MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health

Darius Adam Rohani, Andrea Quemada Lopategui, Nanna Tuxen, Maria Faurholt-Jepsen, Lars V. Kessing, Jakob Eyvind Bardram

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationCHI '20 : Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Number of pages13
PublisherAssociation for Computing Machinery
Publication date2020
Article number750
ISBN (Electronic)978-1-4503-6708-0
DOIs
Publication statusPublished - 2020
EventCHI '20: CHI Conference on Human Factors in Computing Systems - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020

Conference

ConferenceCHI '20: CHI Conference on Human Factors in Computing Systems
Country/TerritoryUnited States
CityHonolulu
Period25/04/202030/04/2020

Keywords

  • Depression
  • Recommendation
  • Behavioral Activation
  • Planning
  • Activities
  • Well-being
  • Smartphone
  • Mental health

Fingerprint

Dive into the research topics of 'MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health'. Together they form a unique fingerprint.

Cite this