Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence

BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses. Traditional prevention programs, while effective, remain underutilized due to poor adherence and participation. Extended reality (XR) technologies, combined with artificial intelligence (AI), are emerging as promising solutions to mitigate fall-related outcomes.ObjectiveTo evaluate existing literature on extended (XR) and virtual reality (VR) based interventions and the role of AI in improving balance, mobility and fall outcomes among older adults.MethodologyA rapid review was conducted using Cochrane Rapid Review methods and conducted in Ovid MEDLINE, Scopus, and IEEE Xplore. Studies meeting eligibility criteria were screened and extracted using Covidence. Data on study design, participant characteristics, intervention components, outcome measures, and key findings were extracted and synthesized, narratively with attention to AI use within XR interventions.ResultsAfter dual screening, four studies met the inclusion criteria. XR and VR interventions showed improvements in mobility, movement quality and muscular strength. Two studies used AI primarily for movement classification and postural control analysis. One exergame study without AI showed mixed results. Participant engagement was generally high, with low dropout rates. Most full-text exclusions were due to absence of AI methods, highlighting the limited AI-specific evidence base. No study measured fall incidence directly.ConclusionThis rapid review highlights the effectiveness of XR interventions in enhancing mobility and participant adherence. However, evidence is limited as the available publications remain preliminary and do not demonstrate fall reduction and the added benefits of AI. Larger, well-designed studies are needed to establish the clinical value of these technologies.

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Publication Details

Journal
Neurorehabilitation
Published
2026-09-17
DOI
https://doi.org/10.1177/10538135261488430
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence

Hamdoni K. Pangandaman, Mirella Veras, Muhammad Haris, Mê‐Linh Lê et al.
Neurorehabilitation
Balance, Gait, and Falls Prevention
article

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence

Hamdoni K. Pangandaman, Mirella Veras, Muhammad Haris, Mê‐Linh Lê, Ke Peng, Mina Park, Barbara Augusta Martins, Bonnie Dueck, Jesse Gibbons, Bowen Cai, Zahra Moussavi, Brittany Elliott
article en

Abstract

BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses. Traditional prevention programs, while effective, remain underutilized due to poor adherence and participation. Extended reality (XR) technologies, combined with artificial intelligence (AI), are emerging as promising solutions to mitigate fall-related outcomes.ObjectiveTo evaluate existing literature on extended (XR) and virtual reality (VR) based interventions and the role of AI in improving balance, mobility and fall outcomes among older adults.MethodologyA rapid review was conducted using Cochrane Rapid Review methods and conducted in Ovid MEDLINE, Scopus, and IEEE Xplore. Studies meeting eligibility criteria were screened and extracted using Covidence. Data on study design, participant characteristics, intervention components, outcome measures, and key findings were extracted and synthesized, narratively with attention to AI use within XR interventions.ResultsAfter dual screening, four studies met the inclusion criteria. XR and VR interventions showed improvements in mobility, movement quality and muscular strength. Two studies used AI primarily for movement classification and postural control analysis. One exergame study without AI showed mixed results. Participant engagement was generally high, with low dropout rates. Most full-text exclusions were due to absence of AI methods, highlighting the limited AI-specific evidence base. No study measured fall incidence directly.ConclusionThis rapid review highlights the effectiveness of XR interventions in enhancing mobility and participant adherence. However, evidence is limited as the available publications remain preliminary and do not demonstrate fall reduction and the added benefits of AI. Larger, well-designed studies are needed to establish the clinical value of these technologies.

Neurorehabilitation
Riverview Hospital (CA), Mindanao State University (PH), University of Manitoba (CA)
No poverty
Openalex Percentile: Top 6%
Balance, Gait, and Falls Prevention
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