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

(PQA4) GPS 暴露于环境中

• 批准号: 8722512
• 负责人: Jacqueline Kerr
• 金额: $ 71.56万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8722512
• 关键词: Address; Adult; Age; Behavior; Biological; Biological Markers; Buffers; Calories; Categories; Censuses; Child; Code; Colon Carcinoma; Data; Devices; Diet; Eating; Ensure; Environment; Environmental Exposure; Equilibrium; Ethnic Origin; Exposure to; Fatty acid glycerol esters; Food; Gender; Health; Health Food; Hispanics; Home environment; Image; Income; Inflammation; Information Systems; Insulin; Insulin Resistance; Interleukin-6; Life; Location; Machine Learning; Malignant Neoplasms; Marketing; Measures; Methods; Neighborhoods; Obesity; Outcome; Participant; Pathway interactions; Patient Self-Report; Persons; Pharmaceutical Preparations; Physical Function; Physical activity; Positioning Attribute; Recreation; Recruitment Activity; Research; Resources; Restaurants; Risk; Risk Factors; Sampling; Seasons; Self Perception; Social Behavior; Social Environment; Social Interaction; Speed; Statistical Models; Surveys; System; Techniques; Text; Time; Transportation; Variant; Walking; Weight; base; cancer risk; demographics; density; fast food; food environment; good diet; inflammatory marker; land use; learned behavior; malignant breast neoplasm; novel; public health relevance; residence; sedentary; sugar; tv watching

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.

描述（由申请人提供）：身体上的不健康，不健康的饮食和肥胖与乳腺癌和结肠癌直接以及通过胰岛素和炎症途径有关。在居民社区中获得健康环境的机会更大，与较高的体育锻炼（PA），更健康的饮食，较低的BMI以及一项研究有关，胰岛素抵抗较低。尽管有一些重要的发现，但由于所评估的环境（仅家庭社区）与行为发生的位置之间的不匹配，建筑环境研究中的效果大小可能很小。在几天内和整个日内行为和位置的时间变化也被忽略了。我们建议通过通过将全球定位系统（GPS）数据与地理信息系统（GIS）数据动态整合到健康环境中的邻里访问和对健康环境的全面环境的曝光方法来推进癌症风险暴露评估的方法。我们假设基于动态的GPS的环境暴露量度将与行为，胰岛素和炎症生物标志物更加密切，而基于静态的基于基于的GIS的GIS量度。我们将研究大量的成年人（n = 700），40-75岁，他们至少在他们的住所生活了至少一年。我们将招募专门选择以随着收入，步行性和食物环境而变化的人口普查区块的参与者，以确保在随机样本中无法实现环境变异性。我们将确保跨种族，性别，年龄和季节的人口普查区块类型（步行性和食物环境）的均衡招募。一半的样本将是西班牙裔，以探索种族潜在的相互作用效果。参与者将完成有关其PA，久坐行为，环境看法，自我选择，癌症风险和人口统计的调查。 PA和久坐行为还将通过加速度计评估，并且将采用机器学习技术来客观地识别可能与已建立环境相关的特定行为，例如步行，骑自行车，骑车，屏幕时间等。参与者将完成ASA 24，以评估饮食，总卡路里和脂肪卡路里。子样本（n = 50）将佩戴Sensecam来评估社会环境，验证GIS建立的环境测度并验证机器学习的类别。动态的GIS暴露量度将从7天的人员磨损的GPS数据匹配到支持性PA和食品环境的GIS指标（例如公园，可步行街道，新鲜农产品市场等），并通过时间，速度，运输方式和环境的特征加权。包裹尺寸。将在参与者家周围的1公里街网络缓冲区内创建静态的住宅GIS缓冲区。使用多层次的统计模型调整聚类，我们将调查基于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危险因素（包括行为（PA，久坐行为和饮食），BMI和饮食），BMI和胰岛素抵抗和炎症的生物标志物（例如，对社区的疾病（例如，crp crp crp crp crp crp crp，ir-ir），是否与乳腺癌和结肠癌危险因素（包括行为（PA，久坐行为和饮食））相关。