Heart rate circadian phase and hyperarousal as wearable digital phenotyping of insomnia: An interpretable machine...

This study evaluates ML approaches for insomnia classification using physiological and behavioral data from wearable devices. SHAP analysis identifies key predictors, highlighting the relationship between sleep disturbances and digital phenotypes and emphasizing clinical plausibility as a criterion for model selection.

Three hundred thirty-eight participants (249 with insomnia and 89 controls) aged 19-70 years were instructed to wear Fitbit Inspire 3 devices for 4 weeks to record heart rate, activity, and sleep metrics. Insomnia classification was based on Insomnia Severity Index scores (≥8 insomnia and ≤7 controls). Filter- and wrapper-based feature-selection methods were applied to the 120 extracted features. Multiple ML algorithms were evaluated using five-fold cross-validation, with the clinical plausibility of the feature relationships explicitly considered in the final model selection.

LightGBM model trained on 60 ANOVA-selected features achieved the highest performance (F1 score = 0.868 ± 0.027). The key predictive features identified by SHAP analysis included delayed acrophase of the heart rate cosinor rhythm, higher self-reported stress and maximum heart rates that aligned with sleep-wake physiology. However, several features exhibited patterns that contradicted previously known clinical expectations, highlighting the disconnection between statistical optimization and clinical utility.

Machine learning models trained on wearable data can effectively classify insomnia. SHAP analysis suggested that altered activity patterns reflect sleep disturbance, while also highlighting the necessity for further clinical validation. Clinical plausibility must be integrated as a fundamental criterion in model development, to ensure clinically trustworthy ML applications in sleep medicine.