Abstract
Background: Urban greenness may protect against obesity, but very few studies have assessed 'street view' (SV) greenness metrics, which may better capture people's actual exposure to greenness compared to commonly-used satellite-derived metrics. We aimed to investigate these associations further in a Chinese adult study. Methods: Our analysis included 24,845 adults in the 33 Chinese Community Health Study in 2009. SV images from Tencent Map, segmented by machine learning algorithms, were used to determine the average proportion of green vegetation in SV images at community level in 800m road network buffer. Sensitivity analyses were performed with an alternative buffer size. Overall greenness was assessed as normalized difference vegetation index (NDVI) in 800 m buffer. We used predicted PM2.5 and monitored NO2 as proxies of air pollution. Body mass index (BMI), waist circumference (WC) and hip circumference (HC) were regressed on SV greenness by generalized linear mixed models, with adjustment for covariates. Mediation analyses were performed to assess the mediation effects of air pollution. Results: Each interquartile range (IQR = 3.6%) increase in street view greenness was associated with a 0.15 kg/ m2 (95% CI: -0.22, -0.09) decrease in BMI and 0.23 cm (95% CI: -0.35, -0.11) reduction in HC, and was associated with 7% lower odds of overweight (OR = 0.93, 95% CI:0.90, 0.96) and 18% lower odds of obesity (OR = 0.82, 95% CI:0.76, 0.89). Similar effect estimation was observed compared with commonly-used NDVI measures. PM2.5 and NO2 mediated 15.5% and 6.1% of the effects of SV greenness with BMI, respectively. Conclusions: Our findings suggest beneficial associations between community-level SV greenness and lower body weight in Chinese adults. The effects were observed in women but not in men. Air pollution may partially mediate the association. These findings may have implications to support efforts to promote greening in urban areas.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin > Institut und Poliklinik für Arbeits-, Sozial- und Umweltmedizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 0013-9351 |
Sprache: | Englisch |
Dokumenten ID: | 103188 |
Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:42 |
Letzte Änderungen: | 17. Sep. 2024, 10:37 |