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Bi-exponential modelling of $$W^{^{\prime}}$$ reconstitution kinetics in trained cyclists

Alan Chorley, Richard Bott, Simon Marwood, Kevin Lamb

2021European Journal of Applied Physiology10 citationsDOIOpen Access PDF

Abstract

Abstract Purpose The aim of this study was to investigate the individual $$W^{^{\prime}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>W</mml:mi> <mml:msup> <mml:mrow/> <mml:mo>′</mml:mo> </mml:msup> </mml:msup> </mml:math> reconstitution kinetics of trained cyclists following repeated bouts of incremental ramp exercise, and to determine an optimal mathematical model to describe $$W^{^{\prime}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>W</mml:mi> <mml:msup> <mml:mrow/> <mml:mo>′</mml:mo> </mml:msup> </mml:msup> </mml:math> reconstitution. Methods Ten trained cyclists (age 41 ± 10 years; mass 73.4 ± 9.9 kg; $$\dot{V}{\text{O}}_{2\max }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>V</mml:mi> <mml:mo>˙</mml:mo> </mml:mover> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>max</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> 58.6 ± 7.1 mL kg min −1 ) completed three incremental ramps (20 W min −1 ) to the limit of tolerance with varying recovery durations (15–360 s) on 5–9 occasions. $$W^{^{\prime}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>W</mml:mi> <mml:msup> <mml:mrow/> <mml:mo>′</mml:mo> </mml:msup> </mml:msup> </mml:math> reconstitution was measured following the first and second recovery periods against which mono-exponential and bi-exponential models were compared with adjusted R 2 and bias-corrected Akaike information criterion (AICc). Results A bi-exponential model outperformed the mono-exponential model of $$W^{^{\prime}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>W</mml:mi> <mml:msup> <mml:mrow/> <mml:mo>′</mml:mo> </mml:msup> </mml:msup> </mml:math> reconstitution (AICc 30.2 versus 72.2), fitting group mean data well (adj R 2 = 0.999) for the first recovery when optimised with parameters of fast component (FC) amplitude = 50.67%; slow component (SC) amplitude = 49.33%; time constant ( τ ) FC = 21.5 s; τ SC = 388 s. Following the second recovery, W ′ reconstitution reduced by 9.1 ± 7.3%, at 180 s and 8.2 ± 9.8% at 240 s resulting in an increase in the modelled τ SC to 716 s with τ FC unchanged. Individual bi-exponential models also fit well (adj R 2 = 0.978 ± 0.017) with large individual parameter variations (FC amplitude 47.7 ± 17.8%; first recovery: ( τ ) FC = 22.0 ± 11.8 s; ( τ ) SC = 377 ± 100 s; second recovery: ( τ ) FC = 16.3.0 ± 6.6 s; ( τ ) SC = 549 ± 226 s). Conclusions W′ reconstitution kinetics were best described by a bi-exponential model consisting of distinct fast and slow phases. The amplitudes of the FC and SC remained unchanged with repeated bouts, with a slowing of W′ reconstitution confined to an increase in the time constant of the slow component.

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AlgorithmArtificial intelligenceComputer scienceSports injuries and preventionSports Performance and TrainingLower Extremity Biomechanics and Pathologies
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