Table of Contents

Abstract

Falls in older adults result in serious, life-limiting consequences. An increasing number of fall prevention interventions have used technology to reduce the number of falls in community-dwelling adults. Various types of e-interventions are being tested in clinical trials and in the community. These include telehealth, exergames, cognitive games, socialized training, smart home systems and non-conventional balance training. Currently, no systematic review and meta-analysis has assessed the overall effectiveness of e-interventions and compared the effectiveness of the different types.The aim of this review was to synthesize best available evidence concerning the effectiveness of e-interventions on fall, neuromuscular functions and quality of life in community-dwelling older adults.A rigorous three-step search was conducted in nine online databases for published and unpublished randomized controlled trials studying e-interventions. Studies were screened and assessed for individual and overall risk of bias by two independent reviewers. Six fall-related outcomes were evaluated in the meta-analysis: fall risk, balance, lower extremity strength, fall efficacy, cognitive function and health-related quality of life. Subgroup and sensitivity analysis were conducted during meta-analysis.Thirty-one studies fit the eligibility criteria and had an overall 74.7% low risk of bias. A total of 4,877 older adults from 17 countries were included in narrative synthesis and meta-analysis. Telehealth combined with exercise programmes and smart home systems were able to reduce fall risk significantly (risk ratio=0.79, 95% CI [0.72, 0.86]). E-interventions also significantly improved balance and fall efficacy (standardized mean difference=0.28, 95% CI [0.04, 0.53]). However, lower extremity strength, cognitive function and health-related quality of life did not show significant improvements.Telehealth combined with exercise and smart home systems demonstrated the best evidence of effectiveness in reduction of falls in community-dwelling older adults. Future research should focus on forecasting falls using smart home technology and Artificial Intelligence, and testing promising e-interventions on larger samples to improve the strength of evidence of fall prevention by e-interventions.