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

Formative Evaluation of Digital Health Interventions (2875) Depression and Mood Disorders; Suicide Prevention (1541) Web-based and Mobile Health Interventions (3696) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (542) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1851)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53768, first published .
Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Authors of this article:

Franziska Wenger1 Author Orcid Image ;   Caroline Allenhof2 Author Orcid Image ;   Simon Schreynemackers3 Author Orcid Image ;   Ulrich Hegerl1, 3 Author Orcid Image ;   Hanna Reich1, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Reuther C, von Essen L, Mustafa M, Saarijärvi M, Woodford J. Engagement With an Internet-Administered, Guided, Low-Intensity Cognitive Behavioral Therapy Intervention for Parents of Children Treated for Cancer: Analysis of Log-Data From the ENGAGE Feasibility Trial. JMIR Formative Research 2025;9:e67171 View