Multidimensional sleep health approach to evaluate the risk of morbidity and mortality in diverse adult populations.
Multidimensional sleep health approach to evaluate the risk of morbidity and mortality in diverse adult populations.
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多维睡眠健康方法评估不同成年人群的发病和死亡风险。
DOI：
10.1093/sleep/zsad075
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发表时间：
2023
期刊：
影响因子：
5.6
通讯作者：
Kaufmann,ChristopherN
中科院分区：
文献类型：
--
作者：
Lee,Soomi;Kaufmann,ChristopherN
“睡眠健康”被定义为基于多个睡眠维度的积极属性。由Buysee [1]提出，该概念确定了对成人健康和功能重要的六个特定睡眠维度：（1）规律性，（2）满意度，（3）警觉性，（4）时间，（5）效率和（6）持续时间（“Ru-SATED”）。睡眠研究领域的一个范式转变，这一概念促使人们意识到，一个人的睡眠健康需要从多个维度来理解，而不是从个人的睡眠特征（如睡眠持续时间）来理解。在这个问题上，Chung et al. [2]利用这种最先进的整体方法，研究更好的睡眠健康是否会降低死亡风险。自其概念以来，睡眠健康已经在各种研究中以各种方式运作。例如，Buxton et al. [3]研究了中年工人的多种睡眠参数及其与10年估计心脏代谢风险评分（CRS）的相关性。他们发现，更多的睡眠呼吸暂停症状与CRS的风险较高有关。这种关联被一些不良睡眠健康特征的存在所改变，例如，在睡眠呼吸暂停症状较多且睡眠持续时间短或小睡较多（因此警觉性较低）的人群中，CRS的风险增加。虽然这项研究证明了检查多个睡眠变量及其如何影响健康的重要性，但作者并没有充分考虑一个人的整体睡眠健康。其他研究人员已经采取了一种综合方法来表征睡眠健康，方法是计算最佳或次佳睡眠健康二元指标的综合得分[4-9]。最后，另一种方法使用以人为中心的方法来识别特定研究样本或队列中睡眠健康特征组合的潜在组[10-13]。例如，具有相似睡眠健康特征的参与者被分配到同一组。后两种方法具有许多优势，例如能够检查个体内出现的“多少”或“哪种类型”的睡眠健康问题。然而，也有一些方法问题需要进一步审议。值得注意的是，睡眠健康可能会在不同人群中具有不同的含义-例如，社会人口特征和地理区域[14-17]。Chung等人的研究[2]通过使用来自动脉粥样硬化队列多种族研究的数据部分解决了这一问题，该队列包括中年和老年人的种族和种族多样性样本。在这个多样化的样本中，他们发现睡眠健康评分高1个标准差与死亡风险低25%相关。他们还发现，睡眠规律性更强，睡眠时间更长，睡眠呼吸暂停不太严重是这种联系的主要驱动因素。虽然这些结果来自不同的样本，但对于未来的研究来说，重要的是要检查观察到的关联是否在种族和族裔群体之间存在差异。值得注意的是，睡眠健康差异是有据可查的;例如，少数群体的睡眠质量较差，如自我报告和客观睡眠评估所示[15，18，19]。检查睡眠健康的程度，作为一个整体，不同群体之间的差异可能使我们能够进一步完善睡眠健康作为一个可推广的结构。此外，Chung等人的研究[2]基于自我报告和客观（即腕动描记术和多导睡眠描记术）睡眠测量创新性地表征了睡眠健康。重要的是，自我报告的睡眠测量并不能显著预测死亡率。这可能意味着将客观的睡眠测量纳入睡眠健康...
“Sleep health” is defined as positive attributes based on a number of sleep dimensions. Proposed by Buysee [1], the concept identified six specific sleep dimensions that are important for adult health and functioning:(1) regularity,(2) satisfaction,(3) alertness,(4) timing,(5) efficiency, and (6) duration (“Ru-SATED”). A paradigm shift for the sleep research field, the concept prompted awareness that one’s sleep health needs to be understood from multiple dimensions rather than individual sleep characteristics (eg sleep duration only). In this issue, Chung et al.[2] capitalizes upon this state-of-the-art, holistic approach, to examine whether better sleep health reduces mortality risk. Since its conception, sleep health has been operationalized in a variety of ways across studies. For example, Buxton et al.[3] examined multiple sleep parameters and their associations with 10-year estimated cardiometabolic risk scores (CRS) in midlife workers. They found that more sleep apnea symptoms were associated with a higher risk of CRS. This association was modified by presence of a number of poor sleep health characteristics such that risk for CRS was increased among those with more sleep apnea symptoms combined with either short sleep duration or more naps (thus lower alertness). While this study demonstrated the importance of examining multiple sleep variables and how they impact health, the authors did not fully consider one’s sleep health as a whole. Other investigators have taken a comprehensive approach to characterize sleep health by computing a composite score summing across binary indicators of optimal or suboptimal sleep health [4–9]. Finally, another approach uses person-centered methodology to identify latent groups of combinations of sleep health characteristics in the specific study sample or cohorts [10–13]. For example, participants with similar sleep health characteristics are assigned to the same group. These latter two approaches have many strengths, such as the ability to examine “how many” or “which type” of sleep health issues arise within an individual. Yet, there are also some methodological issues that require further consideration. It is important to note that sleep health may take on different meanings across populations—for example, by sociodemographic characteristics and geographic regions [14–17]. The Chung et al. study [2] partly addresses this issue by using data from the Multi-Eethnic Study of Atherosclerosis cohort which includes a racially and ethnically diverse sample of middle-aged and older adults. In this diverse sample, they found that a 1-standard deviation higher sleep health score was associated with 25% lower mortality risk. They also found that greater sleep regularity, longer sleep time, and less severe sleep apnea were main drivers of this association. While these results come from a diverse sample, it will be important for future research to examine whether observed associations differ across racial and ethnic groups. Of note, there are well-documented sleep health disparities; for example, minoritized populations have poorer sleep quality as seen in self-reports and objective sleep assessments [15, 18, 19]. Examining the extent to which sleep health, as a whole, differs across diverse groups may enable us to further refine sleep health as a generalizable construct. Furthermore, the Chung et al. study [2] innovatively characterized sleep health based on both self-reported and objective (ie wrist actigraphy and polysomnography) sleep measures. Importantly, self-reported sleep measures did not significantly predict mortality. This may suggest incorporating objective sleep measurements into the sleep health …
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影响因子：
4.1
作者：
Chen,Tuo-Yu;Lee,Soomi;Buxton,OrfeuM
通讯作者：
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影响因子：
5.6
作者：
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影响因子：
4.8
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影响因子：
4.4
作者：
Fietze, Ingo;Laharnar, Naima;Penzel, Thomas
通讯作者：
Penzel, Thomas
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影响因子：
5.6
作者：
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