Department of Epidemiology, University of North Carolina, 137 E. Franklin Street, Suite 306, Chapel Hill, NC, 27514, USA.
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
J Urban Health. 2017 Aug;94(4):459-469. doi: 10.1007/s11524-017-0164-z.
Increasing physical activity (PA) at the population level requires appropriately targeting intervention development. Identifying the locations in which participants with various sociodemographic, body weight, and geographic characteristics tend to engage in varying intensities of PA as well as locations these populations underutilize for PA may facilitate this process. A visual location-coding protocol was developed and implemented in Google Fusion Tables and Maps using data from participants (N = 223, age 18-85) in five states. Participants concurrently wore ActiGraph GT1M accelerometers and Qstarz BT-Q1000X GPS units for 3 weeks to identify locations of moderate-to-vigorous (MVPA) or vigorous (VPA) bouts. Cochran-Mantel-Haenzel general association tests examined usage differences by participant characteristics (sex, age, race/ethnicity, education, body mass index (BMI), and recruitment city). Homes and roads encompassed >40% of bout-based PA minutes regardless of PA intensity. Fitness facilities and schools were important for VPA (19 and 12% of bout minutes). Parks were used for 13% of MVPA bout minutes but only 4% of VPA bout minutes. Hispanics, those without a college degree, and overweight/obese participants frequently completed MVPA bouts at home. Older adults often used roads for MVPA bouts. Hispanics, those with ≤high school education, and healthy/overweight participants frequently had MVPA bouts in parks. Applying a new location-coding protocol in a diverse population showed that adult PA locations varied by PA intensity, sociodemographic characteristics, BMI, and geographic location. Although homes, roads, and parks remain important locations for demographically targeted PA interventions, observed usage patterns by participant characteristics may facilitate development of more appropriately targeted interventions.
要在人群中增加身体活动（PA），就需要有针对性地制定干预措施。确定具有不同社会人口统计学、体重和地理位置特征的参与者在何处进行不同强度的 PA，以及这些人群在何处未充分利用 PA，这可能有助于这一过程。使用来自五个州的 223 名年龄在 18-85 岁之间的参与者的数据，在 Google Fusion Tables 和 Maps 中开发并实施了一种可视化位置编码协议。参与者同时佩戴 ActiGraph GT1M 加速度计和 Qstarz BT-Q1000X GPS 装置进行 3 周，以确定中度至剧烈（MVPA）或剧烈（VPA）活动的位置。Cochran-Mantel-Haenzel 总体关联检验检查了参与者特征（性别、年龄、种族/民族、教育程度、体重指数（BMI）和招募城市）的使用差异。无论 PA 强度如何，家庭和道路都占活动时间的 40%以上。健身设施和学校对 VPA 很重要（分别占活动时间的 19%和 12%）。公园用于 13%的 MVPA 活动时间，但仅用于 4%的 VPA 活动时间。西班牙裔、没有大学学历和超重/肥胖参与者经常在家中完成 MVPA 活动。老年人经常在道路上进行 MVPA 活动。西班牙裔、受教育程度≤高中的人和健康/超重的参与者经常在公园进行 MVPA 活动。在多样化的人群中应用新的位置编码协议表明，成年人的 PA 位置因 PA 强度、社会人口统计学特征、BMI 和地理位置而异。尽管家庭、道路和公园仍然是有针对性的 PA 干预的重要场所，但根据参与者特征观察到的使用模式可能有助于制定更有针对性的干预措施。
