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      Impacts of Individual Daily Greenspace Exposure on Health Based on Individual Activity Space and Structural Equation Modeling

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          Abstract

          Previous studies on the effects of greenspace exposure on health are largely based on static contextual units, such as residential neighborhoods, and other administrative units. They tend to ignore the spatiotemporal dynamics of individual daily greenspace exposure and the mediating effects of specific activity type (such as physical activity). Therefore, this study examines individual daily greenspace exposure while taking into account people’s daily mobility and the mediating role of physical activity between greenspace exposure and health. Specifically, using survey data collected in Guangzhou, China, and high-resolution remote sensing images, individual activity space for a weekday is delineated and used to measure participants’ daily greenspace exposure. Structural equation modeling is then applied to analyze the direct effects of individual daily greenspace exposure on health and its indirect effects through the mediating variable of physical activity. The results show that daily greenspace exposure directly influences individual health and also indirectly affects participants’ health status through physical activity. With respect to the total effects, daily greenspace exposure helps improve participants’ mental health and contributes to promoting their social health. It also helps improve participants’ physical health, although to a lesser extent. In general, the higher the daily greenspace exposure, the higher the physical activity level and the better the overall health (including physical, mental, and social health).

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          A systematic review of evidence for the added benefits to health of exposure to natural environments

          Background There is increasing interest in the potential role of the natural environment in human health and well-being. However, the evidence-base for specific and direct health or well-being benefits of activity within natural compared to more synthetic environments has not been systematically assessed. Methods We conducted a systematic review to collate and synthesise the findings of studies that compare measurements of health or well-being in natural and synthetic environments. Effect sizes of the differences between environments were calculated and meta-analysis used to synthesise data from studies measuring similar outcomes. Results Twenty-five studies met the review inclusion criteria. Most of these studies were crossover or controlled trials that investigated the effects of short-term exposure to each environment during a walk or run. This included 'natural' environments, such as public parks and green university campuses, and synthetic environments, such as indoor and outdoor built environments. The most common outcome measures were scores of different self-reported emotions. Based on these data, a meta-analysis provided some evidence of a positive benefit of a walk or run in a natural environment in comparison to a synthetic environment. There was also some support for greater attention after exposure to a natural environment but not after adjusting effect sizes for pretest differences. Meta-analysis of data on blood pressure and cortisol concentrations found less evidence of a consistent difference between environments across studies. Conclusions Overall, the studies are suggestive that natural environments may have direct and positive impacts on well-being, but support the need for investment in further research on this question to understand the general significance for public health.
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            Ecosystem services in urban areas

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              The Uncertain Geographic Context Problem

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                22 October 2018
                October 2018
                : 15
                : 10
                : 2323
                Affiliations
                [1 ]School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; zhanglin8@ 123456mail2.sysu.edu.cn (L.Z.); FeiFei17213454@ 123456163.com (F.C.); linrp3@ 123456mail2.sysu.edu.cn (R.L.)
                [2 ]Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
                [3 ]Department of Geography and Geographic Information Science, Natural History Building, 1301 W Green Street, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; mpk654@ 123456gmail.com
                [4 ]Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
                Author notes
                [* ]Correspondence: eeszsh@ 123456mail.sysu.edu.cn ; Tel.: +86-138-2504-4799
                Author information
                https://orcid.org/0000-0002-0235-7818
                https://orcid.org/0000-0001-8602-9258
                Article
                ijerph-15-02323
                10.3390/ijerph15102323
                6210249
                30360440
                62660494-3de1-4c5b-9b41-de137fa02db9
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 September 2018
                : 16 October 2018
                Categories
                Article

                Public health
                greenspace exposure,health,human mobility,physical activity,structural equation modeling,guangzhou

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