PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 10, 2026SLEEP0 citations

0555 Neighborhood Green Space and Objective Measures of Sleep: The Multi-Ethnic Study of Atherosclerosis (MESA) Sleep and MESA-Air

View Full Paper
JNJanice NorthUniversity of WashingtonMBMartha BillingsUniversity of WashingtonCSCoralynn SackWashington State Department of Health

Key Points

  • This research investigates the relationship between neighborhood greenness and the risk of obstructive sleep apnea as measured by polysomnography.
  • Repeated cross-sectional design involving geospatial data from MESA participants during Exams 5 and 7.
  • Normalized Difference Vegetation Index (NDVI) assessed within 300m, 900m, and 1200m buffers around participants' homes.
  • Generalized Estimating Equations and regression models adjusted for various covariates were applied for analysis.
  • NDVI within 900m showed no association with obstructive sleep apnea (PR = 0.98; 95% CI: 0.83, 1.16).
  • For a 300m buffer, each interquartile-range increase in NDVI correlated with a 9% lower risk of sleep apnea (PR = 0.91; 95% CI: 0.77, 1.08).
  • Higher NDVI was linked to longer durations of oxygen saturation below 90% during sleep (Coef = 1.48; 95% CI: 0.1, 2.85).

Abstract

Abstract Introduction Obstructive sleep apnea is common yet underdiagnosed, with possible environmental factors contributing to disease risk. The effect of greenspace on sleep apnea, particularly when assessed using polysomnography (PSG), remains understudied. Methods We examined associations between residential greenness, measured by the Normalized Difference Vegetation Index (NDVI), and PSG-measured sleep among participants in the Multi-Ethnic Study of Atherosclerosis. We used a repeated cross-sectional design including participants with geospatial data who participated in the Sleep Ancillary Study during Exam 5 (2010–2013) and/or Exam 7 (2022–2024). NDVI was calculated within a 900m buffer around participants’ homes; 300m and 1,200m buffers were assessed in sensitivity analyses. Primary outcome was moderate-to-severe OSA (AHI 4% ≥15 events/hour). Secondary outcomes were proportion of sleep time when arterial oxygen saturation falls below 90% (T90), nadir SpO2, and oxygen desaturation index (ODI 4%). Sensitivity analyses included AHI 3% ≥15 and ODI 3%. We applied Generalized Estimating Equations with an exchangeable correlation structure to account for repeated measures. Linear regression models were used for continuous outcomes and Poisson regression with robust standard errors for dichotomous outcomes. Models were adjusted for demographic, socioeconomic, neighborhood, behavioral, and clinical covariates. Results The final sample included 2,148 observations from 1,789 unique participants aged 54-95 years. NDVI within 900m was not associated with OSA (PR = 0.98; 95% CI: 0.83, 1.16). However, associations were suggestive at smaller buffers: each interquartile-range increase in annual NDVI within a 300m buffer corresponded to a 9% lower OSA (PR = 0.91; 95% CI: 0.77, 1.08), and for summer NDVI, a 12% lower OSA (PR = 0.88; 95% CI: 0.73, 1.06). No significant associations were observed for secondary outcomes, except for T90 within 900m (Coef = 1.48; 95% CI: 0.1, 2.85), indicating higher NDVI was linked to more time with oxygen saturation below 90% during sleep. Conclusion Although estimates include the null, findings suggest a potential inverse relationship between neighborhood greenness and OSA risk at smaller buffers and during summer months. The unexpected positive association with T90 may reflect seasonal or site-specific effect (e.g., vegetation-related allergies exacerbating OSA). Future studies should confirm these findings and clarify underlying mechanisms to inform sleep-friendly urban planning. Support (if any)

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

North et al. (2026) studied this question.

synapsesocial.com/papers/6a002222c8f74e3340f9d212https://doi.org/10.1093/sleep/zsag091.0554
Ask AI
Helpful
Bookmark
Share
View Full Paper