Longitudinal wearable monitoring and polygenic risk for incident major depressive disorder in the All of Us Research Program
This study addresses the integration of genetic susceptibility and real-world behavioral dynamics to improve risk prediction for major depressive disorder (MDD). By combining polygenic risk scores (PRS), electronic health records, and longitudinal behavioral data from Fitbit wearable devices, the authors employ time-varying Cox regression models to examine the joint and interactive effects of PRS and dynamic behavioral features—such as daily step count and sleep stability—on MDD incidence. This work presents the first real-world implementation of a joint gene–digital-phenotype modeling framework, achieving an increase in model C-index from 0.637 to 0.705. Notably, behavioral factors exhibited stronger associations with MDD risk among individuals with high PRS, offering empirical support for genetically informed, personalized prevention strategies.