A Scoping Review of AI-Driven mHealth Systems for Precision Hydration: Integrating Food and Beverage Water Content for Personalized Recommendations PDF Free Download
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Academic Editor: Stephan Schlögl
Received: 30 September 2025
Revised: 3 November 2025
Accepted: 6 November 2025
Published: 8 November 2025
Citation: Apergi, K.; Styliaras, G.D.;
Tsirogiannis, G.; Beligiannis, G.N.;
Malisova, O. A Scoping Review of
AI-Driven mHealth Systems for
Precision Hydration: Integrating Food
and Beverage Water Content for
Personalized Recommendations.
Multimodal Technol. Interact. 2025,9,
112. https://doi.org/10.3390/
mti9110112
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Review
A Scoping Review of AI-Driven mHealth Systems for Precision
Hydration: Integrating Food and Beverage Water Content for
Personalized Recommendations
Kyriaki Apergi , Georgios D. Styliaras * , George Tsirogiannis, Grigorios N. Beligiannis and Olga Malisova
Department of Food Science and Technology, University of Patras, G Seferi 2, 30100 Agrinio, Greece;
kyapergi@upatras.gr (K.A.); gbeligia@upatras.gr (G.N.B.); omalisova@upatras.gr (O.M.)
*Correspondence: gstyl@upatras.gr
Abstract
Background: Precision nutrition increasingly integrates mobile health (mHealth) and arti-
ficial intelligence (AI) tools. However, personalized hydration remains underdeveloped,
particularly in accounting for both food- and beverage-derived water intake. Objective:
This scoping review maps the existing literature on mHealth applications that incorpo-
rate machine learning (ML) or AI for personalized hydration. The focus is on systems
that combine dietary (food-based) and fluid (beverage-based) water sources to generate
individualized hydration assessments and recommendations. Methods: Following the
PRISMA-ScR guidelines, we conducted a structured literature search across three databases
(PubMed, Scopus, Web of Science) through March 2025. Studies were included if they
addressed AI or ML within mHealth platforms for personalized hydration or nutrition,
with an emphasis on systems using both beverage and food intake data. Results: Of the
43 included
studies, most examined dietary recommender systems or hydration-focused
apps. Few studies used hydration assessments focusing on both food and beverages or
employed AI for integrated guidance. Emerging trends include wearable sensors, AR
tools, and behavioral modeling. Conclusions: While numerous digital health tools address
hydration or nutrition separately, there is a lack of comprehensive systems leveraging AI to
guide hydration from both food and beverage sources. Bridging this gap is essential for
effective, equitable, and precise hydration interventions. In this direction, we propose a
hydration diet recommender system that integrates demographic, anthropometric, psycho-
logical, and socioeconomic data to create a truly personalized diet and hydration plan with
a holistic approach.
Keywords: hydration; personalized nutrition; artificial intelligence; mobile health;
precision
health; recommender systems; scoping review
1. Introduction
Modern lifestyles with unbalanced diet patterns, along with increased sedentary
behaviors, such as tobacco use, contribute to the escalating global disease burden of
Non-Communicable Diseases (NCDs) [
1
,
2
]. At the same time, dehydration is a rather
common condition in adults, especially among vulnerable populations, including older
adults and patients with chronic diseases [
3
]. Epidemiological studies show a connection
between dehydration and increased hospital admissions and poorer health outcomes [
3
–
5
].
Excessive consumption of alcohol or sugar-sweetened beverages, if used for rehydration,
Multimodal Technol. Interact. 2025,9, 112 https://doi.org/10.3390/mti9110112
Multimodal Technol. Interact. 2025,9, 112 2 of 29
may actually exacerbate dehydration due to their diuretic effects or high osmotic load.
Thus, their use for rehydration is not recommended as this can increase the risk for various
chronic conditions, from urolithiasis and constipation to obesity and hypertension [
6
–
8
],
imposing a significant burden for healthcare systems.
Despite the fact that water balance maintenance (euhydration) is often neglected, it
stands as the most fundamental nutritional requirement for human health, since water is
the predominant component of human’s body, enabling critical physiological processes,
such as metabolic reactions, thermoregulation, nutrient transport, and electrolyte homeosta-
sis [
6
,
8
]. It has been demonstrated that hydration levels impact overall health, with even
mild fluid deficits significantly impairing physical capabilities and cognitive function [
9
,
10
].
Current scientific evidence further indicates that optimal hydration levels, along with a diet
rich in food with high water content, such as fresh fruit and vegetables, play a preventative
role against various forms of cancer and other NCDs [
4
,
5
,
7
,
9
]. Current research dispro-
portionately focuses on dietary impacts on health outcomes, overlooking hydration as a
critical health determinant. In addition, sustainable lifestyle modifications—particularly
regarding diet and hydration practices—remain exceptionally challenging to monitor and
implement [11].
Moreover, existing dietary and hydration guidelines often overlook the substantial
inter-individual variations in physiological responses to dietary and fluid intake pat-
terns [
12
,
13
]. Recent evidence supports the idea that personalized nutrition can enhance
health outcomes more than generic advice [
14
–
16
]. Since food contributes significantly to
fluid intake and the consumption of alcohol and high-caloric beverages is connected to
various health issues, precision nutrition could be tailored further, providing hydration
guidance, considering the intake of water from all sources. In this context, precision hy-
dration with recommendations based on each individual’s biological, anthropometric, diet
patterns and preferences, as well as environmental and other lifestyle characteristics, could
improve hydration status and reduce disease risk. Precision hydration systems aim to
proactively optimize fluid intake, taking into account individual variability in fluid needs,
environmental factors, and activity levels, which may not be fully captured by subjective
thirst signals alone, as thirst is a lagging indicator that typically arises after dehydration
has already begun.
In today’s digital age, with the advancement of precision medicine, and person-
alized health interventions based on individual genetic, metabolic, psychological, and
lifestyle factors, there is growing potential to apply this approach to diet and hydration
strategies [17,18]
. Modern data-driven technologies, such as artificial intelligence (AI)
and machine learning (ML), enable the integration of diverse data sources, considering
individual differences in metabolism, psychological influences, social behaviors, and envi-
ronmental factors to develop precision hydration plans. This holistic personalized approach
not only optimizes hydration, but also enhances sustainable change, promoting a balanced
diet and potentially reducing NCD risk. However, the applications of mHealth remain
limited and there is growing concern about their equitable accessibility, particularly for
vulnerable populations at high risk [
17
,
18
]. Additionally, there is a significant gap in
real-world evidence regarding the effectiveness, feasibility, and long-term sustainability of
precision interventions across diverse populations [17,18].
Technological advances in AI are transforming the way we monitor and manage
nutrition and hydration. From wearable devices to image recognition and predictive
modeling, AI has been applied to estimate nutrient intake, detect dehydration risk, and
personalize recommendations about food or fluid intake. Despite this growing interest, the
existing literature lacks a unified overview of how these systems could provide personalized
advice for improved hydration, with recommendations about both fluid and food intake
Multimodal Technol. Interact. 2025,9, 112 3 of 29
simultaneously, taking into account the water content in foods. While some narrative
overviews exist, a structured mapping of empirical research using AI in these areas is
lacking. Therefore, a scoping review is an appropriate way to map the current evidence,
technologies, and research gaps.
The aim of this scoping review is to systematically identify and map the current
landscape of mHealth and AI-driven tools that provide personalized hydration recommen-
dations. Special attention is given to systems that integrate fluid intake from both beverages
and food, leveraging machine learning for individualized assessments and behavior-driven
guidance [
13
,
17
–
19
]. Unlike traditional approaches that focus exclusively on drinks, our
review highlights the critical need for dual-source hydration tracking within intelligent
nutrition recommender systems. The review further examines how such strategies can be
embedded into public health initiatives aimed at reducing the risk of non-communicable
diseases at both the individual and population levels. The main contributions of this
review are threefold: first, we propose a conceptual framework for incorporating precision
hydration into AI-driven dietary guidance platforms, addressing the current gap in per-
sonalized fluid intake recommendations that also take into account the water content in
foods. Second, we assess the multifactorial approach required for effective hydration opti-
mization, highlighting the interplay between anthropometric, behavioral, and contextual
influences on dietary patterns. Third, we identify the key challenges and opportunities for
implementing precision hydration technologies in real-world public health systems, with
particular attention to equity, accessibility, and the unique needs of vulnerable populations
at elevated risk of dehydration-related chronic conditions.
2. Materials and Methods
This scoping review was conducted in accordance with the PRISMA-ScR (Preferred
Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)
guidelines [
20
]. This review aimed to explore the breadth of the literature on AI-integrated
hydration and nutrition systems and identify key concepts, technological frameworks, and
research gaps. The protocol was developed a priori and registered in Open Science Frame-
work (OSF) DOI 10.17605/OSF.IO/YQJ5F. No funding was received for the
present study
.
