Abstract
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).
Highlights
Depression has affected approximately 25% of the world’s population at least once in their lifetime, and around 7% of the population annually [1]
We proposed the development of Short-Term Depression Detector (STDD) to prove the possibility of a depression group classification system and deliver design implications
We proposed STDD, a framework that can track five factors influencing depression and perform group classification based on the passive sensing information
Summary
Depression has affected approximately 25% of the world’s population at least once in their lifetime, and around 7% of the population annually [1]. People with depression experience psychological difficulties and physical symptoms and show higher suicide rates (200% compared to general population), resulting in higher medical costs [2,3]. Depression has become a salient public health issue in this era. Assessing symptoms of depression, and their severity is not straightforward. The most widely used methods among mental health professionals are interviews and self-report questionnaires. These methods are time-consuming, expensive, and often require the involvement of professionals
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