Accessibility settings

This paper is in the following e-collection/theme issue:

Wearables and MHealth Reviews (331) mHealth for Wellness, Behavior Change and Prevention (3472) Use and User Demographics of mHealth (437) Evaluation and Research Methodology for mHealth (261) Innovations and Technology for Healthy Eating Education (514) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (707)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82276, first published .
Key Features of Engagement Strategies in Nutrition Apps for Adults: Scoping Review

Key Features of Engagement Strategies in Nutrition Apps for Adults: Scoping Review

Key Features of Engagement Strategies in Nutrition Apps for Adults: Scoping Review

Authors of this article:

Maria F Vasiloglou1 Author Orcid Image ;   Zoë van der Heijden2 Author Orcid Image ;   Eric Antoine Scuccimarra1 Author Orcid Image ;   Alberto Conde Freniche1 Author Orcid Image ;   Desiree A Lucassen2 Author Orcid Image ;   Nienke de Vlieger3 Author Orcid Image ;   Frédéric Ronga4 Author Orcid Image ;   Elske Brouwer-Brolsma2 Author Orcid Image ;   Tamara Bucher3, 5 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Review

    1Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland

    2Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands

    3School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia

    4Strategic Research Operations Unit, Nestlé Research, Lausanne, Switzerland

    5Division of Nutrition and Dietetics, Bern University of Applied Sciences, Bern, Switzerland

    Corresponding Author:

    Maria F Vasiloglou, MSc, PhD

    Nestlé Institute of Health Sciences, Nestlé Research

    Rte du Jorat 57

    Lausanne, 1000

    Switzerland

    Phone: 41 0762438288

    Email: maria.vasiloglou@rd.nestle.com


    Background: Nutrition apps offer scalable opportunities to support dietary behavior change and prevent chronic diseases. Their success depends on sustained user engagement, which is essential yet challenging to achieve and, consequently impacts the long-term effectiveness of these digital tools. Engagement strategies have been widely explored in digital health, but a comprehensive synthesis focusing on nutrition apps for adults is lacking.

    Objective: This scoping review aimed to map the current engagement approaches and metrics implemented in nutrition apps targeting adults and to identify how user engagement is defined across studies.

    Methods: We conducted a search of the PubMed, Scopus, Cochrane, and Web of Science databases for relevant studies published from January 1, 2013, to June 30, 2024. The inclusion criteria included original adult interventional or observational studies that evaluated nutrition apps and reported user‑engagement strategies or metrics. Two reviewers independently screened records in Covidence, with discrepancies resolved by a third reviewer. Data were charted across study characteristics, engagement strategies, and engagement metrics and then synthesized narratively.

    Results: A total of 59 studies that used apps to improve dietary behaviors were included in our analysis, including randomized controlled trials, observational trials, and mixed methods studies. Most of these apps were designed for adults who were overweight and obese. The studies were primarily conducted in North America and Europe and were randomized controlled trials or nonrandomized intervention studies, with varying durations and sample sizes. Engagement strategies varied widely, and engagement was typically measured by frequency of specific function use and frequency of app use, followed by retention rate. The most common engagement strategies reported in studies were push notifications (n=29, 49%), behavioral theory integration (n=24, 41%), personalization and customization (n=19, 32%), and goal‑setting features (n=18, 31%). Only 31% (n=18) of studies provided an explicit definition of “user engagement,” and definitions were highly heterogeneous. Engagement measurement was dominated by quantitative system‑recorded metrics, including time and frequency of using specific functions (n=38, 64%), app use frequency (n=34, 58%), and retention (n=17, 29%). Few studies assessed qualitative or long‑term engagement dimensions, and long‑duration studies rarely integrated adaptive or contextualized engagement mechanisms. Research apps more frequently used theory‑driven strategies compared with commercial apps, which tended to emphasize streamlined user experience.

    Conclusions: Although several engagement strategies are commonly used, their implementation is inconsistent and often lacks grounding in conceptual frameworks. Research in the future needs to prioritize the use of common definitions for user engagement and measurement criteria while implementing user-centered design methods and using multiple research approaches to study the complex patterns of user engagement. The evidence base for engagement strategies needs strengthening because it will support the development of sustainable nutrition mobile health interventions.

    JMIR Mhealth Uhealth 2026;14:e82276

    doi:10.2196/82276

    Keywords

    mobile health; mHealth; nutrition apps; user engagement; digital health; behavior change; scoping review; mobile apps; dietary behavior 


    Background

    Noncommunicable diseases, including cardiovascular disease, cancer, respiratory conditions, and diabetes, are the leading causes of death globally, accounting for more than 70% of deaths worldwide [1]. Overweight and obesity significantly increase the risk of these noncommunicable diseases, impacting morbidity and mortality rates [1]. The global obesity epidemic is worsening, with significant implications for physical and economic health [2].

    Digital health tools for diet monitoring, planning, and precision nutrition have the potential to provide scalable tools to support health care professionals in managing nutrition-related diseases and obesity [3]. Despite the potential benefits, the integration of digital health tools into large, complex health systems remains limited. Challenges such as the need for user-centered, context-dependent, and customizable designs hinder widespread adoption, particularly in low-income countries with limited financial resources [4].

    Mobile health (mHealth) apps, specifically those targeting nutrition, have proliferated over the past decade, aiding users in tracking food intake, providing personalized dietary advice, and promoting healthier lifestyles. Τhere were more than 330,000 digital health apps available, with a substantial proportion focusing on nutrition [5]. These apps are increasingly being recognized for their potential to improve poor dietary habits and diet-related chronic diseases. The scalability and wide reach of mHealth apps, combined with their capacity to deliver personalized dietary guidance at a low cost, position them as a valuable approach to promote healthier diets and thereby support public health efforts to reduce the burden of chronic disease [6]. Two primary categories can be identified based on the developers’ affiliations and goals: academic apps are created by nutrition or dietetics experts to offer a dependable, scientifically validated tool, primarily for research purposes. Conversely, consumer apps are usually developed by private companies specializing in digital development, aimed at the general public, with a primary focus on commercial objectives [7].

