0218 N2 and wakefulness drive subjective sleep satisfaction in adults. (25th May 2022) - Lok, Renske; Chawra, Dwijen; Hon, Flora; Ha, Michelle; Kaplan, Kate; Zeitzer, Jamie
Abstract: Introduction: The measurable aspects of brain function derivable from polysomnography (PSG) that are correlated with sleep satisfaction are poorly understood. Previously, a weak association of PSG measures with subjectively rated sleep depth and restfulness was shown. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we revisit whether whole night PSG measurements are associated with sleep satisfaction. Additionally, we investigate if PSG data collected closer to wake time explains the subjective sleep experience better than whole night PSG. Methods: Random Forest machine learning was used to investigate the relationship between PSG data from the Sleep Heart Health Study (N=3, 165, middle-aged and older adults) and self-reported sleep satisfaction (restfulness, depth). PSG were rescored using a novel automated algorithm that generates both a sleep stage for each 15-s epoch as well as a stage matching probability. Data were also parsed into 20 minute-fragments based on time relative to wake. Results: Models explained 30% of subjective sleep depth and 27% of subjective sleep restfulness, with a similar top four predictors: minutes of N2 and wake after sleep onset (WASO), sleep efficiency, and age, capturing 28% (restfulness) and 26% (depth) of the relative model variance. With increasing subjective sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM. In comparing those with the best and worst subjective experience of sleep, there is a range of approximately 30 minutes more N2, 30 minutes less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Random Forest models derived from PSG fragments closer to the offset of sleep did not provide better explanatory power compared to the whole-night data set. Conclusion: Approximately one-third of the variance in two measures of self-reported sleep experience can be explained by whole-night PSG variables, notably an increase in N2 and decrease in wake that led to improved sleep efficiency. Interventions that specifically target these may be suitable for improving the self-reported sleep experience. Support (If Any):