A new $1.77 million study of “smart” technology has the potential to improve retirement community residents’ quality of life by extending their ability to remain independent and age in place, according to researchers. Caregiver burden could be lessened, too.
The five-year grant from the National Institute on Nursing Research to Washington State University will allow researchers to study of how the technology can help monitor health and safety and enable real-time assessment and interventions for retirement community residents and other older adults.
The funds will continue the work that Roschelle “Shelly” Fritz, Ph.D., an assistant professor in the WSU College of Nursing in Vancouver, WA, began during a pilot project at Touchmark on South Hill, a retirement community in Spokane, WA.
At the community, Fritz has deployed five health-assistive smart homes with support from the Touchmark Foundation. She is evaluating the clinical relevance of raw data collected by the sensors to see whether intelligent algorithms can be trained to detect health changes in older adults who have multiple chronic conditions.
In addition to Fritz, also working under the grant are Diane Cook, Ph.D., holder of the Huie-Rogers Chair Professor in the WSU School of Electrical Engineering and Computer Science, and Maureen Schmitter-Edgecombe, Ph.D., the Herbert L. Eastlick Professor in the College of Arts and Sciences’ Department of Psychology.
The study also builds on Cook and Schmitter-Edgecombe’s work in developing a health-assistive smart home that uses intelligent algorithms capable of detecting and labeling more than 40 activities of daily living and behavior patterns for older adults with more than 98% accuracy.
Under the new funds, the three researchers will design and pilot test smart-home technology to automatically identify health events for adults with chronic conditions. As sensors record information, a healthcare professional will identify data that are relevant to a person’s health and safety. Then engineers will create computer algorithms to recognize meaningful behavioral patterns.
For example, according to the university, a sensor might detect motion in a person’s kitchen around the same time each night that the person gets a glass of water before bed; the clinician would flag that pattern as important, and the engineer would create an algorithm to trigger an automatic alert to caregivers in the absence of that motion.