It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).

No abstract

Smartphones are often distraction for everyday life activities. In this work, we envision designing a context-aware system that helps users better manage smartphone distractions. This system design requires us to have an in-depth understanding of users' contexts of smartphone distractions and their coping strategies. However, there is a lack of knowledge regarding the contexts in which users perceive that smartphones are distracting in their everyday lives. Furthermore, prior studies did not systematically examine users' preferred coping strategies for handling interruptions caused by smartphones, possibly supported by context-aware systems that proactively manage smartphone distraction. To bridge this gap, we collect in-situ user contexts and their corresponding levels of perceived smartphone distraction as well as analyze the daily contexts in which users perceive smartphones as distracting. Moreover, we also explore how users want to manage phone distraction by asking them to write simple if-then rules. Our results on user contexts and coping strategies provide important implications for designing and implementing context-aware distraction management systems.

No abstract