【論文摘要】The Effects of Error Augmentation on Upper Extremity Robotic Assisted Training After Stroke: A Systematic Review and Meta-Analysis
2019
Background and Purpose: Error augmentation (EA) is a feedback strategy which artificially increases performance error during practice. Additional information carried by EA is hypothesized to enhance error-based learning. Currently, EA has been adopted by upper extremity robotic assisted training (RAT) to enhance motor performances after stroke. However, the effects of EA on RAT remain to be determined. Therefore, the aim of this study was to investigate the effects of EA on upper extremity RAT in post-stroke subjects with quantitative meta-analyses through a systematic review. Methods: PubMed, Scopus, and Physiotherapy Evidence Database (PEDro) were searched with keywords (error augmentation, robotic assisted training, stroke, upper-extremity, motor recovery, etc.) to locate relevant studies upto November 2018. Two reviewers independently screened abstracts and full-text articles for eligibility of studies. Comprehensive Meta-Analysis software (ver. 3.0, Biostat, Englewood, NJ, USA) was used to analyze standard difference in means (SDM) on two categories of outcome: (1) Arm Motor Fugl-Meyer Assessment (AMFM) and (2) kinematic error (KE) of reaching task. Results: Seven studies (PEDro score = 5.00 ± 1.73) were eligible for systematic review while six studies were available for meta-analysis. RAT with EA significantly improved motor control ability (AMFM: SDM = 0.589, 95% confidence interval [CI] = 0.096-1.083) and motor performance (KE: SDM = 0.985, 95% CI = 0.397-1.573) better than RAT without EA right after training. Conclusion: EA, as a feedback modulation strategy, could be adopted by upper extremity RAT to enhance its training effects on patients with stroke. Clinical Relevance: Therapists may try to adapt EA to their regular training to optimize motor performances of stroke patients.
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