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

Journal: Journal of medical Internet research
PMID: 33112241

Abstract

BACKGROUND: User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data-self-reported as well as system-generated data-produced by the path (or journey) an individual takes to navigate through a digital health intervention.

Authors

  • Vincent Bremer
    Institute of Information Systems, Leuphana University, Lüneburg, Germany.
  • Philip I Chow
    School of Medicine, University of Virginia, 1215 Lee St, Charlottesville, VA 22903 USA.
  • Burkhardt Funk
    Institute of Information Systems, Leuphana University, Lüneburg, Germany.
  • Frances P Thorndike
    Center for Behavioral Health & Technology, University of Virginia School of Medicine, Charlottesville, VA, United States.
  • Lee M Ritterband
    School of Medicine, University of Virginia, 1215 Lee St, Charlottesville, VA 22903 USA.

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