Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity

Orianna Demasi, Adrián Aguilera, Benjamin Recht

2016 IEEE Wireless Health (WH)
October 25, 2016

Depression is the most common mental disorder and is negatively impactful to individuals and their social networks. Passive sensing of behavior via smartphones may help detect changes in depressive symptoms, which could be useful for tracking and understanding disorders. Here the authors look at a passive way to detect changes in depressive symptoms from data collected by users' smartphones. In particular, the authors take two modeling approaches to understand what features of physical activity, sleep, and user emotional wellbeing best predict changes in depressive symptoms. The authors find overlap in the features selected by two modeling approaches, which implies the importance of certain features. Characteristics around sleep, such as change and irregularity of sleep duration, appear as meaningful predictors, as does personality. The work corroborates prior results that sleep is strongly related to changes in depressive symptoms, but the authors show that even a very coarse measure has some predictive capability.



Featured Fellows

Orianna DeMasi

Electrical Engineering and Computer Sciences
Alumni - Data Science Fellow