(PQA4) GPS exposure to environments & relations with biomarkers of cancer risk

描述（由申请人提供）：缺乏身体活动、不健康饮食和肥胖与乳腺癌和结肠癌直接相关，并通过胰岛素和炎症途径与乳腺癌和结肠癌相关。居民区更容易获得健康的环境与更高的体力活动 (PA)、更健康的饮食、更低的 BMI 以及一项研究中更低的胰岛素抵抗有关。尽管有一些重要的发现，但建筑环境研究的影响大小可能很小，因为评估的环境（仅限家庭社区）与行为发生的位置之间不匹配。几天之内和不同天内的行为和位置的时间变化也被忽略了。我们建议通过动态集成全球定位系统（GPS）数据与地理信息系统（GIS）数据来测量社区对健康环境的访问和总环境暴露，从而改进癌症风险暴露评估的方法。我们假设基于动态 GPS 的环境暴露测量与基于静态寻址的 GIS 访问测量相比，与行为、胰岛素和炎症生物标志物的相关性更强。我们将研究一个大样本的成年人（N=700），年龄在40-75岁，在他们的住所住了至少一年。我们将从根据收入、步行能力和食物环境专门选择的人口普查区块中招募参与者，以确保随机样本中不会出现环境变化。我们将确保不同种族、性别、年龄和季节的人口普查街区类型（步行适宜性和饮食环境）的均衡招聘。一半的样本将是西班牙裔，以探索不同种族之间潜在的相互作用影响。参与者将完成有关他们的 PA、久坐行为、环境感知、自我选择、癌症风险和人口统计数据的调查。 PA 和久坐行为也将通过加速度测量进行评估，并且机器学习技术将用于客观地识别可能与建筑环境相关的特定行为，例如步行、骑自行车、乘车、看屏幕时间等。参与者将完成 ASA 24 来评估饮食、总热量和脂肪热量。子样本 (N=50) 将佩戴 SenseCam 来评估社会背景、验证 GIS 构建环境措施并验证机器学习类别。动态 GIS 暴露测量将根据 7 天的人员佩戴 GPS 数据创建，该数据与支持性 PA 和食品环境（例如公园、步行街道、新鲜农产品市场等）的 GIS 指标相匹配，并按时间、速度、交通方式和环境特征（例如）进行加权。包裹大小。将在参与者家周围 1 公里的街道网络缓冲区内创建访问社区资源的静态住宅 GIS 缓冲区。使用针对聚类进行调整的多级统计模型，我们将调查基于 GPS 的动态 GIS 测量健康食品暴露和 PA 支持环境是否比静态 GIS 测量邻里资源获取与乳腺癌和结肠癌风险因素（包括行为（PA、久坐行为和饮食）、BMI 以及胰岛素抵抗和炎症的生物标志物（例如 CRP、IL-6、HOMA-IR））更密切相关。

DESCRIPTION (provided by applicant): Physical inactivity, an unhealthy diet, and obesity are related to breast and colon cancer directly and through insulin and inflammation pathways. Greater access to healthy environments in residential neighborhoods is associated with higher physical activity (PA), a healthier diet, lower BMI, and in one study lower insulin resistance. Despite some significant findings, the effect sizes in built environment research have been small likely because of a mismatch between the environment assessed (home neighborhoods only) and location where the behaviors occur. Temporal variation in behaviors and locations within and across days has also been ignored. We propose to advance methods of cancer risk exposure assessment by measuring both neighborhood access and total environment exposure to healthy environments by dynamically integrating Global Positioning System (GPS) data with Geographical Information System (GIS) data. We hypothesize that dynamic GPS based measures of environmental exposure will be more strongly related to behavior and insulin and inflammation biomarkers than static addressed based GIS measures of access. We will study a large sample of adults (N=700), 40-75 years old, who have lived at their residence at least one year. We will recruit participants from census blocks specially selected to vary by income, walkability, and food environments to ensure environmental variability not achieved in a random sample. We will ensure balanced recruitment by census block type (walkability & food environments) across ethnicity, gender, age, and season. Half the sample will be Hispanic to explore potential interaction effects by ethnicity. Participants will complete surveys about their PA, sedentary behavior, environmental perceptions, self-selection, cancer risk, and demographics. PA and sedentary behavior will also be assessed by accelerometry and Machine Learning techniques will be employed to objectively identify specific behaviors likely related to the built environment e.g. walking, biking, riding in a car, screen time etc. Participants will complete the ASA 24 to assess diet, total calories and fat calories. A subsample (N=50) will wear a SenseCam to assess social context, validate GIS built environment measures, and validate the Machine Learned categories. Dynamic GIS measures of exposure will be created from 7 day person worn GPS data matched to GIS indicators of supportive PA and food environments (e.g. parks, walkable streets, fresh produce markets etc.) weighted by time, speed, transportation mode and features of the environment e.g. parcel size. Static residential GIS buffers of access to neighborhood resources will be created within a 1km street network buffer around a participant's home. Using multilevel statistical models adjusting for clustering, we will investigate whether GPS based Dynamic GIS measures of exposure to healthy food and PA supportive environments are more strongly associated with breast and colon cancer risk factors - including behaviors (PA, sedentary behavior, & diet), BMI, and biomarkers of insulin resistance and inflammation (e.g. CRP, IL-6, HOMA-IR) than Static GIS measures of access to neighborhood resources.