Extended reality in the management of upper limb musculoskeletal conditions: A scoping review
Bibliographic record
Abstract
BACKGROUND: Extended reality (XR) is increasingly used in the management of upper limb musculoskeletal conditions. PURPOSE: To systematically map reported interventions using XR, explore their effects, and identify gaps in knowledge. STUDY DESIGN: Scoping review. METHODS: Searches were conducted in CINAHL, ClinicalTrials.gov, Embase, MEDLINE, PEDro, and Web of Science, covering publication from 2006 to September 2024. Primary research studies were included if they focused on adults with upper limb musculoskeletal disorders, evaluated at least one XR intervention, and reported at least one outcome related to pain, range of motion, or function. The quality of the studies was assessed using the PEDro scale and Joanna Briggs Institute checklists. The present scoping review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping reviews guidelines. RESULTS: A total of 19 studies were included. Fifteen were randomized controlled trials, one was a nonrandomized controlled trial, two were case series, and one was a case report. The majority (n = 15) focused on shoulder conditions, with no studies identified for elbow, nontraumatic wrist, or hand conditions. Among the included studies, none evaluated mixed reality, only one investigated augmented reality, and 18 focused on virtual reality. XR interventions seem to be more effective at improving range of motion and upper limb disability than alleviating pain in people with upper limb conditions. CONCLUSIONS: While XR shows potential for improving range of motion and disability in people with upper limb musculoskeletal conditions, its clinical applications are hindered by methodological inconsistencies and limited evidence. Future research should prioritize high-quality randomized controlled trials with larger and more diverse populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".