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

整合新颖的 GIS 和 GPS 数据，评估建筑环境对 BMI、体力活动和癌症相关生物标志物变化的影响，在两项成功的女性减肥干预措施中

• 批准号: 10378748
• 负责人: Tarik Benmarhnia
• 金额: $ 39.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-09 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10378748
• 关键词: Accelerometer; Address; Advocacy; Affect; Age; Aging; Air Pollution; Automobile Driving; Behavior; Behavioral; Big Data Methods; Biological Markers; Body Weight decreased; Body mass index; Breast Cancer Risk Factor; Breast Cancer survivor; Businesses; Cancer Center; Cancer Patient; Cancer Survivor; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Cluster Analysis; Communities; Cues; Data; Data Analyses; Elderly; Environment; Environmental Exposure; Environmental Risk Factor; Exposure to; Food; Funding; Future; Glucose; Goals; Government; Green space; Health; Health Food; Home; Home environment; Individual; Inflammation; Insulin; Intervention; Learning; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Monte Carlo Method; Movement; National Institute of Environmental Health Sciences; Neighborhoods; Obesity; Outcome; Oxidative Stress; Pathway interactions; Pattern; Physical activity; Policies; Pollution; Population; Population Group; Psychosocial Factor; Recording of previous events; Research; Resources; Risk; Sampling; Source; Standardization; Surgeon; Surveys; Techniques; Technology; Testing; Time; Transportation; Vitamin D; Walking; Woman; active lifestyle; adaptive intervention; base; behavior change; behavior influence; built environment; cancer biomarkers; cancer prevention; cancer risk; connected care; demographics; design; environmental change; food environment; frailty; hospital readmission; improved; intervention participants; lifestyle intervention; mHealth; malignant breast neoplasm; mortality; multilevel analysis; novel; personalized approach; personalized medicine; prediction algorithm; predictive modeling; prevent; programs; remote location; social media; unsupervised learning; walkability; weight loss intervention

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.

2018年，预计有266,120例新的侵入性乳腺癌病例。乳腺癌和环境 由NIEHS和NCI资助的研究计划确定了更好地了解环境的需求 暴露于告知癌症预防工作。此外，及时自适应干预措施（JITAIS）雇用 时间和空间提示以促使行为改变，但对行为的空间预测指标知之甚少 在分钟和家庭之外。在有风险的女性中，有两项成功的减肥试验 乳腺癌，在NCI资助的跨学科研究和能量学下与和谐进行 癌症中心，提供了一个独特的机会来检查改变环境暴露的健康影响 在异质样品中。一项针对老年乳腺癌幸存者的试验，另一个针对妇女的乳腺癌幸存者 由于其肥胖状况，乳腺癌风险增加的年龄范围。研究包括许多 基线和6个月的相同度量，包括生物标志物，GP和加速度计测量值， 和感知的环境调查。我们建议调查分钟目标之间的关系 GIS测量的步行性，绿色空间，污染和食物环境以及BMI的变化，体育锻炼 （PA）和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 使用客观的每日措施进行损失干预措施，而没有包括癌症风险的生物标志物。此外，不 研究采用了新的全GPS测量环境暴露，可以随着行为而改变 改变干预措施。评估建筑环境对干预结果的影响和 调查随着时间的推移暴露变化的变化将提供更多的因果证据，以告知政策议程。 大多数关于建筑环境和健康的数据都是横截面。我们需要纵向，因果证据 支持城市设计的政策变化，这将对大人口群体以及 风险，由WHO，IOM和CDC推荐。此外，我们还将使用估计的暴露变化 当前的研究旨在模拟Jitais的潜在影响，并确定决策点，决策规则和 为将来的干预措施调整变量。当前的研究将地理编码每个GPS坐标（4200万）， 使用GIS的步行性，绿地，污染和食物环境整合构建的环境数据 经过验证的集成数据分析技术，并研究环境是否影响变化 生物标志物，BMI和PA。生态模型认为，个人，人际关系和 社区层面可以影响行为和健康。这些分析将评估多层预测因子，而 调整人际关系和个体协变量。结果将被传播到现有社区 癌症，衰老和运输计划的合作伙伴为当地的倡导工作提供了信息。这项研究也将 告知未来的RCT，以控制基线的个人和环境预测指标，并通知Jitais 制定和测试分钟水平的空间，时间和行为规则。