Antonakos Cathy, Baiers Ross, Dubowitz Tamara, Clarke Philippa, Colabianchi Natalie
Environment and Policy Lab, School of Kinesiology, University of Michigan, Ann Arbor, MI, USA.
RAND Corporation, Pittsburgh, PA, USA.
J Transp Health. 2020 Jun;17:100867. doi: 10.1016/j.jth.2020.100867. Epub 2020 May 3.
The built environment has been shown to influence health in studies of disadvantaged populations using different measurement methods. This study determined whether environmental exposures derived from GigaPan® images could serve as valid predictors of body mass index (BMI), walking and moderate to vigorous physical activity (MVPA) in a longitudinal study of low-income adults living in two primarily African American neighborhoods in Pittsburgh, Pennsylvania, USA. GigaPan® is a robotic system used to obtain high-resolution, panoramic images of environments.
Microscale environmental features along 481 streets were audited in 2015-2016 using an audit form. Environmental exposures were estimated for 731 adult participants, using a sample of street segments within a 0.4 km (0.25 mile) network distance from each participant's residential address. Summary environmental exposures were constructed using factor analysis. We tested associations between participant-level environmental exposures and objectively measured BMI, self-reported walking and objectively measured MVPA in regression models controlling for baseline health and demographic variables.
Three factors representing participants' environmental exposures were constructed: pedestrian bicycle-amenities; hilly-vacant-boarded; physical activity-recreation/low housing density. Environments with infrastructure and amenities supportive of walking and bicycling were associated with lower BMI (Coef. = ‒0.47, p = 0.02). Frequent walking was less likely in environments with more physical activity and recreation venues/low housing density (OR = 0.81, 95% CI [0.67, 0.96]). MVPA was not associated with any of the environmental measures and the hilly-vacant-boarded factor was not associated with any of the outcomes.
Predictive validity was demonstrated for an environmental exposure factor that captured features supportive of walking and cycling in a model predicting BMI, using built environment audit data from GigaPan® imagery. A complementary analysis found lower odds of frequent walking in the neighborhood among participants with exposure to more physical activity and recreational features, but fewer types and lower density of housing.
在使用不同测量方法对弱势群体进行的研究中，已表明建筑环境会影响健康。本研究确定了在一项针对居住在美国宾夕法尼亚州匹兹堡两个主要为非裔美国人社区的低收入成年人的纵向研究中，源自GigaPan®图像的环境暴露是否可作为体重指数（BMI）、步行以及中度至剧烈身体活动（MVPA）的有效预测指标。GigaPan®是一种用于获取环境的高分辨率全景图像的机器人系统。
2015年至2016年期间，使用一份审核表对481条街道沿线的微观环境特征进行了审核。利用距离每位参与者居住地址0.4公里（0.25英里）网络距离内的街道段样本，对731名成年参与者的环境暴露进行了估计。使用因子分析构建了综合环境暴露指标。在控制基线健康和人口统计学变量的回归模型中，我们测试了参与者层面的环境暴露与客观测量的BMI、自我报告的步行情况以及客观测量的MVPA之间的关联。
构建了代表参与者环境暴露的三个因子：行人自行车便利设施；多山、空置、有围挡；体育活动、娱乐/低住房密度。拥有支持步行和骑自行车的基础设施及便利设施的环境与较低的BMI相关（系数= -0.47，p = 0.02）。在体育活动和娱乐场所较多/住房密度较低的环境中，经常步行的可能性较小（比值比= 0.81，95%置信区间[0.67, 0.96]）。MVPA与任何环境指标均无关联，且多山、空置、有围挡因子与任何结果均无关联。
利用来自GigaPan®图像的建筑环境审核数据，在一个预测BMI的模型中，一个捕捉支持步行和骑行特征的环境暴露因子显示出预测效度。一项补充分析发现，暴露于更多体育活动和娱乐特征但住房类型较少且密度较低的参与者，在邻里中经常步行的几率较低。
