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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (493) Machine Learning (1465) Tools, Programs and Algorithms (266) Artificial Intelligence (86) Artificial Intelligence (1371)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43633, first published .
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

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 Tweetations (4) Metrics

Aida Brankovic   1 , PhD ;   Gilly A Hendrie   2 , PhD ;   Danielle L Baird   2 , BSc ;   Sankalp Khanna   1 , PhD

1 The Australian e-Health Research Centre, Health & Biosecurity, Commonwealth Scientific Industrial Research Organisation, Brisbane, Australia

2 Human Health Program, Health & Biosecurity, Commonwealth Scientific Industrial Research Organisation, Adelaide, Australia

Corresponding Author:

  • Aida Brankovic, PhD
  • The Australian e-Health Research Centre
  • Health & Biosecurity
  • Commonwealth Scientific Industrial Research Organisation
  • STARS building Level 7
  • Herston
  • Brisbane, 4029
  • Australia
  • Phone: 61 732533629
  • Email: aida.brankovic@csiro.au