Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial

Abstract

Physical inactivity is a public health issue. Mobile health interventions to promote physical activity often still experience dropout, resulting in people not adhering to the interventions. This paper aims to further improve mobile health apps with innovatively applied techniques from recommender system algorithms to increase personalization for physical activities and practical tips to reduce sedentary behavior. Personalization in our mobile health recommender is achieved with a seven-step algorithm: filtering on user profile (1), current weather and daylight (2), pre-filtering with a micro-profile on current mood and motivation (3), content-based recommendations using our own two datasets extended with 24 attributes (4), post-filtering on estimated current situation (5), adapting and gradually increasing duration and intensity (6), and generating just-in-time adaptive interventions (7). To analyze the effectiveness of steps 3, 4, and 5, a double-blind randomized controlled trial is conducted in which only the experimental group receives the three additional personalization steps, while the control group replaces these steps with a random selection. As such, the control group’s recommendations are still partly personalized with the other steps. Participants install the app on their Android smartphone and use the app for eight weeks, with a pretest and posttest questionnaire, and a follow-up after six months. The experimental group assigned significantly higher star ratings to the recommendations, and significantly higher momentary motivation for physical activities, tips, and manual user refreshes, compared to the control group. Additionally, there was less dropout and a significantly stronger increase in duration and intensity of the performed physical activities in the experimental group. Because the experimental group received the three additional personalization steps with micro-profiling, content-based recommender, and post-filtering on estimated situation, our results suggest that these three steps resulted in more personalized recommendations that motivate users more. Future research should aim to further improve personalization to increase the effectiveness of mobile health interventions and effectively motivate people to move more.