Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (16): 4219-4228.doi: 10.12307/2026.710
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Network meta-analysis of robot-assisted gait training interventions on lower limb motor function in stroke patients
Gao Zan1, Liu Yixuan2, Zhang Lichen1, Hou Bing1, Tang Yalei1, Li Shumei1, Che Pengcheng1, Dou Na1
- 1College of Nursing and Rehabilitation, 2School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan 063210, Hebei Province, China
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Received:2025-07-29Accepted:2025-08-27Online:2026-06-08Published:2025-11-28 -
Contact:Dou Na, MS, Associate professor, College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan 063210, Hebei Province, China Co-corresponding author: Che Pengcheng, Professor, College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan 063210, Hebei Province, China -
About author:Gao Zan, MS candidate, College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan 063210, Hebei Province, China Liu Yixuan, School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan 063210, Hebei Province, China Gao Zan and Liu Yixuan contributed equally to this work. -
Supported by:Hebei Provincial Medical Science Research Project in 2024, No. 20240161 (to DN); Hebei Provincial Key Laboratory of Rehabilitation Engineering and Regenerative Medicine, No. SZX202327 (to CPC); Hebei Provincial “Robot+” Application “Best Candidate” Project (to CPC)
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Gao Zan, Liu Yixuan, Zhang Lichen, Hou Bing, Tang Yalei, Li Shumei, Che Pengcheng, Dou Na. Network meta-analysis of robot-assisted gait training interventions on lower limb motor function in stroke patients[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(16): 4219-4228.
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2.1 文献检索结果 初步检索中文数据库得到中国知网665篇、维普196篇和万方1 080篇；英文数据库：Cochrane Library 215篇、PubMed 367篇、Web of Science 865篇和Embase 706篇以及SinoMed 276篇，经过层层筛选，最终纳入22项研究[14-35]。具体筛选流程图见图2。"
2.2 文献纳入质量评估结果 图3偏倚风险评估结果，随机序列生成、分配隐藏、对受试者和研究人员的盲法、对结果评估者的盲法、结果数据完整性、选择性报告和其他偏倚7个方面均可能有一定偏倚风险，其中随机序列生成、对结果评估者的盲法、不完整的结局数据和选择性报告主要为低偏倚风险；随机序列生成、分配隐藏、对结果评估者的盲法有不确定偏倚风险；对受试者和研究人员的盲法及其他偏倚有不确定和高偏倚风险。 2.3 Meta分析结果 2.3.1 FMA-LE评分 FMA-LE展示评分结果见图4，共纳入14项研究[14-15，18，22-26，28-31，34-35]，机器人组351例，对照组348例(P=0.04，I2= 43%)，异质性小，采用固定效应模型。结果分析显示，FMA-LE评分机器人组改善优于对照组(MD=4.02，95%CI：3.51-4.53，P < 0.05)。 2.3.2 FAC评分 FAC展示评分结果见图5。共纳入10项研究[15，17，19-20，22-25，28，33]， 机器人组192例，对照组184例(P < 0.000 1，I2=74%)，异质性大，采用随机效应模型。结果分析显示，FAC评分机器人组改善优于对照组(MD=0.44，95%CI：0.02-0.87，P < 0.05)。 2.3.3 BBS评分 BBS展示评分结果见图6，共纳入10项研究[14-15，17，20，23-24，28，30-31，34]，机器人组219例，对照组209例(P < 0.000 1，I2=78%)，异质性大，采用随机效应模型。结果分析显示，BBS评分机器人组改善优于对照组(MD=3.59，95%CI：0.25-6.93，P < 0.05)。 2.3.4 6 min步行试验评分 6 min步行试验展示评分结果如图7，共纳入10项研究[16-19，21-22，27，32-34]，机器人组190例，对照组185例(P=0.12，I2=36%)，异质性小，采用固定效应模型。结果分析显示，6 min步行试验评分机器人组改善优于对照组(MD=20.62，95%CI：12.71-28.53，P < 0.05)。 2.4 亚组分析 机器人组根据运动处方、患者病程和机器人类型进行亚组分析。①按照运动时间不同分为2个亚组：20-35 min/次训练组和40-"
