Contributed Papers (Oral)
11:30 - 12:15 | Wed 26 Oct | Main Auditorium | WeBT1
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 we look at a passive way to detect changes in depressive symptoms from data collected by users' smartphones. In particular we take two modeling approaches to understand what features of physical activity, sleep, and user emotional wellbeing best predict changes in depressive symptoms. We find overlap in the features selected by our 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. Our work corroborates prior results that sleep is strongly related to changes in depressive symptoms, but we show that even a very coarse measure has some predictive capability.
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