    Although there is modest evidence that nutrition apps can be effective in promoting healthier dietary behaviors [6], a significant challenge lies in maintaining long-term user engagement with the app (“Little e”), which is necessary to ensure sufficient exposure to relevant behavior change techniques [8] and ultimately engagement in the behavior change process and outcomes (“Big E”) [9].

    Although there is modest evidence that nutrition apps can be effective in promoting healthier dietary behaviors [6], a significant challenge lies in maintaining long-term user engagement, which is necessary to ensure sufficient exposure to relevant behavior change techniques [8]. User engagement is a multifaceted concept and includes both the extent of app use, such as frequency, duration, and depth of access, as well as the quality of the user experience, including attention, interest, and emotional involvement. Despite the growing popularity of mHealth apps, many nutrition apps struggle to retain user engagement over time [10-12]. For example, users of the commercial weight loss app Lose It! engaged for an average of only 29 days [13]. Similarly, in a short-term intervention, participants recorded vegetable intake on just 11 out of 28 days, with engagement declining as the study progressed [14]. Low engagement is a common challenge in app-based interventions, with approximately 71% of users discontinuing within the first 90 days. Since this significantly undermines effectiveness [15-17], it is crucial to understand the factors that support sustained user engagement [18].

    In an effort to understand user engagement in mHealth apps, several systematic reviews have been conducted to synthesize the different approaches in digital health interventions, particularly in mental health [19-23], physical activity, and chronic disease prevention and management [18,24,25]. In addition to these focused reviews, broader systematic reviews have also been performed, encompassing digital health interventions through multiple modes of delivery beyond mHealth apps, combining a variety of lifestyle behaviors [26,27]. Furthermore, specific reviews have examined digital health interventions for population groups, such as children [28] or older adults [29]. These studies provide an overview of useful engagement strategies in mHealth more broadly and for specific applications (ie, physical activity, mental health, and chronic disease).

    To address the challenge of sustaining engagement in nutrition apps, various strategies have been implemented to enhance motivation and user experience, including gamification, personalization [30], and the integration of social features [31]. However, to the best of our knowledge, no review has yet provided a comprehensive overview of the full range of strategies used to promote user engagement in nutrition apps for the general adult population.

    Objectives

    This scoping review aims to provide an overview of the current landscape of predefined strategies implemented in apps used for nutrition interventions to enhance user engagement, identify gaps in the literature, and guide future research and app development. Another goal is to map and synthesize the definitions of user engagement used in the studies, creating a comprehensive and unified definition.


    Overview

    We chose a scoping review to explore how user engagement is defined, measured, and promoted in nutrition apps due to the broad and heterogeneous nature of the literature across disciplines such as nutrition, behavioral science, and digital health. This approach enables a comprehensive overview of existing evidence without limiting study design or outcomes. This review is guided by the methodological framework originally proposed by Arksey and O’Malley [32], which outlines 5 key stages: identifying the research question, identifying relevant studies, study selection, charting the data, and summarizing and reporting the results. This scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, which can be found in Multimedia Appendix 1.

    Search Strategy and Selection Criteria

    We searched 4 databases, namely PubMed, Scopus, Cochrane, and Web of Science, for relevant studies published between January 1, 2013, and June 30, 2024. The detailed search strategy can be found in Multimedia Appendix 1.

    Eligibility Criteria

    Our inclusion and exclusion criteria followed the population, intervention, comparison, outcome, and study design framework (Multimedia Appendix 1). This scoping review included studies that examined engagement strategies within nutrition apps used by adults aged above 18 years, across a range of health conditions. The review focused on interventions that incorporate elements such as gamification, personalized content, social integration, and behavior change techniques aimed at enhancing user engagement. Excluded were studies involving children and adolescents below 18 years, and those focusing on non–nutrition-related apps (eg, fitness-only or medication adherence apps without a primary nutrition component). Furthermore, studies that assessed engagement without reporting specific metrics such as daily active users, time spent in the app, or adherence rates were excluded. Only original interventional and observational studies were considered, excluding any type of review (eg, umbrella reviews, scoping reviews, or narrative reviews); conference abstracts; case reports; expert opinions without empirical data; and editorials, as well as quality and content analyses of apps found in app stores.

    Screening and Data Extraction

    Covidence software was used to manage the screening flow, beginning with the removal of duplicate records, followed by title and abstract screening conducted by pairs of reviewers (MFV, EAS, EB-B, TB, ACF, FR, SL, NvD, and ZvdH). All potentially eligible abstracts underwent full-text assessment by pairs of reviewers (MFV, EAS, SL, ACF, EB-B, FR, ZvdH, NdV, and DAL). Data extraction of eligible papers was performed by a single investigator and then verified by a second investigator (MFV or ZvdH). Any discrepancies were resolved through discussion with a third reviewer (EB-B).

    An Excel (Microsoft Corp) data extraction spreadsheet was developed to systematically compile the following study-level information: author, year, aim, study design, location, total number of participants, female or male, intervention group, control group, duration, age, BMI, condition or disease, type of app, commercial or research, app features, intervention description, incentive, user-engagement strategies, description of those strategies, engagement definition, app evaluation criteria, specification of metrics, description of metrics, relationship between user engagement with smartphone apps and main outcome, app evaluation results, and strengths and limitations. Extracted data on engagement strategies were classified into the following categories: push notifications, personalization, goal setting, game-like features, multimedia formats, monitoring (real time or passive), peer-to-peer communication, access to professionals, content reliability, quality of feedback, behavioral theory, or none or insufficiently specified. User metrics (ie, engagement metric or indicators as specified in the protocol) were categorized into average session length, daily app use, drop-offs, most visited or active screens, overall time used, retention rate, time and frequency of using specific functions, frequency of app use, and others.

