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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Lippincott Williams & Wilkins
2023
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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 |
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author | Parks, Jaclyn Baghela, Millie Bhatti, Parveen |
author_facet | Parks, Jaclyn Baghela, Millie Bhatti, Parveen |
author_sort | Parks, Jaclyn |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9916058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-99160582023-02-10 Examining the influence of built environment on sleep disruption Parks, Jaclyn Baghela, Millie Bhatti, Parveen Environ Epidemiol Original Research Article 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. Lippincott Williams & Wilkins 2023-01-09 /pmc/articles/PMC9916058/ /pubmed/36777529 http://dx.doi.org/10.1097/EE9.0000000000000239 Text en Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Original Research Article Parks, Jaclyn Baghela, Millie Bhatti, Parveen Examining the influence of built environment on sleep disruption |
title | Examining the influence of built environment on sleep disruption |
title_full | Examining the influence of built environment on sleep disruption |
title_fullStr | Examining the influence of built environment on sleep disruption |
title_full_unstemmed | Examining the influence of built environment on sleep disruption |
title_short | Examining the influence of built environment on sleep disruption |
title_sort | examining the influence of built environment on sleep disruption |
topic | Original Research Article |
url | 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 |
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