32: Modernizing Mental Health Assessment -- Statistical Innovations in the Age of Digital Data

Music City Center 

~ 75% of mental illness symptoms occur before age 24. Today's Gen Z and Alpha have grown up with daily exposure to digital technology. Yet widely-used mental health assessment tools like the GAD and PHQ surveys, developed over 2 decades ago, rely on episodic, simple, self-reporting methods capturing limited data about patients' lived experiences. In our increasingly digital world, new markers -- location data and screen-time patterns -- can be collected passively & continuously with minimal user burden. Analyzing this rich dataset requires sophisticated statistical approaches to handle its complexity (e.g. big data, repeated measures & and time-series data), and offers the potential for more accurate psychological assessment. We have developed an ML app that integrates passively collected data with self-reported check-ins, on over 40 million data points from > 250 students. We have identified associations between daily moods, habits, social interactions, and feelings of loneliness and acceptance, demonstrating the feasibility of novel digital biomarkers for tracking behavioral and mental health. Statisticians have the opportunity to modernize mental health tools and improve lives.

mental health

digital biomarkers

ML/AI modeling of big data 

Mental Health Statistics Section