背景:COVID-19大流行引发了各种遏制策略,例如在家工作政策和减少的社会接触,这极大地改变了人们的睡眠习惯。虽然以前的研究强调了这些限制对睡眠的负面影响,他们往往缺乏综合考虑其他因素的综合观点,如季节性变化和体力活动(PA),这也会影响睡眠。
目的:本研究旨在使用重复问卷和可穿戴传感器的高分辨率被动测量相结合,纵向检查COVID-19大流行期间工作成年人睡眠模式的详细变化。我们调查睡眠和5组变量之间的关联:(1)人口统计学;(2)睡眠相关习惯;(3)PA行为;和外部因素,包括(4)大流行特定的限制和(5)研究期间的季节性变化。
方法:我们在COVID-19大流行后期进行了一项为期1年的研究(2021年6月至2022年6月)。我们从参与者佩戴的健身追踪器收集了多传感器数据,以及通过每月问卷调查与工作和睡眠相关的措施。此外,我们在不同时间点使用芬兰的严格性指数来估计研究期间与大流行相关的封锁限制的程度.我们应用线性混合模型来检查大流行后期睡眠模式的变化及其与5组变量的关联。
结果:分析了112名在职成年人27,350晚的睡眠模式。更严格的大流行措施与总睡眠时间(TST)增加(β=.003,95%CI0.001-0.005;P<.001)和睡眠中期(MS)延迟(β=.02,95%CI0.02-0.03;P<.001)相关。倾向于贪睡的个体在TST(β=.15,95%CI0.05-0.27;P=.006)和MS(β=.17,95%CI0.03-0.31;P=.01)方面均表现出更大的变异性。观察到睡眠模式的职业差异,服务人员经历较长的TST(β=0.37,95%CI0.14-0.61;P=.004)和较低的TST变异性(β=-.15,95%CI-0.27至-0.05;P<.001)。当天晚些时候参与PA与更长的TST相关(β=.03,95%CI0.02-0.04;P<.001)和更小的TST变异性(β=-.01,95%CI-0.02至0.00;P=.02)。较高的静息活动节律与较短的TST相关(β=-0.26,95%CI-0.29至-0.23;P<.001),早期MS(β=-0.29,95%CI-0.33至-0.26;P<.001),TST变异性降低(β=-0.16,95%CI-0.23至-0.09;P<.001)。
结论:我们的研究提供了在大流行后期影响睡眠模式的因素的综合观点。当我们在大流行后驾驭未来的工作时,了解如何安排工作,生活方式的选择,和睡眠质量互动对于优化员工的福祉和绩效至关重要。
BACKGROUND: The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people\'s sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep.
OBJECTIVE: This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated
questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period.
METHODS: We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly
questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables.
RESULTS: The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (β=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (β=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (β=.15, 95% CI 0.05-0.27; P=.006) and MS (β=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (β=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (β=-.15, 95% CI -0.27 to -0.05; P<.001). Engaging in PA later in the day was associated with longer TST (β=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (β=-.01, 95% CI -0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (β=-.26, 95% CI -0.29 to -0.23; P<.001), earlier MS (β=-.29, 95% CI -0.33 to -0.26; P<.001), and reduced variability in TST (β=-.16, 95% CI -0.23 to -0.09; P<.001).
CONCLUSIONS: Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce.