Remote digital phenotyping in serious mental illness: Focus on negative symptoms, mood symptoms, and self-awareness

Abstract

The serious mental illness (SMI) phenotype is marked by several different symptom domains and biomedical challenges. The nature of SMI renders in-person assessment challenging, due to problems in event recall, response biases, lack of experience in real-world functional domains, and difficulties identifying informants. Digital strategies offer a promising alternative to in-person assessments and allow for remote delivery of cognitive and social cognitive assessments in addition to continuous momentary assessment of activities, moods, symptoms, expressions, experiences, and psychophysiological variables. Remote assessments of mood, emotion, behavior, cognition, and self-assessment have been successfully collected across various SMI conditions. Both active (paging and triggered observations of facial and vocal expressions) and passive (global positioning, actigraphy) methods have been deployed remotely, similarly to in-person assessments previously conducted in the laboratory. Advanced strategies in data analysis are used to examine this information and to guide the development of newer advances in assessment of phenotypic variation in SMI.