High Resolution Measures of Behavioral Cancer Risk Factors From Mobile Technology

 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身体活动和睡眠不足有关。地理背景措施，包括社区可步行和获得绿地，已被证明影响身体活动、睡眠模式和肥胖。这些因素通常是用问卷来衡量的，问卷有很大的误差。新的移动技术，如支持全球定位系统(GPS)的智能手机和消费者可穿戴的加速度计设备，可以提供高时空分辨率的高效、严格和客观的地理环境、体力活动和睡眠测量。然而，管理、处理和分析流动的高维数据带来了巨大的后勤和分析挑战，特别是在将这些数据与来自大型预期队列的现有数据联系起来时。我的长期职业目标是评估地理环境的动态测量对体力活动和睡眠的客观测量的影响，以及随后在全面护士健康研究3(NHS3)中的肥胖和癌症风险。NHS3是一个基于网络的、全国性的、具有前瞻性的开放队列，目前注册的大约有40,000名年龄在19-46岁的男女护士。在本提案的目标1和目标2中，我将通过在NHS3的一个子样本(n=500)中部署智能手机应用程序和可穿戴设备来衡量地理环境、体力活动、睡眠和肥胖之间的相互依赖关系。我将使用移动技术收集流媒体、地理环境(可步行和绿地)、身体活动和睡眠的高时空分辨率测量，在七天的监控期内，一年内四次。然后，我将应用最先进的统计方法来检查这些高维、大数据的背景和行为衡量标准之间的相互关系。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NHS3队列中环境/行为的误差校正测量与肥胖之间的关系。我非常适合进行这项研究，基于1)我过去在情境测量和健康方面的研究经验，2)我组建的卓越的指导团队，以确保这项研究的最高质量，以及3)NHS3的独特资源。这项研究将使我能够严格量化背景暴露、体力活动和睡眠，并确定地理背景因素对这些相互依赖的癌症和肥胖行为风险因素的影响。我将在一个世界级的导师团队的指导下，通过移动技术、大数据方法和测量误差校正来扩展我在客观测量地理环境、体力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，成为癌症行为风险因素流行病学的领导者。

 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.