    The effect on health outcomes were extracted from the included studies and were first categorized as follows: (1) positive outcomes—significant improvements (P<.05) or positive trends (P<.10), (2) negative outcomes—significant deterioration (P<.05) or negative trends (P<.10), (3) neutral or no outcomes—no significant changes (P≥.10), or (4) not reported—no or insufficient data on health outcomes. Subsequently, the number of studies reporting positive, neutral, or neutral or no outcomes was counted for each category of user metrics to describe how frequently each metric appeared in studies with different types of outcomes. This counting procedure was purely descriptive and was not intended to infer causality, comparative effectiveness, or the predictive value of specific engagement metrics.

    Finally, the duration of the studies was defined as short when it lasted less than a month, medium when it lasted 1 to 3 months, and long when it lasted more than 3 months. These classifications were developed specifically for this study by the authors.


    In the following section, the outcomes from the screening and analysis process are presented, including the characteristics of the included studies, the engagement strategies, and the metrics identified.

    Search Results

    Our searches retrieved 4045 records, and after the exclusion of duplications, 2630 (65%) records underwent abstract screening, of which 282 (7%) underwent full-text review (Figure 1). On the basis of the established inclusion and exclusion criteria, 59 studies were included in our scoping review (Multimedia Appendix 1).

    ‎
    Figure 1. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart.

    Characteristics of Included Studies

    Population

    The scoping review included a total of 59 studies examining nutrition apps. The most common study design was a randomized controlled trial (RCT), accounting for 41% (24/59) of the studies. The remaining study designs were nonrandomized intervention studies (11/59, 19%), mixed methods studies (10/59, 17%), observational studies (8/59, 14%), and secondary analyses (6/59, 9%). Most studies (26/59, 44%) were conducted in North America, followed by Europe (14/59, 24%), Australia (9/59, 15%), Asia (7/59, 12%), and New Zealand (2/59, 3%); 1 (N=59, 2%) study did not specify a location. Sample sizes varied across studies from 12 to 1,011,008. Most of the participants (50/59, 85%) were adults (aged 18-55 years). In 24% (14/59) of the studies, women made up 75% to 90% of the participants, and in 39% (23/59) of the studies, women constituted 50% to 75% of the study population.

    The duration of studies also varied widely. Most studies (22/59, 37%) had a medium duration of 1 to 3 months. A smaller proportion (10/59, 17%) had a short duration of less than 1 month, including 2 (N=59, 3%) studies that lasted 1 week or less. Studies with a long duration of more than 3 months accounted for 32% (19/59), spanning from over 3 months to over a year. A notable portion did not specify the study duration or left it unreported (8/59, 14%). Most studies (24/59, 41%) focused on weight management. Diabetes (type 1, type 2, and gestational) was the focus in 10% (6/59) of the studies, while pregnancy was the target in 7% (4/59) of the studies. Table 1 provides detailed information about the studies and their populations.

    In our analysis, the distribution between commercial and research-based nutrition apps was assessed. Research apps constituted the majority, comprising 75% (44/59) of the studies included in our review. In contrast, commercial apps were represented in 25% (15/59) of the studies. Detailed characteristics of the included studies can be found in Multimedia Appendix 1.

    Table 1. Basic characteristics of the study populations (N=59).
    CharacteristicStudies, n (%)
    Participants

    		≤206 (10)

    		20-509 (15)

    		50-10014 (24)

    		100-20014 (24)

    		200-5007 (12)

    		500-10003 (5)

    		>10006 (10)
    Duration 

    		≤1 month10 (17)

    		1 month-3 months22 (37)

    		≥3 months19 (32)

    		Not specified or not applicable8 (14)
    Mean age group (y) 

    		18-6553 (90)

    		≥653 (5)

    		Not reported3 (5)
    Mean BMI (kg/m2)

    		Underweight (<18.5)0 (0)

    		Normal (18.5-24.9)4 (7)

    		Overweight (25.0-29.9)14 (24)

    		Obesity 1 (30.0-34.9)19 (32)

    		Obesity 2 (35.0-39.9)6 (10)

    		Not reported20 (34)
    Condition or disease 

    		Overweight or obesity24 (41)

    		Diabetes (type 1, type 2, or gestational)6 (10)

    		Pregnancy4 (7)

    		Mental health conditions3 (5)

    		Older adults2 (3)

    		Suboptimal lifestyle habits2 (3)

    		Others7 (14)

    		Not reported 12 (20)
    Features

    A variety of user-engagement strategies were used across the included nutrition apps (refer to Figure S1 in Multimedia Appendix 1). The most frequently applied strategies were push notifications or prompts (29/59, 49%), behavioral theory (24/59, 41%), personalization and customization (19/59, 32%), and goal setting (18/59, 31%). Less commonly reported were features such as peer-to-peer communication (9/59, 15%), game-like features (8/59, 14%), and multimedia formats (5/59, 8%). A total of 4 (N=59, 7%) studies did not report on user-engagement strategies applied or described them insufficiently.

    To better understand how these strategies were operationalized, data were extracted on the specific app features used. The most common feature was self-monitoring or tracking (44/59, 75%), followed by prompts or notifications (25/59, 41%). Personalized feedback (22/59, 37%), goal-setting tools (15/59, 27%), and outcome tracking or progress summaries (13/59, 22%) were also frequently included. Less commonly used features included multimedia materials and social features (both 11/59, 19%), data sharing or professional access (9/59, 15%), and more advanced technologies such as artificial intelligence chatbots or image recognition (2/59, 3%). A small number of studies reported features such as rewards (4/59, 7%), reflective logging tools (7/59, 12%), or photo-based modules (6/59, 10%).

    User-Engagement Metrics Included

    To evaluate how users interacted with these features, most of the studies (38/59, 64%) included metrics of functions related to time and frequency of specific functions. Many of them also included frequency of app use (34/59, 58%). Moreover, some of the studies (17/59, 29%) included retention rate and others included the number of drop-offs (15/59, 25%). Fewer studies (n=11, 19%) included metrics such as the overall time that an app was used, the daily app use time (9/59, 15%), the most visited or the most active screens (8/59, 14%), and the average session length (3/59, 5%; Figure S2 in Multimedia Appendix 1).

