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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (482) Machine Learning (1436)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17738, first published .
Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

Authors of this article:

Vincent Bremer1 Author Orcid Image ;   Philip I Chow2 Author Orcid Image ;   Burkhardt Funk1 Author Orcid Image ;   Frances P Thorndike2 Author Orcid Image ;   Lee M Ritterband2 Author Orcid Image
Article Authors Cited by (15) Tweetations (6) Metrics

Journals

  1. Mukhiya S, Lamo Y, Rabbi F. A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study. JMIR Human Factors 2022;9(2):e31029 View
  2. Linardon J, Fuller‐Tyszkiewicz M, Shatte A, Greenwood C. An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. International Journal of Eating Disorders 2022;55(6):845 View
  3. Bricker J, Miao Z, Mull K, Santiago-Torres M, Vock D. Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials. Journal of Medical Internet Research 2023;25:e43629 View
  4. Oinas-Kukkonen H, Pohjolainen S, Agyei E. Mitigating Issues With/of/for True Personalization. Frontiers in Artificial Intelligence 2022;5 View
  5. Chen X, Cheng G, Wang F, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Informatics 2022;9(1) View
  6. Moshe I, Terhorst Y, Paganini S, Schlicker S, Pulkki-Råback L, Baumeister H, Sander L, Ebert D. Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials. Journal of Medical Internet Research 2022;24(8):e38261 View
  7. Naegelin M, Weibel R, Kerr J, Schinazi V, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics 2023;139:104299 View
  8. Zantvoort K, Scharfenberger J, Boß L, Lehr D, Funk B. Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions. Journal of Healthcare Informatics Research 2023;7(4):447 View
  9. Hornstein S, Zantvoort K, Lueken U, Funk B, Hilbert K. Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms. Frontiers in Digital Health 2023;5 View
  10. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
  11. Kötting L, Derksen C, Keller F, Lippke S. Comparing the Effectiveness of a Web-Based Application With a Digital Live Seminar to Improve Safe Communication for Pregnant Women: 3-Group Partially Randomized Controlled Trial. JMIR Pediatrics and Parenting 2023;6:e44701 View
  12. Zantvoort K, Hentati Isacsson N, Funk B, Kaldo V. Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions. DIGITAL HEALTH 2024;10 View
  13. Mishra S, Chaudhury P, Tripathy H, Sahoo K, Jhanjhi N, Hassan Elnour A, Abdelmaboud A. Enhancing health care through medical cognitive virtual agents. DIGITAL HEALTH 2024;10 View