Integrating novel GIS and GPS data to assess the impact of built environments on changes in BMI, physical activity and cancer-related biomarkers in two successful weight loss interventions in women at

2018年，预计将有266,120例新的浸润性乳腺癌病例。乳腺癌与环境 由NIEHS和NCI资助的研究计划确定了更好地了解环境的必要性 为癌症预防工作提供信息的暴露。此外，即时自适应干预(JITAI)采用 提示行为改变的时间和空间线索，但对行为的空间预测因素知之甚少 在微不足道的层面上，甚至超越家庭社区。两项成功的减肥试验在有风险的女性身上进行 乳腺癌，在NCI资助的跨学科研究和能量学的协调下进行 癌症中心，提供了一个独特的机会来检查变化的环境暴露对健康的影响 在一个不同种类的样本中。一项试验关注的是老年乳腺癌幸存者，另一项试验的对象是女性 由于肥胖状况，乳腺癌风险增加的年龄范围。这些研究包括许多 基线和6个月时的相同测量，包括生物标志物、全球定位系统和加速度计测量， 和感知环境调查。我们建议调查分钟水平目标与 地理信息系统测量了可步行、绿地、污染和食物环境以及体重指数、体力活动的变化。 (PA)，以及与癌症相关的生物标志物。很少有研究评估建筑环境对体重的影响。 使用客观日常测量的损失干预，并且没有包括癌症风险的生物标记物。更进一步说，不 研究采用了新的GPS测量方法来测量总环境暴露，这种测量可以随着行为的变化而变化 干预中的变化。评估建成环境对干预结果的影响以及 调查风险敞口随时间的变化将提供更多因果证据，为政策议程提供信息。 大多数关于建筑环境和健康的数据都是横截面的。我们需要纵向的、有因果关系的证据 支持城市设计中的政策变化，这些变化将对庞大的人口群体和 世界卫生组织、国际移民组织和疾控中心建议的风险。此外，我们将使用风险敞口变化的估计 目前的研究旨在模拟联合技术援助的潜在影响，并确定决策点、决策规则和 为未来的干预量身定做变量。目前的研究将对每个GPS坐标(4200万)进行地理编码， 在地理信息系统中集成关于可步行、绿地、污染和食物环境的建成环境数据 经过验证的集成数据分析技术，并调查环境是否影响 生物标记物、BMI和PA。生态模型假设因素在个人、人际和 社区层面可以影响行为和健康。这些分析将评估多层次预测因素，而 对人际和个人协变量进行调整。结果将向现有社区传播 来自癌症、老龄化和交通规划的合作伙伴，为当地的宣传努力提供信息。这项研究还将 向未来控制个人和环境预测因素的区域协调小组通报基线情况，并通过以下方式通知日本国际技术援助机构 开发和测试细微级别的空间、时间和行为规则。

In 2018, 266,120 new cases of invasive breast cancer are expected. The Breast Cancer and Environment Research Program, funded by NIEHS and NCI, identified a need to better understand environmental exposures to inform cancer prevention efforts. Further, Just in Time Adaptive Interventions (JITAIs) employ temporal and spatial cues to prompt behavior change, but little is known about spatial predictors of behaviors at the minute level and beyond home neighborhoods. Two successful weight loss trials in women at risk for breast cancer, conducted in harmony under the NCI-funded Transdisciplinary Research and Energetics in Cancer Center, offer a unique opportunity to examine the health impacts of changing environmental exposures in a heterogeneous sample. One trial focused on older breast cancer survivors, the other on women across the age range at increased breast cancer risk due to their obesity status. The studies included numerous identical measures at baseline and 6 months, including biomarkers, GPS and accelerometer measurements, and perceived environment surveys. We propose to investigate the relationship between minute level objective GIS measured walkability, greenspace, pollution and food environments and changes in BMI, physical activity (PA), and cancer related biomarkers. Few studies have assessed the impact of the built environment on weight loss interventions using objective daily measures, and none included biomarkers of cancer risk. Further, no studies have employed novel GPS measures of total environment exposure that can change as behaviors change in an intervention. Assessing the effects of built environments on intervention outcomes and investigating changes in exposure over time will provide more causal evidence to inform the policy agenda. Most data on built environment and health are cross sectional. We need longitudinal, causal evidence to support policy changes in urban design that will have lasting impact on large population groups and those at risk, recommended by the WHO, IOM and CDC. In addition, we will use estimates of exposure change from the current study to simulate the potential impact of JITAIs and to identify decision points, decision rules and tailoring variables for future interventions. The current study will geocode each GPS coordinate (42 million), integrate built environment data on walkability, greenspace, pollution and food environments in GIS using validated integrated data analysis techniques, and investigate whether the environment influences changes in biomarkers, BMI and PA. The Ecological model posits that factors at the individual, interpersonal, and community level can influence behavior and health. These analyses will assess the multi-level predictors, while adjusting for interpersonal and individual covariates. Results will be disseminated to existing community partners from cancer, aging and transportation planning to inform local advocacy efforts. This study will also inform future RCTs controlling for individual and environmental predictors at baseline and inform JITAIs by developing and testing minute level spatial, temporal and behavioral rules.