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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (840) Tools, Programs and Algorithms (607) Machine Learning (3340) Artificial Intelligence (5022)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43633, first published .
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Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study

Predicting Disengagement to Better Support Outcomes in a Web-Based Weight Loss Program Using Machine Learning Models: Cross-Sectional Study

Authors of this article:

Aida Brankovic1 Author Orcid Image ;   Gilly A Hendrie2 Author Orcid Image ;   Danielle L Baird2 Author Orcid Image ;   Sankalp Khanna1 Author Orcid Image
Article Authors Cited by (5) Tweetations (4) Metrics

Journals

  1. Batterham M, Wakefield B. Evaluation of Intermittent Restricted Eating Using the Interval Weight Loss Online Platform in an Everyday Setting. Nutrients 2025;17(2):332 View
  2. Brankovic A, Hendrie G. Perspectives, challenges and future of artificial intelligence in personalised nutrition research. Proceedings of the Nutrition Society 2025:1 View
  3. Joshi S, Dwivedi A, Kumar A, Aayush K, Bisht B, Kumar S, Kumar V. Revolutionizing Nutrition Care: The Role of Artificial Intelligence in Personalized Nutrition. Journal of Food Science 2026;91(7) View
  4. Mitsea E, Drigas A, Skianis C. AΙ-Driven Interventions for Neurocognitive, Self-Regulation, and Adaptive Skill Development in Neurodevelopmental and Cognitive Disorders: A Systematic Review of Randomized Controlled Trials. Healthcare 2026;14(18):3102 View

Books/Policy Documents

  1. Chaudary S, Ranade P, Verma I. Data Science and Applications. View