Università degli Studi di Genova
Genova, 16122, Italy
NCT Number: NCT06069440
During a submaximal task, gradual muscle fatigue occurs, which inevitably results in a decline in performance (mechanical failure). Elite athletes are known to employ unconscious compensatory strategies during fatiguing submaximal tasks in an attempt to delay the onset of mechanical failure as long as possible.
The purpose of this study was to gain valuable insight into the strategies used by elite swimmers to cope with mechanical failure. Twenty-two swimmers were subjected to a swim test consisting of swimming as long as possible at a predetermined and controlled pace. A light strip positioned at the bottom of the pool allows athletes to get feedback on which gait to keep. The kinematics (stroke rate, stroke length, and efficiency index) and electrical activity of 10 muscle groups were analyzed and compared at the beginning of the test (non-fatiguing conditions), just before the athlete lost the ability to maintain the predetermined pace (pre-mechanical failure), and after the athlete lost the ability to maintain the pace (mechanical failure). It is hypothesized that as fatigue becomes more pronounced and the point of inability to maintain a predetermined speed is approached, increased EMG activity will occur in key muscles while other muscle groups may show more obvious signs of fatigue. In addition, changes in the rhythm and coordination of upper limb movements may occur.
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Notify Me16 year–24 year
All sexes
Interventional
Not applicable
Genova, 16122, Italy
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Swim freestyle at a steady, controlled pace until mechanical failure (i.e., inability to maintain the predetermined speed) is reached.
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
Electromyographic signal analysis (Root Mean Square)
Time frame: up to 10 minutes
stroke frequency (time required to complete a stroke cycle)
Time frame: up to 10 minutes
stroke length (distance traveled during each stroke cycle))
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Time frame: up to 10 minutes
time-course evolution of the median frequency of the power density spectrum (MF [Hz]) of the sEMG signal
Universita degli Studi di Genova
Other
Analysis of Kinematical and Muscular Fatigue in Long Distance Swimmers
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