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. 2021 May 29;2(1):zpab008.
doi: 10.1093/sleepadvances/zpab008. eCollection 2021.

Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness

Renske Lok  1 , Jamie M Zeitzer  1   2
Affiliations

Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness

Renske Lok et al. Sleep Adv. .

Abstract

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.

Keywords: machine learning; polysomnography; sleepiness.

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Figures

Figure 1.
Figure 1.
Overview of inclusion and exclusion of participants. The complete data set was composed of 5804 individuals, of whom 5583 had completed the ESS. Out of these 5583 individuals, 2105 completed all questionnaires and measurement within the category “medical” (i.e. no missing data), as well as a polysomnography recording with quality rated as fair or higher. This analysis was further stratified by age or gender.
Figure 2.
Figure 2.
Variance of importance output of the Random Forest (A) and Lasso (B) regression of the full data set (n = 2105).
See this image and copyright information in PMC

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