2.1. Eligibility Criteria
We included peer-reviewed articles, conference proceedings, and the relevant gray
literature published in English that addressed the following:
-
Studies involving the integration of hydration tracking with dietary patterns using
multi-source (food + drink) water intake and AI or machine learning systems integrat-
ing both food and beverage intake for hydration.
-
mHealth applications that use AI for personalized hydration status assessment
and guidance.
- Behavioral, psychological, or environmental factors influencing hydration.
- Food recommendation systems with hydration components.
We excluded studies for the following reasons:
-
The study was not available in English, to ensure accurate interpretation and consis-
tency in data extraction.
-
The study focused solely on clinical outcomes without technological integration,
as such studies fall outside the scope of our review on AI- and mHealth-based
hydration systems.
-
The study lacked relevance to AI, mHealth, or hydration, in order to maintain a clear
and focused synthesis of studies addressing the intersection of these domains.
Multimodal Technol. Interact. 2025,9, 112 4 of 29
2.2. Information Sources and Search Strategy
A systematic search was conducted in PubMed, Scopus, and Web of Science from
inception to March 2025. The search strategy was developed to capture studies at the
intersection of artificial intelligence, mobile/digital health, precision nutrition, hydration,
and food recommendation systems. Search terms included controlled vocabulary (e.g.,
MeSH terms in PubMed) and free-text keywords, combined using Boolean operators
(Appendix A). No language filters were applied.
2.3. Selection and Data Charting Process
Two reviewers independently screened all titles and abstracts (KA, OM). After re-
moving duplicates, titles and abstracts were screened for relevance. Full-text articles were
assessed for eligibility. Disagreements were resolved through discussion. Data were charted
on the following:
- Study author(s), year of publication;
- Study design, population characteristics;
- ML/AI;
- mHealth technologies used;
- Wearable sensors;
- Food + beverage hydration;
- Personalized guidance;
- Outcome measures (e.g., hydration status, performance metrics);
- Data Fusion/Multimodal Input;
- Behavioral and environmental integration;
- Implementation barriers and policy considerations.
No assumptions were made when data were incomplete. A critical appraisal of the
included sources was not conducted, as the aim of this scoping review was to map the
existing evidence rather than assess study quality. This is consistent with standard scoping
review methodology.
2.4. Synthesis of Results
A qualitative thematic synthesis was conducted to identify recurring patterns, techno-
logical trends, and research gaps. No formal risk of bias assessment was performed due to
the exploratory nature of the review.
3. Results
A total of 42 records were identified through database searches, including PubMed
(
n=8
), Scopus (n = 14), and Web of Science (n = 20). After removing duplicates, 40 unique
records remained for screening. Title and abstract screening excluded 10 records, leaving
30 articles for full-text assessment. Of these, six articles were excluded for reasons such as
lack of relevance to precision hydration (n = 3), insufficient methodological detail (
n=2
),
or duplication of study population (n = 1). Ultimately, 43 studies were included in the
scoping review: 24 were sourced from the databases, while an additional 19 were identified
through citation chaining and hand-searching; a summary of the study selection, including
excluded studies, is presented in the flow diagram (Figure 1).
Multimodal Technol. Interact. 2025,9, 112 5 of 29
Figure 1. Flowchart of studies included in this study.
Table 1outlines the core references reviewed, highlighting their type, contribution, and
relevance for this review, while Table 2presents the studies as a matrix, helping to visualize
the gaps (e.g., lack of food + beverage integration in AI systems) in the current research.
This scoping review is structured as follows. Following this introduction, Section 3.1
discusses the role of food water content in overall hydration status. Section 3.2 examines
existing hydration diet recommendations and their current limitations. Section 3.3 explores
the psychological, social, and environmental drivers that influence fluid intake behavior,
while Section 3.4 discusses the integration of AI and mHealth technologies in hydration diet
recommenders. Section 3.5 reviews the smart applications that are currently available for
assessing hydration status, and finally, Section 4presents our proposed framework for an
advanced hydration diet recommendation system, addresses the public health implications
of implementing precision hydration strategies and identifies key challenges and proposes
solutions for successful implementation.
It is important to note that, within the context of this review, the term ‘recommendation’
encompasses a spectrum of guidance, which ranges from clinically validated dietary
recommendations to more flexible, user-centered suggestions generated by algorithmic
systems. Where appropriate, we distinguish between evidence-based nutritional guidelines
intended to support health outcomes, and behavioral guidance tools, such as recipe or
restaurant suggestion systems, which are primarily designed to enhance user engagement
and personalization without necessarily carrying clinical authority.
Multimodal Technol. Interact. 2025,9, 112 6 of 29
Table 1. Conceptual summary of the reviewed literature based on thematic relevance.
No Author(s)/Source Year Study Type/Source Type Main Theme(s)
1 Shepherd [21] 1999 Review Article Social determinants of food choice, providing behavioral context
for AI-integrated food recommendation systems.
2 Sharp [22] 2007 Review Article Role of whole foods in hydration, informing food-based
hydration recommendation algorithms.
3 Torres and Nowson [23] 2007 Review Article Stress-eating behavior relationships, informing psychological
factors in personalized food recommendation algorithms.
4 Le Bellego et al. [24] 2010 Research Article Fluid consumption pattern analysis supporting the development
of personalized hydration recommendation systems.
5 Ali et al. [11] 2012 Review Article Real-world effectiveness of lifestyle interventions modeled on
diabetes prevention, validating clinical application potential.
6 Thokala and Duenas [25] 2012 Methodological Article
Multiple criteria decision analysis for health technology
assessment, informing evaluation frameworks for AI
nutrition systems.
7 Konstantinidou et al. [16] 2014 Review Article
Links personalized nutrition to cardiovascular disease prevention,
demonstrating the clinical relevance of precision
nutrition systems.
8 Betts and Gonzalez [12] 2016 Commentary Theoretical foundation for personalized nutrition approaches,
supporting individualized dietary guidance systems.
9 Mengistu, Y. et al. [26] 2016 Conference Paper Development of AutoHydrate, a wearable hydration monitoring
system using sensor data.
10 Celis-Morales et al. [14] 2017 Randomized Controlled Trial
Large-scale European RCT demonstrating the effectiveness of
personalized nutrition interventions on health-related
behavior change.
11 Järvelä-Reijonen et al. [27] 2018 Randomized Controlled Trial
RCT evidence of mobile app effectiveness in dietary behavior
change, validating mHealth platforms for personalized
nutrition interventions.
12 Michel and Burbidge [15] 2019 Review Article
Addresses digital tools for solving personalized nutrition
challenges, providing a framework for AI-integrated
recommendation systems.
Multimodal Technol. Interact. 2025,9, 112 7 of 29
Table 1. Cont.
No Author(s)/Source Year Study Type/Source Type Main Theme(s)
13 Liska et al. [4] 2019 Narrative Review
Comprehensive review of hydration and health outcomes,
providing a scientific foundation for hydration
monitoring systems.
14 Perrier [5] 2019 Review Article Historical perspective on hydration research advances,
contextualizing current hydration monitoring technologies.
15 Mathers [18] 2019 Commentary
Population health through personalized nutrition, addressing the
policy integration of AI-driven nutrition systems.
16 Budreviciute et al. [2] 2020 Review Article NCD management and prevention strategies, providing health
system context for AI-integrated nutrition platforms.
17 Beierle et al. [28] 2020 Cross-sectional Study
Smartphone usage patterns and personality traits, informing user
engagement strategies for mHealth nutrition platforms.
18 Liaqat, S. et al. [29] 2020 Research Article ML algorithms for non-invasive skin hydration level estimation.
19 Perrier et al. [10] 2021 Narrative Review Evidence supporting hydration for health hypothesis, validating
the importance of precision hydration guidance systems.
20 Millard-Stafford et al. [30] 2021 Research Article
Beverage hydration index research providing a scientific basis for
fluid recommendation algorithms in AI systems.
21 Jo, S. et al., [31] 2021 Review Article Wearable biosensors for sweat analysis and implications for
real-time hydration tracking.
22 Suppiah, Haresh T. et al. [32] 2021 Research Article Use of ML to classify hydration characteristics in
adolescent athletes.
23 Kulkarni, N. et al. [33] 2021 Conference Paper Development of a non-invasive, context-aware dehydration alert
system using mobile and wearable inputs.
24 Dolci et al. [13] 2022 Research Article
Demonstrates machine learning application for personalized
water intake prediction using clinical data, directly addressing
AI-driven precision nutrition systems.
25 Malik and Hu [34] 2022 Review Article Sugar-sweetened beverages and chronic disease risk, informing
healthy food recommendation system parameters.
Multimodal Technol. Interact. 2025,9, 112 8 of 29
Table 1. Cont.
No Author(s)/Source Year Study Type/Source Type Main Theme(s)
26 Al-Rayes et al. [35] 2022 Review Article Gamification in healthcare applications, informing engagement
strategies for AI-powered nutrition platforms.
