Immersive Virtual Reality in Stroke Rehabilitation: A Systematic Review and Meta-Analysis of Its Efficacy in Upper Limb Recovery

Background: Immersive virtual reality (imVR) has shown promise for upper limb stroke rehabilitation (ULSR). However, optimal implementation and treatment modalities remain unclear. This systematic review and meta-analysis aimed to evaluate imVR’s efficacy in ULSR and determine optimal treatment parameters. Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs), comparing imVR to conventional rehabilitation (CR) in adult stroke patients, was conducted. Databases including, the Web of Science, Scopus, and PubMed, were searched. Meta-regression further explored the relationship between intervention duration, frequency, and outcomes. Results: Twenty-three studies were included in the systematic review, representing 395 patients, with thirteen incorporated into the meta-analysis. imVR showed statistically significant improvements in the Fugl–Meyer Assessment Upper Extremity (FMA-UE) Scale (mean difference (MD) = 3.04, 95% CI 1.46; 4.62, p < 0.001) and the Box and Block Test (BBT) (MD = 2.85, 95% CI 0.70; 4.99, p = 0.009) compared to CR, but not in the Action Research Arm Test (ARAT) (MD = 3.47, 95% CI −0.22; 7.15, p = 0.06). However, these improvements did not reach clinically significant thresholds (7 points for FMA-UE and 6 points for BBT). Clinical subgroup analysis showed significant improvements for both subacute (standardized mean difference (SMD) = 0.92, 95% CI 0.48; 1.36, p = 0.002) and chronic (SMD = 0.69, 95% CI 0.03; 1.35, p = 0.03) stroke stages. Meta-regression indicated that there was a significant positive relationship between the intervention duration and upper limb improvement. Conclusions: imVR demonstrates potential for improving upper limb motor function following stroke, particularly with longer intervention durations and individual session lengths for chronic stroke. However, the improvements observed were not clinically significant, highlighting the need for further research with larger sample sizes and standardized outcome measures to determine optimal treatment protocols.