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

Web-based and Mobile Health Interventions (4018) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Mobile Health (mhealth) (3464) mHealth for Wellness, Behavior Change and Prevention (3315) Obesity and Nutrition as Public Health Problem (397) Telehealth and Telemonitoring (2088) Psychological, Behavioral, Social, and/or Cultural Experiments and Interventions (95) Secondary Use of Clinical Data for Research and Surveillance (474)

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 (7) Tweetations (5) Metrics

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  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
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  6. Johnson H, Clift A, Reisel D, Huang D. Digital engagement significantly enhances weight loss outcomes in adults with obesity treated with tirzepatide: a retrospective cohort study of a digital weight loss service (Preprint). Journal of Medical Internet Research 2025 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