Biol Psychol. 2022 Feb 19:108290. doi: 10.1016/j.biopsycho.2022.108290. Online ahead of print.
ABSTRACT
STUDY OBJECTIVES: The measurable aspects of brain function (polysomnography, PSG) that are correlated with sleep satisfaction are poorly understood. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we examine whether PSG measures are associated with sleep satisfaction.
DESIGN AND SETTING: A single night of PSG data was compared to contemporaneously collected measures of sleep satisfaction with Random Forest regressions. Whole and partial night PSG data were scored using a novel machine learning algorithm.
PARTICIPANTS: Community-dwelling adults (N=3,165) who participated in the Sleep Heart Health Study.
INTERVENTIONS: None MEASUREMENTS AND RESULTS: Models explained 30% of sleep depth and 27% of sleep restfulness, with a similar top four predictors: minutes of N2 sleep, sleep efficiency, age, and minutes of wake after sleep onset (WASO). With increasing self-reported sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM sleep. In comparing those with the best and worst self-reported sleep satisfaction, there was a range of approximately 30minutes more N2, 30minutes less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Examination of sleep most proximal to morning awakening revealed no greater explanatory power than the whole-night data set.
CONCLUSIONS: Higher N2 and concomitant lower wake is associated with improved sleep satisfaction. Interventions that specifically target these may be suitable for improving the self-reported sleep experience.
PMID:35192907 | DOI:10.1016/j.biopsycho.2022.108290
