Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.

Journal: Journal of medical Internet research
PMID:

Abstract

BACKGROUND: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing.

Authors

  • Annette Brons
    Digital Life Center, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Shihan Wang
    Faculty of Digital Media and Creative Industries, Digital Life Centre, University of Applied Sciences Amsterdam, Amsterdam, Netherlands.
  • Bart Visser
    Centre of Expertise Urban Vitality, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Ben Kröse
    Faculty of Digital Media and Creative Industries, Digital Life Centre, University of Applied Sciences Amsterdam, Amsterdam, Netherlands.
  • Sander Bakkes
    Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.
  • Remco Veltkamp
    Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.