FHS-NEXT - Framingham Novel EXam using Technology

摘要 传统的流行病学队列研究极大地促进了我们对心血管疾病及其危险因素的理解， 人们对它们的资源密集型性质以及它们未能整合来自 在研究中心外。数字和移动的健康（mHealth）技术提供了关于个人健康的数据。 行为、危险因素和CVD事件。移动健康解决方案的日益普及提供了前所未有的 有机会在队列研究中增加新的“真实的世界”心血管表型。MHealth可能会促进更多 与队列参与者进行频繁、方便和有意义的互动。尽管有无数的 器械，很少有系统被设计用于促进长期心血管表型和CVD 监视我们建议增强FHS-NExT（Frachial Heart Study-Novel Examination， 技术）系统，其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（Gen 3）和多种族使用 Omni 2参与者。我们将确定与长期坚持相关的因素，以及 mHealth和数字表型与健身和动脉僵硬度的验证措施。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 在FHS研究中心应用CVD危险因素调查与常规措施。我们将 增强FHS-NExT应用程序，添加新的消息传递和交互功能，以促进长期使用 FHS-NExT应用程序（n=2250）、智能手表（n=1500）和BP器械（n=1500），并将FHS CVD风险评分相关为 使用应用程序评估研究中心考试的FHS CVD风险评分。 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BP设备系统依从性将根据以下内容定义：1）应用程序完成 每3个月进行一次生活方式和心血管疾病风险因素调查（n=2250）; 2）戴上智能手表并记录 每日步数≥1000步（n=1500）; 3）每周测量血压≥ 1次（n=1500）。我们将确定参与者或 影响12个月内FHS-NExT系统依从性的系统相关因素（n=1500例受试者）。 目标3：运动心肺功能试验对新型运动能力指标的相关性研究 （CPET），平均静息HR和来自30天内智能手表记录的每日步数。 目标4。动脉张力测定与平均周血压及平均血压的关系 6个月内的变异性。 我们汇集了一个高效，多学科的团队，在应用程序开发，移动健康和 数字研究方法和分析，“大数据”整合，行为科学，心血管疾病流行病学。我们 建议为数字和移动健康数据收集建立新的、可扩展的能力，并测试 传统的心血管健康和动脉张力测量的临床测量和新的mHealth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.