27 Oc and Plangger [36] 2022 Research Article Motivational mechanisms in wearable technology for healthy
habits, supporting behavioral design in AI nutrition systems.
28 Rodin, D. et al. [37] 2022 Research Article Validation study of a wearable hydration sensor under
real-life conditions.
29 Sabry, F. et al. [38] 2022 Research Article Machine learning applied to dehydration detection using data
from wearable sensors.
30 Wang, S. et al. [39] 2022 Case Study Personalized hydration prediction models using sweat
biomarkers and physiological data.
31 Hillesheim and Brennan [40] 2023 Secondary Analysis RCT Evidence of metabotype-based personalized dietary advice
effectiveness, validating precision nutrition frameworks.
32 Rod et al. [41] 2023 Methodological Article Systems-based public health intervention research, providing
framework for implementing AI nutrition systems in healthcare.
33 Tsolakidis et al. [17] 2024 Review Article
Comprehensive review of AI and ML technologies in
personalized nutrition, providing technological framework for
intelligent food recommendation systems.
34 de Castro et al. [42] 2024 Scoping Review
Examines recommender systems in weight management mHealth
interventions, bridging AI technology with mHealth applications.
35 Dimou et al. [43] 2024 Educational Tool Study
Augmented reality application for hydration education,
showcasing innovative mHealth technologies for
nutritional guidance.
36 Tonello, S. et al. [44] 2024 Research Article Design and testing of a wearable multi-sensing hydration
monitoring system.
37 Chiao, J.C. et al. [45] 2024 Review Article Technical and clinical potential of noninvasive wearable devices
for dehydration monitoring.
38 Li, J.H. et al. [46] 2024 Research Article ML-based fluid intake recognition using multi-sensor fusion in
fluid intake behavior monitoring.
Multimodal Technol. Interact. 2025,9, 112 9 of 29
Table 1. Cont.
No Author(s)/Source Year Study Type/Source Type Main Theme(s)
39 Alaslani, R. et al. [47] 2024 Preprint/arXiv Real-time hydration level estimation using a smartphone camera
and computer vision.
40 Sreeharsha, A. et al. [48] 2024 Systematic Review
Review of wearable sensors and ML algorithms for hydration
monitoring, highlighting sensor limitations and
model performance.
41 Waterllama [49] 2025 Mobile Application Commercial hydration tracking platform demonstrating current
mHealth implementation for fluid intake monitoring.
42 Water Time Drink Tracker [50] 2025 Mobile Application Exemplifies mobile reminder systems for fluid intake behavior
modification and real-time fluid intake guidance.
43 Plant Nanny Water Tracker [51] 2025 Mobile Application Demonstrates gamified mHealth approach to hydration tracking,
illustrating behavioral engagement strategies in mobile platforms.
Table 2. Thematic matrix of the studies included in the scoping review.
No Author(s)/Source Year Wearable
Sensors
Machine
Learning/AI
Health/App-
Based
Food +
Beverage Hydration Personalized
Guidance
Data Fusion/
Multimodal
Input
Behavioral
and Environ-
mental
Integration
Implementation
Barriers and
Policy Consid-
erations
1 Shepherd [21] 1999 X Yes Yes Yes X Yes X Yes Yes
2 Sharp [22] 2007 X Yes Yes Yes Yes Yes X Yes Yes
3Torres and Nowson
[23]2007 X Yes Yes Yes X Yes X Yes Yes
4 Le Bellego et al. [24] 2010 X Yes Yes Yes Yes Yes X Yes Yes
5 Ali et al. [11] 2012 X Yes Yes X X Yes X Yes Yes
6Thokala and Duenas
[25]2012 X Yes Yes X X Yes X Yes Yes
7Konstantinidou et al.
[16]2014 X Yes Yes Yes X Yes X Yes Yes
8Betts and Gonzalez
[12]2016 X Yes Yes Yes X Yes X Yes Yes
9 Mengistu, Y. et al. [26] 2016 Yes Yes Yes X Yes Yes Yes Yes Yes
10 Celis-Morales et al.
[14]2017 X Yes Yes X X Yes X Yes Yes
11 Järvelä-Reijonen et al.
[27]2018 X Yes Yes Yes X Yes X Yes Yes
12 Michel and Burbidge
[15]2019 X Yes Yes Yes X Yes X Yes Yes
Multimodal Technol. Interact. 2025,9, 112 10 of 29
Table 2. Cont.
No Author(s)/Source Year Wearable
Sensors
Machine
Learning/AI
Health/App-
Based
Food +
Beverage Hydration Personalized
Guidance
Data Fusion/
Multimodal
Input
Behavioral
and Environ-
mental
Integration
Implementation
Barriers and
Policy Consid-
erations
13 Liska et al. [4] 2019 X X Yes X Yes X X Yes X
14 Perrier [5] 2019 X X Yes X Yes X X Yes X
15 Mathers [18] 2019 X Yes Yes Yes X Yes X Yes Yes
16 Budreviciute et al. [2] 2020 X Yes Yes Yes X Yes X Yes Yes
17 Beierle et al. [28] 2020 X Yes Yes X X Yes X Yes X
18 Liaqat, S. et al. [29] 2020 Yes Yes Yes X Yes Yes Yes Yes Yes
19 Perrier et al. [10] 2021 X X Yes X Yes X X Yes X
20 Millard-Stafford et al.
[30]2021 X Yes Yes Yes Yes Yes X Yes X
21 Jo, S. et al. [31] 2021 Yes X X X Yes X X Yes X
22
Suppiah, H.T. et al. [
32
]
2021 Yes Yes Yes X Yes Yes X Yes X
23 Kulkarni, N. et al. [33] 2021 Yes Yes Yes X Yes Yes Yes Yes X
24 Dolci et al. [13] 2022 X Yes Yes X Yes Yes Yes Yes X
25 Malik and Hu [34] 2022 X Yes Yes Yes X X X Yes Yes
26 Al-Rayes et al. [35] 2022 X Yes Yes Yes X X X Yes X
27 Oc and Plangger [36] 2022 Yes Yes Yes X X X Yes Yes X
28 Rodin, D. et al. [37] 2022 Yes X X X Yes X X X X
29 Sabry, F. et al. [38] 2022 Yes Yes X X Yes X X X X
30 Wang, S. et al. [39] 2022 Yes Yes X X Yes Yes Yes Yes X
31 Hillesheim and
Brennan [40]2023 X Yes Yes Yes X Yes X Yes Yes
32 Rod et al. [41] 2023 X Yes Yes X X Yes X Yes Yes
33 Tsolakidis et al. [17] 2024 X Yes Yes Yes X Yes Yes Yes X
34 de Castro et al. [42] 2024 X Yes Yes X X Yes Yes Yes X
35 Dimou et al. [43] 2024 X X Yes X Yes Yes Yes Yes X
36 Tonello, S. et al. [44] 2024 Yes Yes Yes X Yes Yes Yes Yes X
37 Chiao, J.C. et al. [45] 2024 Yes X Yes X Yes X X X X
38 Li, J.H. et al. [46] 2024 Yes Yes Yes Yes Yes Yes Yes Yes X
39 Alaslani, R. et al. [47] 2024 X Yes Yes X Yes X X X X
40 Sreeharsha, A. et al.
[48]2024 Yes Yes Yes X Yes X X Yes X
41 Waterllama [49] 2025 X X Yes X Yes Yes X Yes X
42 Water Time Drink
Tracker [50]2025 X X Yes X Yes Yes X Yes X
43 Plant Nanny Water
Tracker [51]2025 X X Yes X Yes Yes X Yes X
Multimodal Technol. Interact. 2025,9, 112 11 of 29
3.1. Food Water Content
Water that is contained in foods contributes significantly to total fluid intake and
thus can have an important role in hydration, yet this is often overlooked in many dietary
recommendations and applications. Scientific authorities, such as the European Food Safety
Authority (EFSA) and the Institute of Medicine (IOM), define the Total Water Intake (TWI)
as the sum of water obtained from beverages, food moisture, and metabolic oxidation.
According to EFSA, approximately 70–80% of TWI is typically derived from beverages,
while about 20–30% might come from foods, although this balance is quite variable and
depends on factors such as dietary patterns, types of food and drink consumed, and
individual factors, namely age and physical activity level [
19
]. Additionally, oxidation
water produced from the metabolism of macronutrients may contribute an estimated
250–600 mL/day
, particularly in active individuals [
20
]. The IOM offers similar guidance,
recommending adequate TWI, while noting that most water intake comes from beverages,
without issuing separate values for Total Fluid Intake (TFI) [
52
]. This distinction is especially
important for the development of personalized hydration support systems, as food-based
water may play a proportionally greater role in some individuals’ hydration profiles. These
proportions may vary significantly depending on age, dietary habits, and environmental
conditions [53].
While most hydration guidelines focus mostly on plain water and beverages, it is rather
important to recognize that many foods, particularly fresh fruits, vegetables, and soups,
contain high water content that contributes to the individual’s overall fluid intake [
22
,
24
,
54
].
