Passive Digital Phenotyping for Identifying Dynamic Symptom Patterns: Toward Precision Monitoring in Psychiatry

Abstract:Background:Mental health symptoms fluctuate over short timeframes, yet clinical assessment typically relies on infrequent self-report, limiting the ability to capture dynamic symptom patterns. Passive digital phenotyping enables continuous, low-burden monitoring of behavioral signals associated with mental health, but the clinical meaning of short-term passive symptom trajectories remains poorly understood. Precision psychiatry calls for approaches that enable individualized characterization of symptom trajectories to inform stratified and responsive treatment.Methods:Anonymized passive digital phenotyping data from 13,098 adults were analyzed using a smartphone application that generated daily Mental Health Similarity Scores (MHSS) for depression, anxiety, stress, and attention-deficit/hyperactivity disorder (ADHD) across seven consecutive days, approximating the interval between routine weekly clinical assessments, using validated machine-learning models anchored to standard clinical questionnaires. Latent class growth analyses identified short-term trajectories, and bivariate models examined depression-anxiety co-fluctuation and stratification by depression burden.Results:Depression and anxiety demonstrated heterogeneous short-term trajectories, including stable low, stable high and fluctuating patterns, suggesting distinct short-term symptom dynamics. In contrast, stress and ADHD were largely stable over time. Bivariate analyses identified four synchronized depression-anxiety trajectory classes, ranging from stable low to fluctuating co-occurring profiles. Higher depression was associated with persistently elevated and less variable anxiety patterns.Conclusions:Passive digital phenotyping offers a scalable, low-burden approach for continuous symptom monitoring, enabling identification of dynamic risk and resilience profiles at the individual level. Trajectory-based digital phenotypes may support precision psychiatry by informing patient stratification, early detection of symptom change, and more adaptive and personalized approaches to monitoring and intervention.Key Words: Passive digital phenotyping; Mental health; Precision Psychiatry