When a gold standard isn’t so golden: Lack of prediction of subjective sleep quality from sleep polysomnography

Abstract

Background Reports of subjective sleep quality are frequently collected in research and clinical practice. It is unclear, however, how well polysomnographic measures of sleep correlate with subjective reports of prior-night sleep quality in elderly men and women. Furthermore, the relative importance of various polysomnographic, demographic and clinical characteristics in predicting subjective sleep quality is not known. We sought to determine the correlates of subjective sleep quality in in older adults using more recently developed machine learning algorithms that are suitable for selecting and ranking important variables. Methods Community-dwelling older men (n=1024) and women (n=459), a subset of those participating in the Osteoporotic Fractures in Men study and the Study of Osteoporotic Fractures study, respectively, completed a single night of at-home polysomnographic recording of sleep followed by a set of morning questions concerning the prior night's sleep quality. Questionnaires concerning demographics and psychological characteristics were also collected prior to the overnight recording and entered into multivariable models. Two machine learning algorithms, lasso penalized regression and random forests, determined variable selection and the ordering of variable importance separately for men and women. Results Thirty-eight sleep, demographic and clinical correlates of sleep quality were considered. Together, these multivariable models explained only 11-17% of the variance in predicting subjective sleep quality. Objective sleep efficiency emerged as the strongest correlate of subjective sleep quality across all models, and across both sexes. Greater total sleep time and sleep stage transitions were also significant objective correlates of subjective sleep quality. The amount of slow wave sleep obtained was not determined to be important. Conclusions Overall, the commonly obtained measures of polysomnographically-defined sleep contributed little to subjective ratings of prior-night sleep quality. Though they explained relatively little of the variance, sleep efficiency, total sleep time and sleep stage transitions were among the most important objective correlates.

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

“…Furthermore, including qEEG correlates does not help capture the subjective phenomenology we are recording. These findings are consistent with our previous paper [8], though the previous data were limited to older adults (ages 73 and above); our current data span the range of 39–90 years of age. While it is possible that stronger associations exist between qEEG, PSG-derived variables and subjective sleep quality for adults younger than 39 years of age, our data indicate that these variables do not adequately capture several features of subjective sleep quality in midlife and beyond.…”

“…In a small sample using a longitudinal design, Akerstedt and colleagues found sleep continuity to be most important to ratings of subjective sleep quality [27]. In our recent investigation of sleep quality in a very large cohort of older adults, we similarly showed sleep efficiency to be most important to ratings of sleep quality [8]. However we should note that, while we found a strong sex effect in our older adult sample, sex was not determined to be an important predictor of subjective sleep quality in this midlife sample.…”

“…Instead, we used two machine learning methods built for multivariable analyses and designed to minimize bias and error: lasso penalized regressions [24] and random forests [25]. A fuller description of both methods as applied to sleep data can be found in Ref [8]. R packages “glmnet” and “rfsrc” were used to run lasso and random forest analyses, respectively.…”

“…Using novel machine learning algorithms, we have recently shown in older adults that traditional PSG metrics, demographics, and clinical characteristics do not predict subjective reports of sleep quality well [8]. Quantitative EEG (qEEG) characteristics have been postulated as promising [9] in providing more informative indices that may explain subjective sleep quality reports, but to date have not been systematically examined.…”

Do Older Adults Need Sleep? A Review of Neuroimaging, Sleep, and Aging Studies

“…Surprising, such associations may not observed in older adults [12]. One potential resolution is that sleep macro architecture (e.g., sleep stages) is not as sensitive to biological age as is sleep micro architecture (e.g., sleep spindles)[13].…”