For example, fruits such as watermelon and citrus and vegetables such as cucumbers and
tomatoes contain water in concentrations of more than 90%, contributing significantly
to hydration levels. In addition, these food-based fluids contain electrolytes, which help
enhance water retention, making them particularly valuable [
22
,
24
]. They also provide
essential nutrients, like vitamins, minerals and fiber, which contribute to overall health
and well-being. Despite the importance of fluids in foods for hydration, this factor is
frequently neglected, not only in generic applications that monitor diet intake, but also in
applications created specifically to assist hydration, such as nutrition trackers and mobile
apps that send reminders regarding water consumption [
54
]. These tools often focus solely
on tracking the consumption of plain water and sometimes additionally tea, coffee, and
other beverages, missing a substantial portion of the daily hydration intake from foods. As a
result, individuals may be unaware of the full contribution their food choices make toward
their hydration needs. If fluid intake from food is not properly considered, hydration
strategies may fail to capture the full picture of an individual’s hydration status. This can be
particularly problematic when implementing precision nutrition and hydration programs,
where accurate data is critical for personalized recommendations. Without considering the
contribution of fluids from foods, hydration plans may miss opportunities to optimize fluid
intake, especially for individuals who may struggle to drink adequate amounts of water.
Moreover, the calorie content of beverages plays a significant role in hydration strate-
gies and can impact health risks, particularly when it comes to NCDs [
34
]. While water,
herbal teas, and low-calorie drinks are ideal for hydration, without contributing exces-
sive calories, sugary beverages such as sodas, energy drinks, milk, and sweetened juices
can lead to an increased caloric intake. Regular consumption of these high-calorie bev-
erages can contribute to weight gain, insulin resistance, and higher risks of obesity, type
2 diabetes, and cardiovascular disease [
34
]. Moreover, sugary drinks often provide little
or no nutritional value in terms of hydration, and beverages high in caffeine or alcohol
may promote dehydration due to their high sugar content, which can have a diuretic
effect [
55
,
56
]. Therefore, education on balancing beverage choices is essential not only for
Multimodal Technol. Interact. 2025,9, 112 12 of 29
maintaining proper hydration, but also for reducing the long-term risk of metabolic and
cardiovascular diseases.
However, the calories in beverages can be beneficial in certain cases, particularly for
individuals who have higher energy needs or specific health conditions. For instance,
athletes or individuals engaging in intense physical activity may benefit from the partial
substitution of water with caloric beverages, such as sports drinks, to replenish lost energy,
and restore glycogen stores and electrolytes, supporting recovery and performance [
57
,
58
].
Similarly, individuals who are underweight, struggle to tolerate food volume, or have
difficulty consuming enough calories through food alone, such as those with certain medical
conditions or in recovery from surgery, may find calorie-containing beverages helpful.
High-calorie drinks like smoothies, milkshakes, or meal replacement drinks can provide
water, protein, essential nutrients, and extra calories needed to assist hydration while at
the same time helping to prevent malnutrition and promote weight gain or maintenance.
Also, in cases of dehydration, particularly when fluid losses are substantial, such as during
intense heat, exercise, or fever, beverages containing calories and electrolytes, such as oral
rehydration solutions, can be beneficial. They help restore both hydration and electrolyte
balance, thereby reducing the risk of complications associated with dehydration [30].
A diet proposed for optimal hydration can naturally align with a diet designed for
the prevention of NCDs, as both approaches emphasize the importance of whole, nutrient-
dense foods. Hydration-focused diets often recommend water-rich foods like fruits and
vegetables, which are not only high in water but also packed with essential vitamins,
minerals and fiber. These foods support hydration while simultaneously contributing
to the prevention of NCDs such as cardiovascular disease, diabetes, and certain cancers.
For example, consuming foods like cucumbers, watermelon, oranges, and leafy greens
helps maintain hydration levels while providing antioxidants, potassium, and dietary fiber,
nutrients known to lower blood pressure, support healthy blood sugar levels, and reduce
inflammation, all of which are critical factors in NCD prevention.
Incorporating the fluid content of foods into hydration assessments and recommen-
dations, and providing education on proper hydration choices, either through food or
beverages, could offer a more comprehensive and effective approach to managing hydra-
tion and reduce NCD risk. By accounting for both food and beverage sources of fluids,
this approach enables more accurate monitoring of hydration status and ensures that di-
etary interventions are better aligned with an individual’s specific needs and preferences.
Adopting a holistic perspective on hydration bridges the gap between food and beverage
consumption, making it easier for individuals to adhere to diet and hydration guidelines
and, ultimately, improving health outcomes. This shift in perspective could also have
significant implications for public health campaigns, promoting a more inclusive approach
to hydration that recognizes all sources of fluids, ultimately fostering more sustainable and
effective hydration habits across diverse populations.
3.2. Hydration Diet Recommenders
Precision medicine is an approach to medical treatment that considers individual
variability in genes, environment, and lifestyle for each person. It aims to tailor pre-
vention, diagnosis, and treatment strategies to specific patient characteristics rather than
using a one-size-fits-all approach [
12
,
18
]. It is a data-driven approach that harvests in-
formation such as genomics, intestinal microbiota, and metabolomics to improve patient
outcomes [15,17,40,59].
Personalized diet recommendation systems play a crucial role in tailoring diet advice
to individual needs, leveraging tools like AI and ML to create customized hydration plans.
These systems typically fall into three main categories: 1. diet recommendation systems,
Multimodal Technol. Interact. 2025,9, 112 13 of 29
which generate customized daily or weekly meal plans by analyzing a person’s profile and
utilizing multidimensional data; 2. personalized guidance on recipes, offering personalized
recipe suggestions based on user preferences, dietary needs, and other relevant data; and 3.
personalized guidance on restaurant recommendations, which assist individuals in making
informed choices, suggesting food items in the menu that align with their nutritional
profiles [17].
Currently, these systems primarily rely on basic demographic characteristics, includ-
ing gender and age, and anthropometric data to make recommendations. In addition to
these functions, these systems could also integrate hydration monitoring by incorporating
the fluid content of meals and beverages. For example, meal plans could consider the water
content of foods, such as fruits, vegetables, and soups, while recipe suggestions could
include options that help meet daily hydration goals. Restaurant recommendations could
guide individuals toward menu items with adequate hydration potential, ensuring a more
comprehensive approach to both nutrition and hydration. By combining personalized
dietary advice with hydration monitoring, these systems could promote more balanced and
sustainable health habits for better hydration and NCDs prevention. This additional data,
such as fluid intake patterns and activity levels, could elevate these systems to provide
precision hydration. This approach would further enhance the accuracy of recommenda-
tions, improve adherence to hydration guidelines, and contribute to better health outcomes,
particularly in reducing the long-term risk of NCDs.
3.3. Psychological, Social, and Environmental Drivers of Fluid Intake Behavior
Among the included studies, several examined psychological and social factors in-
fluencing fluid intake behaviors. Studies identified stress, mood, and emotional states as
factors affecting diet and beverage choices [
23
,
60
,
61
], with some evidence that negative
emotions may increase consumption of caffeinated or alcoholic beverages over water [
61
].
Cultural norms and beverage preferences were noted as influences on hydration pat-
terns [
21
], and socioeconomic factors including education level and access to clean water
were identified as determinants of beverage quality and availability [62,63].
However, the included studies provided limited empirical evidence specifically ex-
amining the integration of psychological assessment tools or personality-based tailoring
within hydration recommendation systems. While some studies discussed behavioral
factors conceptually [
27
,
28
], none of the included AI-driven or mHealth hydration systems
incorporated validated psychological instruments such as the Big Five Inventory for per-
sonalized recommendations. This represents a significant gap in current research and an
opportunity for future system development.
3.4. Integration of AI and mHealth Technologies in Hydration Diet Recommenders
The term mobile-health (mHealth) refers to the practice of medicine and public health
facilitated by mobile devices [
64
]. The World Health Organization (WHO) has identified
mHealth as a pivotal health-promotion strategy with the potential to enhance global
health outcomes across low-, middle-, and high-income countries [
65
]. Modern mHealth
applications, powered by AI algorithms and advanced ML models, can offer significant
advantages regarding precision hydration [
66
]. These technologies allow for the collection
of real-time hydration and other lifestyle data. For example, analyzing fluid intake patterns
plays a fundamental role in AI-driven hydration recommendation systems, as it establishes
the foundation for accurately assessing both the quantity and variety of fluids consumed.
This field has seen significant advancements in recent years, with various AI-powered
hydration platforms. The systems can be categorized based on their specific functions and
the different methodologies and datasets used. The fragmentation of existing approaches
Multimodal Technol. Interact. 2025,9, 112 14 of 29
and lack of integrated solutions becomes evident through a detailed examination of separate
technologies. This fragmentation is precisely what our proposed framework aims to
address. AI algorithms using massive amounts of data collected from individuals can
identify patterns and trends in drinking and other lifestyle behaviors that offer insights
into an individual’s modifiable habits associated with hydration risk.
