Subjective sleep quality predict clinical pain severity more strongly than polysomnographic parameters: Machine learning findings from a cross-sectional study.

Poor sleep quality amplifies pain experience. However, it remains uncertain regarding whether subjectively or physiologically assessed sleep parameters better predict pain, given reported discrepancies between the subjective and physiological measures of sleep quality. This secondary analysis examined data from 556 adults in the Cleveland Family Study. Clinical pain severity was assessed using the two-item SF-36 pain subscale. Subjective sleep quality was measured by the Functional Outcomes of Sleep Questionnaire, and physiological sleep quality was assessed via polysomnography. To examine complex sleep-pain relationships, machine learning (ML) models were utilized. The dataset was split into training (70 %) and testing (30 %) subsets. After feature selection, Gradient Boosted Regression (GBR) was applied to develop a predictive model, validated by Random Forest (RF) and Elastic Net Regression (ENR). Results indicated similar prediction accuracy and feature importance across GBR, RF, and ENR. Sociodemographic, clinical, and anthropometric factors explained 32 % of the variance in clinical pain using GBR; inclusion of sleep profiles increased explained variance to 35 %. Analysis of the feature importance revealed subjective experiences of post-sleep activity and vigilance as the strongest predictors of pain severity, rather than physiologically measured sleep quality. We further confirm this finding using K-means clustering analysis. These findings highlight the significant role of subjective sleep quality compared to physiological sleep measures in influencing clinical pain severity. They also suggest the potential value of further investigating whether interventions aimed at improving perceived sleep quality may more effectively improve pain outcomes.