Zhang, Jinguang and Liu, Yuhong and Zhou, Shuai and Cheng, Yingyi and Zhao, Bing (2022) Do various dimensions of exposure metrics affect biopsychosocial pathways linking green spaces to mental health? A cross-sectional study in Nanjing, China. Landscape and Urban Planning, 226. ISSN 0169-2046, DOI https://doi.org/10.1016/j.landurbplan.2022.104494.
Full text not available from this repository.Abstract
The theoretically indicated pathways linking green space (GS) exposure to mental well-being have been consistently examined. However, few studies have deciphered the association between multiple GS exposure metrics and biopsychosocial pathways. Here, we aimed to develop a systematic exposure framework addressing different aspects of GS, including one subjective metric (self-reported GS usage) and four objective metrics, namely, quantity-based GS availability, distance-based GS accessibility, quality-based GS attractiveness, and streetscape-based GS visibility. We then explored whether, or to what extent, these various exposure metrics affect the hypothesized paths: physical activity, social cohesion, stress, and environmental stressors (i.e., air quality and noise). A cross-sectional study was conducted using a population sample (n = 1984) from Nanjing, China. Spearman rank correlation was calculated to examine the relationship between various exposure metrics. A multivariate linear regression model was used to assess the association between GS exposure and self-reported mental well-being (i.e., WHO-5 scores). Structural equation modelling (SEM) techniques were further conducted to examine the parallel/serial mediation effects of GS exposure and mental well-being. The results showed the following: (1) multiple exposure metrics were only moderately to weakly correlated, all of which were significantly associated with respondents' self-rated mental well-being, mediated through one or more of the hypothesized pathways; (2) different exposure metrics significantly affected these mediating variables; structured GS exposure metrics were more associated with physical activity, while unstructured GS exposure metrics were more related to environmental stressors and mental stress; (3) in the parallel mediation model, stress is a relatively important mediator linking GS to mental well-being; the serial mediation model further indicated that physical activity, environmental stressors, and social cohesion worked as antecedents to reduce stress, which, in turn, was associated with better mental well-being. Our findings could help epidemiologists clarify the pathways and mechanisms by which GSs affect mental well-being, as well as assist urban planners in implementing health-based GS interventions.
| Item Type: | Article |
|---|---|
| Funders: | Jiangsu Provinical Department of Science and Technology [SBK2022041803] |
| Uncontrolled Keywords: | Urban nature; Public health; Exposure; Mediation analysis; Deep learning |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences R Medicine T Technology > TE Highway engineering. Roads and pavements |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Sep 2023 07:38 |
| Last Modified: | 22 Sep 2023 07:38 |
| URI: | http://eprints.um.edu.my/id/eprint/41443 |
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