    Table 2 presents the health outcomes reported in studies that included a given engagement metric. For each metric, we examined only the subset of studies that reported that specific metric and assessed whether they described improvements, no changes, or insufficient detail regarding effects on health outcomes. Most metrics were included in studies reporting positive effects. There were no studies that reported a negative association of a metric with health outcomes or components.

    Table 2. Frequency of engagement metrics applied, used, and corresponding health outcomes reported in studies that included the specific metric (N=59).
    Engagement metricImprovements or positive changes, nNo changes, nNot reported or insufficient detail, nStudies, n (%)
    Time or frequency of using specific functions1451938 (64)
    Frequency of app use1261634 (58)
    Retention62917 (29)
    Drop-offs231015 (25)
    Overall time used43411 (19)
    Retention30710 (17)
    Daily app use4149 (15)
    Most visited or active screens2158 (14)
    Commercial Versus Research Apps

    A total of 15 (N=59, 25%) commercial apps and 44 (75%) research apps were analyzed to compare user-engagement strategies. Push notifications or prompts were used in 33% (5/15) of commercial apps and in 55% (24/44) of research apps, suggesting that research apps may more frequently incorporate reminder-based engagement. Personalization and customization features were equally present in commercial and research apps, each used by 33% (5/15) and 32% (14/44) of the apps, respectively. Goal setting was more common in research apps (15/44, 34%) compared to commercial apps (3/15, 20%). Game-like features were used by 13% (2/15) of commercial apps and 14% (6/44) of research apps.

    App Evaluation Results Among Apps That Included Evaluation

    In our review, 32 (54%) of the 59 included studies reported various dimensions of app evaluation. The most frequently assessed aspect was user satisfaction, noted in 50% (16/32) of the studies that included evaluations. This was followed by ease of use, reported in 25% (8/32) of the studies. App functionalities were evaluated in 28% (9/32) of the studies. Aesthetics, intention to use, and attitude toward the app were each evaluated in 9 (n=32, 28%) studies. Attitude toward one’s own health was assessed in only 3% (1/32) of the studies. Suggestions for improvement were noted in 19% (6/32) of the studies. Additionally, 19% (6/32) of the studies did not report specific categories of user feedback.

    App Evaluation Criteria

    Feasibility was the most frequently assessed criterion, reported in 13 of 59 (22%) studies. This was closely followed by user satisfaction, which was evaluated in 12 of 59 (20%) studies. Usability and acceptability were each assessed in 10 of 59 (17%) studies. Finally, 26 of 59 (44%) studies did not report any specific evaluation criteria.

    Key Engagement Strategies Among Adult Populations With Specific Needs

    Key approaches varied across the 30 studies analyzing engagement strategies in apps targeting specific populations of adults. Among the 24 studies on apps for individuals with overweight and obesity, push notifications and prompts (n=12, 50%), behavioral theory (n=12, 50%), goal setting (n=8, 33%), peer-to-peer communication (n=5, 21%), and access to professionals (n=5, 21%) appeared as key engagement strategies. Engagement tracking focused on assessing the time and frequency of using specific functions (15/24, 63%), app use frequency (15/24, 63%), and the retention rate (11/24, 46%). Similarly, the 6 studies on apps for individuals living with diabetes prioritized behavioral theory (n=5, 83%), with half (n=3, 50%) of them incorporating push notifications, professional support, and real-time monitoring features, while goal setting (n=1, 17%) and passive data monitoring (n=2, 33%) were less common.

    Variation in Engagement Strategies Across Study Durations

    In studies with different durations, distinct patterns emerged. Of the 10 (17%) short-duration studies (<1 month), 8 (80%) studies used push notifications or prompts extensively. The 22 (N=59, 37%) medium-duration studies (1-3 months) highlighted the importance of behavioral theory, with 11 (50%) of them incorporating it. The 19 (N=59, 32%) long-duration studies (>3 months) also favored behavioral theory and push notifications equally, each used in 42% (8/19) of the cases.

    Short-duration studies prioritized tracking the time and frequency of using specific functions (7/10, 70%) and the frequency of app use (6/10, 60%). In medium-duration studies, 73% (16/22) focused on the time and frequency of using specific functions, and 55% (12/22) focused on the frequency of app use. Long-duration studies showed a significant measurement of time and frequency of using specific functions (9/19, 47%) and retention rates (6/19, 32%).

    The presence of defined engagement strategies was examined across 59 studies. Of these, 18 (31%) studies provided an explicit definition of user engagement, while 9 (15%) studies presented an implicit conceptual definition. However, most of the studies (32/59, 55%) did not define user engagement at all. Among the 27 studies that contained either explicit or implicit definitions, various dimensions were highlighted. The most referenced criterion was use patterns, mentioned in 21 (n=27, 78%) studies. Feature interaction was the second most common, appearing in 8 (n=27, 30%) studies, followed by behavioral indicators in 6 (n=27, 22%) studies and user feedback and responses in 5 (n=27, 19%) studies. Outcome-based definitions, linking engagement to specific user achievements or health outcomes, were noted in 3 (n=27, 11%) studies. The least common approach was game-like features, mentioned in just 1 (n=27, 4%) study.

    To summarize how user engagement was operationalized across the included studies, we derived a working description based on those studies that provided an explicit definition of user engagement (18/59, 31%). This description is intended as a synthesis of observed operationalizations rather than a novel or normative definition:

    User engagement is operationalized as the extent, frequency, and persistence of users’ interactions with the app over time. It’s typically quantified using system-recorded metrics, such as the number of active or logged-in days, total time spent in the app, frequency of accessing app modules, and the number or intensity of recorded actions (e.g., logging meals or viewing content). In some cases, these indicators are combined into composite scores, points totals, or weekly usage summaries, while other studies operationalize express engagement as the proportion of days with recorded activity. A small number of studies additionally incorporate the user’s reflections on factors that support or hinder continued app usage.

