Abstract
To test the hypothesis that a system’s metabolic rate and the complexity of fluctuations in the output of that system are related, thirteen healthy participants performed intermittent isometric knee extensor contractions at intensities where a rise in metabolic rate would (40% maximal voluntary contraction, MVC) and would not (20% MVC) be expected. The contractions had a 60% duty factor (6 sec contraction, 4 sec rest) and were performed until task failure or for 30 min, whichever occurred sooner. Torque and surface EMG signals were sampled continuously. Complexity and fractal scaling of torque were quantified using approximate entropy (ApEn) and the detrended fluctuation analysis (DFA) α scaling exponent. Muscle metabolic rate was determined using near‐infrared spectroscopy. At 40% MVC, task failure occurred after (mean ± SD) 11.5 ± 5.2 min, whereas all participants completed 30 min of contractions at 20% MVC. Muscle metabolic rate increased significantly after 2 min at 40% MVC (2.70 ± 1.48 to 4.04 ± 1.23 %·s‐1, P < 0.001), but not at 20% MVC. Similarly, complexity decreased significantly at 40% MVC (ApEn, 0.53 ± 0.19 to 0.15 ± 0.09; DFA α, 1.37 ± 0.08 to 1.60 ± 0.09; both P < 0.001), but not at 20% MVC. The rates of change of torque complexity and muscle metabolic rate at 40% MVC were significantly correlated (ApEn, ρ = −0.63, P = 0.022; DFA, ρ = 0.58, P = 0.037). This study demonstrated that an inverse relationship exists between muscle torque complexity and metabolic rate during high‐intensity contractions.
Highlights
Life, according to Macklem (2009), can only exist in a phase transition between order and chaos, wherein a dynamic balance between stability and adaptability exists
The latter represents the amplitude of fluctuations, whilst the former identifies the temporal structures within a timeseries (Goldberger et al, 2002a)
Complexity is quantified using algorithms drawn from information theory, such as approximate entropy (ApEn; Pincus, 1991), which quantifies the irregularity of a system’s output, and from fractal geometry, such as detrended fluctuation analysis
Summary
Life, according to Macklem (2009), can only exist in a phase transition between order and chaos, wherein a dynamic balance between stability and adaptability exists. This balance results in constant fluctuations in physiological time-series (such as heart rate, gait and muscle torque; Lipsitz and Goldberger, 1992; Goldberger et al, 2002a), which do not represent an error signal in homeostatic control. Complexity is quantified using algorithms drawn from information theory, such as approximate entropy (ApEn; Pincus, 1991), which quantifies the irregularity of a system’s output, and from fractal geometry, such as detrended fluctuation analysis
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