Comparing EMG amplitude patterns of responses during dynamic exercise: Polynomial vs log-transformed regression

R. J. Blaesser, L. M. Couls, C. F. Lee, J. M. Zuniga, M. H. Malek

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The purposes of this study were to determine if (a) the log-transformed model can be applied to dynamic exercise and (b) the slope and y-intercept terms can provide additional information above and beyond the polynomial regression analyses. Eleven physically active individuals performed incremental cycle ergometry on a single occasion. Electromyographic electrodes were placed on the three superficial quadriceps muscles to record muscle activation during the exercise test. The patterns of responses for electromyographic amplitude vs power output were analyzed with polynomial and log-transformed regression models. The results of the polynomial regression for the composite data indicated that the best-fit model for the vastus lateralis muscle was linear (R2=0.648, P<0.0001), whereas the best-fit model for the rectus femoris (R2=0.346, P=0.013) and vastus medialis (R2=0.764, P=0.020) muscles was quadratic. One-way repeated measures analyses indicated no significant differences (P>0.05) across the three superficial quadriceps muscles for the slope and y-intercept terms. These findings suggest that the log-transformed model may be a more versatile statistical approach to examining neuromuscular responses during dynamic exercise.

Original languageEnglish (US)
Pages (from-to)159-165
Number of pages7
JournalScandinavian Journal of Medicine and Science in Sports
Volume25
Issue number2
DOIs
StatePublished - Apr 1 2015
Externally publishedYes

Keywords

  • Cycle ergometry
  • Exercise physiology
  • Muscular fatigue
  • Quadriceps muscles

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation

Fingerprint

Dive into the research topics of 'Comparing EMG amplitude patterns of responses during dynamic exercise: Polynomial vs log-transformed regression'. Together they form a unique fingerprint.

Cite this