Psychometric properties of the two-item Pittsburgh Sleep Quality Index (PSQI-2) in a cohort of community-dwelling older men

Background The full Pittsburgh Sleep Quality Index (PSQI) is a widely used measure of sleep quality, but can be impractical in large studies due to its length. The abbreviated two-item version (PSQI-2) is a promising alternative, yet its longitudinal psychometric properties remain underexplored in community-based cohorts of older adults. Objective To comprehensively evaluate the cross-sectional and longitudinal validity of the PSQI-2 against the full PSQI in a cohort of older men. Methods This longitudinal analysis utilized data from 2,911 participants in the Osteoporotic Fractures in Men (MrOS) Sleep Study with complete sleep data at two visits. Cross-sectional validity was assessed using mixed-effects regression and Bland-Altman analysis. Diagnostic accuracy for poor sleep quality (PSQI > 5, >7, and > 10) was evaluated with Receiver Operating Characteristic (ROC) curves. Longitudinal properties included test-retest reliability (Intraclass Correlation Coefficient, ICC), correlation of change scores (Δ), and the accuracy of the PSQI-2 in detecting clinically meaningful change (ΔPSQI > 3) using the area under the curve (AUC). Results The PSQI-2 showed a strong cross-sectional association with the full PSQI (β = 2.08, p < 0.001), explaining 72% of its variance. For identifying poor sleep quality, the PSQI-2 demonstrated excellent accuracy (AUC = 0.89) with an optimal cutoff of ≥ 2 (sensitivity = 77.5%, specificity = 84.5%). Longitudinally, both instruments showed moderate test-retest reliability (PSQI-2 ICC = 0.579; PSQI ICC = 0.639). The correlation between their change scores was strong (r = 0.682, p < 0.001), and the PSQI-2 showed reasonable accuracy (AUC = 0.716) in detecting clinically meaningful change (PSQI > 3). Conclusion The PSQI-2 is a valid and reliable tool for cross-sectional screening of poor sleep quality in older men at the cutoff of ≥ 2. It is also responsive to directional change over time and can identify individuals with clinically significant changes in sleep.