Sense4Safety Intervention

Abstract 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. This project targets community dwelling OA with mild cognitive impairment (MCI). 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. We have developed and pilot-tested an innovative technology-supported intervention called Sense4Safety to 1) identify escalating risk for falls real-time through in-home passive sensor monitoring; 2) employ machine learning to inform individualized alerts for fall risk; and 3) link `at risk' older adults with a coach who will guide them in implementing evidence-based individualized plans to reduce fall-risk. The purpose of this study is to assess the effectiveness of Sense4Safety in reducing fall risk with a randomized clinical trial, and understand implementation factors to improve the scalability of Sense4Safety in diverse community settings.