Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (28): 7475-7484.doi: 10.12307/2026.818

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Meta-analysis of robot-assisted walking training on lower limb motor function improvement in Parkinson's disease patients#br#

Ren Yi1, Wang Qing1, Yu Shaohong2, Qiu Zhengang1   

  1. 1School of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China; 2Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250001, Shandong Province, China 

  • Received:2025-09-25 Revised:2025-12-13 Online:2026-10-08 Published:2026-02-27
  • Contact: Qiu Zhengang, PhD, Associate professor, School of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China
  • About author:Ren Yi, MS candidate, School of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China
  • Supported by:
    Shandong Provincial Natural Science Foundation, No. ZR2024MH048 (to YSH); 2025 Postgraduate Quality Improvement and Innovation Project of Shandong University of Traditional Chinese Medicine, No. YJSTZCX2025077 (to RY)

Abstract: OBJECTIVE: Studies have confirmed that robot-assisted walking training can effectively improve motor function in patients with neurological diseases such as stroke, spinal cord injury, and multiple sclerosis. Currently, different robot-assisted gait training devices differ in design and function, but their impact on Parkinson’s disease patients remains unclear. Different robots can provide different motion parameters, motion frequencies, and training modes, but related research is scarce. Therefore, this article systematically evaluates the impact of robot-assisted walking training on lower limb motor function in Parkinson’s disease patients.
METHODS: Randomized controlled trials addressing the impact of robot-assisted walking training on lower limb motor function in patients with Parkinson’s disease were retrieved from English databases (PubMed, Web of Science, Embase, Cochrane Library) and Chinese databases (CNKI, WanFang, and VIP) by computer. The retrieval time limit was from the establishment of each database to April 20, 2025. The methodological quality of the included studies was evaluated according to the Cochrane Risk of Bias Assessment Tool. Meta-analysis was performed using RevMan 5.3 software.
RESULTS: (1) A total of 12 articles involving 526 patients with Parkinson’s disease were included. (2) Meta-analysis results showed that compared with the control group, robot-assisted walking training was superior to the control group in the improvement of Berg scale score [MD=4.08, 95%CI(2.59, 5.58), P < 0.000 01], Activities-specific Balance Confidence Scale [MD=4.31, 95%CI(2.97-5.83), P < 0.000 01], 6-minute walk test distance [MD=32.62, 95%CI(13.41, 51.83), P = 0.000 9], Timed Up and Go test time [MD=-1.88, 95%CI(-2.58, -1.18), P < 0.000 01], cadence(MD=2.98, 95%CI(0.67, 5.29), P=0.01], stride length (MD=9.11, 95%CI(7.06, 11.15), P < 0.000 01], walking speed (MD=0.04, 95%CI(0.02, 0.06), P=0.000 1], Unified Parkinson’s Disease Rating Scale Part II score (MD=-2.05, 95%CI(-2.55, -1.55), P < 0.000 01], and Unified Parkinson’s Disease Rating Scale Part III score (MD=-3.73, 95%CI(-4.17, -3.29), P < 0.000 01].
CONCLUSION: Robot-assisted walking training can effectively improve the lower limb motor function of patients with Parkinson’s disease. Specifically, it enhances balance function and walking ability, and improves gait parameters. Notably, an intervention period of 8 weeks or longer shows a more significant improvement in walking endurance (measured by the 6-Minute Walk Test) and dynamic balance (measured by the Timed Up and Go Test), with an average increase of 0.04 m/s in walking speed and 9.11 cm in step length. However, further verification through large-sample, high-quality randomized controlled trials is still required.

Key words: Parkinson’s disease, robot-assisted walking training, lower limb motor function, meta-analysis, evidence-based medicine

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