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60 min/次训练组；②按照运动频率不同分为2个亚组：2-5次/周组和6-10次/周组；③按照运动周期不同分为2个亚组：2-6周组和8-12周组；④按照病程分为2个亚组：病程< 6个月组和病程> 6个月组；⑤将机器人的类型分为2种：减重机器人组和地面行走机器人组。由于FAC、BBS和6 min步行试验评分的研究数量较少和样本量较小，仅对FMA-LE评分进行了亚组分析。 2.4.1 运动时间对脑卒中患者FMA-LE评分的影响 每个亚组运动时间的FMA-LE评分见图8。有10项研究[14-15，22-23，25-26，28，31，34-35]，展示了运动时间为20-35 min/次运动，结果为：P=0.05，I2=47%，采用固定效应模型进行分析。有4项研究[18，24，29-30]，展示了运动时间为40-60 min/次运动，结果为：P=0.47，I2=0%，采用固定效应模型进行分析。结果表明两种运动时间下机器人组下肢功能改善优于对照组(P < 0.05)，亚组分析组内无显著异质性。 2.4.2 运动频率对脑卒中患者FMA-LE评分的影响 每个亚组运动评频率的FMA-LE评分见图9。有7项研究[14-15，18，22，28，34-35]，展示了运动频率为2-5次/周，结果为：P=0.12，I2=41%，采用固定效应模型进行分析。有5项研究[24-26，29-30]，展示运动时间为6-10次/周，结果为：P=0.29，I2=19%，采用固定效应模型进行分析。结果表明两种运动频率下机器人组下肢功能改善均优于对照组(P < 0.05)，亚组分析组内无显著异质性。 2.4.3 运动周期对脑卒中患者FMA-LE评分的影响 每个亚组运动周期的FMA-LE评分见图10。有7项研究[18，22，24，28-29，31，34]，展示了运动周期为2-6周，结果为：P=0.11，I2=42%，采用固定效应模型进行分析。有6项研究[14-15，23，25，30，35]，展示了运动周期为8-12周，结果为：P=0.06，I2=53%，采用固定效应模型进行分析。结果表明两种运动周期下机器人组下肢功能改善均优于对照组，亚组分析组内无显著异质性。"
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2.4.4 病程对脑卒中患者FMA-LE评分的影响 基于患者病程每个亚组的FMA-LE评分见图11。有6项研究[18，23，25，29，31，35]，展示了病程< 6个月，结果为：P=0.10，I2=47%，采用固定效应模型进行分析。有2项研究[18，28]，展示了病程> 6个月，结果为：P=0.43，I2=0%，采用固定效应模型进行分析。结果表明两种病程中机器人组下肢功能改善均优于对照组，亚组分析组内无显著异质性。 2.4.5 机器人类型对脑卒中患者FMA-LE评分的影响 基于机器人类型每个亚组的FMA-LE评分见图12。有4项研究[15，22，28，34]，展示了地面行走机器人，结果为：P=0.13，I2=47%，采用固定效应模型进行分析。有10项研究[14，18，22-23，25，29-31，34-35]，展示了减重机器人，结果为：P=0.02，I2=56%，采用随机效应模型进行分析。结果表明两种类型机器人干预下，机器人组下肢功能改善均优于对照组(P < 0.05)，亚组分析组内无显著异质性。 2.5 Meta分析网络证据图 采用与亚组分析指标FMA-LE一致的方式进行网络荟萃分析，绘制网络证据图。在FMA-LE评分的网状Meta分析中，各干预措施之间的关系通过节点和连线进行了直观展示，每个节点代表一种特定的干预措施，节点的大小与参与该干预的受试者人数呈正比。节点之间的实线连接反映了直接比较这些干预措施的研究数量，其中粗线表示较多研究支持，反之较少，见图13。由于研究网络中不存在闭环结构，因此无需进行不一致性检验。 2.6 基于运动处方进行分组的累计概率 不同运动时间、运动频率和运动周期FMA-LE评分网络分析的累积概率见图14 。运动时间曲线下面积、累积排序概率曲线(SUCRA)、曲线下面积的大小通常反映治疗效果的不同。曲线下面积越大，表明治疗效果越显著。 2.6.1 FMA-LE评分运动时间网状Meta分析结果 机器人辅助步态训练20-35 min/次(MD=35.63， 95%CI：14.47-87.73) 和机器人辅助步态训练"
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40-60 min/次(MD=129.59，95%CI：35.50-473.03) 与常规康复治疗相比均能改善FMA-LE评分 (P < 0.05)，机器人辅助步态训练20-35 min/次与机器人辅助步态训练40-60 min/次相比差异无显著性意义 (P > 0.05)，见表1。 受试者运动时间曲线下面积显示训练时间效果排序为：机器人辅助步态训练40-60 min/次> 机器人辅助步态训练20-35 min/次> 常规康复。表明训练40-60 min/次是最佳训练时间，见图14A。 2.6.2 FMA-LE 评分运动频率网状Meta分析结果 机器人辅助步态训练2-5次/周(MD=27.41，95%CI：6.74-111.44) 和6-10次/周(MD=64.47，95%CI：23.73-175.14) 与常规康复治疗相比均能改善FMA-LE评分的运动频率(P < 0.05)，机器人辅助步态训练2-5次/周与6-10次/周相比差异无显著性意义 (P > 0.05)，见表2。 受试者运动频率曲线下面积显示运动频率效果排序为：机器人辅助步态训练6-10次/周> 机器人辅助步态训练2-5次/周> 常规康复。表明训练6-10次/周是最佳训练频率，见图14B。 2.6.3 FMA-LE 评分运动周期网状Meta分析结果 机器人辅助步态训练2-6周(MD=42.98，95%CI：12.04-153.48) 和8-12周(MD= 72.47，95%CI：21.93-239.49) 与常规康复治疗相比均能改善运动周期的FMA-LE评分(P < 0.05)，机器人辅助步态训练2-6周与8-12周相比差异无显著性意义 (P > 0.05)，见表3。 受试者运动周期曲线下面积显示运动周期效果排序为：机器人辅助步态训练8-12周> 机器人辅助步态训练2-6周> 常规康复。表明训练8-12周是最佳训练周期，见图14C。 2.7 发表偏倚 为评估运动处方指标的发表偏倚，采用漏斗图进行了分析。结果显示：运动时间、运动频率及运动周期的散点分布较为均匀且集中在漏斗图的预期范围内，图形整体呈现对称性，未发现明显偏倚迹象,这表明研究结果的稳定性较高,见图15。"
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2.8 结局指标敏感性分析 以FMA-LE评分、FAC分级、BBS分级及6 min步行试验距离为结局指标的分析显示，研究间未发现显著异质性。图16敏感性分析进一步验证了临床疗效的稳定性，表明研究结果具有较高的可靠性。"
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