A large number of current studies have demonstrated that the growing integration of
wearable sensors, AI, and ML in the field of hydration monitoring is possible and effective.
Several works [
38
,
44
,
45
] explore the use of multi-sensor or wearable technologies, utilizing
biometric and environmental data to assess hydration in real time, and are considered
key mHealth-based technologies enabling personalized hydration guidance, which this
scoping review advocates. Suppiah et al. (2021) [
32
] and Kulkarni et al. (2021) [
33
]
incorporated ML methods to adapt recommendations based on individual behavior or
context, which is critical for developing adaptive hydration plans. Jo, S. et al. (2021) [
31
]
and Chiao et al. (2024) [
45
] provide some important overviews of biosensing and wearable
technologies, helping to contextualize the types of data that could be integrated into AI-
powered hydration recommender systems, while skin sensors and smartphone cameras
can help in data collection [29,47].
A recent systematic review by Sreeharsha et al. (2024) [
48
] evaluated wearable sensor-
based hydration monitoring systems powered by machine learning. While this review
confirmed the accuracy of ML techniques for dehydration detection, it did not address the
integration of food-based water intake or personalized recommendation functionalities
within mHealth platforms. This further highlights the gap in the current research between
monitoring and personalized guidance systems that combine AI, hydration tracking, and
nutritional inputs.
Other promising studies have applied ML algorythms to monitor hydration status
or detect fluid intake behaviors using wearable technologies. For instance, Mengistu et al.
(2016) [
26
] have developed a wearable system called AutoHydrate, combining a throat
microphone, smartwatch, and embedded processing unit to automatically detect drinking
activities and physical movement. The system employed Support Vector Machines for
acoustic signal classification and Gradient Boosting Decision Trees for activity recogni-
tion, achieving high accuracy in detecting fluid intake and calculating personalized fluid
recommendations based on Dietary Reference Intakes.
Importantly, a subset of these studies has included the validation of model outcomes.
For instance, Liaqat et al. (2020) [
29
] and Alaslani et al. (2024) [
47
] have successfully shown
that non-invasive methods, which range from skin sensors to smartphone cameras, can now
be used to estimate hydration status with accuracy, making the data collection even more
accurate, accessible, and user-friendly. Similarly, in 2022, Wang et al. [
39
] explored hydration
status prediction using non-invasive physiological and sweat biomarkers during endurance
exercise. Their single-subject study used nonlinear ML models to predict dehydration
levels, with heart rate and whole-body sweat rate being the most accurate indicators. In
another study, Li et al. (2024) [
46
] proposed a multimodal drinking activity detection
system using wrist, container movement, and swallowing acoustics. Their multi-sensor
fusion model, using SVM and Extreme Gradient Boosting, significantly outperformed
unimodal approaches, achieving F1-scores up to 96.5% for event-based evaluations. Dolci
et al. (2022) [
13
] present a notable example of personalized hydration modeling using ML,
focusing mainly on the prediction of optimal daily water intake with the goal of achieving a
target urine osmolality of about a 500 mOsm/kg, which is consindered a clinical marker for
optimal hydration status. Their model combines both intrinsic variables (age, sex, height,
weight) and extrinsic factors, including food and beverage intake, which makes it one
of the few studies to account for dietary water content in its predictive framework. By
Multimodal Technol. Interact. 2025,9, 112 15 of 29
analyzing data from multiple hydration-focused clinical trials, the authors evaluated a
range of ML methods and found that XGBoost outperformed other models in predicting
urine osmolality (Mean Absolute Error = 124.99), with a classification accuracy of 85.5%,
compared to 77.8% for standard dietary guidelines. This work exemplifies the potential
of data-driven approaches, not just for hydration assessment but also for personalized,
actionable guidance. Importantly, this study stands out among the current literature for
explicitly incorporating food-derived water intake into its algorithm, aligning closely with
the aims of integrated hydration recommender systems. However, while the model offers
predictive recommendations for fluid intake, it does not integrate real-time, context-aware
feedback via mHealth platforms or behavioral data from wearable sensors. Bridging this
gap between clinical modeling and digital, personalized health tools represents a key area
for future development.
The above-mentioned systems have shown the technical feasibility of real-time hy-
dration monitoring using ML and sensor data. However, none of the reviewed studies
incorporate water content from food or generate comprehensive recommendations that
combine both beverage and dietary sources of hydration. This represents a key gap in
the field. For optimal hydration, especially in free-living and diverse populations, future
AI-driven systems should integrate both fluid- and food-derived water intake and offer
context-aware, personalized guidance grounded in user behavior, environmental factors,
and nutritional needs. The integration of AI algorithms with mHealth platforms to deliver
personalized recommendations for diet and water intake holds significant potential to
advance accessible precision hydration in a cost-effective and user-friendly manner. The
abovementioned studies establish a technological foundation for an AI-integrated, fluid
hydration recommendation system, clearly showing that while monitoring capabilities
are advancing, the integration of these tools into real-time, personalized, mHealth-guided
dietary interventions with food-and-fluid hydration recommendation systems remains
largely unexplored.
Personalized hydration advice can account for an individual’s metabolic profile, which
in turn may further improve the efficacy of interventions regarding dehydration, car-
diometabolic health, and NCD prevention. Moreover, these systems usually integrate
multiple data sources, such as wearable sensing devices and health records, to provide a
more comprehensive analysis of an individual’s temperature, heartbeat, exercise levels,
sleep environmental characteristics, etc. This integration aims to achieve a better under-
standing of how various factors, such as genetics, stress, activity, environmental indices,
and fluid intake, interact and influence hydration and health outcomes [31,33,37].
An AI-powered hydration diet recommender system that can provide personalized
hydration advice but also allows for personalized Specific, Measurable, Achievable, Real-
istic, and Timely (SMART) goals could improve adherence to health-promoting diet and
hydration patterns and reduce the risk of dehydration and developing NCDs. AI systems,
in addition to providing diet and hydration guidance, can also monitor adherence to rec-
ommendations and make adjustments if necessary, ensuring users stay on track with their
objectives [
17
,
67
]. For example, when a user deviates from the suggested drinking patterns
or SMART goals, the system could provide reminders or suggest alternative beverages
that align with their goals. Also, wearable sensors can provide recommendation systems
with data for more accurate and on-site recommendations. Additionally, the use of im-
ages/videos, analyzed by AI and ML algorithms, can provide an input to recommendation
systems regarding food and beverage recognition, as well as intake volumes. By providing
personalized, real-time feedback, AI-powered mHealth tools could lead to higher user
satisfaction and increase app use, offering support and motivation, so that users remain
engaged and motivated to maintain healthy hydration habits.
Multimodal Technol. Interact. 2025,9, 112 16 of 29
3.5. Smart Applications Assessing Hydration
Smartphone applications designed to support individuals in monitoring and im-
proving hydration generally offer personalized hydration goals, progress tracking, and
reminders. These apps are useful tools for promoting optimal hydration but often focus
exclusively on liquid intake, overlooking the significant contribution of water in foods.
For example, “Waterllama” is an iOS app that is compatible with Apple Watch and
other widgets and integrates gamification to encourage hydration, motivating users to
meet their goals through interactive features. However, it primarily tracks beverages and
does not account for water content in foods [
49
]. “Water Time Drink Tracker & Reminder”
is another Android smart app that offers users the ability to set daily hydration targets
with reminders to promote regular intake. The app focuses solely on liquid intake and
does not include food-derived water in its assessments [
50
]. “My Water” is an Android
and iOS app, also compatible with Apple watch, that allows users to log various beverages
and adjust hydration goals based on personal variables. Like many other apps, it focuses
on liquids and does not incorporate the water content from food sources [
68
]. Similarly,
“WaterMinder”, which runs also on desktops as well as mobile devices, provides an intuitive
platform for tracking daily water intake and integrates with devices like the Apple Watch
for ease of use. However, it does not consider water in food, limiting its assessment to
liquids [
69
]. Also, “Aqualert”, running on both Android iOS and placing more emphasis
on purposely colorful graphical elements than the previous apps, allows for customizable
hydration goals and flexible reminder settings, yet it only tracks liquid consumption and
excludes the contribution from food-derived water [70].
“Hydro Coach” operates on both main mobile platforms, calculates hydration goals
based on user data, and adjusts for weight changes. Rewards for achieving certain hy-
dration goals appear as amusing animations. It sends reminders but does not integrate
water content from foods, focusing only on beverages [
71
]. “Plant Nanny” is an app that
has been popular for more than 10 years and combines hydration tracking with a plant-
growing game to motivate users to meet their water goals. However, it does not address
food-derived hydration [
51
]. “Daily Water Tracker Reminder- Waterful” is an Android app
that provides a simple interface for logging water intake and setting personalized goals.
Like other apps, it does not take into account water from food sources.
