Depression is one of the most prevalent mental illnesses which requires early detection and intervention to successfully overcome it. However, treatment for depression is being conducted insufficiently due to lack of information about the illness and concern about the stigma. Moreover, traditional assessment for depression mostly depends on self-report, which may hinder appropriate assessment and intervention with inaccurate retrospection.
Recently, attempts have been made to detect depression early by automatically measuring user biometrics, mobile phone usage patterns, and behavioral characteristics through mobile and wearable devices. However, existing mobile applications for depression detection have limitations such as insufficient consideration of diagnostic criteria or inclusion of self-report.
Therefore, this study aims to develop an integrative ICT service platform, which automatically measures 5 depression index(mood, food intake, sleep, physical activity, social activity), assesses depression level based on the measured data, and provides intervention by level.