Temporal dynamics of subjective sleepiness: A convergence analysis of two scales

TL;DR

This study compares two subjective sleepiness scales, SSS and KSS, analyzing their convergence and dynamics in adults, revealing their correlation, differences in sensitivity, and distribution patterns across sleep-wake cycles.

Contribution

It introduces the SSDD dataset and provides a detailed analysis of the convergence and features of SSS and KSS in sleepiness assessment.

Findings

Sleepiness increased from evening to morning.

SSS is more sensitive to sleep disorders.

SSS and KSS strongly correlate and converge.

Abstract

While sleepiness assessment metrics were initially developed in medical research to study the effects of drugs on sleep, subjective sleepiness assessment is now widely used in both fundamental and applied studies. The Stanford Sleepiness Scale (SSS) and the Karolinska Sleepiness Scale (KSS) are often considered the gold standard in sleepiness research. However, only a few studies have applied both scales, and their convergence and specific features have not been sufficiently investigated. The present study aims to analyse the dynamics and convergence of subjective sleepiness as measured by the KSS and SSS in a population of adults. To achieve this, we present the Subjective Sleepiness Dynamics Dataset (SSDD), which collects evening and morning data on situational subjective sleepiness. A total of 208 adults participated in the experiment. Our findings suggest that sleepiness generally increased from the evening till night and was highest early in the morning. The SSS score appeared to be more sensitive to certain factors, such as the presence of a sleep disorder. The SSS and KSS scores strongly correlated with each other and converged on sleepiness assessment. However, the KSS showed a more even distribution of scores than the SSS. Currently, we are continuously expanding the SSDD.