ArticleJMIR mHealth and uHealth2023
Patterns and Predictors of Engagement With Digital Self-Monitoring During the Maintenance Phase of a Behavioral Weight Loss Program: Quantitative Study.
Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Randomized Controlled Trials to Treat Obesity in Military Populations: A Systematic Review and Meta-Analysis.Nutrients · 2023Pooled it
- Action Planning for Reducing Sugar-Sweetened Beverage Intake in Appalachian Adults: Longitudinal Process Evaluation of a Digital Behavioral Health Intervention.JMIR mHealth and uHealth · 2026Trial
- Engagement and Weight Change in an Online Weight Management Program in Clinical Settings: An Implementation Project.Journal of general internal medicine · 2026Article
- Weight- and Sugar-Sweetened Beverage-Related Information Avoidance: Implications for Self-Monitoring in a Behavioral Intervention.Journal of nutrition education and behavior · 2026Article
- Do routinized eating behaviors support weight loss? An examination of food logs from behavioral weight loss participants.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2026Article
- Digital Monitoring of Pre-Exposure Prophylaxis Users Through Electronic Patient-Reported Outcome Measures and Electronic Patient-Reported Experience Measures: Multicenter Prospective Study on Feasibility, Safety, and Predictive Modeling of Digital Engagement.Journal of medical Internet research · 2026Observational
- Beyond the digital divide: multi-group SEM examination of socioeconomic status, mHealth utilization, and urban-rural physical activity disparities in Indonesia.Frontiers in digital health · 2026Article
- Digital health technologies in obesity: trajectory, emerging hotspots, and future perspectives.Frontiers in medicine · 2026Review
- Participant food tracking in psychiatric ketogenic metabolic therapy: an unresolved clinical, psychosocial, and implementation question.Frontiers in nutrition · 2026Review
- Exploring the Dynamics of Dietary Self-Monitoring Adherence Among Participants in a Digital Behavioral Weight Loss Program: Model Development Study.Journal of medical Internet research · 2025Article
- Review
- The Mobile lifestyle intervention for food and exercise (mLife) study: Protocol of a remote behavioral weight loss randomized clinical trial for type 2 diabetes prevention.Contemporary clinical trials · 2025Article
- The impact of weight self-stigma on weight-loss treatment engagement and outcome.Obesity science & practice · 2024Article
- Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment.Journal of behavioral medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLong-term self-monitoring (SM) of weight, diet, and exercise is commonly recommended by behavioral weight loss (BWL) treatments. However, sustained SM engagement is notoriously challenging; therefore, more must be learned about patterns of engagement with digital SM tools during weight loss maintenance (WLM). In addition, insight into characteristics that may influence SM engagement could inform tailored approaches for participants at risk for poor adherence.
objectiveThis study explored patterns of digital SM of weight, diet, and exercise during WLM (aim 1) and examined timing, patterns, and rates of disengagement and reengagement (aim 2). This study also assessed relationships between individual-level factors (weight-related information avoidance and weight bias internalization) and SM engagement (aim 3).
methodsParticipants were 72 adults enrolled in a BWL program consisting of a 3-month period of weekly treatment designed to induce weight loss (phase I), followed by a 9-month period of less frequent contact to promote WLM (phase II). Participants were prescribed daily digital SM of weight, diet, and exercise. At baseline, self-report measures assessed weight-related information avoidance and weight bias internalization. SM adherence was objectively measured with the days per month that participants tracked weight, diet, and exercise. Repeated-measures ANOVA examined differences in adherence across SM targets. Multilevel modeling examined changes in adherence across phase II. Relationships between individual-level variables and SM adherence were assessed with Pearson correlations, 2-tailed independent samples t tests, and multilevel modeling.
resultsDuring WLM, consistently high rates of SM (≥50% of the days in each month) were observed for 61% (44/72) of the participants for exercise, 40% (29/72) of the participants for weight, and 21% (15/72) of the participants for diet. Adherence for SM of exercise was higher than that for weight or diet (P<.001). Adherence decreased over time for all SM targets throughout phase II (P<.001), but SM of exercise dropped off later in WLM (mean 10.07, SD 2.83 months) than SM of weight (mean 7.92, SD 3.23 months) or diet (mean 7.58, SD 2.92 months; P<.001). Among participants with a period of low SM adherence (ie, <50% of the days in a month), only 33% (17/51 for weight, 19/57 for diet) to 46% (13/28 for exercise) subsequently had ≥1 months with high adherence. High weight-related information avoidance predicted a faster rate of decrease in dietary SM (P<.001). Participants with high weight bias internalization had the highest rates of weight SM (P=.03).
conclusionsParticipants in BWL programs have low adherence to the recommendation to sustain daily SM during WLM, particularly for SM of diet and weight. Weight-related information avoidance and weight bias internalization may be relevant indicators for SM engagement. Interventions may benefit from innovative strategies that target participants at key moments of risk for disengagement.
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