Smart Wearable Technologies for Balance Rehabilitation in Older Adults at Risk of Falls: Scoping Review and Comparative Analysis.

Abstract

Background: Falls among older adults are a significant public health concern, often leading to severe injuries, decreased quality of life, and substantial health care costs. Smart wearable technologies for balance rehabilitation present a promising avenue for addressing the falls epidemic, capable of providing detailed objective movement data, engaging visuals, and real-time feedback. With the recent and rapid evolution of innovative technologies, including artificial intelligence (AI), augmented reality (AR) or virtual reality (VR), and motion tracking, there is a need to evaluate the market to identify the most effective and accessible smart balance systems currently available.

Objective: This study aims to evaluate the current landscape of smart wearable technology systems for balance rehabilitation in older adults at risk of falls. In addition, it aims to compare market-available systems to the telerehabilitation of balance clinical and economic decision support system (TeleRehab DSS), a recently developed smart balance system.

Methods: A scoping review and strengths, weaknesses, opportunities, and threats (SWOT) analysis was completed, exploring the landscape of smart balance systems in older adults at risk of falls. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, electronic databases PubMed, MEDLINE, and Cochrane were systematically searched for articles in English from July 1, 2014, to July 1, 2024. Gray literature searches of relevant institutions and web pages were also conducted. The database search and commercial systems were then compared against the TeleRehab DSS in a SWOT analysis.

Results: The scoping review yielded 17 systems that met the inclusion criteria; 10 investigational systems and 7 commercially available systems. Out of 10 studies, only 1 reported the use of intelligent learning or AI, 8 studies reported the use of motion tracking, and 9 studies used virtual reality. Of the studies incorporating motion tracking, 3 provided feedback as either visual or auditory. All but 2 studies reported the use of gamification, and 7 studies incorporated balance exercises. In total, 2 studies reported remote delivery, with 5 being clinician-supervised and 4 providing a clinician report. The SWOT analysis of TeleRehab DSS against the 7 market-available smart balance systems revealed several unique advantages, including personalized therapy with AI-DSS, AR for real-world interaction, enhanced clinician involvement, and comprehensive data analytics.

Conclusions: The findings from this scoping review highlight the rapid evolution of smart balance systems, yet significant gaps remain in AI integration, remote accessibility, and clinician-driven data analytics. Despite limitations such as cost, accessibility, and user training requirements, TeleRehab DSS emerges as a significant innovation, addressing many of these gaps through AI-driven personalization, AR for real-world interaction, and real-time clinician monitoring. These features position it as a next-generation solution that aligns closely with the evolving needs of patients and clinicians. The results of this review provide valuable insights for future research, supporting the need for further validation studies and the development of more intelligent and accessible balance rehabilitation technologies.