Literature DB >> 35248281

Contribution of components of the quadriceps femoris muscle for producing external torque in patients with patellofemoral pain syndrome.

Kazem Malmir1, Gholam Reza Olyaei2, Saeed Talebian3, Fahimeh Khaleghi4.   

Abstract

INTRODUCTION: Different muscular activities of the quadriceps components for producing necessary torque may change in patients with patellofemoral pain syndrome (PFPS). The aim of the current study, therefore, was to assess the contribution of each component of the quadriceps femoris muscle for producing external torque in patients with PFPS.
METHOD: Twelve females with PFPS (24.7 ± 2.3 years) and twelve healthy matched females (25.4 ± 2.4 years) performed three consecutive knee flexion and extension movements with maximum effort at 45°/s and 300°/s using a Biodex system 3 dynamometer. Simultaneously, electromyographic (EMG) activities of the vastus medialis oblique (VMO), RF (rectus femoris) and vastus lateralis (VL) muscles were recorded using a DataLog instrument. Standard multiple regressions were used to assess the ability of EMG activities of the VMO, RF and VL muscles to predict normalized quadriceps femoris isokinetic concentric and eccentric torques at 45°/s and 300°/s in the normal and patient groups.
RESULTS: In the normal group, the VL and the VMO were the good predictors of quadriceps concentric torque at 45°/s and 300°/s, respectively. The VL and the RF were the good predictors of quadriceps eccentric torque at 300°/s in the patient group. No other conditions showed a considerable prediction for quadriceps torque in the normal or patient group.
CONCLUSION: Females with PFPS differ with normal females in terms of the contribution of each component of the quadriceps femoris for producing external torque. Training the VMO for concentric contraction at both high and low velocities should be included in the management of the patients with PFPS.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Electromyography; Prediction; Quadriceps; Torque

Mesh:

Year:  2021        PMID: 35248281     DOI: 10.1016/j.jbmt.2021.11.002

Source DB:  PubMed          Journal:  J Bodyw Mov Ther        ISSN: 1360-8592


  1 in total

1.  A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome.

Authors:  Baoping Xiong; Yaozong OuYang; Yiran Chang; Guoju Mao; Min Du; Bijing Liu; Yong Xu
Journal:  Front Neurosci       Date:  2022-07-29       Impact factor: 5.152

  1 in total

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