    Principal Findings

    This scoping review provides a comprehensive overview of 59 mHealth nutrition studies, focusing on user-engagement strategies and metrics. Most apps targeted adults with overweight and/or obesity and were tested in RCTs and nonrandomized intervention studies. The included study samples comprised a predominantly female population. Study durations and sample sizes varied. Engagement was typically measured by the time and frequency of using specific functions and overall app use frequency. Push notifications and behavioral theory elements were the most frequently reported engagement strategies.

    Push notifications and behavioral theory (elements) were commonly used to enhance engagement, aligning with findings from other digital health interventions, including mental and social well-being [33], diabetes management [34], weight loss interventions [35], or physical activity promotion [36]. While their widespread use suggests versatility across health contexts and adult populations, poorly timed or excessive push notifications can lead to user fatigue or disengagement, emphasizing the need to optimize the implementation of the engagement strategies. Notably, user engagement is known to be influenced by individual factors, including an individual’s digital literacy and technology acceptance [37,38], health and demographic characteristics [39], or behavioral and psychological factors [40]. In addition, contextual factors also affect user engagement. For instance, sending notifications at specific times during weekends may yield better engagement rates [41,42]. Therefore, a standardized approach may not engage all users effectively, highlighting the need for adaptive frameworks that tailor engagement strategies to individual user characteristics and contextual factors.

    Looking into the differences between commercial and research apps, the latter often used structured engagement tools such as push notifications and goal setting, likely reflecting a focus on behavioral theory and evidence-based practices. In contrast, commercial apps tended to emphasize user experience and simplicity since they are designed for broader appeal, with minimal features to reduce user burden. Both app types similarly adopted personalization and game-like elements, suggesting their broad value. For future studies and development, commercial apps could adopt more evidence-based strategies, while research apps might benefit from commercial design strengths.

    The adoption of evidence-based strategies in mHealth apps is essential to ensure their effectiveness, equity, and sustainability in real-world settings. Evidence-based approaches integrate the best available scientific evidence with professional expertise and contextual considerations, thereby reducing reliance on ad hoc or intuition-driven decision-making [43]. In this review, engagement was primarily assessed using quantitative metrics, often referred to as “Little e” engagement, such as time spent on specific functions and overall app use, aligning with existing literature [25]. While standardized quantitative metrics aid comparability and benchmarking, they provide only a limited view on user engagement, which also involves emotional, cognitive, and behavioral dimensions [44]. Most studies focused on short-term metrics, limiting insights into sustained engagement. Therefore, longitudinal assessments are needed to better understand long-term user interaction, while qualitative measures, such as interviews and in-app feedback, could provide deeper insights into user experience motivations, satisfaction, perceived usability, and barriers, and for explaining why certain engagement strategies succeed or fail [44-46]. Therefore, a mixed methods approach may better capture engagement dynamics and may help optimize future mHealth nutrition tools to better meet user needs and preferences. This variation between research and commercial apps underscores the need for standardized guidelines to ensure consistent quality and effectiveness in behavior change apps, an aim already addressed by several ongoing initiatives [47].

    This review highlights gaps in how user engagement is conceptualized, defined, and measured. Only 31% (n=18) of studies explicitly defined engagement, limiting comparability and generalizability, an issue noted in multiple previous reviews on the topic [48,49]. Despite existing conceptual frameworks [44,45], their adoption remains limited, leading to high heterogeneity in engagement metrics [50]. This inconsistency hinders the understanding of real-world challenges, such as low uptake of nutrition apps, and impedes the development of standardized measures. These measures are crucial for comparability, establishing benchmarks, and deriving robust conclusions on effective engagement strategies. Future research should prioritize implementing established frameworks and developing standardized metrics to enhance cross-study synthesis and support evidence-based best practices for mHealth nutrition tools.

    Limitations

    Despite the broad scope of this review, several limitations must be acknowledged. The diversity of the included studies, both in design and in the involved engagement strategies, made direct comparisons of effectiveness challenging. Additionally, the findings highlight the need for further investigation into how engagement strategies can be tailored to individual user characteristics and contextual factors. The included studies use a wide range of methodologies, including RCTs, pilot RCTs, secondary analyses of RCTs, observational cohort studies, and mixed methods research, such as feasibility and acceptability studies. There is also significant variability in sample sizes, ranging from as few as 12 participants in the study by Robinson et al [51] to more than a million (1,011,008) in the study by Serrano et al [13]. Moreover, while this review analyses engagement among users who accessed the included interventions, it is crucial to acknowledge that nonuse may reflect structural and contextual barriers rather than a lack of interest. Several barriers, including limited access to digital infrastructure, low digital literacy, and socioeconomic constraints, may prevent certain populations from engaging with nutrition apps. The latter could affect both the extent and quality of recorded engagement, potentially leading to underestimation or misrepresentation of true user behavior. Thus, it is important to note that our findings represent the experiences of the included active users.

    A significant limitation observed across the reviewed studies is the lack of clear, standardized definitions for essential terms such as “engagement,” which is often undefined, as well as broad concepts such as “acceptability” and “feasibility.” Furthermore, few studies include reflective discussions on how to improve user engagement, despite its critical role in the success of interventions [44]. The absence of standardized reporting metrics further complicates the synthesis of findings, limiting the ability to draw meaningful comparisons across studies. It should be noted that the counting of studies reporting positive, negative, or neutral findings across engagement‑metric categories was purely descriptive. This approach does not allow any causal, comparative, or predictive inferences regarding the effectiveness of specific engagement metrics, and the observed distribution reflects reporting patterns in the included studies rather than true differences in impact.

    Finally, another limitation of this study was the uneven distribution between commercial and research-based nutrition apps across the reviewed articles, with the latter constituting a majority (n=44, 75%). Whereas commercial apps typically prioritize consumer design principles and advanced user interface aesthetics, research apps may focus more on evidence‐based content, and, in some cases, compensate users to sustain use. Therefore, the insights gained from this review can be further explored in future studies that incorporate a broader range of commercial apps.

    Future Directions

    Future research should focus on more rigorous evaluations, potentially through RCTs or long-term observational studies, to establish clearer insights into the efficacy of these strategies.

