of actual sleep did you get at night? (This
may be different than the number of hours spent in bed.)
HOURS OF SLEEP PER NIGHT____
For each of the remaining questions, check the one best response. Please answer all
questions.
5. During the past month, how often have you had trouble sleeping because . . .
a) Cannot get to sleep within 30 minutes
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
142
b) Wake up in the middle of the night or early morning
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
c) Have to get up to use the bathroom
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
d) Cannot breathe comfortably
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
e) Cough or snore loudly
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
f) Feel too cold
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
g) Feel too hot
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
h) Had bad dreams
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
i) Have pain
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
143
j) Other reason(s), please describe________________________________________
How often during the past month have you had trouble sleeping because of this?
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
6. During the past month, how would you rate your sleep quality overall?
Very good ___________
Fairly good __________
Fairly bad ___________
Very bad ____________
7. During the past month, how often have you taken medicine to help them sleep
(prescribed or "over the counter")?
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
8. During the past month, how often have you had trouble staying awake while
driving, eating meals, or engaging in social activity?
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
9. During the past month, how much of a problem has it been for you to keep up
enough enthusiasm to get things done?
No problem at all______________
Only a very slight problem_______
Somewhat of a problem_________
A very big problem_____________
144
10. Do you have a bed partner or roommate?
No bed partner or roommate _______________
Partner/roommate in other room ____________
Partner in same room, but not same bed ______
Partner in same bed ______________________
If you have a roommate or bed partner, ask him/her how often in the past month you have
had . . .
a) Loud snoring
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
b) Long pauses between breaths while asleep
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
c) Legs twitching or jerking while asleep
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
d) Episodes of disorientation or confusion during sleep
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
e) Other restlessness while you sleep; please describe________________________
Not during the Less than Once or twice Three or more
past month_____ once a week_____ a week_____ times a week_____
145
APPENDIX D: PRE-SLEEP AROUSAL SCALE
Please describe how intensely you experience each of the symptoms mentioned
below as you attempt to fall asleep by circling the appropriate number.
Not at all
Slightly
Moderately
Alot
Extremely
1. Worry
about
falling
asleep
1
2
3
4
5
2. Review or
ponder the
events of the
day
1
2
3
4
5
3. Depressing or
anxious thoughts
4. Worry about
1
2
3
4
5
problems other
1
2
3
4
5
than sleep
5. Being
mentally
alert, active
1
2
3
4
5
6. Can’t shut off
your thoughts
1
2
3
4
5
7. Thoughts keep
running through
your head
1
2
3
4
5
8. Being
distracted by
1
2
3
4
5
sounds, noise in
the environment
9. Heart racing,
pounding or
1
2
3
4
5
beating
Irregularly
10. A jittery,
nervous feeling
1
2
3
4
5
in your body
11. Shortness of
breath or labored
1
2
3
4
5
breathing
146
12. A tight, tense
feeling in your
1
2
3
4
5
Muscles
13. Cold feeling
in your hands,
1
2
3
4
5
feet or your body
in general
14. Have
stomach upset
(knot or
nervous
feeling in
1
2
3
4
5
stomach,
heartburn,
nausea, gas,
etc.)
15. Perspiration
in palms of your
1
2
3
4
5
hands or other
parts of your
body.
16. Dry feeling
in
1
2
3
4
5
mouth or throat
147
Table 1
Participants
N
%
Experimental Group
54
50.0%
Control Group
52
48.1%
Missing
System
2
1.9%
Participants
.
N
%
Experimental
Group
Missing
System
2
100.0%
Experimental
Group
Control Group
18
23
42.6%
19
11
20.4%
20
14
25.9%
21
6
11.1%
Control Group
18
17
32.7%
19
14
26.9%
20
10
19.2%
Ethnicity
Participants
N
%
.
Missing
System
2
100.0%
Experimental
Group
Australian
18
33.3%
Aboriginal
5
9.3%
American
25
46.3%
Other
6
11.1%
Control Group
Australian
20
38.5%
Aboriginal
4
7.7%
American
24
46.2%
Other
4
7.7%
Gender
Participants
N
%
.
