Sensing Applications and Public Datasets for Digital Phenotyping of Mental Health (Preprint)
BACKGROUND Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as Digital Phenotyping of Mental Health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. OBJECTIVE This article aims to identify and characterize technically the sensing applications and public datasets for DPMH. METHODS We performed a systematic review of scientific literature and datasets. We searched digital libraries and dataset repositories to find results that met the selection criteria. RESULTS After applying inclusion and exclusion criteria, 31 articles and 8 datasets were selected for data extraction, in which we summarized their characteristics and identified trends and research opportunities. CONCLUSIONS Results evidenced growth in proposals for DPMH sensing applications in recent years as opposed to a scarcity of public datasets. This systematic review provides in-depth analysis regarding solutions for DPMH.