    Co-design approaches in the development of nutrition and health apps have been shown to enhance user engagement and improve intervention effectiveness. Many studies have used participatory methods, involving stakeholders in the design process to ensure that the app aligns with user needs and preferences. Some commonly used co-design techniques include participatory research approaches, stakeholder engagement, and iterative design workshops. According to Davis et al [52] and Jackson et al [53], the benefits of co-design include higher user satisfaction, improved app usability, and a greater likelihood of meeting users’ needs, particularly by addressing cultural considerations. While co-design in mHealth is still an emerging field, it has demonstrated effectiveness across various health domains, including disease management and health promotion. Further research is needed to fully implement co-design processes and evaluate their impact on nutrition interventions.

    Although many studies report use patterns, such as log in frequency and session duration, few provide insights into how users engage with specific behavioral components of nutrition apps. This lack of detail makes it difficult to identify which features drive engagement and behavior change. Additionally, engagement metrics vary widely across studies, making comparisons and synthesis challenging. For example, “active use” may be defined as a single session in one study but as daily interactions in another, complicating efforts to draw meaningful conclusions.

    Moreover, most studies rely on static, one-size-fits-all engagement metrics that do not account for changes in user behavior over time. Without adaptive metrics, where use patterns dynamically inform engagement strategies, apps risk missing opportunities for personalization and sustained engagement. This static approach limits the potential for iterative improvements and real-time feedback loops that could optimize user experience and long-term outcomes. A recent methodological framework has been developed to analyze and model the dynamic processes involved in participant engagement with mHealth apps and identify factors that foster or undermine engagement in an individual's respective context [54,55]. However, the uptake of this type of approach is still very limited. Future research on user engagement may find this type of approach particularly valuable to understand how different contextual factors impact engagement as a dynamic process.

    Conclusions

    This review provides a broad overview of the various strategies used to enhance user engagement in nutrition apps. mHealth nutrition apps predominantly target adults with overweight or obesity and are often evaluated through controlled trials with female-majority samples. Despite variability in study design, engagement is commonly tracked via use patterns, with push notifications and behavioral theory–based strategies emerging as key tools to enhance user involvement. By understanding and implementing these strategies, we can design more engaging and effective digital tools to promote healthy dietary behaviors and ultimately contribute to improved public health outcomes.

    Funding

    The authors declared that no financial support was received for this work.

    Data Availability

    The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    MFV, TB, and EB-B designed, conceptualized, and defined the methods in this study. All the authors contributed to the screening of the studies. MFV drafted the manuscript, and all the authors reviewed the final manuscript. All authors contributed to screening abstracts and full texts. MFV and ZvdH conducted the data extraction.

    Conflicts of Interest

    At the time of publication, MFV, ACF, EAS, and FR were employees of Nestlé Research, which is part of the Société des Produits Nestlé S.A.

    Multimedia Appendix 1

    Search strategy, PICOS, user-engagement definitions, list of included studies, basic study characteristics, and characteristics of the apps.

