Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial
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Abstract
Background: Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. Objective: The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. Methods: Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. Results: The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. Conclusions: This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). Trial Registration: ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585 International Registered Report Identifier (IRRID): DERR1-10.2196/33568
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A microrandomized trial has been successfully applied to evaluate dynamic and user-tailored eHealth interventions focused on weight management, smoking cessation, and mental health.
“an MRT has been successfully applied to evaluate dynamic and user-tailored eHealth interventions focused on weight management [52], smoking cessation [53], and MH [54].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [52]
The Abstract Methods describes a planned microrandomized trial of an adaptive intervention to prevent dietary lapses during web-based obesity treatment, supporting the weight-management portion attributed to this source. But the Results says recruitment was ongoing, so this protocol does not establish that the trial had successfully evaluated the intervention. The smoking-cessation and mental-health examples have separate citations in the citing sentence. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCompared with other methodologies used for intervention evaluation such as an RCT, the MRT design makes it possible to observe proximal changes in participants' emotional states directly after they complete an exercise, in addition to requiring fewer participants to achieve sufficient power to detect the proximal main effect of an intervention component.
“Compared with other methodologies used for intervention evaluation such as an RCT, the MRT design makes it possible to observe proximal changes in participants’ emotional states directly after they complete an exercise, in addition to requiring fewer participants to achieve sufficient power to detect the proximal main effect of an intervention component [52].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [52]
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Experimental — accuracy not yet measuredThe 60:40 ratio was chosen to ensure a higher chance of receiving an intervention at every decision point.
“The 60:40 ratio was chosen to ensure a higher chance of receiving an intervention at every decision point. This strategy has been used in previous MRT studies too [51,52].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [52]
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Self-citationRoefs, A., Boh, B., Spanakis, G., Nederkoorn, C., Lemmens, L. H. J. M., & Jansen, A. T. M. (2019) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12693 · Cited by 36 · see citing works
Dempsey, W., Liao, P., Kumar, S., & Murphy, S. A. (2020) · The Annals of Applied Statistics · doi:10.1214/19-aoas1293 · Cited by 33 · see citing works
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Bell, L., Garnett, C. V., Qian, T., Perski, O., POTTS, H. W. W., & Williamson, E. (2020) · JMIR Research Protocols · doi:10.2196/18690 · Cited by 28 · see citing works
Coping with dietary relapse crises and their aftermath
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Self-citationThe Impact of Walking and Resting on Wrist Motion for Automated Detection of Meals
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Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials
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Self-citationPrior Knowledge of the Mediterranean Diet Is Associated With Dietary Adherence in Cardiac Patients
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Optimization of Just-in-Time Adaptive Interventions Using Reinforcement Learning
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Self-citationEffective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth InterventionPreprint
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Flagged first, then by citations
Research works printed but not linked: 3
- [43]Hayes S Strosahl K Wilson K Acceptance and Commitment Therapy: The Process and Practice of Mindful Change 2011 New York City Guilford Press
- [77]Snidjers T Bosker R Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling 1999 Thousand Oaks, CA SAGE publications
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Cited by
Sorted by citations
Developing personalized algorithms for sensing mental health symptoms in daily life
Timmons, A. C., Tutul, A. A., Avramidis, K., Duong, J. B., Carta, K. E., Walters, S. N., Jumonville, G. A., Carrasco, A. S., Freitag, G. F., Romero, D. N., et al. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00147-5 · Cited by 7 · see citing works
Laure, T., Engels, R. C. M. E., Remmerswaal, D., Spruijt-Metz, D., Konigorski, S., & Boffo, M. (2023) · JMIR Research Protocols · doi:10.2196/46603 · Cited by 6 · see citing works
“This strategy has been used in previous MRT studies too [51,52].”
BackgroundMethod
- Authors
- Goldstein, S. P., Zhang, F., Klasnja, P., Hoover, A., Wing, R. R., & Thomas, J. G.
- Affiliations
- Brown University (US)
- Drexel University (US)
- University of Michigan (US)
- Clemson University (US)
- Corresponding author
- Goldstein, Stephanie P
- ORCID
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2021-12-06
- Volume
- 10
- Issue
- 12
- Pages
- e33568
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC8691411
- OpenAlex
- W4200299471
- Cited by
- 26
- References
- 102
- Citations by year
- 2026: 10 · 2025: 7 · 2024: 1 · 2023: 6 · 2022: 2
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL153543)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- just-in-time adaptive interventions, dietary adherence, dietary lapses, smartphone-based intervention, weight loss, cardiovascular risk, self-efficacy, motivation, self-regulation, theory-based intervention, contextual moderators
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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