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.

Inclusion Criteria:

• aged ≥ 65 years
• reside in senior living communities and plan to be in the area for more than 6 months
• be able to walk household distances
• score of at least 1 of 12 on the Short Physical Performance Battery (SPPB)
• meet MCI criteria (MCI is defined as a score ≥17 Montreal Cognitive Assessment)

Exclusion Criteria:

Exclusion criteria include terminal illness, central nervous system condition such as stroke or Parkinson's disease, diagnosis of dementia, severe vision or hearing deficits, and reliant on wheelchair or scooter within living quarters.