    DOCX File , 226 KB
    Multimedia Appendix 2

    Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

    DOCX File , 87 KB
    1. Noncommunicable diseases. World Health Organization. URL: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [accessed 2025-04-03]
    2. Gregg EW, Shaw JE. Global health effects of overweight and obesity. N Engl J Med. Jul 06, 2017;377(1):80-81. [FREE Full text] [CrossRef] [Medline]
    3. Abeltino A, Riente A, Bianchetti G, Serantoni C, De Spirito M, Capezzone S, et al. Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutr Rev. Feb 01, 2025;83(2):e574-e601. [CrossRef] [Medline]
    4. Ruyobeza B, Grobbelaar SS, Botha A. Hurdles to developing and scaling remote patients' health management tools and systems: a scoping review. Syst Rev. Aug 30, 2022;11(1):179. [FREE Full text] [CrossRef] [Medline]
    5. Digital health trends 2024. IQVIA. Dec 12, 2024. URL: https:/​/www.​iqvia.com/​insights/​the-iqvia-institute/​reports-and-publications/​reports/​digital-health-trends-2024 [accessed 2026-04-21]
    6. Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. Dec 07, 2016;13(1):127. [FREE Full text] [CrossRef] [Medline]
    7. Khazen W, Jeanne JF, Demaretz L, Schäfer F, Fagherazzi G. Rethinking the use of mobile apps for dietary assessment in medical research. J Med Internet Res. Jun 18, 2020;22(6):e15619. [FREE Full text] [CrossRef] [Medline]
    8. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. Jun 29, 2017;19(6):e232. [FREE Full text] [CrossRef] [Medline]
    9. Cole-Lewis H, Ezeanochie N, Turgiss J. Understanding health behavior technology engagement: pathway to measuring digital behavior change interventions. JMIR Form Res. Oct 10, 2019;3(4):e14052. [FREE Full text] [CrossRef] [Medline]
    10. Zarnowiecki D, Mauch CE, Middleton G, Matwiejczyk L, Watson WL, Dibbs J, et al. A systematic evaluation of digital nutrition promotion websites and apps for supporting parents to influence children's nutrition. Int J Behav Nutr Phys Act. Feb 10, 2020;17(1):17. [FREE Full text] [CrossRef] [Medline]
    11. Kohl LF, Crutzen R, de Vries NK. Online prevention aimed at lifestyle behaviors: a systematic review of reviews. J Med Internet Res. Jul 16, 2013;15(7):e146. [FREE Full text] [CrossRef] [Medline]
    12. Grady A, Pearson N, Lamont H, Leigh L, Wolfenden L, Barnes C, et al. The effectiveness of strategies to improve user engagement with digital health interventions targeting nutrition, physical activity, and overweight and obesity: systematic review and meta-analysis. J Med Internet Res. Dec 19, 2023;25:e47987. [FREE Full text] [CrossRef] [Medline]
    13. Serrano KJ, Coa KI, Yu M, Wolff-Hughes DL, Atienza AA. Characterizing user engagement with health app data: a data mining approach. Transl Behav Med. Jun 2017;7(2):277-285. [FREE Full text] [CrossRef] [Medline]
    14. Nour M, Chen J, Allman-Farinelli M. Young adults' engagement with a self-monitoring app for vegetable intake and the impact of social media and gamification: feasibility study. JMIR Form Res. May 10, 2019;3(2):e13324. [FREE Full text] [CrossRef] [Medline]
    15. Maher CA, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. Are health behavior change interventions that use online social networks effective? A systematic review. J Med Internet Res. Feb 14, 2014;16(2):e40. [FREE Full text] [CrossRef] [Medline]
    16. Laranjo L, Arguel A, Neves AL, Gallagher AM, Kaplan R, Mortimer N, et al. The influence of social networking sites on health behavior change: a systematic review and meta-analysis. J Am Med Inform Assoc. Jan 2015;22(1):243-256. [FREE Full text] [CrossRef] [Medline]
    17. Cavallo DN, Tate DF, Ward DS, DeVellis RF, Thayer LM, Ammerman AS. Social support for physical activity-role of Facebook with and without structured intervention. Transl Behav Med. Dec 2014;4(4):346-354. [FREE Full text] [CrossRef] [Medline]
    18. Pham Q, Graham G, Carrion C, Morita PP, Seto E, Stinson JN, et al. A library of analytic indicators to evaluate effective engagement with consumer mHealth apps for chronic conditions: scoping review. JMIR Mhealth Uhealth. Jan 18, 2019;7(1):e11941. [FREE Full text] [CrossRef] [Medline]
    19. Gan DZ, McGillivray L, Larsen ME, Christensen H, Torok M. Technology-supported strategies for promoting user engagement with digital mental health interventions: a systematic review. Digit Health. Jun 01, 2022;8:20552076221098268. [FREE Full text] [CrossRef] [Medline]
    20. Alkhaldi G, Hamilton FL, Lau R, Webster R, Michie S, Murray E. The effectiveness of prompts to promote engagement with digital interventions: a systematic review. J Med Internet Res. Jan 08, 2016;18(1):e6. [FREE Full text] [CrossRef] [Medline]
    21. Elkes J, Cro S, Batchelor R, O'Connor S, Yu LM, Bell L, et al. User engagement in clinical trials of digital mental health interventions: a systematic review. BMC Med Res Methodol. Aug 24, 2024;24(1):184. [FREE Full text] [CrossRef] [Medline]
    22. Borghouts J, Eikey E, Mark G, De Leon C, Schueller SM, Schneider M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. J Med Internet Res. Mar 24, 2021;23(3):e24387. [FREE Full text] [CrossRef] [Medline]
    23. Szinay D, Jones A, Chadborn T, Brown J, Naughton F. Influences on the uptake of and engagement with health and well-being smartphone apps: systematic review. J Med Internet Res. May 29, 2020;22(5):e17572. [FREE Full text] [CrossRef] [Medline]
    24. Abqari U, van 't Noordende AT, Richardus JH, Isfandiari MA, Korfage IJ. Strategies to promote the use of online health applications for early detection and raising awareness of chronic diseases among members of the general public: a systematic literature review. Int J Med Inform. Jun 2022;162:104737. [FREE Full text] [CrossRef] [Medline]
    25. Eaton C, Vallejo N, McDonald X, Wu J, Rodríguez R, Muthusamy N, et al. User engagement with mHealth interventions to promote treatment adherence and self-management in people with chronic health conditions: systematic review. J Med Internet Res. Oct 24, 2024;26:e50508. [FREE Full text] [CrossRef] [Medline]
    26. Mclaughlin M, Delaney T, Hall A, Byaruhanga J, Mackie P, Grady A, et al. Associations between digital health intervention engagement, physical activity, and sedentary behavior: systematic review and meta-analysis. J Med Internet Res. Feb 19, 2021;23(2):e23180. [FREE Full text] [CrossRef] [Medline]
    27. Aguiar M, Trujillo M, Chaves D, Álvarez R, Epelde G. mHealth apps using behavior change techniques to self-report data: systematic review. JMIR Mhealth Uhealth. Sep 09, 2022;10(9):e33247. [FREE Full text] [CrossRef] [Medline]
    28. Schoeppe S, Alley S, Rebar AL, Hayman M, Bray NA, Van Lippevelde W, et al. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. Int J Behav Nutr Phys Act. Jun 24, 2017;14(1):83. [FREE Full text] [CrossRef] [Medline]
    29. van Acker J, Maenhout L, Compernolle S. Older adults' user engagement with mobile health: a systematic review of qualitative and mixed-methods studies. Innov Aging. 2023;7(2):igad007. [FREE Full text] [CrossRef] [Medline]
