Preventing falls in older adults with mild cognitive impairment
A team led by researchers in the School of Nursing has developed a multicomponent intervention that uses passive monitoring and behavioral support to help reduce falls in at-risk older adults.
4 min. read
Falls pose a significant risk to older adults, often resulting in injuries that lead to other health problems, decreased independence, and a lower quality of life. They also pose a considerable burden on the health care system—fall-related injuries are associated with an increased use of services, making them among the most expensive medical conditions to treat.
“We’re focusing on falls because they are a critical issue for older adults—sometimes a fall is the beginning of a lot of other health-related complications or adverse events,” says Penn Integrates Knowledge University Professor George Demiris of the School of Nursing and the Perelman School of Medicine. He adds that they are often the results of accumulated vulnerabilities such as cognitive status, income, living conditions, the environment, and health.
“[A fall] is often a harbinger of an individual’s decline,” agrees Nancy A. Hodgson, Claire M. Fagin Leadership Professor in Penn Nursing. “And so, we are trying to predict upstream what's happening—changes in gait or whatever’s going on before the fall event—so that we can intervene sooner and prevent whatever’s going to happen after that fall.
In this study, they recruited 11 adults aged 65 years and older who had mild cognitive impairment to receive the intervention for three months.
“Executive functioning and processing speed are all essential for maintaining balance and navigating your home safely,” says Hodgson. “People with mild cognitive impairment might have trouble with things like judging distances or remembering to use an assistive device or processing multiple tasks like simultaneously walking and talking.”
“Older adults with mild cognitive impairment who live alone in low-income housing or are socially vulnerable are among the highest risks for falling,” adds Demiris. “So, we wanted to first see if [the intervention] would work in a population that would have the most to gain from a fall prevention strategy.”
Sense4Safety, the intervention developed by the research team, includes both passive monitoring via an in-home depth sensor and active engagement and education with a coach who has expertise with exercise programs.
“Sense4Safety is a multicomponent intervention that looks at both getting technology-mediated assessments—so better understanding what’s going on in the home with the depth sensors—but also having a coach who can work with the older adult to figure out if there are environmental modifications that can be made,” explains Demiris. “For example, could we do something about the poor lighting in the hallway or the loose rug in the living room?”
The intervention also has a tailored exercise program—the Otago Exercise Program—which can be tailored for people’s abilities.
“Adding an exercise component for community-dwelling older adults reduces the fall risk by more than a third,” says Hodgson. “Any well-designed exercise program that brings in balance and strength training can reduce the fall risk.”
The study finds that most participants found the intervention to be useful, providing them with a sense of safety and helping them be more aware of their home environment. They also saw value in the behavioral components—coaching sessions, exercise, and education.
The intervention developed by the research team includes both passive monitoring via an in-home depth sensor (pictured) and active engagement and exercise and education with a coach.
“We had one participant who consistently—when they got up—would sit down walking backwards without looking at where the chair was. They had two near falls because the chair had moved,” says Demiris. “So, looking at the video actually helped them realize how risky this was.”
Potential privacy issues are addressed through data processing—images appear only as silhouettes, and the algorithms used for the depth sensors are tuned for only the study participant.
“If a neighbor visits, their gait characteristics are not going to be captured,” explains Demiris, adding that they chose this model of passive monitoring because they wanted something that did not require participants to have to learn any new hardware or software.
“We wanted to use this technology because when it comes to fall prevention in this population, it is not easy to use wearables. With wearables, you have to remember to charge them, take them off, and put them back on, and you have to operate a system,” says Demiris.
He adds that they tried to include the study participants in the process as well. “So rather than just saying, ‘Hey, we have this in your home, and we'll let you know if something happens,’ we have been trying to show them their own data. Part of our effort has been, can we create a more user-friendly dashboard that has gait-related information not for the clinician—which is the current dashboard we have—but also for the older adults themselves?”
For their next step, the team hope to run a clinical trial with participants randomly assigned to either the control group, who will just receive passive monitoring, or the invention group, who will receive the Sense4Safety invention that includes the coaching exercise and educational components in addition to passive monitoring via depth sensors.
The researchers hope one day that this intervention will be available to populations at high risk for falls—older adults recently released from the hospital, for example.
“Preventing falls can be not only significant in improving quality of life for people, especially those living alone, but it can also be a significant cost saving for our health care system,” says Demiris.
George Demiris is the Mary Alice Bennett University Professor and Penn Integrates Knowledge University Professor with joint faculty appointments in the Department of Biobehavioral Health Sciences at Penn Nursing and the Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine at the University of Pennsylvania.
Nancy A. Hodgson is the Claire M. Fagin Leadership Professor and professor and chair of the Department of Biobehavioral Health Sciences at Penn Nursing.
Other authors are Sean Harrison and Therese S. Richmond of Penn Nursing; Justine Sefcik of Drexel University; and Marjorie Skubic of the University of Missouri.
This work was supported by the National Institute on Aging of the National Institutes of Health (grant numbers R03AG069793 and P30AG073105).
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