Evidence map›Paper›PMID 40045360›Full record

ArticleJournal of neuroengineering and rehabilitation2025

Impact of an upper limb motion-driven virtual rehabilitation system on residual motor function in patients with complete spinal cord injury： a pilot study.

Yanqing Xiao, Yang Gao, Hongming Bai, Guiyun Song, Hanming Wang, Jia-Sheng Rao, Aimin Hao, Xiaoguang Li, Jia Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yanqing XiaoBeijing Key Laboratory for Biomaterials and Neural Regeneration, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering; Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China.
Yang Gao *The State Key Laboratory of VR Technology and Systems, Unit of Virtual Body and Virtual Surgery (2019RU004), Beihang University, Chinese Academy of Medical Sciences, Beijing, 100050, China. gaoyangvr@buaa.edu.cn.
Hongming BaiThe State Key Laboratory of VR Technology and Systems, Unit of Virtual Body and Virtual Surgery (2019RU004), Beihang University, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Guiyun SongDepartment of Rehabilitation Evaluation, Rehabilitation Research Center, Beijing, China.
Hanming WangRehabilitation Treatment Center of Beijing Rehabilitation Hospital, Affiliated to Capital Medical University, Beijing, China.
Jia-Sheng RaoBeijing Key Laboratory for Biomaterials and Neural Regeneration, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering; Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China.
Aimin HaoThe State Key Laboratory of VR Technology and Systems, Unit of Virtual Body and Virtual Surgery (2019RU004), Beihang University, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Xiaoguang Li *Beijing Key Laboratory for Biomaterials and Neural Regeneration, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering; Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China. lxgchina@sina.com.
Jia Zheng *Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China. lwc20002@126.com.

Funding

Beijing Science and Technology Project No. Z231100005923039CAMS Innovation Fund for Medical Sciences No. 2019-I2M-5-016Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems No. VRLAB2023A05
6 · The paper itself

Abstract

backgroundAssessing residual motor function in motor complete spinal cord injury (SCI) patients using surface electromyography (sEMG) is clinically important. Due to the prolonged loss of motor control and peripheral sensory input, patients may struggle to effectively activate residual motor function during sEMG assessments. The study proposes using virtual reality (VR) technology to enhance embodiment, motor imagery (MI), and memory, aiming to improve the activation of residual motor function and increase the sensitivity of sEMG assessments.

methodsBy Recruiting a sample of 12 patients with AIS A/B and capturing surface electromyographic signals before, druing and after VR training,

resultsMost patients showed significant electromyographic improvements in activation frequency and or 5-rank frequency during or after VR training. However, one patient with severe lower limb neuropathic pain did not exhibit volitional electromyographic activation, though their pain diminished during the VR training.

conclusionsVR can enhance the activation of patients' residual motor function by improving body awareness and MI, thereby increasing the sensitivity of sEMG assessments.

Indexed as

Spinal Cord InjuriesUpper ExtremityVirtual RealityAdultElectromyographyFemaleHumansMaleMiddle AgedPilot ProjectsYoung AdultResidual motor control abilitysEMGSpinal cord injuryVR

Identifiers

PMID40045360
PMCPMC11881371

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.