George Demiris PhD, University of Pennsylvania; Therese S. Richmond PhD, RN University of Pennsylvania; Nancy A. Hodgson PhD, RN University of Pennsylvania
Competition Sponsor: National Institute on Aging, National Institutes of Health
Falls and fall-related injuries are significant public health issues for adults 65 years of age and older. Over a third of older adults (OA) fall each year and 10-20% of falls result in serious injuries such as fractures and head trauma. The annual direct medical costs in the US as a result of falls are estimated to exceed $50 billion, and this estimate does not include the indirect costs of disability, dependence, and decreased quality of life. Mild cognitive impairment (MCI) is a leading risk factor for falls in OA. Approximately 15%-20% of OA have MCI, and over 60% of OA with MCI fall annually – two to three times the rate of those without cognitive impairment. Socially vulnerable OA living in low-resource neighborhoods with poor housing conditions have twice the risk of falling. We have developed an innovative technology-supported nursing-driven intervention called Sense4Safety to identify escalating risk for falls real-time through in-home passive sensor monitoring, employ machine learning to inform individualized alerts for fall risk and link ‘at risk’ socially vulnerable older adults with a nurse tele-coach who will guide them in implementing evidence-based individualized plans to reduce fall-risk. The purpose of this study is to refine the Sense4Safety intervention based on extensive feedback by low income OA with MCI, their family members or trusted others and clinicians in order to ensure that the passive monitoring system to predict fall risk for OA with MCI can generate actionable and tailored information
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