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.
TigerPlace (named after the University of Missouri mascot, the tiger) is a specially designed elder housing project that was initiated by the MU Sinclair School of Nursing (SON) and designed by MU faculty working with the Americare Corporation of Sikeston, Missouri.
TigerPlace is built to nursing home standards, but not the typical configuration. The building has 32 private apartments with fully accessible bathrooms, kitchens, and screened porches. Private garages and a private dining room for special family occasions are available, as are beautiful common spaces such as a large living room, dining room, meeting room, library, sports bar, and beauty shop. Included in this list of amenities that surpasses the typical list of long term care options are TigerPlace Pet Initiative (TiPPI) Veterinary Medicine Clinic, TigerCare Wellness Center, and TigerCize Exercise and Spa area.
A major goal for MU is to design and implement exciting research, education, and practice opportunities at TigerPlace while integrating TigerPlace into the MU campus and the Columbia community. From the resident’s point of view, on-going assessment, early illness recognition, health promotion activities, and a well-designed housing environment will help older people stay healthier and active longer, avoid expensive and debilitating hospitalizations, and for most residents, avoid relocation to a nursing home. The links with MU are important as seniors become involved in the student learning projects and take advantage of classes and cultural activities of their interest at MU.
In the area below you will find a list of recently added TigerPlace research articles.
Passive sensor networks were deployed in independent living apartments to monitor older adults in their home environments to detect signs of impending illness and alert clinicians so they can intervene and prevent or delay significant changes in health or functional status. A retrospective qualitative analysis was undertaken to refine health alerts to improve clinical relevance to clinicians as they use alerts in their normal work flow of routine care delivery to older adults. Clinicians completed text boxes to describe actions taken (or not) as a result of each alert. The significance for each health alert was also rated on a scale of 1-5. Two samples for analysis after alert algorithms had been adjusted based on results of a pilot study using health alerts.
In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time and step length from a three-dimensional voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the lab with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. An excellent agreement with intra-class correlation coefficients of 0.99 and repeatability coefficients between 0.7% and 6.6% was found for walking speed, step time and step length given the limitation of frame rate and voxel resolution. The system was further tested with 10 seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.
As people age, they want to remain as active and independent as possible for as long as possible. They want to age at home, not in institutions like nursing homes (Marek & Rantz, 2000). According to a 2010 AARP survey, 88 percent of people over age 65 want to stay in their residence for as long as possible (AARP, 2010). Technology has the potential to help people remain at home by monitoring their health status, detecting emergency situations such as debilitating falls, and notifying health care providers to potential changes in health status or emergency situations. Researchers at the University of Missouri (MU) are using sensor technology at TigerPlace (a senior housing complex that enables residents to Age in Place) to detect changes in health status of the residents, alert health care providers, and augment traditional healthcare. This article reviews the Aging in Place research, TigerPlace as a state sponsored Aging in Place site, and the sensor technology developed by MU to support Aging in Place.
We present an example of unobtrusive, continuous monitoring in the
home for the purpose of assessing early health changes. Sensors embedded in
the environment capture activity patterns. Changes in the activity patterns are
detected as potential signs of changing health. A simple alert algorithm has
been implemented to generate health alerts to clinicians in a senior housing
facility. Clinicians analyze each alert and provide a rating on the clinical
relevance. These ratings are then used as ground truth in developing classifiers.
Here, we present the methodology and results for two classification approaches
using embedded sensor data and health alert ratings collected on 21 seniors over
nine months. The results show similar performance for the two techniques,
where one approach uses only domain knowledge and the second uses
supervised learning for training.
The purpose of the study is to investigate whether motion density maps based on passive infrared (PIR) motion sensors and the dis-similarity measure of the density maps, along with relative energy expenditure estimates derived from motion density are sensitive enough to detect changes in mental health over time.
Our team has developed a technological innovation that detects changes in health
status that indicate impending acute illness or exacerbation of chronic illness before usual assessment methods or self-reports of illness. We successfully used this information in a 1-year prospective study to alert health care providers so they could readily assess the situation and initiate early treatment to improve functional independence. Intervention participants showed significant improvements (as compared with the control group) for the Short Physical Performance Battery gait speed score at Quarter 3 (p = 0.03), hand grip-left at Quarter 2 (p = 0.02), hand gripright at Quarter 4 (p = 0.05), and the GAITRite functional ambulation profile score at Quarter 2 (p = 0.05). Technological methods such as these could be widely adopted in older adult housing, long-term care settings, and in private homes where older adults wish to remain independent for as long as possible.