ReviewJMIR XR and spatial computing2025
Virtual, Augmented, and Mixed Reality for Motor Neurorehabilitation: Scoping Review Focused on the Role of Body Representation.
Review in JMIR XR and spatial computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Extended reality (XR), encompassing virtual reality, augmented reality (AR), and mixed reality, is increasingly being used in neurorehabilitation to provide multisensory feedback and promote neural plasticity in sensorimotor networks. Objective: This scoping review aimed to (1) examine how XR technologies are applied in motor neurorehabilitation, (2) explore how body representation and somatic embodiment are addressed, and (3) analyze the methodological designs of XR-based interventions. Methods: This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, with a comprehensive search across PubMed, Embase, Scopus, and Web of Science from inception to December 2023. Eligible studies included original research involving XR-based interventions explicitly targeting neurorehabilitation. Studies related to somatic embodiment and reporting data on implementation and user outcomes were considered without date restriction. Three independent reviewers conducted screening in Covidence. The following variables were extracted: study design, participant characteristics, XR devices and software, experimentation details, treatment approaches, and evaluation methods. Methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale and the Murad Scale. Findings have been presented in tabular and narrative formats. Results: Twenty-six studies met the inclusion criteria, and these were mainly clinical trials involving patients with neurological conditions, particularly poststroke status (n=6) and spinal cord injury (n=2). Several studies provided physiological data, including electroencephalography (n=12), electromyography (n=2), magnetic resonance imaging (n=1), galvanic skin response (n=1), electrodermal activity (n=1), and motor-evoked potential data (n=1). Two studies used noninvasive brain stimulation, and another two used eye tracking. Most studies (n=17) used built-in motion sensors; however, some (n=8) analyzed the data quantitatively. Unity 3D was the most frequently used development platform (n=8). First-person (n=20) and third-person (n=2) perspectives were used, and 4 studies combined both perspectives. Interventions mainly targeted sensorimotor deficits, with improvements in motor and cognitive performance. Sixteen studies addressed body perception, focusing on limb embodiment. Questionnaires were the most frequently used evaluation tools (n=18), and 3 studies used standardized tests. Some studies (n=7) investigated body ownership under visuomotor inconsistencies with or without visuotactile stimulation. XR was primarily applied to enhance sensorimotor recovery and assess device feasibility. Few studies directly measured embodiment (n=4), ownership (n=2), or self-location (n=2). The ability of XR platforms to deliver multisensory feedback appears to facilitate sensorimotor learning and support a more accurate body schema. Conclusions: Evidence from the studies supports the usefulness of XR in enhancing reinforcement learning and facilitating recovery in neurorehabilitation. Tailored XR approaches, which are grounded in embodiment principles and patient-specific needs, show promise for improving outcomes in neurological rehabilitation programs. The AR paradigm, which could offer several advantages, was not explored in depth, perhaps due to its difficult implementation during the period considered.
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