Predicting responsiveness to a dialectical behaviour therapy skills training app for recurrent binge eating: A machine learning approach
ML models were unable to accurately predict responsiveness using self-reported baseline predictors alone. Predicting outcomes with greater precision may require consideration of how predictors change over time and interact with a user's context. Modelling usage pattern data appears to improve prediction of dropout, highlighting the potential value of tracking intervention usage to identify individuals at risk of disengagement.
Authors
- Jake Linardon (ORCID: https://orcid.org/0000-0003-4475-7139)
- Matthew Fuller‐Tyszkiewicz (ORCID: https://orcid.org/0000-0003-1145-6057)
- Zoe McClure (ORCID: https://orcid.org/0000-0003-3926-8058)
- Mariel Messer (ORCID: https://orcid.org/0000-0002-7186-7264)
- Christopher Greenwood (ORCID: https://orcid.org/0000-0002-9211-6312)
Institutions
- Royal Children's Hospital (AU)
- Deakin University (AU)
Publication Details
- Journal
- Behaviour Research and Therapy
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1016/j.brat.2025.104755
- Citations
- 1
- Primary Topic
- Eating Disorders and Behaviors
- Type
- article
- Field-Weighted Citation Impact
- 2.31
Funders
- National Health and Medical Research Council