Abstract Machine and deep learning methods, today's core of artificial intelligence, have
been applied with increasing success and impact in many commercial and research …

Highlights•Smartphone Sensing Methods (SSMs) permit continuous and real-time
behavioral observation in the context of people's daily lives.•SSMs provide objective …

There are rising rates of depression on college campuses. Mental health services on our
campuses are working at full stretch. In response researchers have proposed using mobile …

Background The COVID-19 pandemic has increased the impact and spread of mental
illness and made health services difficult to access; therefore, there is a need for remote …

Background: Several studies have recently reported on the correlation between objective
behavioral features collected via mobile and wearable devices and depressive mood …

Depression is a prevailing issue and is an increasing problem in many people's lives.
Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting …

Prior research has recently shown that passively collected sensor data collected within the
contexts of persons daily lives via smartphones and wearable sensors can distinguish those …

Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant
Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and …

Background Uncertainty surrounds the ethical and legal implications of algorithmic and data-
driven technologies in the mental health context, including technologies characterized as …

Continuously monitoring schizophrenia patients' psychiatric symptoms is crucial for in-time
intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey …