Multidimensional sleep health in a diverse, aging adult cohort: Concepts, advances, and implications for research and intervention.
Multidimensional sleep health in a diverse, aging adult cohort: Concepts, advances, and implications for research and intervention.
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DOI：
10.1016/j.sleh.2021.08.005
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发表时间：
2021-12
期刊：
影响因子：
4.1
通讯作者：
Redline S
中科院分区：
文献类型：
--
作者：
Chung J;Goodman M;Huang T;Bertisch S;Redline S
为了说明多维睡眠健康的两个框架转换：i)复合睡眠指标的使用；ii)睡眠维度之间的相互关系。735名年龄为65岁的不同背景的成年人参与了动脉粥样硬化的多种族研究。家庭多导睡眠监测(PSG)、7天手腕活动监测和有效问卷调查。Buysse Ru状态模型-睡眠规律性、满意度、警觉性、时间、效率、持续时间-被操作，然后扩展，包括PSG对睡眠结构和睡眠呼吸暂停的额外测量，从问卷调查启动睡眠的困难，以及来自活动记录仪的睡眠开始潜伏期和持续时间规律性。我们对睡眠变量进行二分法，将最优和非最优范围分别运算为‘1’和‘0’，并将其相加为睡眠健康评分，然后通过主成分分析(PCA)计算整体睡眠健康评分。参与者在睡眠时间(30分钟标准差[SD]；21.4%)和持续时间(<；60分钟标准差；36.9%)方面表现出较低的睡眠规律性。虽然62.7%的参与者通过肌动描记显示良好的睡眠时间，但很少有人达到良好的%N3(11.4%)或%REM(34.1%)的标准。平均睡眠健康得分为5.6分(13分中越高越好)。睡眠变量之间存在不同程度的相关性(r=0到r=−0.72)。每一次睡眠健康操作的第一主成分(PC)可被解释为“健康”分数；所有汇总分数都包含变量，但在每个睡眠变量中系统地向更有利的睡眠转变。多维睡眠健康可以通过互补的综合得分以及对多个个体维度的考虑来衡量。
To illustrate two frame-shifts of multidimensional sleep health: i) use of composite sleep metrics; and ii) the inter-correlations among sleep dimensions. 735 adults of diverse backgrounds aged <65 years who participated in the Multi-Ethnic Study of Atherosclerosis. In-home polysomnography (PSG), 7-day wrist actigraphy, and validated questionnaires. The Buysse Ru SATED model – sleep Regularity, Satisfaction, Alertness, Timing, Efficiency, Duration – was operationalized, then extended by including additional measures of sleep architecture and sleep apnea from PSG and difficulties initiating sleep from questionnaire and sleep onset latency and duration [ir]regularity from actigraphy. We dichotomized sleep variables, operationalizing optimal and non-optimal ranges as ‘1’ and ‘0’, respectively, summed into a Sleep Health Score, and computed global sleep health scores via Principal Components Analysis (PCA). Participants showed low prevalence of sleep regularity in timing (<30 minutes Standard deviation [sd]; 21.4% favorable) and duration (<60 minutes sd; 36.9%). Although 62.7% of participants demonstrated favorable sleep duration by actigraphy, few met criteria for favorable levels of % N3 (11.4%) or % REM (34.1%). The average Sleep Health Score was 5.6 of 13 (higher is better). Sleep variables were variably inter-correlated (r=0 to r=−0.72). The first Principal Component (PC) for each operationalization of sleep health was interpretable as a ‘health’ score; all summary scores captured variable but systematic shifts towards more favorable sleep in each sleep variable. Multidimensional sleep health can be measured by complementary composite scores as well as consideration of multiple individual dimensions.
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影响因子：
29.9
作者：
Djonlagic, Ina;Mariani, Sara;Fitzpatrick, Annette L.;Van der Klei, Veerle M. G. T. H.;Johnson, Dayna A.;Wood, Alexis C.;Seeman, Teresa;Nguyen, Ha T.;Prerau, Michael J.;Luchsinger, Jose A.;Dzierzewski, Joseph M.;Rapp, Stephen R.;Tranah, Gregory J.;Yaffe, Kristine;Burdick, Katherine E.;Stone, Katie L.;Redline, Susan;Purcell, Shaun M.
通讯作者：
Purcell, Shaun M.
影响因子：
33.9
作者：
Liu, Yong;Wheaton, Anne G.;Croft, Janet B.
通讯作者：
Croft, Janet B.
影响因子：
5
作者：
Kessler, RC
通讯作者：
Kessler, RC
影响因子：
5.6
作者：
Bertisch, Suzanne M.;Sillau, Stefan;Redline, Susan
通讯作者：
Redline, Susan
影响因子：
4.1
作者：
Hirshkowitz, Max;Whiton, Kaitlyn;Hillard, Paula J. Adams
通讯作者：
Hillard, Paula J. Adams