Feasibility and Acceptability of a Technology Mediated Fall Risk Prevention Intervention for Older Adults with Mild Cognitive Impairment.

Abstract

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 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 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 1) identify escalating risk for falls real-time through in-home passive sensor monitoring (including depth sensors); 2) employ machine learning to inform individualized alerts for fall risk; and 3) link 'at risk' socially vulnerable older adults 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 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 older adults with MCI.