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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Nursing Research (NINR)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21NR011197-01
Application #
7638239
Study Section
Nursing Science: Adults and Older Adults Study Section (NSAA)
Program Officer
Jett, Kathleen
Project Start
2009-08-13
Project End
2011-07-31
Budget Start
2009-08-13
Budget End
2010-07-31
Support Year
1
Fiscal Year
2009
Total Cost
$221,032
Indirect Cost
Name
University of Missouri-Columbia
Department
Type
Schools of Nursing
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
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Rantz, Marilyn J; Skubic, Marjorie; Popescu, Mihail et al. (2015) A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults. Gerontology 61:281-90
Su, Bo Yu; Ho, K C; Rantz, Marilyn J et al. (2015) Doppler radar fall activity detection using the wavelet transform. IEEE Trans Biomed Eng 62:865-75
Stone, Erik; Skubic, Marjorie; Rantz, Marilyn et al. (2015) Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders. Gait Posture 41:57-62
Krampe, Jean; Miller, Steven J; Echebiri, Chinonye et al. (2014) Nighttime restfulness during daytime dance therapy: an exploratory study using bed sensors. West J Nurs Res 36:362-73
Rantz, Marilyn J; Banerjee, Tanvi S; Cattoor, Erin et al. (2014) Automated fall detection with quality improvement ""rewind"" to reduce falls in hospital rooms. J Gerontol Nurs 40:13-7
Wang, Fang; Skubic, Marjorie; Rantz, Marilyn et al. (2014) Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment. IEEE Trans Biomed Eng 61:2434-43
Rantz, Marilyn J; Scott, Susan D; Miller, Steven J et al. (2013) Evaluation of health alerts from an early illness warning system in independent living. Comput Inform Nurs 31:274-80
Rantz, Marilyn J; Skubic, Marjorie; Abbott, Carmen et al. (2013) In-home fall risk assessment and detection sensor system. J Gerontol Nurs 39:18-22
Galambos, Colleen; Skubic, Marjorie; Wang, Shaung et al. (2013) Management of Dementia and Depression Utilizing In- Home Passive Sensor Data. Gerontechnology 11:457-468

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