Evidence map›Paper›PMID 39367757›Full record

ArticleOrthopaedic surgery2025

Surface Electromyography and Gait Features in Patients after Anterior Cruciate Ligament Reconstruction.

Xipeng Wu, Hao Zhang, Hongxing Cui, Wenbin Pei, Yixuan Zhao, Shanshan Wang, Zhijie Cao, Wei Li

Abstract read
In one paragraph

Article in Orthopaedic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xipeng WuSchool of Rehabilitation Medicine, Binzhou Medical University, Yantai, China.ORCID https://orcid.org/0009-0000-3512-0502
Hao ZhangSchool of Rehabilitation Medicine, Binzhou Medical University, Yantai, China.
Hongxing CuiDepartment of Rehabilitation, Binzhou Medical University Hospital, Binzhou, China.
Wenbin PeiSchool of Rehabilitation Medicine, Binzhou Medical University, Yantai, China.
Yixuan ZhaoSchool of Rehabilitation Medicine, Binzhou Medical University, Yantai, China.
Shanshan WangDepartment of Rehabilitation, Binzhou Medical University Hospital, Binzhou, China.
Zhijie CaoDepartment of Rehabilitation, Binzhou Medical University Hospital, Binzhou, China.
Wei LiDepartment of Rehabilitation, Binzhou Medical University Hospital, Binzhou, China.

Funding

Natural Science Foundation of Shandong Province ZR2022MH063
6 · The paper itself

Abstract

objectiveAn important reason for the poor recovery of anterior cruciate ligament (ACL) injuries is the poor recovery of muscle function. Therefore, we used surface electromyography (sEMG) and gait analysis to explore the muscle activation patterns and gait characteristics between lower limbs under different exercise states in patients, following anterior cruciate ligament reconstruction (ACLR).

methodsForty-one adults with unilateral ACL injuries in Binzhou Medical University Hospital from October 2022 to June 2023 were allocated to three groups according to the time after ACL reconstruction: group A (≤3 months, 16), group B (3 months-1 year, 13), and group C (>1 year, 12). Patients were tested by sEMG and gait, while straight leg raising (SLR), walking at normal speed, fast walking, and walking up and down the stairs. Two related sample tests were performed for the normalized root mean square (RMS) values and gait parameters.

resultsMuscle function changes varied in different training tasks. The RMS value of the involved side was more than the uninvolved side in biceps femoris and semitendinosus of group A (p < 0.010), and for the bilateral rectus femoris (RS), vastus medialis (VM), and vastus lateralis in group B, only the comparison of the RS was significant in group C during fast walking and going up and down the stairs. The ground impact (0.90 [0.63, 1.33] vs. 0.71 [0.43, 1.02], p = 0.035) of the uninvolved side was significantly decreased compared to those of the involved side in patients with ACLR when going down the stairs.

conclusionDifferent muscles need to be focused on at different stages of the postoperative period. sEMG and gait analysis can guide the development of a rehabilitation program.

Indexed as

Anterior Cruciate Ligament InjuriesAnterior Cruciate Ligament ReconstructionElectromyographyGaitGait AnalysisMuscle, SkeletalAdolescentAdultFemaleHumansMaleYoung AdultAnterior Cruciate Ligament ReconstructionGaitLower LimbsSurface Electromyography

Identifiers

PMID39367757
PMCPMC11735373

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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.