Literature DB >> 24742937

Diagnostic accuracy of the electromyography parameters associated with anterior knee pain in the diagnosis of patellofemoral pain syndrome.

Deisi Ferrari1, Heloyse Uliam Kuriki1, Cristiano Rocha Silva2, Neri Alves1, Fábio Mícolis de Azevedo3.   

Abstract

OBJECTIVE: To assess the diagnostic accuracy of the surface electromyography (sEMG) parameters associated with referred anterior knee pain in diagnosing patellofemoral pain syndrome (PFPS).
DESIGN: Sensitivity and specificity analysis.
SETTING: Physical rehabilitation center and laboratory of biomechanics and motor control. PARTICIPANTS: Pain-free subjects (n=29) and participants with PFPS (n=22) selected by convenience.
INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The diagnostic accuracy was calculated for sEMG parameters' reliability, precision, and ability to differentiate participants with and without PFPS. The selected sEMG parameter associated with anterior knee pain was considered as an index test and was compared with the reference standard for the diagnosis of PFPS. Intraclass correlation coefficient, SEM, independent t tests, sensitivity, specificity, negative and positive likelihood ratios, and negative and positive predictive values were used for the statistical analysis.
RESULTS: The medium-frequency band (B2) parameter was reliable (intraclass correlation coefficient=.80-.90), precise (SEM=2.71-3.87 normalized unit), and able to differentiate participants with and without PFPS (P<.05). The association of B2 with anterior knee pain showed positive diagnostic accuracy values (specificity, .87; sensitivity, .70; negative likelihood ratio, .33; positive likelihood ratio, 5.63; negative predictive value, .72; and positive predictive value, .86).
CONCLUSIONS: The results provide evidence to support the use of EMG signals (B2-frequency band of 45-96 Hz) of the vastus lateralis and vastus medialis muscles with referred anterior knee pain in the diagnosis of PFPS.
Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Anterior knee pain syndrome; Electromyography; Rehabilitation; Sensitivity; Specificity

Mesh:

Year:  2014        PMID: 24742937     DOI: 10.1016/j.apmr.2014.03.028

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  9 in total

1.  Universal spectral profile and dynamic evolution of muscle activation: a hallmark of muscle type and physiological state.

Authors:  Sergi Garcia-Retortillo; Rossella Rizzo; Jilin W J L Wang; Carol Sitges; Plamen Ch Ivanov
Journal:  J Appl Physiol (1985)       Date:  2020-07-16

Review 2.  Does the Foot and Ankle Alignment Impact the Patellofemoral Pain Syndrome? A Systematic Review and Meta-Analysis.

Authors:  Nicolò Martinelli; Alberto Nicolò Bergamini; Arne Burssens; Filippo Toschi; Gino M M J Kerkhoffs; Jan Victor; Valerio Sansone
Journal:  J Clin Med       Date:  2022-04-17       Impact factor: 4.964

Review 3.  Anterior knee pain: an update of physical therapy.

Authors:  Suzanne Werner
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2014-07-06       Impact factor: 4.342

4.  Diagnosis of Patellofemoral Pain Syndrome Based on a Multi-Input Convolutional Neural Network With Data Augmentation.

Authors:  Wuxiang Shi; Yurong Li; Baoping Xiong; Min Du
Journal:  Front Public Health       Date:  2021-02-11

5.  Different pain responses to distinct levels of physical activity in women with patellofemoral pain.

Authors:  Ronaldo V Briani; Marcella F Pazzinatto; Danilo De Oliveira Silva; Fábio M Azevedo
Journal:  Braz J Phys Ther       Date:  2017-03-17       Impact factor: 3.377

6.  Does the relative muscle activation of the vastus medialis, rectus femoris, and vastus lateralis, during the various activities, change in relation to the quadriceps angle?

Authors:  Nakyung Lee
Journal:  J Phys Ther Sci       Date:  2018-04-13

7.  Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network.

Authors:  Wuxiang Shi; Yurong Li; Dujian Xu; Chen Lin; Junlin Lan; Yuanbo Zhou; Qian Zhang; Baoping Xiong; Min Du
Journal:  Front Public Health       Date:  2021-04-16

8.  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

Review 9.  The Role of Botulinum Toxin Type A in the Clinical Management of Refractory Anterior Knee Pain.

Authors:  Barbara J Singer; Benjamin I Silbert; Peter L Silbert; Kevin P Singer
Journal:  Toxins (Basel)       Date:  2015-08-25       Impact factor: 4.546

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.