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BACKGROUND: Falls and fall-related injuries are significant public health issues for adults 65 years of age and older. The annual direct medical costs in the United States 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. This project targets community-dwelling older adults (OA) with mild cognitive impairment (MCI) who are socially vulnerable and thus at high risk for falling. METHODS: We have developed an innovative technology-supported nursing-driven intervention called Sense4Safety to (a) identify escalating risk for falls real time through in-home passive sensor monitoring (including depth sensors) ; (b) employ machine learning to inform individualized alerts for fall risk; and (c) link "at risk" socially vulnerable OA with a coach who guides them in implementing evidence-based individualized plans to reduce fall risk. The purpose of this study was to assess the feasibility and acceptability of the Sense4Safety intervention through participant interviews. RESULTS: We recruited a cohort of 11 low-income OA with MCI who received the intervention for 3 months. Our study findings indicate the overall feasibility of the intervention with most participants (n = 9; 82%) having confidence in the passive monitoring system to effectively predict fall risk and generate actionable and tailored information that informs educational and exercise components. CONCLUSIONS: Passive sensing technologies can introduce acceptable platforms for fall prevention for community-dwelling OA with MCI.
Demiris et al. (2025) studied this question.