Drink Water Reminder helps users establish regular hydration routines and tracks liq-
uid consumption. It does not factor in the water content from foods, limiting its scope [
72
].
While the above-mentioned hydration tracking apps offer valuable features, like reminders
and personalized goals, they typically neglect the significant role of water in food. Addi-
tionally, mHealth is a broad field that encompasses the use of mobile technologies, such as
smartphones, tablets, and wearable devices, to support various health-related activities,
including disease prevention, diagnosis, treatment, and monitoring. mHealth includes a
wide range of services, from chronic disease management to fitness tracking, mental health
support, and telemedicine [
64
]. It involves the use of mobile applications, sensors, and
data analytics to track and manage various aspects of health, offering a comprehensive
approach to health management [
42
,
64
,
66
]. For example, mHealth apps can help users
monitor chronic conditions like diabetes or hypertension, track physical activity, provide
remote consultations with healthcare providers, or offer mental health support through
mood tracking or stress management. The existing hydration apps are a specific subset
of mHealth applications focused solely on monitoring and improving hydration status.
These apps help users track their daily water intake and set hydration goals, and provide
reminders to encourage regular hydration. While hydration apps can be considered as a
useful tool for promoting adequate hydration, they typically have a narrower focus com-
pared to the broader range of health functions that can be offered by an mHealth solution.
Multimodal Technol. Interact. 2025,9, 112 17 of 29
For instance, the current hydration apps primarily track liquid intake and generally do not
integrate with other health data or devices in the way that more comprehensive mHealth
apps do. Thus, they may not provide the same level of support for managing chronic
diseases, tracking fitness metrics, or offering mental health services. Future developments
should consider integrating these apps into mHealth solutions and consider the food water
content, personalized goals and needs, and extra data from other devices, such as stress
levels, physical activity status, environmental conditions, etc., in order to provide a more
comprehensive assessment of hydration.
4. Discussion
4.1. Proposal
Existing hydration apps typically only track fluid intake from beverages. Meanwhile,
nutrition-focused AI tools rarely include hydration, let alone water from food. This review
identifies a critical opportunity: integrating food- and beverage-derived water tracking
within intelligent systems powered by AI and mHealth. These systems could generate
real-time, personalized hydration recommendations, addressing individual physiological,
behavioral, and environmental contexts, thereby closing a gap in both clinical practice and
public health tools.
The proposed hydration diet recommender integrates demographic, anthropometric,
psychological, and socioeconomic data to create a truly personalized diet and hydration
plan with a holistic approach. Users begin by selecting SMART goals tailored to their
needs, based on a precision diet and hydration plan. To support this tailored approach,
smartwatches and activity trackers help monitor key health metrics like heart rate, activity
levels, and environmental conditions. At the same time, smart mobile apps allow users to
track their eating and drinking habits, while smart water bottles provide data about current
water intake. By continuously collecting this information and integrating it with other
factors influencing hydration and eating behavior, the proposed hydration recommendation
system can refine and adjust diet and hydration guidance according to environmental
conditional and lifestyle habits in real-time. Users can monitor their progress, adapt
their SMART goals as needed, and receive ongoing, personalized feedback that educates
them in real-world scenarios. This dynamic process helps to increase satisfaction with the
comprehensive hydration diet recommender, assisting the users in staying motivated in
the long-term in a way that is both practical and achievable.
Figure 1illustrates the proposed HydrationApp system architecture, a comprehensive
precision nutrition platform that integrates artificial intelligence, mHealth technologies,
and augmented reality features to address the growing need for personalized dietary and
hydration guidance. The system represents an innovative approach to precision nutrition
specifically designed to deliver individualized recommendations for improved hydration
through mobile and augmented reality technologies, particularly targeting young adult
populations. The architecture employs a systematic four-stage data pipeline that begins
with the collection of multidimensional input parameters, including individual demo-
graphic profiles, behavioral patterns, environmental contexts, and real-time physiological
monitoring data. This diverse information is subsequently stored in structured databases
alongside comprehensive nutritional knowledge bases and trained machine learning mod-
els. The core processing layer utilizes sophisticated artificial intelligence algorithms that
synthesize these multidimensional data streams while accounting for the complex interplay
of social, psychological, and environmental factors known to influence dietary behaviors.
The platform’s strength lies in its ability to transform this complex data integration into
actionable, evidence-based recommendations delivered through intuitive user interfaces,
including mobile application dashboards, immersive augmented reality educational ex-
Multimodal Technol. Interact. 2025,9, 112 18 of 29
periences, real-time health monitoring systems, and seamless integration with broader
healthcare infrastructures. Critically, the system incorporates a continuous learning mecha-
nism that enables machine learning algorithms to refine their predictive capabilities based
on user interactions and health outcomes, thereby improving recommendation accuracy
and personalization effectiveness over time. This architecture aligns with contemporary
precision nutrition principles and addresses the critical gap between population-based
dietary guidelines and individual-specific nutritional needs, ultimately supporting the
prevention of dehydration-related health issues and non-communicable diseases through
the promotion of evidence-based nutritional behaviors.
4.2. Public Health Implications
The integration of mHealth tools into public health frameworks holds significant po-
tential for improving population health outcomes. By accounting for individual and
population-level variability, integration enhances efforts to prevent hydration-related
chronic conditions such as kidney stones, urinary tract infections, and cardiovascu-
lar diseases [
66
]. This personalized approach helps public health strategies and pol-
icy recommendations to be more effective, targeted, and inclusive, ultimately fostering
healthier communities.
Policymakers have a critical role in creating supportive frameworks for the imple-
mentation of precision hydration technologies. Integrating these tools into existing health
systems requires the development of policies that provide resources for implementation,
establish reimbursement strategies, and promote training for healthcare providers to effec-
tively utilize related technologies [65].
Standards for data privacy and security must be prioritized to protect individuals’
sensitive information, and ethical guidelines should inspect the use of AI in diet and
hydration for fairness and transparency. Furthermore, integrating diet and hydration tools
with electronic health records can open the way for the use of precision diet and hydration
interventions within clinical environments. These proposals could bridge the gap between
technological advancements and practical public health applications, making precision
hydration part of future healthcare strategies.
4.3. Challenges and Proposed Solutions
Despite the tremendous AI and technological advances, vulnerable populations, who
are often at higher risk for developing NCDs, face significant barriers to accessing even
basic healthcare, let alone precision hydration services. The barriers often stem from the
limited availability of healthcare professionals, but also the lack of local resources, plus
the high costs of specialized interventions. Furthermore, mobile apps and AI-driven tools
require a certain level of digital literacy, which can be a significant obstacle for vulnerable
groups, particularly older adults, those with disabilities, or those with lower levels of
education [
65
]. These populations may struggle to access or use effectively innovative
approaches, including mHealth apps. Additionally, language difficulties and varying levels
of health literacy can further complicate the delivery of effective mHealth interventions [
41
].
Additionally, an important consideration in AI- and ML-driven hydration recommen-
dation systems is the scope and, of course, the quality of the training data. Current models
predominantly rely on structured datasets from controlled studies or sensor-based measure-
ments, which may not fully capture dynamic biological feedback, such as thirst perception,
hormonal regulation, or any individual variability in hydration needs. Similarly, contextual
and environmental factors, which also include the accessibility of drinking water, public
health infrastructure, or practical app features such as barcode scanning of beverages, are
rarely incorporated, potentially limiting the real-world applicability of these tools. Ad-
Multimodal Technol. Interact. 2025,9, 112 19 of 29
dressing these gaps will require larger, more diverse datasets that integrate physiological,
behavioral, social, and environmental dimensions. Incorporating such multidimensional
data could enhance predictive accuracy, allow for context-aware recommendations, and
improve both individual adherence and public health impact, thereby bridging the gap
between technological innovation and practical hydration interventions.
Personalized studies using mHealth tools face several challenges, including the need
for participant screening and targeted recruitment based on genetic profiles, health status,
and lifestyle behaviors. Additionally, social and psychological factors, such as cultural influ-
ences, emotional drinking and eating patterns, and social dynamics, must be accounted for
to ensure diverse and representative participant groups. These problems make it difficult to
achieve adequate statistical power to evaluate within- and between-group variability, which
can be both time-intensive and resource-intensive. Moreover, the predictive potential of a
diet and hydration tool determining dehydration and NCD risk is still in the development
phase, with current models largely overlooking the interplay of psychological, social, and
environmental factors. This limitation highlights the need for more integrative approaches
to improve the applicability and effectiveness of findings in real-world settings.
