城市体力活动相关建成环境评价工具信度和效度研究进展

体力活动(physical activity)水平是影响个体健康重要的、可干预的因素之一[1].个体保持适当的体力活动水平能够有效降低诸多慢性病,如心血管疾病、呼吸系统疾病等的发病风险[2,3].建成环境(built environment)指人为建设改造的各种建筑物、场所,尤其指那些可以通过政策、人为行为改变的环境[4].建成环境不仅包括公园、公共绿地,还包括人行道、交通流量、公共场所的清洁和维护、社区及社会安全感、区域划分及土地综合利用、人口密度等各个方面[5].近年来,人们的关注点逐渐集中于建成环境与个体体力活动关系的探讨[6,7].国外研究显示[8,9],良好的运动环境会促进人们更多地进行体力活动.围绕建成环境与个体及人群健康间关系的研究证据必将成为日后健康城市建设、城市规划的重要决策依据.而在此类研究中,建成环境和体力活动的评价方法及其测量工具是影响研究真实性的一个重要环节.     

杭州市城区不同特征人群对体力活动相关建成环境的主观感知评价

目的 了解杭州市城区不同特征人群的体力活动相关建成环境主观感知情况。方法 采用多阶段分层随机抽样方法在杭州市城区抽取25~59岁常住居民开展面对面问卷调查。社区步行环境量表简版(NEWS-A)用于评价居民对社区内住宅密度、场所设施多样性、公共服务可及性、街道连通性、步行道和自行车道适宜性、美观舒适度、交通安全、治安8个建成环境维度的主观感知。采用两水平logistic回归模型分析社会人口学特征和BMI等个体因素对居民建成环境主观感知的影响。结果 共纳入1 362例常住居民,性别、婚姻状况、是否有工作与建成环境主观感知各维度得分关联均无统计学意义。调整其他因素影响后,年龄45~59岁与街道连通性感知得分呈正相关(OR=2.02,95%CI:1.30~3.15)。大专及以上文化程度与人口密度感知得分存在正相关(OR=1.97,95%CI:1.29~3.00),与场所设施多样性感知得分呈负相关(OR=0.65,95%CI:0.43~0.97)。超重肥胖与步行道/自行车道(OR=0.67,95%CI:0.48~0.95)和社区治安得分(OR=0.75,95%CI:0.57~0.99)均呈负相关。相对于Ⅰ类区域,Ⅲ类区域居民对场所设施多样性(OR=0.11,95%CI:0.04~0.30)、公共服务可及性(OR=0.33,95%CI:0.11~0.95)、街道连通性(OR=0.30,95%CI:0.11~0.86)、交通安全(OR=0.39,95%CI:0.17~0.91)的主观感知得分更低。结论 杭州市城区居民对体力活动相关的建成环境主观感知与年龄、文化程度、BMI和居住区域存在相关性,开展人群体力活动的环境干预时需要考虑个体特征。     

苏州市社区建成环境与居民体力活动的关联分析

目的 了解苏州市居民体力活动水平,探索社区建成环境与居民体力活动的关联。方法 2017年采用多阶段整群随机抽样方法抽取苏州市25~64岁常住人群进行面对面调查,采用国际体力活动量表长卷（IPAQ-L）评估居民体力活动水平,采用居民环境步行量表简表（NEWS-A）评价社区建成环境主观感知。结果 苏州市居民过去1周总体力活动水平M=3 610.42 MET-min/w,以工作相关体力活动水平为主,交通、家务及休闲相关体力活动水平较低。控制社会人口学因素后,公共服务可及性与社区居民的总体力活动水平呈负相关（OR=0.522,95%CI：0.329~0.830）,场所设施多样性与工作相关体力活动水平呈负相关（OR=0.701,95%CI：0.492~0.999）,步行和自行车道设施与工作相关体力活动水平呈正相关（OR=1.603,95%CI：1.004~2.559）;交通安全与交通相关体力活动水平呈负相关（OR=0.642,95%CI：0.416~0.990）;住宅密度与休闲相关体力活动水平呈正相关（OR=1.001,95%CI：1.000~1.002）;此外,社区美观与舒适的主观感知程度越高,工作、交通、家务及总体力活动水平越高（OR=1.889,95%CI：1.176~3.033;OR=1.671,95%CI：1.120~2.495;OR=1.775,95%CI：1.143~2.756;OR=1.593,95%CI：1.079~2.350）。结论 完善步行道和自行车道设施、提高社区的美观和舒适程度对于增加居民体力活动有重要作用。     

