Monitoring Health in Older Adults Using mHealth Technology: Framingham Offspring Study

摘要 如何在纵向移动健康研究中吸引和留住老年人几乎是未知的。这真是太棒了 公共卫生的重要性，因为许多老年人在获得医疗保健方面存在障碍，而技术可能 通过允许对包括认知功能在内的健康状况进行家庭监测来克服。使用移动健康技术 为了更频繁地在现实生活环境中获取数据，对比研究中心相隔数年进行的考试 为队列研究提供了一个转变的机会，并可能导致监测风险的实用方法 阿尔茨海默病及相关疾病(ADRD)。我们之前开发了一种创新的、可扩展的数字 心血管健康监测系统，“电子弗雷明翰心脏研究”(EFHS)8，用于中期- 老年人，从而提供了支持这一提议的基础经验。我们将把eFHS扩展到 增强为年长参与者量身定做的智能手机应用程序。这款应用程序将捕捉患者报告的结果， 认知、身体表现、社会支持和健康事件(跌倒、住院)。我们计划 调查mHealth系统在监控与健康相关的行为方面的有效性，我们将介绍 脑老化的数字数据，包括使用面对面研究级神经心理学(NP)的临床前ADRD 测试和脑核磁共振数据。具体目标： 目标1.加强参与战略并确定与成功使用 3个月和6个月的老年人使用智能手机应用程序和智能手表。成功的使用将是 根据遵守情况定义，包括：1)基于应用程序完成调查以及认知和绩效 任务(n=850)；2)每天佩戴智能手表(n≈500)。我们将确定参与者(例如，年龄、性别、认知能力 状态)和与系统有关的因素(例如，用户界面、导航)，这些因素影响遵守情况超过3个月和6个月。 目标2.将基于智能手机应用程序的数据与大脑健康(NP)的研究级测量进行比较 在FHS研究中心收集的相同参与者的测试电池、大脑核磁共振)。我们会 将基于APP的测量与脑健康测量进行比较，包括NP测试测量(全球认知 和4个领域：执行功能、记忆、语言、视觉空间)和脑MRI标记物(全脑 体积、海马体积、白质高信号体积)。 次目标2a：构建数字脆弱表型。 目标3.检查Apple Watch在90天内的体力活动和心率 大脑健康的研究等级测量(见目标2)。 我们的应用程序将提供老年人如何使用mHealth设备的迫切需要的知识 并将提供有关mHealth数据的益处的见解，以在临床和 研究设置。数字生物标记物可能是监测ADRD风险因素/行为的强大工具 确定有认知和身体衰退风险的老年人。

Abstract How to engage and retain older adults in longitudinal mHealth research is virtually unknown. This is of great public health importance as many older adults have barriers to obtaining health care that technology may overcome by permitting in-home monitoring of health including cognitive function. Using mHealth technologies to obtain data more frequently and in real life settings, vs. research center examinations that occur years apart presents a transformational opportunity for cohort studies and may lead to practical methods for monitoring risk of Alzheimer’s disease and related disorders (ADRD). We previously developed an innovative, scalable digital cardiovascular health monitoring system, the “electronic Framingham Heart Study” (eFHS)8 for use in middle- aged adults, thus providing foundational experience that will support this proposal. We will extend eFHS to enhance a smartphone app tailored for older participants. The app will capture patient-reported outcomes, cognition, physical performance, social support, and health events (falls, hospitalizations). We plan to investigate the usefulness of the mHealth system for monitoring health-related behaviors and we will relate the digital data to brain aging including preclinical ADRD using in-person research grade neuropsychological (NP) testing and brain MRI data. Specific Aims: Aim 1. Enhance engagement strategies and identify factors associated with successful use of the smartphone app and smartwatch among older adults at 3 months and 6 months. Successful use will be defined based on adherence, including: 1) app-based completion of surveys and cognitive and performance tasks (n=850); 2) wearing the smartwatch daily (n≈500). We will identify participant (e.g., age, sex, cognitive status) and system-related factors (e.g., user-interface, navigation), that affect adherence over 3 and 6 months. Aim 2. Compare the smartphone app-based data to research grade measures of brain health (NP testing batteries, brain MRIs) collected in the FHS Research Center on the same participants. We will compare the app-based measures with measures of brain health, including NP test measures (global cognition and 4 domains: executive function, memory, language, visuospatial) and brain MRI markers (total brain volume, hippocampal volume, white matter hyperintensity volume). Sub Aim 2a: Construct a digital frailty phenotype. Aim 3. Examine measures of physical activity and heart rate from the Apple Watch over 90 days with research grade measures of brain health (see Aim 2). Our application will provide critically needed knowledge on how older adults engage with mHealth devices and will provide insights on the benefit of mHealth data to monitor the health of older adults in clinical and research settings. Digital biomarkers may be powerful tools to monitor risk factors/behaviors for ADRD and to identify older adults at risk for cognitive and physical decline.