Correlates of sleep quality in midlife and beyond: a machine learning analysis

Katherine A Kaplan, Prajesh P Hardas, Susan Redline, Jamie M Zeitzer, Sleep Heart Health Study Research Group, Katherine A Kaplan, Prajesh P Hardas, Susan Redline, Jamie M Zeitzer, Sleep Heart Health Study Research Group

Abstract

Objectives: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whether quantitative electroencephalography (qEEG) is associated with sleep quality. Therefore, we examined the relationship between subjective sleep quality and objective sleep characteristics (standard PSG and qEEG) across middle to older adulthood.

Methods: Using cross-sectional analyses of 3173 community-dwelling men and women aged between 39 and 90 participating in the Sleep Heart Health Study, we examined the relationship between a morning rating of the prior night's sleep quality (sleep depth and restfulness) and polysomnographic, and qEEG descriptors of that single night of sleep, along with clinical and demographic measures. Multivariable models were constructed using two machine learning methods, namely lasso penalized regressions and random forests.

Results: Little variance was explained across models. Greater objective sleep efficiency, reduced wake after sleep onset, and fewer sleep-to-wake stage transitions were each associated with higher sleep quality; qEEG variables contributed little explanatory power. The oldest adults reported the highest sleep quality even as objective sleep deteriorated such that they would rate their sleep better, given the same level of sleep efficiency. Despite this, there were no major differences in the predictors of subjective sleep across the age span.

Conclusion: Standard metrics derived from PSG, including qEEG, contribute little to explaining subjective sleep quality in middle-aged to older adults. The objective correlates of subjective sleep quality do not appear to systematically change with age despite a change in the relationship between subjective sleep quality and objective sleep efficiency.

Keywords: Aging; Machine learning; Polysomnography; Sex differences; Sleep quality.

Conflict of interest statement

Conflict of Interest: The authors report no conflict of interest associated with this manuscript.

Published by Elsevier B.V.

Figures

Figure 1
Figure 1
Top ten predictors of sleep quality by random forest models, stratified by age quartile, for Sleep Depth (top) and Sleep Restfulness (bottom). Note: The models above include all 48 variables listed in Table 1. See text for details on each measure. Education, level of education; Fast, High-Frequency Sigma; HST, habitual sleep duration; %N1, %N2, %N3/4, percent of sleep spent in NREM stage 1, NREM stage 2, and NREM stage 3 or 4, respectively; S->W, number of transitions between sleep and wake; RDI, Respiratory Disturbance Index; REML, latency to REM sleep; SE, sleep efficiency; SF36M, Mental Component Score of the Medical Outcomes Study 36-item short-form survey; SF36P, Physical Component Score of the Medical Outcomes Study 36-item short-form survey; TST, total sleep time; Slow, slow oscillations; WASO, wake after sleep onset
Figure 2
Figure 2
Partial dependence plots of objective sleep efficiency predicting subjective sleep depth and sleep restfulness, stratified by age quartile.

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