Wang Yueying, Zhang Peipei, Li Chao
College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Department of Rehabilitation Medicine, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Neurol Sci. 2023 Nov;44(11):3863-3875. doi: 10.1007/s10072-023-06964-w. Epub 2023 Jul 26.
This study aimed to evaluate the effectiveness of robot-assisted gait training (RAGT) in treating lower extremity function in patients with cerebral palsy (CP) and compare the efficacy differences between different robotic systems.
PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP, CBM, and Wanfang databases were searched to collect randomized controlled trials of RAGT for lower extremity dysfunction in patients with CP from the time the databases were created until December 26, 2022. The D and E of Gross Motor Function Measure-88 (GMFM-88) assessed lower limb motor function. Berg Balance Scale (BBS) was used to assess balance function. Walking endurance and speed were assessed using the 6-minute walk test (6MWT) and walking speed. The modified Ashworth Scale (MAS) was used to assess the degree of muscle spasticity in the lower extremities. The Cochrane Risk Assessment Scale and the Physiotherapy Evidence Database (PEDro) scale were used for qualitative assessment in the studies included. RevMan 5.4 was used for data merging and statistical analysis. R 4.2.0 and ADDIS 1.16.8 were used to map the network relationships and to perform the network meta-analysis.
A total of 14 studies were included in the review. The meta-analysis showed that RAGT significantly improved GMFM-88 D and E, BBS, and 6MWT scores in CP patients compared with conventional rehabilitation. However, for walking speed and MAS, the intervention effect of RAGT was insignificant. The network meta-analysis showed that the best probability ranking for the effect of the 3 different robots on the GMFM-88 D score was LokoHelp (P = 0.66) > Lokomat (P = 0.28) > 3DCaLT (P = 0.06) and the best probability ranking for the GMFM-88 E score was LokoHelp (P = 0.63) > 3DCaLT (P = 0.21) > Lokomat (P = 0.16).
RAGT positively affects walking and balance function in patients with CP, while efficacy in improving gait speed and muscle spasticity is unknown. The best treatment among the different robots is LokoHelp. Future high-quality, long-term follow-up studies are needed to explore the clinical efficacy of RAGT in depth.
本研究旨在评估机器人辅助步态训练（RAGT）对脑瘫（CP）患者下肢功能的治疗效果，并比较不同机器人系统之间的疗效差异。
检索PubMed、Web of Science、Cochrane图书馆、Embase、中国知网（CNKI）、维普资讯（VIP）、中国生物医学文献数据库（CBM）和万方数据库，收集从数据库创建至2022年12月26日期间关于RAGT治疗CP患者下肢功能障碍的随机对照试验。采用粗大运动功能测量量表-88（GMFM-88）的D和E部分评估下肢运动功能。采用伯格平衡量表（BBS）评估平衡功能。采用6分钟步行试验（6MWT）和步行速度评估步行耐力和速度。采用改良Ashworth量表（MAS）评估下肢肌肉痉挛程度。纳入研究采用Cochrane风险评估量表和物理治疗证据数据库（PEDro）量表进行定性评估。使用RevMan 5.4进行数据合并和统计分析。使用R 4.2.0和ADDIS 1.16.8绘制网络关系图并进行网络荟萃分析。
本综述共纳入14项研究。荟萃分析表明，与传统康复相比，RAGT显著改善了CP患者的GMFM-88 D和E部分、BBS及6MWT评分。然而，对于步行速度和MAS，RAGT的干预效果不显著。网络荟萃分析表明，3种不同机器人对GMFM-88 D评分的效果最佳概率排名为LokoHelp（P = 0.66）> Lokomat（P = 0.28）> 3DCaLT（P = 0.06），对GMFM-88 E评分的最佳概率排名为LokoHelp（P = 0.63）> 3DCaLT（P = 0.21）> Lokomat（P = 0.16）。
RAGT对CP患者的步行和平衡功能有积极影响，而在提高步态速度和肌肉痉挛方面的疗效尚不清楚。不同机器人中最佳治疗方案是LokoHelp。未来需要高质量、长期随访研究以深入探索RAGT的临床疗效。
