Sleep Estimation from Low Frequency Smartphone Sensors via Bayesian Hidden Markov Model
Byun, A. J. S., Li, Y., Cong, S., Dhima, A., Wang, S., Flathers, M., & Torous, J.
Preprint.
DOI: 10.21203/rs.3.rs-7217304/v1
Abstract
Sleep disturbances are recognized as transdiagnostic markers and potential mechanistic contributors to psychiatric illness, yet objective sleep monitoring remains rare in large-scale psychiatric research due to infrastructure and methodological barriers. While smartphones enable scalable, real-world behavioral sensing, most current approaches are limited by single-sensor thresholds, proprietary algorithms, or lack of validation in diverse populations. Here, we introduce a probabilistic Bayesian hidden Markov model that integrates accelerometer and screen state data to infer nightly sleep states and extract a set of behavioral sleep metrics. Model performance was evaluated using both empirically derived simulation and real-world self-reported sleep logs. Analyzing 5,888 nights from 516 participants, we identified substantial heterogeneity in individual sleep-symptom coupling, with unsupervised clustering of sensor-derived sleep and symptom dynamics revealing five distinct phenotypes that were consistent with independent clinical assessments. Our approach provides a robust framework for large-scale, non-invasive sleep monitoring, with direct applications in digital psychiatry and individualized intervention.