Multimodal Markers of Transdiagnostic Childhood Mental Health Impairment

Childhood mental health problems are impairing, predictive of health problems later in life, and becoming increasingly prevalent. A critical first step toward addressing this growing crisis is facilitating more widespread screening; however, gold-standard assessments remain subjective, time-consuming, largely unable to capture subthreshold conditions and comorbidity, and limited by access to the clinical experts needed to provide and interpret the results. In response, researchers have developed digital phenotype screening tools that capture and utilize objective physiological and behavioral measures to augment traditional mental health screening. However, the efficacy of these tools has traditionally been evaluated on their ability to predict mental health diagnoses. In contrast, in this work and in line with the Research Domain Criteria (RDoC) framework, we explore the utility of these objective measures for quantifying trans diagnostic severity of impairment across a range of widely used clinical measurements. Using canonical correlation analysis, we find that linear combinations of movement and audio features extracted from smartphone sensor data collected during short (< 7 minutes) objective assessments are significantly correlated with a range of widely used clinical measurements. These findings suggest that easy-to-collect objective physiological and behavioral measures are indicative of severity across a range of psychopathologies.