建成环境对南京市儿童青少年体力活动影响

目的 分析不同建成环境特征对江苏省南京地区儿童青少年体力活动水平的影响,为提高儿童青少年体力活动水平提供科学依据.方法 于2018年5-6月采用现况研究设计,使用多阶段整群随机抽样方法,选择南京市12个行政区4～12年级学生(9～17岁儿童青少年)共4 401人进行问卷调查,采用国际上通用体力活动居住环境量表(PANE...     

四城市成年人主观感知建成环境与休闲性体力活动的相关性研究

目的 探讨4城市社区成年居民主观感知的建成环境与休闲性体力活动之间的相关性。方法 2017年6月至2018年7月采用多阶段整群随机抽样方法抽取杭州、苏州、成都和青岛市25～64岁成年人,使用社区环境步行适宜性量表简版和国际体力活动问卷长版评估建成环境和休闲性体力活动水平,采用广义线性混合模型分析主观感知的建成环境与休闲性体力活动的相关性。结果 共纳入有效样本3 789份。调整可能的混杂因素后,公共服务可及性（OR=1.34,95% CI：1.02～1.75）和美观度（OR=1.37,95% CI：1.09～1.73）与居民自报过去一周有休闲性体力活动的可能性呈正相关;类似地,居民进行休闲性步行的可能性与这2个维度也呈正相关。街道连通性与居民休闲性步行水平呈正相关［exp（β）=1.09,95% CI：1.00～1.19］;居住密度［exp（β）=1.000 4,95% CI：1.000 0～1.000 8］越高、体力活动场所可及性［exp（β）=1.09,95% CI：1.00～1.19］越好,美观度［exp（β）=1.11,95% CI：1.00～1.22］越好,居民休闲性体力活动水平越高,达标的可能性也越高。结论 改善社区某些建成环境维度,有望增加居民进行休闲性体力活动的可能性及相应的水平。     

城市体力活动相关建成环境客观评价工具的信度和效度研究

目的 在杭州市6个社区中研究城市体力活动相关建成环境评价工具的信度和效度.方法 在杭州市按公共建筑开发功能区选取6个社区共205个路段,2名调查员同时独立进行调查评价调查者间信度,间隔7d重复调查约一半路段(104个)评价调查者内信度.采用因子分析方法检验结构效度.结果 问卷调查者间信度Kappa值大部分在0.8以上;调查者内信度低于调查者间信度,Kappa值大部分在0.4以上.问卷表面效度良好.结构效度研究提取了6个因子,符合问卷的初始题目设计,因子负荷矩阵显示问卷结构效度良好.结论 此建成环境评价量表的信度、效度较好,适合在杭州使用.     

体力活动相关环境对健康的影响

<正>随着各城市生态环境建设与城市健康城市建设要求的提出以及《国家环境与健康行动计划(2007年—2015年)》的正式启动,在城市化高速的发展背景下,对体质健康水平和生活方式问题的研究越显示出其研究的紧迫性,城市环境与体力活动的关系成为很多国家研究的热点。然而,我国的体力活动相关环境对健康影响方面的研究至今还鲜有报道,这是环境卫生学面临的又一探索性领域。     

代谢当量应用于浙江省居民体力活动评价

代谢当量(MET)是国际广泛认可的体力活动强度评价方法,WHO全球体力活动问卷(IPAQ)亦基于MET确定体力活动分级标准。本文采用MET评价浙江省居民体力活动现状,探讨IPAQ分级标准在人群中的适用性。1．对象与方法:利用2002年浙江省居民营养与健康状     

西安市公务员体力活动现状评价及其相关因素研究

目的评价西安市公务员体力活动水平现状,并探讨其相关因素,为促进公务员健康提供科学依据。方法采用随机抽样的方法,于2012年7月-2013年7月,分别在承担政府公务员年度体检的西安市两所三级甲等医院体检中心体检的公务员中抽取1 000名。采用国际体力活动量表中文版(IPAQ-C),对其体力活动水平进行调查并评价,采用自编问卷调查研究对象的社会学特征和家族史。采用Mann-Whitney U检验及Kruskal-Wallis H检验分析调查对象社会学特征、家族史与体力活动的关系。结果体力活动总得分为2 227(1 308,3 802)MET-min/week,女性体力活动总得分低于男性(P0.05),51岁及以上老人体力活动得分低于其他年龄组(P0.05),小学和本科及其以上学历活动水平高于其他组别(P0.05),高收入人群体力活动水平高于中等收入人群(P0.05),家族史患者体力活动得分高于非家族史患者(P0.05)。结论目前西安市公务员体力活动得分整体较高,公务员的年龄、性别、文化程度、人均月收入及家族史对体力活动水平有一定影响。     