To address these challenges, innovative methodologies and integrative study designs
are essential. The combination of holistic approaches with high-throughput screening
platforms and advanced ML algorithms can improve the prediction of individual responses
to hydration interventions. Translational research—the process of applying laboratory
discoveries and clinical insights to real-world healthcare solutions—is also needed to bridge
the gap between mHealth, hydration and public health applications, ensuring findings
are actionable and accessible. Incorporating emotional and psychological dimensions into
hydration recommendation tools can create more person-centered interventions. Features
like behavioral factors, culturally sensitive recommendations, and social support can further
promote adherence and address social determinants of health. Public health policies should
prioritize equitable access to precision hydration services, particularly for vulnerable
populations, by offering personalized nutrition and hydration at lower cost, culturally
appropriate diet advice, and support networks within the community. This holistic and
inclusive approach can help reduce health disparities and foster healthier communities by
improving outcomes for hydration-related chronic diseases [41,67].
The integration of data science, diet and hydration science, psychology, and public
health policy could indeed help to accelerate the development of effective tools. Our
proposed framework would particularly benefit from further research in these interdis-
ciplinary fields to enhance both its precision and its applicability. Our review highlights
the need for further comprehensive research to develop and evaluate the effectiveness of
smart hydration diet recommenders across diverse populations, examining both short-term
usability and long-term health outcomes. Such research could optimize the performance of
applications like HydrationApp and ensure more precise, evidence-based decision making
in personalized hydration guidance.
First, further research in developing and validating robust AI algorithms could inte-
grate complex biological, psychological, and social data, which would enhance the precision
of hydration diet recommendations. Also, the integration of physiological, psychological,
and social dimensions into personalized diet and hydration interventions will be crucial
for maximizing their effectiveness. Moreover, more sophisticated virtual- and augmented-
reality technologies, plus gamification, could revolutionize hydration recommendation
tools by simulating social eating and drinking environments or physically demanding
situations, which could provide personalized diet and hydration education and increase
users’ engagement with the tool [
35
,
36
,
43
,
66
]. Additionally, cost-effectiveness studies will
play a pivotal role in evaluating the feasibility of scaling these innovations within healthcare
Multimodal Technol. Interact. 2025,9, 112 20 of 29
systems, particularly in resource-limited settings. Multi-Criteria Decision Analysis (MCDA)
could be a valuable tool for optimizing AI-driven hydration diet recommenders [
25
]. In
this context, MCDA can integrate diverse criteria, such as genetic and metabolic profiles,
food and beverage preferences, cultural considerations, economic constraints, and the
accessibility of food and water sources, ensuring that recommendations are practical, eq-
uitable, and tailored to individual needs. This approach can enhance decision making in
precision nutrition and hydration, leading to more effective and sustainable interventions
and policies.
Finally, addressing health disparities should remain a central focus, with targeted
research aimed at understanding and removing barriers faced by vulnerable populations.
Governments and health organizations should fund and support research that explores the
connections between hydration, genetics, psychology, social behavior, and health outcomes.
By creating comprehensive data repositories, policymakers can develop evidence-based
strategies to integrate precision nutrition and hydration into existing public health frame-
works, establishing effective interventions for short-term dehydration and long-term NCD
risk reduction. Figure 2illustrates an integrated digital framework that takes advantage
of the use of AI along with mHealth technologies to deliver real-time, personalized rec-
ommendations for both food and fluid intake. Positioned at the intersection of precision
nutrition and public health, this system incorporates behavioral, social, and environmental
data to enhance dietary quality and hydration. By addressing the complexity of individual
needs and NCD prevention, the model advocates for inclusive health policies and the
incorporation of precision nutrition strategies into national health systems in a sustainable
and low-cost way, supporting the transformative shift from population-level guidelines to
personalized public health strategies.
Figure 2. AI-Driven precision nutrition: a next-generation framework for personalized food and fluid
guidance in public health.
Multimodal Technol. Interact. 2025,9, 112 21 of 29
4.4. Psychological and Social Dimensions in Future Hydration Systems
On broader behavioral science and precision nutrition literature, we propose that
future hydration recommendation systems should integrate psychological and social deter-
minants to enhance personalization and adherence.
Stress, mood, cognitive biases, and emotional regulation are all patterns that can
significantly alter thirst perception as well fluid intake behaviors. Research in nutritional
psychology demonstrates that individuals may consume more caffeinated or alcoholic
beverages in response to negative emotions [
23
,
61
], potentially disrupting optimal hydra-
tion. While the included studies acknowledged these factors conceptually, none of them
implemented systematic psychological assessment within AI-driven hydration tools.
In addition, beverage preferences are shaped by cultural norms, with some populations
preferring tea, coffee, or sugary drinks over plain water [
21
,
73
]. Socioeconomic status also
determines both affordability and access to quality beverage options [
62
]. Family dynamics,
social support systems, workplace environments, and peer influences can either facilitate
or undermine hydration behaviors [
63
]. Understanding these drivers is more than essential
in developing targeted interventions, especially for children, older adults, and athletes in
vulnerable populations [24].
To further enhance future hydration recommendation systems, we propose integrating
validated psychological assessment tools. Dietary behavior questionnaires could help
provide clear insights into individual fluid intake patterns [
27
]. Additionally, personality
categorization using instruments such as the Big Five Personality Traits (BFI-2) could
refine recommendations [
28
]. For example, by classifying users based on traits such
as conscientiousness, openness to experience, or impulsivity, a hydration system could
tailor guidance to an individual’s psychological profile. However, we emphasize that this
approach requires substantial empirical validation before implementation, as none of the
studies in our review demonstrated such integration in hydration contexts.
Using validated instruments, future hydration recommendation systems could offer
more personalized and adaptive suggestions, potentially improving adherence and maxi-
mizing long-term impact on non-communicable disease risk. This represents a promising,
yet currently underexplored, direction for precision hydration research.
4.5. Strengths and Limitations
This review explores a timely and rapidly evolving topic concerning the use of AI
and mHealth technologies in order to provide personalized guidance for nutrition and
specifically for optimizing hydration.
However, there are several important limitations to acknowledge. Most significantly,
at the time this scoping review was conducted, no empirical studies evaluating AI-based
systems that offer integrated, personalized recommendations for both nutrition and hydra-
tion were identified. As such, the analysis was primarily conceptual and exploratory in
nature and relied on the related literature (e.g., digital nutrition apps, hydration trackers,
and theoretical frameworks) to outline current capabilities and propose future directions.
While this limits the strength of directly applicable evidence, it also underscores the novelty
and need for innovation in this emerging field. Moreover, the included studies provided
limited empirical evidence on several critical dimensions. Behavioral determinants of hy-
dration (e.g., habit formation, social influences on drinking behavior) were rarely examined
in hydration-specific contexts. Technological factors, including sensor accuracy, real-time
data integration challenges, and user interface design solutions for hydration apps, re-
ceived minimal attention in the reviewed literature. Psychological determinants, such
as motivation, self-efficacy, health beliefs, and emotional regulation related to hydration,
were largely absent from hydration-focused studies, though well-established in broader
Multimodal Technol. Interact. 2025,9, 112 22 of 29
nutrition behavior research. Our Discussion necessarily draws on this broader behavioral
and nutritional literature to contextualize the findings, as hydration-specific research on
cultural, psychological, and socioeconomic determinants remains limited.
In this context, several themes discussed in this review, including cultural sensitivity,
socioeconomic accessibility, family influences, and behavioral tailoring, are derived from
general behavioral science, precision nutrition, and mHealth implementation research
rather than hydration-specific empirical studies from our included articles. For example,
barriers related to digital literacy, healthcare access, and resource limitations reflect well-
documented challenges in the broader mHealth literature rather than findings unique
to hydration interventions. Similarly, recommendations for culturally sensitive design
and consideration of social determinants reflect established principles from health equity
research and precision nutrition frameworks, not exclusively hydration-specific evidence.
We have been careful to frame these as important considerations for future system design
and implementation rather than as direct findings from the scoping review.
Consistent with the PRISMA-ScR guidelines for scoping reviews, we did not conduct
formal quality assessment or risk-of-bias evaluation of included studies. The purpose
of this review was to map the landscape of existing research and identify gaps, not to
synthesize evidence for clinical recommendations or evaluate intervention effectiveness.
This approach is appropriate for an emerging field where integrated AI-driven hydration
systems are largely absent from the literature, but it means we cannot comment on the
methodological rigor or robustness of individual included studies. Many findings are
context-specific and may not generalize across populations or settings. Studies examining
hydration apps or nutrition recommenders were often conducted in high-resource settings
with technologically literate populations, limiting their applicability to vulnerable groups,
low-resource contexts, or populations with limited digital access. Additionally, hydration
needs and behaviors are highly variable across age groups, activity levels, climates, and
health conditions—dimensions not adequately represented in the current evidence base.
Several components of our proposed HydrationApp framework (Section 4.1) repre-
sent future research directions rather than validated applications. These include real-time
physiological monitoring integration, augmented reality educational features, and contin-
uous machine learning refinement based on user outcomes. While grounded in existing
technologies from adjacent fields, their application to integrated food-beverage hydration
systems requires empirical validation. When our Discussion moves from evidence synthe-
sis to conceptual proposals for future systems, we added explicit transitional language to
maintain clarity about the speculative nature of these elements.
