Unobtrusive Perceived Sleep Quality Monitoring in the Wild

Perceived sleep quality is a key aspect of sleep health and a crucial factor in mental health. However, predicting it accurately is difficult because of its deeply personal nature and the considerable variability in how individuals perceive their sleep at night. This study presents a robust subject-wise nested cross-validation framework for passive daily monitoring of perceived sleep quality using wearable data through population-level machine learning modeling. A total of 294 participants (mean age 42 (SD = 10) years; 43% female) were monitored over 30 days employing commercial wearable devices in free-living conditions, with daily self-reported sleep quality. A novel adaptation of the person-mean centering approach was employed to split time-varying features into within-person and between-person components, preventing temporal leakage and enabling unbiased daily prediction. Various machine learning models were trained, and SHAP values were used to identify key predictors. Our results show that fully passive prediction of perceived sleep quality is feasible at population-level from the first day of monitoring (ROC AUC 0.715, F1 0.494, BA 0.666), with within-person deviations from individual baselines being the primary predictors. The most influential predictors were found to be deviations in sleep duration and continuity, followed by cardiac, stress-related features, and SF-12 health survey components.