Combined robot-assisted gait training and virtual reality in stroke rehabilitation: a meta-analysis

Introduction: Stroke is a leading cause of long-term disability, with approximately 80% of survivors experiencing initial motor dysfunction. While robot-assisted gait training (RAGT) and virtual reality (VR) have emerged as promising interventions, evidence regarding their combined effectiveness remains inconsistent. This study aims to quantitatively evaluate the effects of combined RAGT and VR interventions on lower limb function in stroke patients, specifically examining gait distance, gait speed, and balance ability. Materials and methods: Five international databases (CINAHL, Embase, MEDLINE, Web of Science, and Google Scholar) were systematically searched from inception to October 2025 for randomized controlled trials (RCTs) evaluating combined RAGT and VR interventions. Quality assessment used the Cochrane Risk of Bias tool. Meta-analysis was performed using RevMan 5.4 with random-effects models. Subgroup analyses examined intervention session number and stroke phase. Results: Seven RCTs involving 299 stroke patients were included. Combined RAGT and VR demonstrated significant improvements in gait distance (standardized mean difference [SMD] = 0.41; 95% confidence interval [CI]: 0.07–0.75; p = 0.02) and balance ability (SMD = 0.81; 95% CI: 0.37–1.26; p = 0.0004). Gait speed improvement was not statistically significant (SMD = 0.29; 95% CI: −0.23–0.82; p = 0.28). Twelve-session protocols benefited gait distance in acute-phase patients, while 18+-session protocols improved balance in chronic-phase patients. Conclusions: Combined RAGT and VR interventions effectively enhance gait distance and balance in stroke patients, with effects varying by intervention intensity and recovery stage. Personalized rehabilitation strategies may optimize therapeutic outcomes.