Technology to Automatically Detect Early Signs of Illness in Senior Housing

描述（由申请人提供）：慢性病管理是当今美国面临的最大卫生保健问题。慢性病尤其影响老年人，80%的老年人至少有一种慢性病，50%有两种或两种以上（CDC，2007）。慢性病管理的主要目标是控制疾病而不是治愈疾病，在治疗最有效和仍有可能预防急剧变化的情况下，及早发现疾病和识别健康状况的微小变化对于及早干预至关重要。早期疾病识别和早期治疗不仅是改善健康状况、在急性疾病或慢性疾病恶化后迅速康复的关键，也是降低老年人发病率和死亡率的关键。基于我们目前使用智能传感器系统回顾性测量老年人功能能力的工作，我们建议开发一种前瞻性的创新技术方法，使用嵌入环境中的廉价传感器进行早期疾病检测和慢性疾病管理。受试者将不使用任何昂贵的远程医疗设备或佩戴任何设备。相反，传感器数据将被动收集，从而消除合规性问题。此外，传感器在受试者在家中进行日常活动时持续监测受试者（运动传感器）。不显眼的床传感器收集有关受试者睡眠时脉搏，呼吸和不安的数据。我们建议使用这些信息来检测健康状况的变化，这可能表明即将发生的急性疾病或慢性疾病的恶化。具体来说，我们建议1）开发一种早期疾病传感器系统，该系统使用传感器数据来检测老年人疾病或功能衰退的早期迹象。我们将进一步开发和完善基于Web的界面，以医疗保健提供者发现易于使用和解释，随时可用和临床相关的格式显示传感器数据。我们将根据传感器数据开发警报，并通知医疗保健提供者老年人的潜在疾病，以便他们能够进一步评估和干预急性疾病或慢性疾病恶化的早期治疗。然后，我们将在一项试点研究中前瞻性地使用早期疾病传感器系统，以2）确定在老年人住房中使用早期疾病传感器系统的干预研究的样本量，以衡量与常规健康评估相比，使用传感器数据检测老年人疾病或功能下降的早期迹象的临床有效性和成本效益。NINR和NIA都将对该应用程序感兴趣。公共卫生相关性：基于我们目前使用智能传感器系统回顾性测量老年人功能能力的工作，我们建议开发一种前瞻性的创新技术方法，使用嵌入环境中的廉价传感器进行早期疾病检测和慢性疾病管理。受试者将不使用任何昂贵的远程医疗设备或佩戴任何设备。我们建议使用这些信息来检测健康状况的变化，这可能表明即将发生的急性疾病或慢性疾病的恶化。

DESCRIPTION (provided by applicant): Chronic disease management is the biggest health care problem facing the United States today. Chronic diseases especially affect older adults, 80% of older adults have at least one chronic condition and 50% have two or more (CDC, 2007). The primary goal of chronic disease management is controlling disease rather than curing it. Early illness detection and recognition of small changes in health conditions are essential for early interventions when treatment is the most effective and when prevention of dramatic changes are still possible. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an acute illness or exacerbation of a chronic illness, but also a key to reducing morbidity and mortality in older adults. Building on our current work using intelligent sensor systems to retrospectively measure functional ability in older adults, we propose to develop a prospective innovative technological approach to early illness detection and chronic disease management using inexpensive sensors embedded in the environment. Subjects will not use any expensive telehealth equipment or wear any devices. Instead, sensor data will be collected passively, thus eliminating compliance issues. In addition, the sensors monitor subjects continuously (motion sensors) while they go about daily activities in their homes. Unobtrusive bed sensors collect data about the subjects pulse, breathing, and restlessness while they sleep. We propose to use this information to detect changes in health status which could indicate an impending acute illness or exacerbation of chronic illness. Specifically, we propose to 1) develop an early illness sensor system that uses sensor data to detect early signs of illness or functional decline in older adults. We will further develop and refine a web-based interface to display sensor data in a format that health care providers find easy to use and interpret, readily available, and clinically relevant. We will develop alerts based on the sensor data and notify health care providers of potential illness in older adults so they can further evaluate and intervene with early treatment of acute illness or exacerbation of chronic illness. Then, we will prospectively use the early illness sensor system in a pilot study to 2) determine the sample size for an intervention study using the early illness sensor system in elder housing to measure the clinical effectiveness and cost-effectiveness of using sensor data to detect early signs of illness or functional decline in older adults as compared to usual health assessment. This application will be of interest to both NINR and NIA. PUBLIC HEALTH RELEVANCE: Project Narrative Building on our current work using intelligent sensor systems to retrospectively measure functional ability in older adults, we propose to develop a prospective innovative technological approach to early illness detection and chronic disease management using inexpensive sensors embedded in the environment. Subjects will not use any expensive telehealth equipment or wear any devices. We propose to use this information to detect changes in health status which could indicate an impending acute illness or exacerbation of chronic illness.