Neuromuscular functions in sportsmen and fibromyalgia patients: a surface EMG study in static and dynamic conditions

E.G. Klaver-Krol

Research output: ThesisPhD Thesis - Research UT, graduation UT

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Abstract

This thesis presents two studies, one involving sportsmen (sprinters versus endurance athletes) and one fibromyalgia patients (patients versus healthy controls). The studies have investigated muscular functions using a non-invasive method: surface electromyography (sEMG). In the sportsmen, differences were found between the muscles of sprinters and of endurance athletes that were in accordance with the composition of their muscle fibers. The findings also suggest differences between sprinters and endurance athletes in the neural regulatory strategies of the muscles. Fibromyalgia is an unexplained affliction characterized by widespread pain, especially in the muscles. In the fibromyalgia patients, disturbances were observed of the muscle membrane with excessive propagation velocities along the membranes. Surprisingly, the higher propagation velocities in fibromyalgia patients appeared to obscure the signs of fatigue in sEMG that would normally be apparent when applying prolonged exercises. Furthermore, a clear positive correlation was found between the degree of membrane disturbance and the extent of the somatic signs (the number of painful spots known as tender points) in the patients. The findings are discussed and compared with the literature. The sEMG method used in this study is innovative. In particular, the dynamic conditions of the experiments are of interest because these have been seldom applied, despite the fact that they reveal a clearer reflection of everyday situations when compared with the static conditions more usually employed. The method involves subjects exerting both short and prolonged efforts. Applying a prolonged effort allows one to observe fatigue processes in a muscle. Novel parameters are used in the sEMG analysis based on the velocities of the peaks (motor unit potentials) and their associated statistics. By applying these parameters, it becomes possible to extract more refined features from an sEMG.
Original languageEnglish
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Rasker, Hans J., Supervisor
  • Zwarts, M.J., Advisor
Award date22 Nov 2012
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-3445-1
DOIs
Publication statusPublished - 22 Nov 2012

Keywords

  • METIS-289491
  • IR-82202

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