Chronic disease management is the biggest health care problem facing the United States today. In 2005, nearly 1 in 2 Americans (133 million) had at least one chronic condition, and 21% of the population had multiple chronic conditions. These numbers will steadily increase over the next 30 years. Chronic diseases especially affect older adults in whom exacerbations result in dramatic changes and decline in health status, hospitalization, complex treatment interventions, and high cost. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an exacerbation of a chronic illness or acute illness, but also a key to reducing morbidity and mortality in older adults and controlling costs of health care. We propose to build on our recently completed pilot intervention study (NINR R21, Rantz, PI). In that study, we developed alerts for our intelligent sensor system (not telehealth that measures traditional vital signs, weight, pulse oximetry, blood sugar) and used it prospectively to measure functional ability in older adults and actually detected changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Inexpensive sensors are embedded in the environment, so subjects do not "have to use" or "wear" any devices. The R21 was to: 1) develop alerts based on the sensor data to notify health care providers of early signs of illness or functional decline so they could further evaluate and intervene with early treatment;2) further develop and refine a web-based interface to display the sensor data to health care providers;and 3) determine the sample size for an intervention study that would measure the clinical effectiveness and cost-effectiveness of using the sensor system with alerts in elder housing. Now, we propose to conduct a prospective intervention study to measure the clinical effectiveness and cost effectiveness of using sensor data to detect early signs of illness or functional decline in older adults compared to usual health assessment. A larger sample of older adults will be recruited;they live in a different independent housing than where the pilot study was conducted. While preparing the staff in the different housing setting to work with alerts from our intelligent sensor system, we will adjust, if necessary, the algorithms for automated alerts or the web-based interface for health care providers. Following the prospective study, we will develop and refine ways of providing sensor information to older adults and informal caregivers to help them directly better manage changes in health status. Our intelligent sensor system enables early detection of illness or functional decline, the key to successful less invasive, time consuming, and expensive interventions. Helping older adults remain healthier, active, and control their chronic illnesses with early detection of changes in health status and early intervention by health care providers, can result in millions of people remaining independent as they age, avoiding or reducing debilitating and costly hospital stays, and for many, avoiding or delaying nursing home care. This application will be of interest to both NINR and NIA.

Public Health Relevance

We will build on our current work using intelligent sensor systems to prospectively measure functional ability in older adults and actually detect changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. We will conduct a prospective intervention study (n=130) using the intelligent sensor system to measure the clinical effectiveness and cost effectiveness of using sensor data to detect early signs of illness or functional decline in older adults compared to usual health assessment. Subjects will not use traditional telehealth equipment or wear any devices.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01NR014255-02
Application #
8662807
Study Section
Nursing and Related Clinical Sciences Study Section (NRCS)
Program Officer
Roary, Mary
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Missouri-Columbia
Department
None
Type
Schools of Nursing
DUNS #
City
Columbia
State
MO
Country
United States
Zip Code
65211
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
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; 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