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

Web-based and Mobile Health Interventions (5846) Mobile Health (mhealth) (5172) mHealth for Wellness, Behavior Change and Prevention (4264) Behavior Change (1083) Mobile Health in Psychiatry (730) Machine Learning (3168) Digital Biomarkers and Digital Phenotyping (437) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (807)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27218, first published .
Man eating fruit salad with a spoon, holding a smartphone.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial

Authors of this article:

Meelim Kim1 Author Orcid Image ;   Jaeyeong Yang2 Author Orcid Image ;   Woo-Young Ahn2, 3 Author Orcid Image ;   Hyung Jin Choi1, 4 Author Orcid Image
Article Authors Cited by (33) Tweetations (6) Metrics

Journals

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  3. Yun J, Shin J, Lee H, Kim D, Choi I, Kim M. Characteristics and Potential Challenges of Digital-Based Interventions for Children and Young People: Scoping Review. Journal of Medical Internet Research 2023;25:e45465 View
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  11. Lee H, Choi E, Shin J, Kim T, Oh J, Shin B, Sim J, Shin J, Kim M. The Impact of Intervention Design on User Engagement in Digital Therapeutics Research: Factorial Experiment With a Mixed Methods Study. JMIR Formative Research 2024;8:e51225 View
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  16. Tak Y, Lee J, Kim J, Lee Y. Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices. Journal of Medical Internet Research 2024;26:e59444 View
  17. Weingott S, Parkinson J. The application of artificial intelligence in health communication development: A scoping review. Health Marketing Quarterly 2025;42(1):67 View
  18. Kim S. Trends and Perspectives of mHealth in Obesity Control. Applied Sciences 2024;15(1):74 View
  19. Yang L, Zhang M, Jia L, Yan Z, Yin Q. Understanding digital therapeutics in disease self-management: A systematic literature review. Technology in Society 2025;81:102831 View
  20. Huang L, Huhulea E, Abraham E, Bienenstock R, Aifuwa E, Hirani R, Schulhof A, Tiwari R, Etienne M. The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations. Medicina 2025;61(2):358 View
  21. Prochnow T, Dunton G, de la Haye K, Pollack Porter K, Lee C. Combining Ecological Momentary Assessment and Social Network Analysis to Study Youth Physical Activity and Environmental Influences: Protocol for a Mixed Methods Feasibility Study. JMIR Research Protocols 2025;14:e68667 View
  22. Lacruz-Pleguezuelos B, Bazán G, Romero-Tapiador S, Freixer G, Tolosana R, Daza R, Fernández-Díaz C, Molina S, Crespo M, Laguna T, Marcos-Zambrano L, Aguilar-Aguilar E, Fernández-Cabezas J, Cruz-Gil S, Fernández L, Vera-Rodriguez R, Fierrez J, Ramírez de Molina A, Ortega-Garcia J, Morales A, Carrillo de Santa Pau E, Espinosa-Salinas I. AI4Food, a feasibility study for the implementation of automated devices in the nutritional advice and follow up within a weight loss intervention. Clinical Nutrition 2025;48:80 View
  23. Yang H, De la Peña-Armada R, Sun H, Peng Y, Lo M, Scheer F, Hu K, Garaulet M. Uncovering key factors in weight loss effectiveness through machine learning. International Journal of Obesity 2025;49(6):1189 View
  24. Liu C, Messer M, Linardon J, Fuller-Tyszkiewicz M. Applying models of self-regulated learning to understand engagement with digital health interventions: a narrative review. Frontiers in Digital Health 2025;7 View
  25. Lee T, Park S, Lee S, Hwangbo A, Bae H, Lee Y, Choi H. Hijacked Brain in Modern Obesity: Cue, Habit, Addiction, Emotion, and Restraint as Targets for Personalized Digital Therapy and Electroceuticals. Journal of Obesity & Metabolic Syndrome 2025;34(3):196 View
  26. Tan C, Koh J, Ang W, Tan X, Koh S, Lin W, Lee J, Chew H. State-of-the-art digital phenotyping methods for cardiometabolic risk prevention and management: a scoping review. International Journal of Medical Informatics 2026;206:106133 View
  27. Aktas M, Cambon L, Aromatario O. Engagement of Users in Digital Health Applications: Scoping Review. JMIR mHealth and uHealth 2026;14:e66002 View
  28. Wei M, Liu C, Zhuang S. Knowledge, attitudes, and proactive practices regarding PICC in gastrointestinal cancer: a mediation analysis of a patient empowerment attempt. Frontiers in Medicine 2026;12 View
  29. Li H, Pan W, Chen H, Hou Y. Can We Predict Older Adults’ Long-Term Healthy Behavior Maintenance? Identifying the Optimal Machine Learning Model. International Journal of Human–Computer Interaction 2026:1 View
  30. Gkintoni E, Vantarakis A. Neuropsychological Mechanisms Associated with the Effectiveness of AI-Delivered Health Promotion Programs: A Comprehensive Meta-Analysis. Brain Sciences 2026;16(4):389 View
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Conference Proceedings

  1. Abedi A, Dayyani F, Chu C, Khan S. 2022 IEEE International Conference on Data Mining Workshops (ICDMW). MAISON - Multimodal AI-based Sensor platform for Older Individuals View