{"title":"研究建筑环境对睡眠中断的影响。","authors":"Jaclyn Parks, Millie Baghela, Parveen Bhatti","doi":"10.1097/EE9.0000000000000239","DOIUrl":null,"url":null,"abstract":"<p><p>Modifying aspects of the built environment may be an effective strategy for population-level improvements to sleep. However, few comprehensive evaluations of built environment and sleep have been completed.</p><p><strong>Methods: </strong>We conducted a cross-sectional study among participants of the British Columbia Generations Project (BCGP) who self-reported sleep duration (n = 28,385). Geospatial measures of light-at-night (LAN), greenness, air pollution (PM<sub>2.5</sub>, NO<sub>2</sub>, SO<sub>2</sub>), and road proximity were linked to participant baseline residential postal codes. Logistic regression models, adjusted for age and sex, were used to estimate the association between these factors and self-reported sleep duration (<7 vs. ≥7 hours).</p><p><strong>Results: </strong>Interquartile range (IQR) increases in LAN intensity, greenness, and SO<sub>2</sub> were associated with 1.04-fold increased (95% CI = 1.02, 1.07), 0.95-fold decreased (95% CI = 0.91, 0.98), and 1.07-fold increased (95% CI = 1.03, 1.11) odds, respectively, of reporting insufficient sleep (i.e., <7 hours per night). Living <100 m from a main roadway was associated with a 1.09-fold greater odds of insufficient sleep (95% CI = 1.02, 1.17). Results were unchanged when examining all factors together within a single regression model. In stratified analyses, associations with SO<sub>2</sub> were stronger among those with lower reported annual household incomes and those living in more urban areas.</p><p><strong>Conclusions: </strong>BCGP's rich data enabled a comprehensive evaluation of the built environment, revealing multiple factors as potentially modifiable determinants of sleep disruption. In addition to longitudinal evaluations, future studies should pay careful attention to the role of social disparities in sleep health.</p>","PeriodicalId":11713,"journal":{"name":"Environmental Epidemiology","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/49/cb/ee9-7-e239.PMC9916058.pdf","citationCount":"0","resultStr":"{\"title\":\"Examining the influence of built environment on sleep disruption.\",\"authors\":\"Jaclyn Parks, Millie Baghela, Parveen Bhatti\",\"doi\":\"10.1097/EE9.0000000000000239\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Modifying aspects of the built environment may be an effective strategy for population-level improvements to sleep. However, few comprehensive evaluations of built environment and sleep have been completed.</p><p><strong>Methods: </strong>We conducted a cross-sectional study among participants of the British Columbia Generations Project (BCGP) who self-reported sleep duration (n = 28,385). Geospatial measures of light-at-night (LAN), greenness, air pollution (PM<sub>2.5</sub>, NO<sub>2</sub>, SO<sub>2</sub>), and road proximity were linked to participant baseline residential postal codes. Logistic regression models, adjusted for age and sex, were used to estimate the association between these factors and self-reported sleep duration (<7 vs. ≥7 hours).</p><p><strong>Results: </strong>Interquartile range (IQR) increases in LAN intensity, greenness, and SO<sub>2</sub> were associated with 1.04-fold increased (95% CI = 1.02, 1.07), 0.95-fold decreased (95% CI = 0.91, 0.98), and 1.07-fold increased (95% CI = 1.03, 1.11) odds, respectively, of reporting insufficient sleep (i.e., <7 hours per night). Living <100 m from a main roadway was associated with a 1.09-fold greater odds of insufficient sleep (95% CI = 1.02, 1.17). Results were unchanged when examining all factors together within a single regression model. In stratified analyses, associations with SO<sub>2</sub> were stronger among those with lower reported annual household incomes and those living in more urban areas.</p><p><strong>Conclusions: </strong>BCGP's rich data enabled a comprehensive evaluation of the built environment, revealing multiple factors as potentially modifiable determinants of sleep disruption. 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引用次数: 0
摘要
改变建筑环境的各个方面可能是改善人口睡眠水平的有效策略。然而,关于建筑环境与睡眠的综合评价却很少。方法:我们对不列颠哥伦比亚省世代计划(BCGP)中自我报告睡眠时间的参与者(n = 28,385)进行了横断面研究。夜间照明(LAN)、绿化、空气污染(PM2.5、NO2、SO2)和道路邻近度的地理空间测量与参与者的基线住宅邮政编码有关。采用逻辑回归模型,对年龄和性别进行调整,以估计这些因素与自我报告的睡眠时间之间的关系(结果:LAN强度、绿化和SO2的四分位数范围(IQR)增加分别与报告睡眠不足的几率增加1.04倍(95% CI = 1.02, 1.07)、减少0.95倍(95% CI = 0.91, 0.98)和增加1.07倍(95% CI = 1.03, 1.11)相关(即,2在报告的家庭年收入较低和生活在更多城市地区的人中更强)。结论:BCGP的丰富数据能够对建筑环境进行全面评估,揭示了多种因素可能是睡眠中断的可变决定因素。除了纵向评估外,未来的研究还应注意社会差异在睡眠健康中的作用。
Examining the influence of built environment on sleep disruption.
Modifying aspects of the built environment may be an effective strategy for population-level improvements to sleep. However, few comprehensive evaluations of built environment and sleep have been completed.
Methods: We conducted a cross-sectional study among participants of the British Columbia Generations Project (BCGP) who self-reported sleep duration (n = 28,385). Geospatial measures of light-at-night (LAN), greenness, air pollution (PM2.5, NO2, SO2), and road proximity were linked to participant baseline residential postal codes. Logistic regression models, adjusted for age and sex, were used to estimate the association between these factors and self-reported sleep duration (<7 vs. ≥7 hours).
Results: Interquartile range (IQR) increases in LAN intensity, greenness, and SO2 were associated with 1.04-fold increased (95% CI = 1.02, 1.07), 0.95-fold decreased (95% CI = 0.91, 0.98), and 1.07-fold increased (95% CI = 1.03, 1.11) odds, respectively, of reporting insufficient sleep (i.e., <7 hours per night). Living <100 m from a main roadway was associated with a 1.09-fold greater odds of insufficient sleep (95% CI = 1.02, 1.17). Results were unchanged when examining all factors together within a single regression model. In stratified analyses, associations with SO2 were stronger among those with lower reported annual household incomes and those living in more urban areas.
Conclusions: BCGP's rich data enabled a comprehensive evaluation of the built environment, revealing multiple factors as potentially modifiable determinants of sleep disruption. In addition to longitudinal evaluations, future studies should pay careful attention to the role of social disparities in sleep health.