PMID- 37495708
OWN - NLM
STAT- Publisher
LR  - 20231015
IS  - 1590-3478 (Electronic)
IS  - 1590-1874 (Print)
IS  - 1590-1874 (Linking)
VI  - 44
IP  - 11
DP  - 2023 Nov
TI  - Systematic review and network meta-analysis of robot-assisted gait training on 
      lower limb function in patients with cerebral palsy.
PG  - 3863-3875
LID - 10.1007/s10072-023-06964-w [doi]
AB  - OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.
CI  - (c) 2023. The Author(s).
FAU - Wang, Yueying
AU  - Wang Y
AD  - College of Rehabilitation Medicine, Shandong University of Traditional Chinese 
      Medicine, Jinan, China.
FAU - Zhang, Peipei
AU  - Zhang P
AD  - Department of Rehabilitation Medicine, The Second Affiliated Hospital of Shandong 
      University of Traditional Chinese Medicine, Jinan, China. 408291294@qq.com.
FAU - Li, Chao
AU  - Li C
AUID- ORCID: 0009-0005-8633-3221
AD  - Department of Rehabilitation and Physiotherapy, The Affiliated Hospital of 
      Shandong University of Traditional Chinese Medicine, Jinan, China. 
      lichao1026@163.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230726
PL  - Italy
TA  - Neurol Sci
JT  - Neurological sciences : official journal of the Italian Neurological Society and 
      of the Italian Society of Clinical Neurophysiology
JID - 100959175
SB  - IM
PMC - PMC10570202
OTO - NOTNLM
OT  - Cerebral palsy
OT  - Lower limb function
OT  - Network meta-analysis
OT  - Robot-assisted walking training
OT  - Systematic review
COIS- The authors declare no competing interests.
EDAT- 2023/07/27 01:09
MHDA- 2023/07/27 01:09
PMCR- 2023/07/26
CRDT- 2023/07/26 23:25
PHST- 2023/04/28 00:00 [received]
PHST- 2023/07/14 00:00 [accepted]
PHST- 2023/07/27 01:09 [pubmed]
PHST- 2023/07/27 01:09 [medline]
PHST- 2023/07/26 23:25 [entrez]
PHST- 2023/07/26 00:00 [pmc-release]
AID - 10.1007/s10072-023-06964-w [pii]
AID - 6964 [pii]
AID - 10.1007/s10072-023-06964-w [doi]
PST - ppublish
SO  - Neurol Sci. 2023 Nov;44(11):3863-3875. doi: 10.1007/s10072-023-06964-w. Epub 2023 
      Jul 26.