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Review
. 2023 Jan 3;13(1):92.
doi: 10.3390/brainsci13010092.

Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis

Affiliations
Review

Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis

Alberto Loro et al. Brain Sci. .

Abstract

Background: Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors.

Methods: PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used.

Results: A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of -0.62 (95%CI - 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (β = -1.019, 95% CI - 1.827; -0.210, p-value = 0.0135).

Conclusions: RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy.

Keywords: balance; gait; rehabilitation; robotics; stroke.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram representing the study selection process for the meta-analysis.
Figure 2
Figure 2
Forest plot regarding BBS meta-analysis. On the right, there are the mean pre-post difference of each selected study and their respective 95% CI. At the bottom, there are total pMD, its 95% CI, and heterogeneity evaluation.
Figure 3
Figure 3
Forest plot regarding TUG meta-analysis. On the right, there are the mean pre-post difference of each selected study and their respective 95% CI. At the bottom, written in bold, there are total pMD, its 95% CI, and heterogeneity evaluation.
Figure 4
Figure 4
Forest plots summarizing the influence analyses performed: (a) pMD obtained omitting one study at a time (on the left) in BBS meta-analyses; (b) pMD obtained omitting one study at a time (on the left) in TUG meta-analyses.
Figure 5
Figure 5
(a) Funnel plot that analyses potential publication bias in BBS meta-analysis. On the top right corner, p-value calculated with Egger’s test for publication bias. (b) Funnel plot that analyses potential publication bias in BBS meta-analysis. On the top right corner, p-value calculated with Egger’s test for publication bias.

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