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Robot-Assisted Gait Training in Individuals With Spinal Cord Injury: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Article en En | WPRIM | ID: wpr-1042065
Biblioteca responsable: WPRO
ABSTRACT
Spinal cord injury (SCI) rehabilitation emphasizes locomotion. Robotic-assisted gait training (RAGT) is widely used in clinical settings because of its benefits; however, its efficacy remains controversial. We conducted a systematic review and meta-analysis to investigate the efficacy of RAGT in patients with SCI. We searched international and domestic databases for articles published until April 18, 2024. The meta-analysis employed a random effects model to determine the effect size as either mean difference (MD) or standardized MD (SMD). Evidence quality was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Twenty-three studies with a total of 690 participants were included in the final analysis. The overall pooled effect size for improvement in activities of daily living was 0.24, with SMD (95% confidence interval [95% CI], 0.04–0.43; GRADE: high) favoring RAGT over conventional rehabilitation. Muscular strength (MD, 0.23; 95% CI, 0.02–0.44; GRADE: high), walking index for SCI (MD, 0.31; 95% CI, 0.07–0.55; GRADE: moderate) and 6 min walk test distance (MD, 0.38; 95% CI, 0.14–0.63; GRADE: moderate) showed significant improvement in the robot group. Subgroup analysis revealed that subacute patients and intervention periods >2 months were more effective. This meta-analysis revealed that RAGT significantly improved activities of daily living, muscular strength, and walking abilities. Additional studies are needed to identify the optimal treatment protocol and specific patient groups for which the protocol is most effective.
Texto completo: 1 Base de datos: WPRIM Idioma: En Revista: Annals of Rehabilitation Medicine Año: 2024 Tipo del documento: Article
Texto completo: 1 Base de datos: WPRIM Idioma: En Revista: Annals of Rehabilitation Medicine Año: 2024 Tipo del documento: Article