A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study.

Background: Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety.

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study.

Background: Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes.