A smartphone-based personalized activity recommender system for patients with depression

Abstract

Depression is a common mental illness worldwide. Apart of pharmacological treatment and psychotherapy, self-management of negative emotions is of paramount importance, because relapse of depression often results from an inadequate response to negative emotions. The purpose of this study is to design and implement a personal recommender system, for emotion regulation. It assists users to be aware of negative emotions and guides them to deal with it with behavioral activation. It analyzes the smartphone usage patterns to predict the emergence of negative emotions, while integrating data obtained from context awareness and psychiatrists' recommendations to suggest relevant emotion-regulating activities. The presented system has a potential to provide personalized and pervasive mental health services for patients with depression.