Abstract
Lower limb muscles activation was assessed during cycling to exhaustion using frequency band analysis. Nine cyclists were evaluated in two days. On the first day, cyclists performed a maximal incremental cycling exercise to measure peak power output, which was used on the second day to define the workload for a constant load time to exhaustion cycling exercise (maximal aerobic power output from day 1). Muscle activation of vastus lateralis (VL), long head of biceps femoris (BF), lateral head of gastrocnemius (GL), and tibialis anterior (TA) from the right lower limb was recorded during the time to exhaustion cycling exercise. A series of nine band-pass Butterworth digital filters was used to analyze muscle activity amplitude for each band. The overall amplitude of activation and the high and low frequency components were defined to assess the magnitude of fatigue effects on muscle activity via effect sizes. The profile of the overall muscle activation during the test was analyzed using a second order polynomial, and the variability of the overall bands was analyzed by the coefficient of variation for each muscle in each instant of the test. Substantial reduction in the high frequency components of VL and BF activation was observed. The overall and low frequency bands presented trivial to small changes for all muscles. High relationship between the second order polynomial fitting and muscle activity was found (R2 > 0.89) for all muscles. High variability (~25%) was found for muscle activation at the four instants of the fatigue test. Changes in the spectral properties of the EMG signal were only substantial when extreme changes in fatigue state were induced.
Highlights
Fatigue has been defined from different perspectives
A comparison of each time-window of the test indicated large decreases in vastus lateralis (VL) and biceps femoris (BF) high frequency component at the 90% of the test compared to the 10% of the test
High levels of variability were found for all four muscles, with coefficients of variation up to 43% for VL, 28% for BF, 30% for GL, and 48% for tibialis anterior (TA), respectively
Summary
Fatigue has been defined from different perspectives. Abbiss and Laursen[1] defined fatigue as the sensation of tiredness associated with decrements in muscular performance. Neuromechanical adaptation to different fatigue states in cycling has been observed via changes in pedal force application[2], joint kinematics[3], and joint kinetics[4]. Potential increases in motor unit recruitment resulted in higher oxygen uptake[6], lower pedaling cadence[7], and higher pedal force[3] during fatigue in cycling. Controversial evidence has been shown in muscle recruitment priority and its effects on electromyography spectral properties during fatigue[10,11,12]. It is not clear whether changes in fatigue state do not affect muscle recruitment or whether changes in muscle activation are not detectable using surface electromyography
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