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基于 GIS 的儿童身体活动与邻里环境关联的研究:系统评价的二次分析。

Associations between Children's Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review.

机构信息

School of Nursing, The University of Auckland, Auckland 1142, New Zealand.

Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3053, Australia.

出版信息

Int J Environ Res Public Health. 2022 Jan 18;19(3):1033. doi: 10.3390/ijerph19031033.

Abstract

Regular participation in physical activity is essential for children's physical, mental, and cognitive health. Neighborhood environments may be especially important for children who are more likely to spend time in the environment proximal to home. This article provides an update of evidence for associations between children's physical activity behaviors and objectively assessed environmental characteristics derived using geographical information system (GIS)-based approaches. A systematic scoping review yielded 36 relevant articles of varying study quality. Most studies were conducted in the USA. Findings highlight the need for neighborhoods that are well connected, have higher population densities, and have a variety of destinations in the proximal neighborhood to support children's physical activity behaviors. A shorter distance to school and safe traffic environments were significant factors in supporting children's active travel behaviors. Areas for improvement in the field include the consideration of neighborhood self-selection bias, including more diverse population groups, ground-truthing GIS databases, utilising data-driven approaches to derive environmental indices, and improving the temporal alignment of GIS datasets with behavioral outcomes.

摘要

定期参加体育活动对儿童的身心健康和认知健康至关重要。对于那些更有可能在离家较近的环境中度过时间的儿童来说,邻里环境可能尤为重要。本文提供了使用基于地理信息系统(GIS)的方法评估儿童身体活动行为与客观评估的环境特征之间关联的证据更新。系统的范围审查产生了 36 篇相关的文章,质量各不相同。大多数研究在美国进行。研究结果强调需要有良好连接、人口密度较高且在邻近社区有多种目的地的社区,以支持儿童的身体活动行为。到学校的距离较短和安全的交通环境是支持儿童积极出行行为的重要因素。该领域需要改进的地方包括考虑邻里的自我选择偏差,包括更多样化的人群,对 GIS 数据库进行实地核实,利用数据驱动的方法来推导环境指数,以及改善 GIS 数据集与行为结果的时间一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46c3/8834090/ec8d55496c47/ijerph-19-01033-g001.jpg

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