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

Engagement with and Adherence to Digital Health Interventions, Law of Attrition (638) Machine Learning (2076)

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 (22) Tweetations (5) Metrics

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  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
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  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
  14. Terceiro L, Mustafa M, Hägglund M, Kharko A. Research Participants’ Engagement and Retention in Digital Health Interventions Research: Protocol for Mixed Methods Systematic Review. JMIR Research Protocols 2025;14:e65099 View
  15. Zantvoort K, Nacke B, Görlich D, Hornstein S, Jacobi C, Funk B. Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions. npj Digital Medicine 2024;7(1) View
  16. Ramesh S, Jull D, Fournier H, Rajabiyazdi F. Exploring Barriers to Patients’ Progression in the Cardiac Rehabilitation Journey From Health Care Providers’ Perspectives: Qualitative Study. Interactive Journal of Medical Research 2025;14:e66164 View
  17. Wang P, Chen H, Li Z, Xu W, Chang Y, Li H. Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach. Smart Health 2025;36:100565 View
  18. Derksen M, van Beek M, Blankers M, Nasri H, de Bruijn T, Lommerse N, van Wingen G, Pauws S, Goudriaan A. Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use. European Addiction Research 2024;31(1):47 View
  19. Zantvoort K, Matthiesen J, Bjurner P, Bendix M, Brefeld U, Funk B, Kaldo V. The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions. Internet Interventions 2025;40:100828 View
  20. Simon L, Steinmetz L, Bendig E, Küchler A, Riemann D, Ebert D, Spiegelhalder K, Baumeister H. Exploring dropout in internet-delivered cognitive behavioral therapy for insomnia: A secondary analysis of prevalence, self-reported reasons, and baseline and intervention data as predictors. International Journal of Clinical and Health Psychology 2025;25(3):100598 View

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