When a gold standard isn't so golden: Lack of prediction of subjective sleep quality from sleep polysomnography.
When a gold standard isn't so golden: Lack of prediction of subjective sleep quality from sleep polysomnography.
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DOI：
10.1016/j.biopsycho.2016.11.010
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发表时间：
2017-02
影响因子：
2.6
通讯作者：
Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures SOF Research Groups
中科院分区：
文献类型：
--
作者：
Kaplan KA;Hirshman J;Hernandez B;Stefanick ML;Hoffman AR;Redline S;Ancoli-Israel S;Stone K;Friedman L;Zeitzer JM;Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures SOF Research Groups
在研究和临床实践中经常收集主观睡眠质量的报告。然而，目前尚不清楚多导睡眠图测量睡眠与老年男性和女性前一晚睡眠质量的主观报告之间的相关性。此外，各种多导睡眠图、人口统计学和临床特征在预测主观睡眠质量方面的相对重要性尚不清楚。我们试图使用最近开发的机器学习算法来确定老年人主观睡眠质量的相关性，这些算法适用于选择和排名重要变量。社区居住的老年男性（n=1024）和女性（n=459），分别参与男性骨质疏松性骨折研究和骨质疏松性骨折研究的一个子集，完成了一个晚上的家庭多导睡眠图睡眠记录，然后是一组关于前一天晚上睡眠质量的早晨问题。在过夜记录之前还收集了关于人口统计学和心理特征的数据，并输入多变量模型。两种机器学习算法，套索惩罚回归和随机森林，分别为男性和女性确定变量选择和变量重要性排序。38个睡眠，人口统计学和睡眠质量的临床相关因素被认为是。总之，这些多变量模型仅解释了预测主观睡眠质量的11-17%的方差。在所有模型中，客观睡眠效率与主观睡眠质量的相关性最强，男女都是如此。更长的总睡眠时间和睡眠阶段转变也是主观睡眠质量的显着客观相关因素。获得的慢波睡眠量并不重要。总体而言，通常获得的多导睡眠图定义的睡眠测量值对前一晚睡眠质量的主观评级贡献不大。虽然它们解释的方差相对较小，但睡眠效率，总睡眠时间和睡眠阶段转换是最重要的客观相关因素。
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. 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. 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. 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.