Catherine Meriano, professor of occupational therapy, is one of several lead investigators in a new two-year research study that will examine whether seniors monitored by Healthsense, Inc.'s wireless sensor technology at the Masonicare healthcare and retirement community in Wallingford, Conn., are able to remain independent for longer, delay being admitted to a hospital or nursing home, and better attend to their own basic daily needs.
The two-year study is the first of its kind in the New England area and among the first nationally to specifically examine whether integrated sensor technology can help seniors age safely and comfortably in their own homes, according to Jim Albert, Masonicare's chief information officer and vice president of information services. Masonicare is the largest senior-focused healthcare system in Connecticut, as well as one of the largest such systems in the country.
"The first goal of the research is fairly simple: we want to see if there is any difference between the two groups over the two-year span of the study in terms of admissions to hospitals and/or nursing homes," Meriano said. "Second, we'll compare the two groups to see how they fare from a functionality standpoint - for example, whether residents with access to the sensor technology are able to maintain their activities of daily living (ADLs), such as dressing, bathing and cooking, for longer."
Developed with grants from the National Institutes of Aging (NIA) and the Defense Advanced Research Projects Agency (DARPA), the eNeighbor System employs a wide range of sensors in seniors' residences to "learn" their daily activities and detect unexplained changes in their behavior that may indicate a need for assistance. These include tilt sensors on medicine boxes to monitor medication usage; motion detectors on walls to detect movement within rooms; contact sensors on kitchen cupboards and refrigerator doors to monitor whether the resident is eating regularly; toilet sensors to monitor toilet usage; pressure sensors on beds to detect when a resident gets in or out of bed, and home-or-away sensors that can detect when the resident leaves or returns to the residence.
Using algorithms to predict residents' behavior based on their individual habits and lifestyle, eNeighbor's "smart" operating system analyzes the correlated data from the sensors to determine whether the resident requires assistance and automatically issues alerts when the data indicate help is needed.