Effects of robot-assisted training on balance function in patients with stroke: A systematic review and meta-analysis
DOI:
https://doi.org/10.2340/16501977-2815Keywords:
robot-assisted therapy, stroke, balance function, Berg Balance Scale, meta-analysisAbstract
Objective: To investigate the effectiveness of robot-assisted therapy on balance function in stroke survivors. Data sources: PubMed, the Cochrane Library, Embase and China National Knowledge Infrastructure databases were searched systematically for relevant studies. Study selection: Randomized controlled trials reporting robot-assisted therapy on balance function in patients after stroke were included. Data extraction: Information on study characteristics, demographics, interventions strategies and outcome measures were extracted by 2 reviewers. Data synthesis: A total of 19 randomized trials fulfilled the inclusion criteria and 13 out of 19 were included in the meta-analysis. Analysis revealed that robot-assisted therapy significantly improved balance function assessed by berg balance scale (weighted mean difference (WMD)?3.58, 95% confidence interval (95% CI) 1.89?5.28, p?<?0.001) compared with conventional therapy. Secondary analysis indicated that there was a significant difference in balance recovery between the conventional therapy and robot-assisted therapy groups in the acute/subacute stages of stroke (WMD 5.40, 95% CI 3.94?6.86, p?<?0.001), while it was not significant in the chronic stages. With exoskeleton devices, the balance recovery in robot-assisted therapy groups was significantly better than in the conventional therapy groups (WMD 3.73, 95% CI 1.83?5.63, p?<?0.001). Analysis further revealed that a total training time of more than 10 h can significantly improve balance function (WMD 4.53, 95% CI 2.31?6.75, p?<?0.001). No publication bias or small study effects were observed according to the Cochrane Collaboration tool. Conclusion: These results suggest that robot-assisted therapy is an effective intervention for improving balance function in stroke survivors.Downloads
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Copyright (c) 2021 Lu Wang, Yu Zheng, Yini Dang, Meiling Teng, Xintong Zhang, Yihui Cheng, Xiu Zhang, Qiuyu Yu, Aimei Yin, Xiao Lu
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