Detecting psychometric and diagnostic performance of the RU_SATED v2.0 multidimensional sleep health scale in community-dwelling adults combining exploratory graph analysis and ROC analysis.

Abstract

Objective: The RU_SATED scale is increasingly used across the globe to measure sleep health. However, there is a lack of consensus around its psychometric and diagnostic performance. We conducted an empirical investigation into the psychometrics of the Chinese version of the RU_SATED (RU_SATED-C) scale, with a focus on structural validity and diagnostic performance.

Methods: 1171 adults were enrolled from three communities in Hangzhou, China in July 2022. The dataset was spilt in half, and we ran a bootstrapped exploratory graph analysis (bootEGA) in one half and a confirmatory factor analysis (CFA) in the other half to assess structural validity. Correlations with insomnia, wellness, anxiety, and depression symptoms were examined in order to assess concurrent validity; and Cronbach's α and McDonald's ω were calculated to assess internal consistency. Additionally, a Receiver Operating Characteristic (ROC) analysis established and externally validated the optimal score for identifying insomnia symptoms.

Results: A one-dimensional structure, as identified by bootEGA, was corroborated in the CFA [comparative fit index = 0.934, root mean square error of approximation = 0.088, standardized root mean square residual = 0.051]. A moderate correlation was shown with insomnia symptoms, while weak correlations were observed with wellness, anxiety, and depression symptoms. The RU_SATED-C scale displayed sub-optimal internal consistency where coefficients dropped if any item was removed. A recommended cutoff score of ≤13 was derived for probable insomnia with a satisfactory diagnostic performance.

Conclusion: The RU_SATED-C scale displayed a one-dimensional model, along with adequate concurrent validity, internal consistency, and diagnostic performance. Further work necessitates multi-scenario testing and additional validation using objective sleep assessments.