Missing
System
2
100.0%
Experimental
Group
Male
26
48.1%
Female
28
51.9%
Control Group
Male
31
59.6%
Female
21
40.4%
148
Table 2
Baseline Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Pre-Nighttime
Awakenings
Experimental
Group
54
1.96
.751
.102
Control Group
52
1.87
.687
.095
Pre-Total Minutes of
Sleep (min)
Experimental
Group
54
435.39
72.998
9.934
Control Group
52
435.04
52.884
7.334
Pre-Time it Takes to
Fall Asleep (min)
Experimental
Group
54
35.87
24.592
3.347
Control Group
52
32.13
17.755
2.462
Pre-PSQI
Experimental
Group
54
8.80
4.218
.574
Control Group
52
8.06
4.513
.626
Pre-PSAS
Experimental
Group
54
33.26
10.387
1.414
Control Group
52
31.98
10.457
1.450
Baseline Independent Samples Test
Levene's
Test for
Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pre-
Nighttime
Awakenings
Equal
variances
assumed
.118
.732
.697
104
.244
.487
.098
.140
-.180
.375
Equal
variances
not
assumed
.698
103.723
.243
.487
.098
.140
-.180
.375
Pre-Total
Minutes of
Sleep (min)
Equal
variances
assumed
4.054
.047
.028
104
.489
.978
.350
12.421
-
24.281
24.982
Equal
variances
.028
96.673
.489
.977
.350
12.348
-
24.157
24.858
149
not
assumed
Pre-Time it
Takes to
Fall Asleep
(min)
Equal
variances
assumed
1.684
.197
.894
104
.187
.374
3.736
4.180
-4.553
12.024
Equal
variances
not
assumed
.899
96.520
.185
.371
3.736
4.155
-4.511
11.982
Pre-PSQI
Equal
variances
assumed
1.111
.294
.871
104
.193
.386
.739
.848
-.943
2.420
Equal
variances
not
assumed
.870
102.852
.193
.386
.739
.849
-.946
2.423
Pre-PSAS
Equal
variances
assumed
.015
.904
.631
104
.265
.529
1.278
2.025
-2.737
5.294
Equal
variances
not
assumed
.631
103.792
.265
.529
1.278
2.025
-2.737
5.294
150
Table 3
Post PSQI and Total Minutes of Sleep Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Post-PSQI
Experimental
Group
54
3.54
3.289
.448
Control Group
52
7.25
4.715
.654
Post-Total Minutes of Sleep
(min)
Experimental
Group
54
451.85
61.555
8.377
Control Group
52
434.42
60.380
8.373
Post PSQI Independent Samples T-Test
Levene's Test
for Equality
of Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-
PSQI
Equal
variances
assumed
11.961
<.001
-
4.717
104
<.001
<.001
-3.713
.787
-5.274
-2.152
Equal
variances
not
assumed
-
4.686
90.812
<.001
<.001
-3.713
.792
-5.287
-2.139
Post-
Total
Minutes
of Sleep
(min)
Equal
variances
assumed
.381
.539
1.471
104
.072
.144
17.429
11.848
-6.067
40.924
Equal
variances
not
assumed
1.472
103.963
.072
.144
17.429
11.844
-6.058
40.916
151
Table 4
Experimental Group PSQI Paired Samples Statistics
N
Correlation
Significance
One-
Sided
p
Two-
Sided
p
Pair
1
Pre-PSQI &
Post-PSQI
54
.708
<.001
<.001
Experimental Group PSQI Paired Samples T-Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std. Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-
PSQI -
Post-
PSQI
5.259
2.992
.407
4.443
6.076
12.918
53
<.001
<.001
152
Table 5
Experimental Group Total Minutes of Sleep Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair
1
Pre-Total Minutes of
Sleep (min)
435.39
54
72.998
9.934
Post-Total Minutes of
Sleep (min)
451.85
54
61.555
8.377
Experimental Group Total Minutes of Sleep Paired Samples T-Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-Total
Minutes of
Sleep (min) -
Post-Total
Minutes of
Sleep (min)
-
16.463
42.308
5.757
-
28.011
-4.915
-
2.859
53
.003
.006
153
Table 6
Post PSAS Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Post-PSAS
Experimental
Group
54
23.72
5.731
.780
Control Group
52
31.37
11.561
1.603
Post PSAS Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-
PSAS
Equal
variances
assumed
32.811
<.001
-
4.337
104
<.001
<.001
-7.643
1.762
-
11.138
-4.148
Equal
variances
not assumed
-
4.287
74.007
<.001
<.001
-7.643
1.783
-
11.195
-4.091
154
Table 7
Table7
Post-Sleep Latency Group Statistics
Participants
N
Mean
Std.
Deviation
Std. Error
Mean
Post-Time it Takes to
Fall Asleep (min)
Experimental
Group
54
19.72
8.322
1.133
Control Group
52
31.54
15.034
2.085
Post Sleep Latency Independent Samples Test
Levene's Test
for Equality
of Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Post-Time
it Takes to
Fall Asleep
(min)
Equal
variances
assumed
20.512
<.001
-
5.031
104
<.001
<.001
-11.816
2.349
-
16.474
-
7.159
Equal
variances
not
assumed
-
4.980
78.927
<.001
<.001
-11.816
2.373
-
16.539
-
7.094
155
Table 8
Experimental Group PSAS Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair 1
Pre-PSAS
33.26
54
10.387
1.414
Post-
PSAS
23.72
54
5.731
.780
Experimental Group PSAS Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-PSAS
- Post-
PSAS
9.537
6.246
.850
7.832
11.242
11.221
53
<.001
<.001
156
Table 9
Experimental Group Sleep Latency Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair
1
Pre-Time it Takes to
Fall Asleep (min)
35.87
54
24.592
3.347
Post-Time it Takes to
Fall Asleep (min)
19.72
54
8.322
1.133
Experimental Group Sleep Latency Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-Time it
Takes to Fall
Asleep (min)
- Post-Time
it Takes to
Fall Asleep
(min)
16.148
21.029
2.862
10.408
21.888
5.643
53
<.001
<.001
157
Table 10
Experimental Group Pre-and Post Nighttime Awakenings Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error Mean
Pair 1
Pre-Nighttime
Awakenings
1.96
54
.751
.102
Post-Nighttime
Awakenings
1.48
54
.637
.087
Experimental Group Pre-and Post Nighttime Awakenings Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-
Nighttime
Awakenings
- Post-
Nighttime
Awakenings
.481
.606
.083
.316
.647
5.836
53
<.001
<.001
158
Table 11
Control Group Pre-and Post Nighttime Awakenings Paired Samples Statistics
Mean
N
Std.
Deviation
Std. Error
Mean
Pair 1
Pre-Nighttime
Awakenings
1.87
52
.687
.095
Post-Nighttime
Awakenings
1.65
52
.590
.082
Control Group Pre-and Post Nighttime Awakenings Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Pair
1
Pre-
Nighttime
Awakenings -
Post-
Nighttime
Awakenings
.212
.412
.057
.097
.326
3.699
51
<.001
<.001
159
Figure 1
160
Figure 2
161
Figure 3
162
Figure 4
163
Figure 5
164
Figure 6
165
Figure 7
166
Figure 8
167
Figure 9
