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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 Gene...

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Detalles Bibliográficos
Autores principales: Parks, Jaclyn, Baghela, Millie, Bhatti, Parveen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9916058/
https://www.ncbi.nlm.nih.gov/pubmed/36777529
http://dx.doi.org/10.1097/EE9.0000000000000239
Descripción
Sumario: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 (PM(2.5), NO(2), SO(2)), 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 SO(2) 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(2) 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.