体力活动推荐量及评价标准

随着科学技术的发展，新技术、新设备的应用，机动车辆使用的增加，人们的职业劳动强度降低，久坐少动生活方式增加，体力活动水平逐渐降低。体力活动不足是心血管疾病、糖尿病、肥胖等的主要危险因素之一，因此，许多国家颁布了国家体力活动指南，评价居民体力活动水平，制定体力活动推荐量，以增加人们的体力活动，促进健康。我国还没有自己的体力活动推荐量，本文就国际上的体力活动推荐量及评价标准进行综述，为研究中国居民体力活动水平、制定体力活动推荐量及评价标准提供资料。     

Mapping the development of research on physical activity and the built environment

Objective

The importance of the built environment for physical activity has been recognized in recent decades, resulting in new research. This study aims to understand the current structure of physical activity and built environment (PABE) research and identify gaps to address as the field continues to rapidly develop.

Methods

Key PABE articles were nominated by top scholars and a snowball sample of 2764 articles was collected in 2013 using citation network links. Article abstracts were examined to determine research focus and network analysis was used to examine the evolution of scholarship.

Results

The network included 318 PABE articles. Of these, 191 were discovery-focused, examining the relationship between physical activity and built environment; 79 were reviews summarizing previous PABE work; 38 focused on theory and methods for studying PABE; six were delivery-focused, examining PABE interventions; and four addressed other topics.

Conclusions

Network composition suggested that PABE is in the discovery phase, although may be transitioning given the large number and central position of review documents that summarize existing literature. The small amount of delivery research was not well integrated into the field. PABE delivery researchers may wish to make explicit connections to the discovery literature in order to better integrate the field.     

GPS-based built environment measures associated with adult physical activity

Studies often rely on home locations to access built environment (BE) influences on physical activity (PA). We use GPS and accelerometer data collected for 288 individuals over a two-week period to examine eight GPS-derived BE characteristics and moderate-to-vigorous PA (MVPA) and light-to-moderate-vigorous PA (LMVPA). NDVI, parks, blue space, pedestrian-orientated intersections, and population density were associated with increased odds of LMVPA and MVPA, while traffic air pollution and noise were associated with decreased odds of LMVPA and MVPA. Associations varied by population density and when accounting for multiple BE measures. These findings provide further information on where individuals choose to be physically active.     

The built environment and recreational physical activity among adults in Curitiba, Brazil

Objective

To examine the relationship between objective measures of the built environment (BE) and recreational physical activity (PA) in adults from Curitiba, Brazil.

Method

A phone survey was carried among a random sample of 1206 people. Walking during leisure time (WLT) and moderate and vigorous recreational PA (MVPA) was measured using IPAQ. Characteristics of the BE were determined in an area of 500 m surrounding respondent's homes. Multivariate logistic regression analysis was used to estimate the associations between recreational PA and BE.

Results

After adjusting for confounders, WLT was associated with area income level US$971.45-3341.64 vs. US$167.05-461.06 (25.7% vs. 11.1% POR = 2.5; 95% CI = 1.5-4.4), having ≥ 2 gyms vs. none (26.1% vs. 12.7%, POR = 1.9; 95% CI = 1.2-3.0) and distance to recreation centers, 1769.1-2835.5 km vs. 2835.6-10,212.3 km (22.1% vs. 11.0%, POR = 2.3; 95% CI = (1.0-2.5). MVPA was associated with neighborhood income US$971.45-3341.64 vs. US$167.05-461.06 (47.6% vs. 22.0% POR = 3.0; 95% CI = 1.5-5.9) and having ≥ 2 gyms vs. none (41.7% vs. 26.0%, POR = 1.5; 95% CI = 1.11-2.1).

