MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health
Darius A. Rohani, Andrea Quemada Lopategui, Nanna Tuxen, Maria Faurholt-Jepsen, Lars Vedel Kessing, Jakob E. Bardram
摘要
Depression is a leading cause of disability worldwide, which has inspired the design of mobile health (mHealth) applications for disease monitoring, prediction, and diagnosis. Less mHealth research has, however, focused on the treatment of depressive disorders. Clinical evidence shows that depressive symptoms can be reduced through a behavior change method known as Behavioral Activation (BA). This paper presents MUBS; a smartphone-based system for BA, which specifically contributes a personalized content-based activity recommendation model using a unique list of validated activities. An 8-week feasibility study with 17 depressive patients provided detailed insight into how MUBS provided inspiration and motivation for planning and engaging in more pleasant activities, thereby facilitating the core components of BA. Based on this study, the paper discusses how recommender technology can be used in the design of mHealth technology for BA.
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