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。及时适应性干预（JITAIs） 时间和空间线索，以促进行为的变化，但很少有人知道的空间预测的行为 在每分钟的水平和超越家庭邻里。两项成功的女性减肥试验 乳腺癌，在国家癌症研究所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，检查不断变化的环境暴露对健康的影响 在异质样品中。一项试验集中在老年乳腺癌幸存者身上，另一项试验集中在 由于肥胖状态，这些年龄段的人患乳腺癌的风险增加。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、GPS和加速计测量， 和感知环境调查。我们建议研究分钟级目标之间的关系 地理信息系统测量了步行性、绿地、污染和食品环境以及体重指数、身体活动 (PA)和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 使用客观的日常措施进行损失干预，没有一个包括癌症风险的生物标志物。均不会及无须 研究采用了新的全球定位系统测量总的环境暴露， 改变干预。评估建筑环境对干预结果的影响， 调查暴露量随时间的变化将提供更多的因果证据，为政策议程提供信息。 关于建筑环境和健康的大多数数据是跨部门的。我们需要纵向的因果证据 支持城市设计方面的政策变化，这些政策变化将对大型人口群体和 风险，由世界卫生组织，国际移民组织和疾病预防控制中心推荐。此外，我们将使用暴露变化的估计， 目前的研究，以模拟JITAI的潜在影响，并确定决策点，决策规则和 为今后的干预措施调整变量。目前的研究将对每个GPS坐标（4200万）进行地理编码， 在GIS中集成关于步行性、绿地、污染和食品环境建筑环境数据 经过验证的综合数据分析技术，并调查环境是否影响 生物标志物、BMI和PA。生态模型假定，个人、人际和 社区水平可以影响行为和健康。这些分析将评估多水平预测因子， 调整人际和个人协变量。结果将分发给现有社区 来自癌症、老龄化和交通规划的合作伙伴为当地的宣传工作提供信息。本研究还将 通过以下方式通知未来RCT控制基线的个体和环境预测因子，并通知JITAI 开发和测试分钟级空间，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.