Wearable Technology Infrastructure to Enhance Capacity for Real-Time, Online Assessment and Mobility (ROAMM) of Intervening Health Events in Older Adults

摘要 美国老年人每年经历大约2900万次福尔斯跌倒和1300万次住院治疗。这些 干预性健康事件（IHE -偶发性福尔斯、受伤、疾病和住院）是 老年人的残疾。由于其情节的性质，IHE是非常难以研究。连续的， 使用可穿戴技术的远程功能进行长期监测是捕获 R21/R33项目旨在开发一个 可持续研究基础设施建立在智能手表应用程序和服务器的基础上， ROAMM（实时在线评估和移动监控）。它将提供长期和持续的 连接性、双向交互性和远程编程。ROAMM将创建一个详细的叙述， 移动性（活动模式、步行速度、生活空间）、患者报告的结果/症状（疼痛，情绪不佳， 疲劳，残疾），认知（工作记忆，处理速度和执行功能）和报告 健康事件（福尔斯和住院）。基础设施由不同的调查人员组成 具备移动的技术/数据科学和应用/医学科学方面的专业知识，将在以下领域任职 核心：可穿戴技术、表型分析、临床结果、数据科学管理和质量，以及 招聘、保留与合规。在R21阶段，我们将创建ROAMM框架， 手表应用程序和配套服务器。我们还将评估重测信度、聚合效度 和参与者可用性/可接受性。每年，一个独立的咨询小组和外部咨询小组 委员会将评估里程碑驾驶活动和我们过渡到R33的Go/No-Go检查点 相位在R33阶段提出的工作将展示ROAMM基础设施， 在200名70岁以上的社区居民中进行的前瞻性纵向研究（范围1.25-2.5年）。 第一阶段将在真实的世界环境中测试ROAMM的实地部署版本，以解决以下假设： 1)ROAMM收集的低活动性、残疾、疲劳、疼痛和抑郁情绪的事件前模式是 IHE事件的独立预测因素; 2）IHE将对ROAMM措施的过程产生负面影响;以及 3)将获得额外的价值，解释变化的变异性和恢复轨迹。探索性 aim将评估使用ROAMM功能时的安全性，并使用这两种功能确定ROAMM依从性的预测因素 关键信息提供者访谈并检查人口统计学和健康史，以确定使用范围 ROAMM和其他类似的系统用于研究和实践中的长期持续监控。我们将 通过瞄准远程患者可穿戴技术激增的资助机会来维持ROAMM 互动，采用许可费，并使我们的服务与更大的实体保持一致，成为 远程数据采集这些活动将建立一个可持续的基础设施，以确保对老年人的研究 正在通过可穿戴技术跟上最先进的“智能和互联”健康的步伐。

ABSTRACT Older Americans experience approximately 29 million falls and 13 million hospitalizations per year. These intervening health events (IHE - episodic falls, injuries, illnesses, and hospitalizations) are strong precipitants of disability in older adults. Because of their episodic nature, IHEs are extremely difficult to study. Continuous, long-term monitoring with remote capabilities using wearable technology is an ideal solution for capturing information surrounding an IHE and in particular, preceding it. This R21/R33 project aims to develop a sustainable research infrastructure built on the foundation of a smart watch application and server called ROAMM (Real-time Online Assessment and Mobility Monitor). It will offer long-term and continuous connectivity, bidirectional interactivity and remote programming. ROAMM will create a detailed narrative about mobility (activity patterns, walking speed, life space), patient reported outcomes/symptoms (pain, poor mood, fatigue, disability), cognition (working memory, processing speed, and executive functioning) and reports of health events (falls and hospitalizations). The infrastructure is composed of a diverse group of investigators with expertise in mobile technology/data science and applied/medical sciences who will serve in the following cores: Wearable Technology, Phenotyping, Clinical Outcomes, Data Science Management & Quality, and Recruitment, Retention & Compliance. In the R21 phase, we will create the ROAMM framework consisting of the watch application and accompanying server. We will also assess test-retest reliability, convergent validity and participant usability/acceptability. Each year, an Independent Advisory Panel and External Advisory Committee will evaluate milestone-driving activities and our Go/No-Go checkpoints for transitioning to the R33 phase. Work proposed in the R33 phase will showcase the ROAMM infrastructure by conducting a prospective, longitudinal study (range 1.25-2.5 yrs) in 200 community-dwelling persons aged 70+ yrs. This phase will test a field deployable version of ROAMM in real world settings to address the following hypotheses: 1) Pre-event patterns of low mobility, disability, fatigue, pain and depressive mood collected by ROAMM are independent predictors of incident IHE's; 2) IHE's will negatively impact the course of ROAMM measures; and 3) Additional value will be gained for explaining the change variability and recovery trajectories. An exploratory aim will evaluate safety while using ROAMM features and identify predictors of ROAMM adherence using both key-informant interviews and examine demographic and health histories to create boundaries for using ROAMM and other systems like it for long-term, continuous monitoring in research and practice. We will sustain ROAMM by targeting grant opportunities for the wearable technology surge for remote patient interaction, adopting licensing fees, and aligning our services with larger entities to become the go-to place for remote data capture. These activities will create a sustainable infrastructure to ensure research on older adults is keeping pace with the state-of-the-art “smart and connected” health with wearable technology.