Conclusion

The presence of some recreational facilities for PA was associated with recommended levels of PA during leisure time in Curitiba, Brazil.     

Associations between the built environment and physical activity among adults with low socio-economic status in Canada: a systematic review

ObjectiveTo synthesize literature on the associations between the built environment and physical activity among adults with low socio-economic status (SES) in Canada.MethodsUsing a pre-specified study protocol (PROSPERO ID: CRD42019117894), we searched seven databases from inception to November 2018, for peer-reviewed quantitative studies that (1) included adults with low SES living in Canada and (2) estimated the association between self-reported or objectively measured built characteristics and self-reported or objectively measured physical activity. Study quality was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Findings were synthesized using a narrative approach.SynthesisOf the 8338 citations identified by our search, seven studies met the inclusion criteria. Most studies included adults living in one province (Alberta, British Columbia, Ontario, or Quebec), with one study including a national sample. All studies were cross-sectional, and none controlled for residential self-selection. Sampling designs and data collection strategies were heterogeneous. Sample sizes ranged between 78 and 37,241 participants. Most studies measured SES using household income. Street connectivity, greenness, destination density, and walkability were positively associated with physical activity. Relative to the objectively measured built environment, associations between the self-reported built environment and physical activity were less consistent. Studies were of fair to good quality.ConclusionFindings suggest that the neighbourhood built environment is associated with physical activity among adults with low SES in Canada. More rigorous study designs are needed to determine whether or not the built environment and physical activity are causally related within this vulnerable population.     

Methodologies for assessing contextual exposure to the built environment in physical activity studies: A systematic review

Growing research has integrated Global Positioning Systems (GPS), Geographic Information Systems (GIS), and accelerometry in studying effects of built environment on physical activity outcomes. This systematic review aimed to summarize current geospatial methods of assessing contextual exposure to the built environment in these studies. Based on reviewing 79 eligible articles, methods were identified and grouped into three main categories based on similarities in their approaches as follows: domain-based (67% of studies), buffer-based (22%), and activity space-based (11%). Additionally, technical barriers and potential sources of uncertainties in each category were discussed and recommendations on methodological improvements were made.     

Development and application of a moveability index to quantify possibilities for physical activity in the built environment of children

Several studies show that urban forms are environmental correlates of physical activity. Most of these studies used data based on questionnaires while only a few used geographic information systems (GIS) to objectively assess urban forms. Based on GIS data, we applied a kernel density method to measure urban forms and combined these measures to a moveability index to assess the opportunities for physical activity in the German intervention region of the IDEFICS study. In this proof-of-principal analysis, we linked the moveability index with physical activity data obtained from the baseline survey of the IDEFICS study. Regression analyses revealed a modest but significant impact of the built environment on the physical activity of 596 school children in the study region, supporting the potential application of the moveability index.     

Using machine learning to examine associations between the built environment and physical function: A feasibility study

Linking geospatial neighbourhood design characteristics to health and behavioural data from population-representative cohorts is limited by data availability and difficulty collecting information on environmental characteristics (e.g. greenery, building setbacks, dwelling structure). As an alternative, this study examined the feasibility of Generative Adversarial Networks (GANs) – machine learning – to measure neighbourhood design using ‘street view’ and aerial imagery to explore the relationship between the built environment and physical function. This study included 3102 adults aged 45 years and older clustered in 200 neighbourhoods in 2016 from the How Areas in Brisbane Influence Health and Activity (HABITAT) project in Brisbane, Australia. Exposure data were Google Street View and Google Maps images from within the 200 neighbourhoods, and outcome data were self-reported physical function using the PF-10 (a subset of the SF-36). Physical function scores were aggregated to the neighbourhood level, and the highest and lowest 20 neighbourhoods respectively were used in analysis. We found that the aerial imagery retrieved was unable to be used to adequately train the model, meaning that aerial imagery failed to produce meaningful results. Of the street view images, n = 56,330 images were downloaded and used to train the GAN model. Model outputs included augmented street view images between neighbourhoods classed as having high function and low function residents. The GAN model detected differences in neighbourhood design characteristics between neighbourhoods classed as high and low physical function at the aggregate level. Specifically, differences were identified in urban greenery (including tree heights) and dwelling structure (e.g. building height). This study provides important lessons for future work in this field, especially related to the uniqueness, diversity and amount of imagery required for successful applications of deep learning methods.