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Technology to Automatically Detect Early Signs of Illness in Senior Housing

自动检测老年住宅早期疾病迹象的技术

基本信息

批准号：
7914329
负责人：
MARILYN J RANTZ
金额：
$ 18.36万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-13 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7914329
关键词：
Activities of Daily Living Acute Affect Age Agitation Beds Blood Glucose Blood Pressure Breathing Caregivers Caring Centers for Disease Control and Prevention (U.S.)Chronic Chronic Disease Clinical effectiveness Communication Complex Data Detection Devices Disease Management Early Diagnosis Early treatment Elderly Environment Equipment Family Goals Health Health Personnel Health Status Healthcare Home Health Agency Home environment Hospitalization Housing Individual Intervention Intervention Studies Length of Stay Life Long-Term Care Measures Monitor Morbidity - disease rate Motion Nursing Homes Outcome Oxygen Patients Persons Physiologic pulse Pilot Projects Population Prevention Publishing Reading Recovery Research Research Personnel Rest Sample Size Sleep System Technology Testing Time United States Work active control base behavior influence clinically relevant compliance behavior cost cost effectiveness disorder control functional decline improved innovation interest mortality prevent programs prospective public health relevance sensor successful intervention telehealth web based interface

项目摘要

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.

描述（由申请人提供）：慢性病管理是美国当今面临的最大的医疗保健问题。慢性病尤其影响老年人，80% 的老年人至少患有一种慢性病，50% 的老年人患有两种或两种以上（CDC，2007）。慢性病管理的首要目标是控制疾病而不是治愈疾病。当治疗最有效并且仍然有可能预防剧烈变化时，早期疾病检测和识别健康状况的微小变化对于早期干预至关重要。疾病的早期识别和早期治疗不仅是急性疾病或慢性疾病恶化后改善健康状况、快速康复的关键，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创新技术方法，使用嵌入环境中的廉价传感器来进行早期疾病检测和慢性疾病管理。受试者不会使用任何昂贵的远程医疗设备或佩戴任何设备。相反，传感器数据将被被动收集，从而消除合规性问题。此外，当受试者在家中进行日常活动时，传感器会持续监控受试者（运动传感器）。不引人注目的床传感器收集有关受试者睡眠时的脉搏、呼吸和不安的数据。我们建议利用这些信息来检测健康状况的变化，这可能表明即将发生急性疾病或慢性疾病恶化。具体来说，我们建议 1) 开发一种早期疾病传感器系统，利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网络的界面，以医疗保健提供者认为易于使用和解释、随时可用且与临床相关的格式显示传感器数据。我们将根据传感器数据制定警报，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，我们将前瞻性地在试点研究中使用早期疾病传感器系统，2) 确定在老年人住宅中使用早期疾病传感器系统进行干预研究的样本量，以衡量与通常的健康评估相比，使用传感器数据检测老年人疾病早期迹象或功能衰退的临床有效性和成本效益。 NINR 和 NIA 都会对此应用感兴趣。公共健康相关性：项目叙述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创新技术方法，使用嵌入环境中的廉价传感器来进行早期疾病检测和慢性病管理。受试者不会使用任何昂贵的远程医疗设备或佩戴任何设备。我们建议利用这些信息来检测健康状况的变化，这可能表明即将发生急性疾病或慢性疾病恶化。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluation of health alerts from an early illness warning system in independent living.

对独立生活早期疾病预警系统健康警报的评估。

DOI：
10.1097/nxn.0b013e318296298f
发表时间：
2013
期刊：
Computers, informatics, nursing : CIN
影响因子：
0
作者：
Rantz,MarilynJ;Scott,SusanD;Miller,StevenJ;Skubic,Marjorie;Phillips,Lorraine;Alexander,Greg;Koopman,RichelleJ;Musterman,Katy;Back,Jessica
通讯作者：
Back,Jessica

Using Technology to Enhance Aging in Place.

利用技术促进就地养老。

DOI：
发表时间：
2008
期刊：
Lecture notes in computer science
影响因子：
0
作者：
Rantz,MJ;Skubic,M;Miller,SJ;Krampe,J
通讯作者：
Krampe,J

Management of Dementia and Depression Utilizing In- Home Passive Sensor Data.

DOI：
10.4017/gt.2013.11.3.004.00
发表时间：
2013-01-01
期刊：
Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society
影响因子：
0
作者：
Galambos, Colleen;Skubic, Marjorie;Rantz, Marilyn
通讯作者：
Rantz, Marilyn

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges.