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

描述（由申请人提供）：缺乏运动，不健康的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。在居民区有更多的健康环境与更高的身体活动（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，但建筑环境研究中的效应量很小，可能是因为评估的环境（仅限家庭社区）与行为发生的地点之间的不匹配。行为和地点的时间变化也被忽略了。我们建议通过动态整合全球定位系统（GPS）数据和地理信息系统（GIS）数据，通过测量社区访问和对健康环境的总环境暴露来改进癌症风险暴露评估方法。我们假设基于动态GPS的环境暴露测量与行为、胰岛素和炎症生物标志物的关系比基于静态定位的GIS测量更密切。我们将研究一个大样本的成年人（N=700）， 40-75岁，住在他们的住所至少一年。我们将从人口普查区招募参与者，这些人口普查区是根据收入、步行性和食物环境而特别选择的，以确保在随机样本中不会出现环境变异性。我们将确保按人口普查街区类型（可步行性和食物环境）、种族、性别、年龄和季节平衡招聘。一半的样本将是西班牙裔，以探索种族间潜在的相互影响。参与者将完成关于他们的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计进行评估，机器学习技术将被用来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、开车、看屏幕等。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建筑环境措施，并验证机器学习类别。动态的GIS暴露测量将由7天的人佩戴的GPS数据与支持PA和食品环境（如公园、步行街道、新鲜农产品市场等）的GIS指标相匹配，根据时间、速度、运输方式和环境特征（如包裹大小）进行加权。将在参与者家周围1公里的街道网络缓冲区内创建获取社区资源的静态住宅GIS缓冲区。使用调整聚类的多层次统计模型，我们将研究基于GPS的动态GIS暴露于健康食品和PA支持环境的测量是否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和胰岛素抵抗和炎症的生物标志物（例如CRP， IL-6, HOMA-IR）的相关性比静态GIS测量获得社区资源的相关性更强。

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.