a National Institutes of Health.
b Indiana University-Purdue University Indianapolis.
Res Q Exerc Sport. 2019 Sep;90(3):395-402. doi: 10.1080/02701367.2019.1609649. Epub 2019 Jun 14.
: Most built environment studies have quantified characteristics of the areas around participants' homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. : Participants ( = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. : Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. : Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.
大多数建筑环境研究都对参与者家周围区域的特征进行了量化。然而，身体活动（PA）的环境暴露是空间动态的，而不是静态的。因此，利用合并的加速度计和全球定位系统（GPS）数据来估计成年人的建筑环境与 PA 之间的关联。
参与者（n=142）在马萨诸塞州的步道上招募，并在 1-4 天内佩戴加速度计和 GPS 设备。创建了两个二进制结果：中高强度 PA（MVPA 与低强度 PA 至久坐）；以及低强度至高强度 PA（LVPA 与久坐）。在 GPS 点周围 50 米缓冲区创建了五个建筑环境变量：人口密度、街道密度、土地利用混合（LUM）、绿化和步行指数。通过广义线性混合模型来检验环境变量与两个结果之间的关联，调整了人口统计学协变量。
总体而言，在完全调整的模型中，绿化与 MVPA 和 LVPA 呈正相关（比值比 [OR] = 1.15，95%置信区间 [CI] = 1.03，1.30 和 1.25，95%CI = 1.12，1.41）。相比之下，街道密度和 LUM 与 MVPA（OR = 0.69，95%CI = 0.67，0.71 和 0.87，95%CI = 0.78，0.97）和 LVPA（OR = 0.79，95%CI = 0.77，0.81 和 0.81，95%CI = 0.74，0.90）呈负相关。人口密度和步行能力与两个结果之间的负相关关系具有统计学意义，但效应大小较小。
使用加速度计和 GPS 单元对活动进行同步监测，使我们能够研究 GPS 点周围客观测量的建筑环境与每分钟 PA 之间的关系。街道密度和 LUM 与 PA 的负相关关系与大多数成年人的建筑环境研究中的证据形成对比。然而，由于大多数先前的研究都集中在家居位置周围的空间固定缓冲区上，而不是 PA 发生的确切位置，因此应该谨慎进行直接比较。
