High resolution measures of behavioral cancer risk factors from mobile technology

通过移动技术对行为癌症危险因素进行高分辨率测量

• 批准号: 9013227
• 负责人: Peter James
• 金额: $ 12.94万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9013227
• 关键词: Accelerometer; Affect; Behavior; Behavioral; Big Data; Breast; Cellular Phone; Cohort Studies; Colon; Colon Carcinoma; Data; Devices; Endometrial; Enrollment; Ensure; Epidemiologic Studies; Epidemiology; Female; General Population; Geographic Information Systems; Goals; Green space; Health; Intervention; Lead; Link; Location; Malignant Neoplasms; Measurement; Measures; Mentors; Methods; Modeling; Monitor; Neighborhoods; Nurses; Nurses' Health Study; Obesity; Online Systems; Patient Self-Report; Pattern; Physical activity; Positioning Attribute; Process; Prostate; Questionnaires; Research; Research Training; Resolution; Resources; Risk Factors; Science; Sleep; Sleep disturbances; Statistical Methods; Stream; System; Time; aged; base; behavior influence; behavior measurement; cancer risk; career; cohort; contextual factors; critical period; design; driving behavior; experience; high risk; insight; male; malignant breast neoplasm; mobile computing; novel; obesity risk; prospective; skills; temporal measurement; walkability

 DESCRIPTION (provided by applicant): Obesity is an established driver of cancer risk, and substantial evidence has linked obesity to inadequate physical activity and sleep. Geographic contextual measures, including neighborhood walkability and access to green space, have been demonstrated to affect physical activity, sleep patterns, and obesity. These factors are typically measured with questionnaires, which have substantial error. Novel mobile technologies, such as global positioning systems (GPS) enabled smartphones and consumer wearable accelerometry devices, can provide efficient, rigorous, and objective measures of geographic context, physical activity, and sleep with high spatio-temporal resolution. However, managing, processing, and analyzing streaming high-dimensional data presents significant logistical and analytical challenges, especially when linking these data to existing data from large prospective cohorts. My long term career goals are to assess the effect of dynamic measures of geographic context on objective measures of physical activity and sleep, as well as subsequent obesity and cancer risk within the full Nurses' Health Study 3 (NHS3). NHS3 is a web-based, nationwide, prospective open cohort with a current enrollment of ~40,000 male and female nurses aged 19-46 years old. In Aims 1 and 2 of this proposal, I will measure the interdependent relationships between geographic context, physical activity, sleep, and obesity by deploying smartphone applications and wearable devices within a subsample (n=500) of the NHS3. I will use mobile technologies to collect streaming, high spatio-temporal resolution measures of geographic context (walkability and green space), physical activity, and sleep over a seven day monitoring period, four times over one year. I will then apply state-of-the-science statistical methods to examine the interrelationships between these high-dimensional, big data measures of context and behavior. For Aim 3, I will apply statistical approaches for measurement error correction to examine the relationship between error-corrected measures of context/behavior and obesity in the full NHS3 cohort. I am well suited to perform this research based on 1) my past research experience in contextual measures and health, 2) the exceptional mentoring team I have assembled to ensure that this research is of the highest quality, and 3) the unique resources of NHS3. This study will enable me to rigorously quantify contextual exposures, physical activity and sleep, and to identify the influence of geographic contextual factors on these interdependent behavioral risk factors for cancer and obesity. I will be guided by a world-class team of mentors to expand my expertise in the objective measurement of geographic context, physical activity, and sleep through mobile technology; big data methods; and measurement error correction. The proposed research and training will provide me the skills to establish an independent career as a leader in the epidemiology of behavioral risk factors for cancer.

 描述（由申请人提供）：肥胖是癌症风险的既定驱动因素，大量证据表明肥胖与体力活动和睡眠不足有关。地理环境的措施，包括邻里步行和进入绿色空间，已被证明会影响身体活动，睡眠模式和肥胖。这些因素通常用问卷来衡量，问卷有很大的误差。新型移动的技术，诸如启用全球定位系统（GPS）的智能手机和消费者可穿戴加速度计设备，可以提供具有高时空分辨率的地理背景、身体活动和睡眠的有效、严格和客观的测量。然而，管理、处理和分析流式高维数据提出了重大的后勤和分析挑战，特别是当将这些数据与来自大型前瞻性队列的现有数据相关联时。我的长期职业目标是评估地理环境的动态测量对身体活动和睡眠的客观测量的影响，以及在完整的护士健康研究3（NHS 3）中随后的肥胖和癌症风险。NHS 3是一项基于网络的全国性前瞻性开放队列研究，目前招募了约40，000名年龄在19-46岁之间的男性和女性护士。在本提案的目标1和2中，我将通过在NHS 3的子样本（n=500）中部署智能手机应用程序和可穿戴设备来测量地理环境、身体活动、睡眠和肥胖之间的相互依赖关系。我将使用移动的技术来收集流媒体，地理环境（步行和绿色空间）的高时空分辨率测量，身体活动，在七天的监测期内睡眠，一年四次。然后，我将应用最先进的统计方法来研究这些高维、大数据的背景和行为测量之间的相互关系。对于目标3，我将应用统计方法进行测量误差校正，以检查在整个NHS 3队列中背景/行为和肥胖的误差校正措施之间的关系。我非常适合进行这项研究，基于1）我过去在环境测量和健康方面的研究经验，2）我组建的优秀指导团队，以确保这项研究具有最高质量，3）NHS的独特资源3。这项研究将使我能够严格量化环境暴露，身体活动和睡眠，并确定地理环境因素对这些相互依赖的癌症和肥胖行为风险因素的影响。我将在世界一流的导师团队的指导下，通过移动的技术、大数据方法和测量误差校正，扩展我在客观测量地理环境、身体活动和睡眠方面的专业知识。拟议的研究和培训将为我提供技能，以建立一个独立的职业生涯，作为癌症行为危险因素流行病学的领导者。