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

Web-based and Mobile Health Interventions (4083) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (653) Mobile Health (mhealth) (3516) mHealth for Wellness, Behavior Change and Prevention (3359) Obesity and Nutrition as Public Health Problem (412) Telehealth and Telemonitoring (2114) Psychological, Behavioral, Social, and/or Cultural Experiments and Interventions (97) Secondary Use of Clinical Data for Research and Surveillance (480)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45469, first published .
App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data

App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data

App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data

Authors of this article:

Marco Lehmann1 Author Orcid Image ;   Lucy Jones2 Author Orcid Image ;   Felix Schirmann1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

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  1. Arigo D, Jake-Schoffman D, Pagoto S. The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024. Journal of Behavioral Medicine 2025;48(1):120 View
  2. Hu Y, Wang J, Zhou J, Gu Y, Nicholas S, Maitland E. Preferences of Individuals With Obesity for Online Medical Consultation in Different Demand Scenarios: Discrete Choice Experiments. Journal of Medical Internet Research 2024;26:e53140 View
  3. Kim H, Chung M. Testing a User Engagement Model of Motivational Technology for Exercise Behavior and Postpartum Weight Management. Health & New Media Research 2025;9(1):1 View
  4. Brügger V, Kowatsch T, Jovanova M. Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management. Scientific Reports 2025;15(1) View
  5. Szypula J, Jarvstad A, Jones L, Tapper K. Personalizing a Weight Loss Program Using Cognitive-Behavioral Phenotypes to Improve Engagement and Weight Loss in Adults With Overweight or Obesity: Quasi-Experimental Study. JMIR Formative Research 2025;9:e72645 View
  6. Johnson H, Clift A, Reisel D, Huang D. Digital Engagement Significantly Enhances Weight Loss Outcomes in Adults With Obesity Treated With Tirzepatide: Retrospective Cohort Study of a Digital Weight Loss Service. Journal of Medical Internet Research 2026;28:e83718 View
  7. Suraya Mohd Dan A, Linoby A, Shahlan Kasim S, Zaki S, Sazali R, Yusoff Y, Nasir Z, Haziq Abidin A. Validation of a personalized AI prompt generator (NExGEN-ChatGPT) for obesity management using fuzzy Delphi method. Biology Methods and Protocols 2025;10(1) View
  8. Wang D, Rojo‐Tirado M, Benito P, Rubio‐Arias J, Ramos‐Campo D, Marques M. Adherence to Behavioral Weight Management: A Scoping Review of Definitions, Measurement, and Components. Obesity Reviews 2025 View
  9. Joanna Barwacz , Dagmara Gładysz , Magdalena Adamik , Layla Settaf-Cherif , Katarzyna Malinowska , Elhatra Settaf-Cherif . THE IMPACT OF MOBILE APPLICATIONS ON HEALTHY LIFESTYLES AND CHRONIC DISEASE MANAGEMENT. International Journal of Innovative Technologies in Social Science 2025;3(4(48)) View