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

Web-based and Mobile Health Interventions (4018) Machine Learning (2076) Digital Biomarkers and Digital Phenotyping (284) Mobile Health in Psychiatry (527) Mobile Health (mhealth) (3464) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) mHealth for Wellness, Behavior Change and Prevention (3315) Behavior Change (863)

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 .
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

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 (27) Tweetations (7) Metrics

Journals

  1. Buller D, Pagoto S, Henry K, Baker K, Walkosz B, Hillhouse J, Berteletti J, Bibeau J. Effects of Engagement with a Social Media Campaign for Mothers to Prevent Indoor Tanning by Teens in a Randomized Trial. Journal of Health Communication 2022;27(6):394 View
  2. Dai R, Kannampallil T, Zhang J, Lv N, Ma J, Lu C. Multi-Task Learning for Randomized Controlled Trials. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  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
  4. Dlima S, Shevade S, Menezes S, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e39618 View
  5. Agachi E, Bijmolt T, van Ittersum K, Mierau J. The Effect of Periodic Email Prompts on Participant Engagement With a Behavior Change mHealth App: Longitudinal Study. JMIR mHealth and uHealth 2023;11:e43033 View
  6. Yuhas M, Brock D, Ritterband L, Chow P, Porter K, Zoellner J. Retention and engagement of rural caregivers of adolescents in a short message service intervention to reduce sugar-sweetened beverage intake. DIGITAL HEALTH 2023;9 View
  7. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View
  8. Zhaunova L, Bamford R, Radovic T, Wickham A, Peven K, Croft J, Klepchukova A, Ponzo S. Characterization of Self-reported Improvements in Knowledge and Health Among Users of Flo Period Tracking App: Cross-sectional Survey. JMIR mHealth and uHealth 2023;11:e40427 View
  9. Phan P, Mitragotri S, Zhao Z. Digital therapeutics in the clinic. Bioengineering & Translational Medicine 2023;8(4) View
  10. Aschbacher K, Rivera L, Hornstein S, Nelson B, Forman-Hoffman V, Peiper N. Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention. Psychosomatic Medicine 2023;85(7):651 View
  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
  12. Metzendorf M, Wieland L, Richter B. Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity. Cochrane Database of Systematic Reviews 2024;2024(2) View
  13. Carrera A, Manetti S, Lettieri E. Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework. BMC Health Services Research 2024;24(1) View
  14. Gutierrez G, Stephenson C, Eadie J, Asadpour K, Alavi N. Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review. Frontiers in Psychiatry 2024;15 View
  15. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  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

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