    30. Zenun Franco R, Fallaize R, Weech M, Hwang F, Lovegrove JA. Effectiveness of web-based personalized nutrition advice for adults using the eNutri web app: evidence from the EatWellUK randomized controlled trial. J Med Internet Res. Apr 25, 2022;24(4):e29088. [FREE Full text] [CrossRef] [Medline]
    31. Lister C, West JH, Cannon B, Sax T, Brodegard D. Just a fad? Gamification in health and fitness apps. JMIR Serious Games. Aug 04, 2014;2(2):e9. [FREE Full text] [CrossRef] [Medline]
    32. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [CrossRef]
    33. Pretolesi D, Motnikar L, Till B, Uhl J. Notifying users: customisation preferences for notifications in health and well-being applications. In: Meschtscherjakov A, Midden C, Ham J, editors. Persuasive Technology. Cham, Switzerland. Springer; 2023.
    34. Narindrarangkura P, Ye Q, Boren SA, Khan U, Simoes EJ, Kim MS. Analysis of healthy coping feedback messages from diabetes mobile apps: validation against an evidence-based framework. J Diabetes Sci Technol. Jan 2023;17(1):152-162. [FREE Full text] [CrossRef] [Medline]
    35. Hales S, Turner-McGrievy G, Fahim A, Freix A, Wilcox S, Davis RE, et al. A mixed-methods approach to the development, refinement, and pilot testing of social networks for improving healthy behaviors. JMIR Hum Factors. Feb 12, 2016;3(1):e8. [FREE Full text] [CrossRef] [Medline]
    36. Schwarz A, Winkens LH, de Vet E, Ossendrijver D, Bouwsema K, Simons M. Design features associated with engagement in mobile health physical activity interventions among youth: systematic review of qualitative and quantitative studies. JMIR Mhealth Uhealth. Mar 06, 2023;11:e40898. [FREE Full text] [CrossRef] [Medline]
    37. Nunes A, Limpo T, Castro SL. Acceptance of mobile health applications: examining key determinants and moderators. Front Psychol. Dec 10, 2019;10:2791. [FREE Full text] [CrossRef] [Medline]
    38. Fan S, Jain RC, Kankanhalli MS. A comprehensive picture of factors affecting user willingness to use mobile health applications. ACM Trans Comput Healthcare. Jan 13, 2024;5(1):1-31. [CrossRef]
    39. Simões de Almeida R, Marques A. User engagement in mobile apps for people with schizophrenia: a scoping review. Front Digit Health. 2022;4:1023592. [FREE Full text] [CrossRef] [Medline]
    40. Steinberg JR, Yeh C, Jackson J, Saber R, Niznik CM, Leziak K, et al. Optimizing engagement in an mHealth intervention for diabetes support during pregnancy: the role of baseline patient health and behavioral characteristics. J Diabetes Sci Technol. Nov 2022;16(6):1466-1472. [FREE Full text] [CrossRef] [Medline]
    41. Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, et al. To prompt or not to prompt? A microrandomized trial of time-varying push notifications to increase proximal engagement with a mobile health app. JMIR Mhealth Uhealth. Nov 29, 2018;6(11):e10123. [FREE Full text] [CrossRef] [Medline]
    42. Bidargaddi N, Pituch T, Maaieh H, Short C, Strecher V. Predicting which type of push notification content motivates users to engage in a self-monitoring app. Prev Med Rep. Sep 2018;11:267-273. [FREE Full text] [CrossRef] [Medline]
    43. Brownson RC, Fielding JE, Maylahn CM. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health. Apr 01, 2009;30:175-201. [CrossRef] [Medline]
    44. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. Jun 2017;7(2):254-267. [FREE Full text] [CrossRef] [Medline]
    45. Short CE, DeSmet A, Woods C, Williams SL, Maher C, Middelweerd A, et al. Measuring engagement in eHealth and mHealth behavior change interventions: viewpoint of methodologies. J Med Internet Res. Nov 16, 2018;20(11):e292. [FREE Full text] [CrossRef] [Medline]
    46. Abusamhadana GA, Elias NF, Mukhtar M, Mokhtar UA. Items for measuring user engagement success in information systems development. In: Proceedings of the 2019 International Conference on Electrical Engineering and Informatics. 2019. Presented at: ICEEI 2019; July 9-10, 2019; Bandung, Indonesia. [CrossRef]
    47. Australian mHealth apps assessment framework: options report. Australasian Institute of Digital Health. 2024. URL: https://digitalhealth.org.au/wp-content/uploads/2024/06/AIDH_mHealth-Framework-Options.pdf [accessed 2026-04-21]
    48. Flaherty SJ, McCarthy M, Collins AM, McCafferty C, McAuliffe FM. Exploring engagement with health apps: the emerging importance of situational involvement and individual characteristics. Eur J Mark. Dec 2020;55(13):122-147. [CrossRef]
    49. König LM, Attig C, Franke T, Renner B. Barriers to and facilitators for using nutrition apps: systematic review and conceptual framework. JMIR Mhealth Uhealth. Apr 01, 2021;9(6):e20037. [FREE Full text] [CrossRef] [Medline]
    50. Ng MM, Firth J, Minen M, Torous J. User engagement in mental health apps: a review of measurement, reporting, and validity. Psychiatr Serv. Jul 01, 2019;70(7):538-544. [FREE Full text] [CrossRef] [Medline]
    51. Robinson E, Higgs S, Daley AJ, Jolly K, Lycett D, Lewis A, et al. Development and feasibility testing of a smart phone based attentive eating intervention. BMC Public Health. Jul 09, 2013;13:639. [FREE Full text] [CrossRef] [Medline]
    52. Davis SR, Peters D, Calvo RA, Sawyer SM, Foster JM, Smith L. "Kiss myAsthma": using a participatory design approach to develop a self-management app with young people with asthma. J Asthma. Sep 2018;55(9):1018-1027. [FREE Full text] [CrossRef] [Medline]
    53. Jackson DN, Sehgal N, Baur C. Benefits of mHealth co-design for African American and Hispanic adults: multi-method participatory research for a health information app. JMIR Form Res. Mar 09, 2022;6(3):e26764. [FREE Full text] [CrossRef] [Medline]
    54. De La Torre SA, El Mistiri M, Hekler E, Klasnja P, Marlin B, Pavel M, et al. Modeling engagement with a digital behavior change intervention (HeartSteps II): an exploratory system identification approach. J Biomed Inform. Oct 2024;158:104721. [FREE Full text] [CrossRef] [Medline]
    55. Hekler EB, Rivera DE, Martin CA, Phatak SS, Freigoun MT, Korinek E, et al. Tutorial for using control systems engineering to optimize adaptive mobile health interventions. J Med Internet Res. Jun 28, 2018;20(6):e214. [FREE Full text] [CrossRef] [Medline]

    ‎
    mHealth: mobile health
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
    RCT: randomized controlled trial

    Edited by L Buis; submitted 28.Aug.2025; peer-reviewed by M Aktas; comments to author 15.Dec.2025; revised version received 24.Mar.2026; accepted 25.Mar.2026; published 12.May.2026.

    Copyright

    ©Maria F Vasiloglou, Zoë van der Heijden, Eric Antoine Scuccimarra, Alberto Conde Freniche, Desiree A Lucassen, Nienke de Vlieger, Frédéric Ronga, Elske Brouwer-Brolsma, Tamara Bucher. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 12.May.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.