Published on: Aug 01, 2025
Falls are a serious health threat for older adults, often leading to injuries that trigger other medical problems, reduce independence, and diminish quality of life. They also place a heavy burden on the health care system, as fall-related injuries drive increased use of services and rank among the most expensive conditions to treat.
In a proof-of-concept study, researchers from the University of Pennsylvania’s School of Nursing, along with collaborators, tested the feasibility of an innovative, technology-supported, nursing-led intervention called Sense4Safety to predict and prevent falls. Their results were published in the Journals of Gerontology, Series A: Biological Sciences and Medical Sciences.
“We’re focused on falls because they often mark the beginning of a cascade of health complications,” said George Demiris, Penn Integrates Knowledge University Professor in Penn Nursing and the Perelman School of Medicine. “They typically result from accumulated vulnerabilities—cognitive decline, financial hardship, living conditions, environmental hazards, and other health issues.
Nancy A. Hodgson, Claire M. Fagin Leadership Professor in Penn Nursing, added, “A fall is often a signal of an individual’s decline. We want to identify what’s happening beforehand—such as changes in gait—so we can intervene early and prevent what might come next.”
The study enrolled 11 adults aged 65 and older with mild cognitive impairment (MCI) to receive the Sense4Safety intervention for three months. People with MCI, Hodgson noted, may struggle with tasks like judging distances, remembering to use assistive devices, or multitasking while moving—factors that can increase fall risk.
Older adults with MCI who live alone in low-income housing or face social isolation are especially vulnerable. “We wanted to start with a population that would have the most to gain from a fall prevention strategy,” Demiris said.
Sense4Safety combines two main components: passive in-home monitoring via a depth sensor and active engagement with a trained coach. The depth sensor collects gait-related data while participants are at home, and the coach provides exercise guidance, education, and environmental safety assessments—such as improving lighting or removing loose rugs.
The exercise element is based on the Otago Exercise Program, which can be tailored to individual abilities. “Adding a balance and strength program for community-dwelling older adults can reduce fall risk by more than a third,” Hodgson explained.
Participants reported that the intervention improved their sense of safety and made them more aware of hazards in their home environment. One participant, for example, often sat down without checking the chair’s position, leading to two near falls. Reviewing video footage helped them recognize and correct the risky behavior.
Privacy concerns were addressed by ensuring the depth sensor only displayed silhouettes and processed data solely for the enrolled participant. If a visitor entered the home, their gait data was not recorded. The team chose this system over wearable devices to avoid the need for charging, removing, or operating additional technology—barriers that could reduce adoption.
Importantly, the researchers involved participants in the process, sharing their own gait data and working toward a user-friendly dashboard designed not just for clinicians but for older adults themselves.
The next phase of research will be a clinical trial, randomly assigning participants to either a control group with passive monitoring only or an intervention group receiving the full Sense4Safety package with coaching, exercise, and education.
Ultimately, the team hopes the intervention will be available to other high-risk populations, such as older adults recently discharged from the hospital.
“Preventing falls can improve quality of life—especially for those living alone—and could also result in significant cost savings for our health care system,” Demiris said.
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