The Quality Improvement Program for Missouri's Long - Term Care Facilities (QIPMO) is committed to Missouri's Elderly.
The "Aging-in-place" model allows older adults to receive health care in their preferred place of living, eliminating the need for a more restricted living space, such as a nursing home.
TigerPlace is a specially designed elder housing project initiated by the MU Sinclair School of Nursing, working to provide elders a better quality of life.
Interdisciplinary research is a major focus for MU faculty and many have come together to focus their research expertise on improving the lives of older people. There are several large research projects funded by the National Institutes of Health (NIH), National Science Foundation (NSF), and Agency for Healthcare Research and Quality (AHRQ) underway developing and applying technology to help the residents of TigerPlace age in place. Research teams are pursuing multiple ways to measure physical function, detect falls, and early illness recognition. Grant proposals to NIH, NSF, AHRQ, and other funding agencies are continuously under development with PIs from our multidisciplinary team.
Environmentally embedded (non-wearable) sensor technology is in continuous use in elder housing to monitor a new set of "vital signs" that continuously measure the functional status of older adults, detect potential changes in health or functional status, and alert healthcare providers for early recognition and treatment of those changes. Older adult participants' respiration, pulse, and restlessness are monitored as they sleep. Gait speed, stride length, and stride time are calculated daily, and automatically assess for increasing fall risk. Activity levels are summarized and graphically displayed for easy interpretation. Falls are detected when they occur and alerts are sent immediately to healthcare providers, so time to rescue may be reduced. Automated health alerts are sent to health care staff, based on continuously running algorithms applied to the sensor data, days and weeks before typical signs or symptoms are detected by the person, family members, or health care providers. Discovering these new functional status "vital signs," developing automated methods for interpreting them, and alerting others when changes occur has the potential to transform chronic illness management and facilitate aging in place through the end of life. Key findings of research in progress at the University of Missouri are discussed in this viewpoint article, as well as obstacles to widespread adoption.
Rantz, M., Skubic, M., Popescu, M., Galambos, C., Koopman, R.J., Alexander, G.L., Phillips, L.J., Musterman, K., Back, J., & Miller, S.J. (2015). A new paradigm of technology enabled "vital signs" for early detection of health change for older adults. Gerontology, published online 11/26/14.*
A study was conducted to assess how a new metric, average in-home gait speed (AIGS), measured using a low-cost, environmentally mounted, continuous monitoring system, compares to a set of traditional physical performance instruments used for mobility and fall risk assessment of elderly adults. Sixteen participants were recruited from a local independent living facility. In addition to having their gait monitored continuously in their home for one-two years, the participants completed a monthly clinical assessment consisting of a set of traditional assessment instruments: Habitual Gait Speed, Timed-Up-and-Go (TUG), Short Physical Performance Battery, Berg Balance Scale – short form, and Multidirectional Reach Test. A methodology is developed to assess which of these instruments may work well with the largest subset of older adults, offers the best sensitivity for detecting changes in an individual over time, and most reliably captures the functional ability level of an individual. Using the ability of an instrument to predict how an individual would score on all the instruments as a metric, AIGS performs best, having better prediction power than the traditional instruments. AIGS also displays the best agreement between observed and filtered values, indicating it has the lowest test-retest variability of the instruments. AIGS, measured continuously, during normal everyday activity, represents a significant shift in assessment methodology as compared to traditional physical performance measures. Continuous, in-home data may provide a more accurate and precise picture of physical function of older adults, revolutionizing mobility and fall risk assessment.
Stone, E., Skubic, M., Rantz, M.J., Abbott, C., & Miller, S. (2015). Average in-home gait speed: Investigation of a new metric for mobility and fall risk assessment of elders. Gait & Posture, 41, 57-62.*