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Biol Psychol. 2017 Feb;123:37-46. doi: 10.1016/j.biopsycho.2016.11.010. Epub 2016 Nov 24.

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

Author information

1
Department of Psychiatry and Behavioral Sciences, Stanford Center for Sleep Sciences and Medicine, Stanford University, Stanford CA 94305, USA.
2
Department of Mathematics, Stanford University, Stanford CA 94305, USA.
3
Department of Psychiatry and Behavioral Sciences, Stanford Center for Sleep Sciences and Medicine, Stanford University, Stanford CA 94305, USA; Mental Illness Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto CA 94304, USA.
4
Department of Medicine, Stanford University, Stanford CA 94305, USA.
5
Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02115, USA.
6
Departments of Psychiatry and Medicine, University of California, San Diego, San Diego CA 92093, USA.
7
California Pacific Medical Center Research Institute, San Francisco CA 94107, USA.
8
Department of Psychiatry and Behavioral Sciences, Stanford Center for Sleep Sciences and Medicine, Stanford University, Stanford CA 94305, USA; Mental Illness Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto CA 94304, USA. Electronic address: jzeitzer@stanford.edu.

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 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.

KEYWORDS:

Aging; Machine learning; Polysomnography; Sex differences; Sleep quality

PMID:
27889439
PMCID:
PMC5292065
DOI:
10.1016/j.biopsycho.2016.11.010
[Indexed for MEDLINE]
Free PMC Article

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