While our proposed framework mentions psychological factors, we refer to health-
related behavioral constructs (e.g., motivation, self-efficacy, health beliefs) commonly
integrated into mHealth interventions, rather than personality profiling or trait-based
tailoring, which would require substantial additional validation before integration into
hydration recommendation systems.
Despite these limitations, this review contributes to identifying key gaps in current
evidence and practice and underscores the need for future empirical research, particularly
in under-monitored and high-risk populations such as children, the elderly, individuals
with chronic diseases, and athletes. One of the main strengths of our work is its broad,
forward-looking perspective. While many existing reviews focus only either on diet or
general health technology, we highlight the unique opportunity to bring nutrition and
hydration together in a single, individualized system. Including hydration is especially
important, as it often is overlooked in health recommendations, despite playing a key role in
overall well-being and chronic disease prevention. Another strength is the multidisciplinary
nature of the review. We bring together ideas and developments from nutrition science,
Multimodal Technol. Interact. 2025,9, 112 23 of 29
digital health, behavioral psychology, and AI. This multidisciplinary approach makes
this work useful not just for researchers in one field, but for anyone interested in how
these areas can work together to improve health outcomes, highlighting the complexity
involved in developing intelligent, context-aware systems that can accommodate real-life
variability in dietary behaviors and hydration needs.There is considerable opportunity
for the development and validation of integrated AI-mHealth platforms that holistically
address both nutritional and hydration needs, particularly in the context of chronic disease
prevention and personalized health promotion. By systematically mapping the current
state of knowledge and clearly delineating the boundaries between empirical findings and
conceptual proposals, this review provides a foundation for rigorous future research in
precision hydration science.
4.6. Figures
The proposed system architecture is organized into four color-coded functional layers
connected by directional data flow arrows and a continuous feedback mechanism (Figure 3).
Blue components represent the Data Input layer, comprising multiple data sources feeding
the system: user profile data (demographics, health conditions, activity levels), behavioral
data (preferences, eating patterns, social context), environmental data (weather, location,
time), and real-time monitoring data (hydration status, food intake, AR interactions).
Green components illustrate the Data Storage layer with organized storage systems,
including a user database for profiles and history, a knowledge base with nutrition and
hydration guidelines, AI models and trained algorithms, and a real-time cache for session
data. Yellow components depict the Data Processing and AI Engine layer, representing
the core intelligence infrastructure: a personalization engine using machine learning,
recommendation algorithms for food and hydration, a risk assessment for dehydration and
non-communicable diseases (NCDs), AR processing for educational content, an integration
layer considering social, psychological, and environmental factors, an analytics engine for
continuous learning, and quality assurance and validation systems. Purple components
show the System Output layer, encompassing user-facing applications and integrations:
a mobile app interface with personalized dashboards, AR experience with interactive
learning, real-time guidance and smart notifications, health monitoring and tracking, an
educational platform with gamified content, analytics and reporting, and integration
with health systems. The red dashed line represents the continuous feedback loop that
enables the system to learn and improve over time through user interaction analysis and
outcome evaluation.
Multimodal Technol. Interact. 2025,9, 112 24 of 29
Figure 3. HydrationApp system architecture and data flow diagram.
5. Conclusions
This scoping review reveals several clear gaps in the current digital health landscape:
the absence of AI- and mHealth-powered systems that provide personalized hydration
recommendations based on both food and beverage water intake. While hydration apps and
dietary recommenders exist, few systems combine these domains into a single, precision-
guided approach. Future innovation must target this intersection, enabling accurate,
personalized, and sustainable hydration strategies for diverse populations. Beyond this, the
review identifies broader challenges and considerations, including policy implementation,
public health impact, and the accessibility and practicability of AI-driven hydration tools.
Rather than replacing standard hydration practices, these emerging innovative technologies
could further extend their reach and adaptability, addressing the individuals’ variability and
behavior patterns, and contextual challenges, such as climate, physical activity, or illness.
Future research should focus on the design, implementation, and validation of AI-
driven recommendation systems that are able to provide more comprehensive, real-time
guidance for both diet intake and hydration. Collaborative efforts between researchers,
healthcare professionals, policymakers, and technology developers are more than essential
to ensure that these tools are scalable, equitable, culturally sensitive, and clinically effective.
Finally, hydration-aware dietary recommendation systems might support more sustainable
health behaviors in the long-term and play a valuable role in reducing the global burden of
non-communicable diseases. Future research should focus on the design, implementation,
Multimodal Technol. Interact. 2025,9, 112 25 of 29
and validation of AI-driven recommendation systems, such as the one proposed in this
paper. By systematically mapping their current capabilities and limitations, this review
provides a foundation for developing next-generation precision hydration tools that inte-
grate AI, mHealth, and comprehensive dietary assessments to support public health and
clinical practice. In this way, technological opportunities and real-world considerations
will be able to translate precision hydration recommendations into scalable, equitable, and
actionable interventions.
Author Contributions: Conceptualization, K.A., G.D.S., G.T., G.N.B., and O.M.; methodology, K.A.,
G.D.S., G.T., G.N.B., and O.M.; investigation, K.A. and O.M.; writing—original draft preparation,
K.A.; writing—review and editing, K.A., G.D.S., G.T., G.N.B., and O.M.; visualization, K.A. and O.M.;
supervision, O.M.; project administration, O.M.; funding acquisition, O.M. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: A complete list of included studies and their sources is provided in the
reference list. No primary datasets were generated as part of this scoping review.
Acknowledgments: Generative AI tools were not used in the design, data collection, analysis, or
interpretation phases of this review. AI assistance was limited to language editing for grammar,
clarity, and formatting purposes during manuscript preparation. Anthropic. (2025). Claude 3.7
Sonnet [Large language model]. https://claude.ai was used for figure generation (accessed on 30
September 2025).
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AI Artificial Intelligence
AR Augmented Reality
CASP Critical Appraisal Skills Programme
EFSA European Food Safety Authority
IOM Institute of Medicine
mHealth Mobile Health
NCDs Non-Communicable Chronic Diseases
ML Machine Learning
MCDA Multi-Criteria Decision Analysis
ROBIS Risk Of Bias In Systematic reviews
SMART Goals Specific, Measurable, Achievable, Realistic, and Timely Goals
TFI Total Fluid Intake
TWI Total Water Intake
WHO World Health Organization
Appendix A
The following five concept groups were used:
1. Artificial intelligence and machine learning
(“artificial intelligence,” “machine learning,” “predictive modeling”)
2. Digital and mobile health
(“mHealth,” “mobile health,” “digital health,” “smartphone application”)
3. Personalized nutrition
(“personalized nutrition,” “precision nutrition,” “individualized diet”)
Multimodal Technol. Interact. 2025,9, 112 26 of 29
4. Hydration and fluid intake
(“hydration,” “fluid intake,” “water consumption,” “dehydration prevention”)
5. Food and nutrition guidance
(“food recommendation,” “dietary guidance,” “nutrition intervention”)
■Database-Specific Search Strings:
■PubMed
(Free text and MeSH terms)
plaintext
“artificial intelligence”[MeSH Terms] OR “artificial intelligence”[All Fields]
OR “machine learning”[MeSH Terms] OR “machine learning”[All Fields]
OR “predictive modeling”[All Fields])
AND
(“mHealth”[All Fields] OR “mobile health”[All Fields] OR “digital health”[All Fields]
OR “smartphone application”[All Fields])
AND
(“personalized nutrition”[All Fields] OR “precision nutrition”[All Fields]
OR “individualized diet”[All Fields])
AND
(“hydration”[MeSH Terms] OR “hydration”[All Fields] OR “fluid intake”[All Fields]
OR “water consumption”[All Fields] OR “dehydration prevention”[All Fields])
AND
(“food recommendation”[All Fields] OR “dietary guidance”[All Fields]
OR “nutrition intervention”[All Fields]))
■Scopus
TITLE-ABS-KEY (
“artificial intelligence” OR “machine learning” OR “predictive modeling”)
AND
(“mHealth” OR “mobile health” OR “digital health” OR “smartphone application”)
AND
(“personalized nutrition” OR “precision nutrition” OR “individualized diet”)
AND
(“hydration” OR “fluid intake” OR “water consumption” OR “dehydration preven-
tion”)
AND
(“food recommendation” OR “dietary guidance” OR “nutrition intervention”)
■Web of Science
TS = (“artificial intelligence” OR “machine learning” OR “predictive modeling”)
AND
TS = (“mHealth” OR “mobile health” OR “digital health” OR “smartphone applica-
tion”)
AND
TS = (“personalized nutrition” OR “precision nutrition” OR “individualized diet”)
AND
TS = (“hydration” OR “fluid intake” OR “water consumption” OR “dehydration
prevention”)
AND
TS = (“food recommendation” OR “dietary guidance” OR “nutrition intervention”)
Multimodal Technol. Interact. 2025,9, 112 27 of 29
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