FHS-NEXT - Framingham Novel EXam using Technology

摘要 传统的流行病学队列对我们对 CVD 及其危险因素的理解做出了巨大贡献，但是 人们对其资源密集型性质以及未能整合来自 研究中心外。数字和移动健康 (mHealth) 技术提供有关个人健康状况的数据 行为、危险因素和 CVD 事件。移动医疗解决方案的日益普及提供了前所未有的 有机会将新的“现实世界”心血管表型添加到队列研究中。移动健康可能会带来更多便利 与队列参与者进行频繁、方便且有意义的互动。尽管有无数的可用 设备，很少有系统被设计来促进长期心血管表型分析和 CVD 监视。我们建议加强 FHS-NExT（弗雷明汉心脏研究 - 小说检查） 技术）系统，其中包括定制智能手机应用程序（app）和来自智能手表的数据 （心率/步数）和数字血压 (BP) 袖带，供 FHS 第 3 代 (Gen3) 和多种族人士使用 Omni 2 参与者。我们将确定与长期坚持相关的因素以及之间的关系 移动健康和数字表型以及经过验证的健身和动脉僵硬度测量。具体目标： 目标 1. 增强 FHS-NExT 智能手机应用程序以促进依从性并比较新的 FHS-NExT 应用 FHS 研究中心针对传统措施进行的 CVD 风险因素调查。我们会 增强FHS-NExT应用程序，添加新的消息传递和交互功能，以方便长期使用 FHS-NExT 应用程序 (n=2250)、智能手表 (n=1500) 和 BP 设备 (n=1500) 并将 FHS CVD 风险评分关联为 使用该应用程序根据研究中心考试的 FHS CVD 风险评分进行评估。 目标 2. 确定与成功长期使用 FHS-NExT 系统相关的因素，包括 应用程序、智能手表和数字血压设备。系统依从性将根据以下因素定义：1) 应用程序完成情况 每 3 个月进行一次生活方式和 CVD 危险因素调查 (n=2250)； 2）戴上智能手表并录音 每天≥1000步（n=1500）； 3) 每周≥一次进行血压测量 (n=1500)。我们将确定参与者或 系统相关因素，影响 12 个月内对 FHS-NExT 系统（n=1500 名参与者）的遵守情况。 目标 3. 通过心肺运动测试检查新运动能力测量的关系 (CPET)，包含 30 天内智能手表记录的平均静息心率和每日步数。 目标 4. 通过张力测量检查动脉僵硬度与平均每周血压和平均血压的关系 6个月内的变化。 我们汇集了一支高效、多学科的团队，他们在应用程序开发、移动医疗和 数字研究方法和分析、“大数据”整合、行为科学和CVD流行病学。我们 建议为数字和移动医疗数据收集建立新的、可扩展的能力，并测试之间的关系 心血管健康和动脉张力测量的传统临床测量以及新颖的移动健康表型。

Abstract Traditional epidemiology cohorts have contributed greatly to our understanding of CVD and its risk factors, but concerns have been raised about their resource intensive nature, as well as their failure to integrate data from outside the research center. Digital and mobile health (mHealth) technologies provide data on individuals’ behaviors, risk factors, and CVD events. The growing ubiquity of mHealth solutions provides an unprecedented opportunity to add novel “real world” cardiovascular phenotypes to cohort studies. MHealth may facilitate more frequent, convenient, and meaningful interactions with cohort participants. Despite the availability of myriad devices, few systems have been designed to facilitate long-term cardiovascular phenotyping and CVD surveillance. We propose to enhance the FHS-NExT (Framingham Heart Study-Novel Examination using Technology) system, which includes a custom smartphone application (app) and data from a smartwatch (heart rate/steps) and digital blood pressure (BP) cuff, for use by FHS Generation 3 (Gen3) and multi-ethnic Omni 2 participants. We will identify factors associated with long-term adherence, and relations between mHealth and digital phenotypes with validated measures of fitness and arterial stiffness. Specific aims: Aim 1. Enhance the FHS-NExT smartphone app to promote adherence and compare the new FHS-NExT app CVD risk factor surveys against conventional measures in the FHS Research Center. We will enhance the FHS-NExT app, add new messaging and interactive capabilities to facilitate long-term use of the FHS-NExT app (n=2250), smartwatch (n=1500), and BP device (n=1500) and correlate FHS CVD risk score as assessed using the app to the FHS CVD risk score from the research center exam. Aim 2. Identify factors associated with successful long-term use of the FHS-NExT system, including the app, smartwatch, and digital BP device. System adherence will be defined based on: 1) app completion of lifestyle and CVD risk factor surveys every 3 months (n=2250); 2) donning the smartwatch and recording ≥1000 steps daily (n=1500); and 3) taking BP measures ≥once weekly (n=1500). We will identify participant or system-related factors, that affect adherence to the FHS-NExT system (n=1500 participants) over 12 months. Aim 3. Examine relations of novel exercise capacity measures by cardiopulmonary exercise testing (CPET) with average resting HR and daily step counts from smartwatch recordings over 30 days. Aim 4. Examine relations of arterial stiffness by tonometry with mean weekly BP and mean BP variability over 6 months. We bring together a highly productive, multi-disciplinary team with expertise in app development, mHealth and digital research methods and analysis, “big data” integration, behavioral science, & CVD epidemiology. We propose to build new, scalable capabilities for digital and mHealth data collection, and test the relations between traditional in-clinic measures of cardiovascular fitness and arterial tonometry and novel mHealth phenotypes.