n African Journal for Physical Health Education, Recreation and Dance - Influence of visual feedback on knee extensor isokinetic concentric and eccentric peak torque : biokinetics

Supplement 1
  • ISSN : 1117-4315


Isokinetic normative data can be invaluable in identifying an individual's strengths and weaknesses, and thus lead to a more effective use of the individual's time to minimise or overcome his weaknesses while maintaining or improving existing strength. However, visual feedback (VF) may significantly affect the result of isokinetic testing, resulting in erroneous conclusions if not accounted for. Additionally, the previous use of VF to obtain increased strength values has resulted in inconsistent findings. The purpose of this study was to examine the effect of VF on concentric and eccentric knee extensor peak torque. Twenty-two sedentary, college-aged male and female volunteers were assigned to either Group 1 (n = 11) or Group 2 (n = 11) to either perform knee extensor concentric-eccentric (con-ecc) isokinetic testing with VF or without VF (no-VF) using a crossover method. After a one-week rest, the two groups underwent knee extensor con-ecc isokinetic testing using the alternative testing condition. Each test consisted of five maximal knee extensor con-ecc isokinetic testing contractions at 60° per second on the Cybex Norm system. The data indicated significant (p < 0.05) differences in the concentric peak torque of Group 1, Group 2 and Combined Group following VF when compared to no-VF. The eccentric peak torque of Group 1, Group 2 and Combined Group was found not to be significantly different following VF when compared to no-VF. Further, no significant interaction effect as a result of the different groups was found. Visual feedback of torque output can improve maximum voluntary concentric contraction in isokinetic dynamometry, but not maximum voluntary eccentric contraction. It is thus recommended that VF should be consistently provided during isokinetic testing, since it can also be used to help detect and correct errors in performance as well as derive reinforcement from correct performances.

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