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In Sleep advances : a journal of the Sleep Research Society

The Epworth Sleepiness Scale is commonly used to examine self-reported daytime sleepiness in clinical populations; the physiologic correlates of this scale, however, are not well understood. Furthermore, how well this scale correlates with parallel objective and self-reported concepts of daytime sleepiness is not well described. As such, we used machine learning algorithms to examine the association between Epworth Sleepiness Scale scores and 55 sleep and medical variables in the Sleep Heart Health Study (N = 2105). Secondary analyses examined data stratified by age and gender and the relationship between the Epworth and other measures of daytime sleepiness. Analyses of the main data set resulted in low explained variance (7.15%-10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3%-13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to Epworth scores. We find that Epworth scores are not well explained by habitual or polysomnographic sleep values, or other biomedical characteristics. These analyses indicate that there are different, potentially orthogonal dimensions of the concept of "daytime sleepiness" that may be driven by different aspects of sleep physiology. As the physiologic correlates of the Epworth Sleepiness Scale remain to be elucidated, interpretation of the clinical meaning of these scores should be done with caution.

Lok Renske, Zeitzer Jamie M

2021

machine learning, polysomnography, sleepiness