W′ Reconstitution Accelerates More with Decreasing Intensity in the Heavy- versus the Moderate-Intensity Domain

Lievens, Maarten; Caen, Kevin; Bourgois, Jan; Vermeire, Kobe; Boone, Jan

doi:10.1249/mss.0000000000002574

Cited by 15 publications

(17 citation statements)

References 39 publications

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“…The mono-exponential W ′ bal model has been validated using similar intermittent protocols in hypoxia (Shearman et al 2016 ; Townsend et al 2017 ), and by retrofitting to the point of exhaustion during training and race data (Skiba et al 2014a ), where the mono-exponential model proved a successful fit against the measurements of W′ reconstitution over the short intermittent recoveries. Validations of the mono-exponential W ′ bal model via different protocols have, however, found significant differences against longer recovery durations (Chorley et al 2019 ) and partial prior depletion of W ′ (Lievens et al 2021 ; Sreedhara et al 2020 ), albeit without τ being individually fitted. Where τ has been individualised, it has only been done so against W′ reconstitution at specific measured time points (Caen et al 2019 ; Chorley et al 2020 ) rather than against a time-course of W ′ reconstitution.…”

Section: Discussionmentioning

confidence: 99%

“…4 . This reliance solely upon D CP for the determination of τ W ′ has been questioned (Chorley and Lamb 2020 ) following significant differences being found between predicted W ′ reconstitution and experimental measurements (Chorley et al 2019 ; Lievens et al 2021 ) and several markers of aerobic fitness together with age and body composition have been correlated with W′ reconstitution (Chorley et al 2020 ). Hence, it is posited that such individual factors might contribute to the accuracy of W ′ reconstitution modelling.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Purpose The aim of this study was to investigate the individual $$W^{^{\prime}}$$ W ′ reconstitution kinetics of trained cyclists following repeated bouts of incremental ramp exercise, and to determine an optimal mathematical model to describe $$W^{^{\prime}}$$ W ′ reconstitution. Methods Ten trained cyclists (age 41 ± 10 years; mass 73.4 ± 9.9 kg; $$\dot{V}{\text{O}}_{2\max }$$ V ˙ O 2 max 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. $$W^{^{\prime}}$$ W ′ reconstitution was measured following the first and second recovery periods against which mono-exponential and bi-exponential models were compared with adjusted R2 and bias-corrected Akaike information criterion (AICc). Results A bi-exponential model outperformed the mono-exponential model of $$W^{^{\prime}}$$ W ′ reconstitution (AICc 30.2 versus 72.2), fitting group mean data well (adjR2 = 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; τSC = 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 τSC to 716 s with τFC unchanged. Individual bi-exponential models also fit well (adjR2 = 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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…To what extent CS may change during severe-intensity exercise of ~13-27 min is unknown. Second, the D´ balance model incorporates assumptions on D´ reconstitution time that may vary depending on the proximity of the recovery speed to CS, individual athlete fitness (especially when differences in aerobic fitness are present), and fatigue development (15,26,27). In the present study, we employed the simplified novel D′ balance model associated with principles of chemical kinetics to derive D´ recovery.…”

Section: Experimental Considerationsmentioning

confidence: 99%

Interaction of exercise bioenergetics with pacing behavior predicts track distance running performance

Kirby

Winn

Wilkins

et al. 2021

Journal of Applied Physiology

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The best possible finishing time for a runner competing in distance track events can be estimated from their critical speed (CS) and the finite amount of energy that can be expended above CS (D'). During tactical races with variable pacing, the runner with the 'best' combination of CS and D' and, therefore, the fastest estimated finishing time prior to the race, does not always win. We hypothesized that final race finishing positions depend on the relationships between the pacing strategy employed, the athletes' initial CS, and their instantaneous D' (i.e., D' balance) as the race unfolds. Using publicly available data from the 2017 IAAF World Championships men's 5,000 m and 10,000 m races, race speed, CS, and D' balance were calculated. The correlation between D' balance and actual finishing positions was non-significant utilizing start-line values but improved to R2 > 0.90 as both races progressed. The D' balance with 400 m remaining was strongly associated with both final 400 m split time and proximity to the winner. Athletes who exhausted their D' were unable to hold pace with the leaders, whereas a high D´ remaining enabled a fast final 400 m and a high finishing position. The D' balance model was able to accurately predict finishing positions in both a 'slow' 5,000 m and a 'fast' 10,000 m race. These results indicate that while CS and D' can characterize an athlete's performance capabilities prior to the start, the pacing strategy that optimizes D' utilization significantly impacts final race outcome.

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“…In this scenario, the MLSS 15-30 and MLSS 20-30 criteria discussed herein could offer valid support to select the most appropriate estimate of CP and consequently identify MMSS. This strategy, which has been previously suggested and used (3,29,30,33,(53)(54)(55), would improve the rigor of MMSS characterization.…”

Section: Discussionmentioning

confidence: 99%

Methodological Reconciliation of CP and MLSS and Their Agreement with the Maximal Metabolic Steady State

Iannetta

Ingram

Keir

et al. 2021

Medicine &Amp; Science in Sports &Amp; Exercise

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The critical power (CP) and maximal lactate steady state (MLSS) are operational surrogates of the maximal metabolic steady state (MMSS). However, their concordance and their agreement with MMSS remain variable likely because of methodological factors. Purpose: This study aimed to compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS. Methods: After a ramp test, 10 recreationally active males performed four to five severe-intensity constant-power output (PO) trials to estimate CP and three to four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the three-parameter hyperbolic (CP 3-hyp ), twoparameter hyperbolic (CP 2-hyp ), linear (CP lin ), and inverse of time (CP 1/Tlim ) models. In addition, the model with the lowest combined parameter error identified the "best-fit" CP (CP best-fit ). MLSS was determined as an increase in blood lactate concentration ≤1 mM during constant-PO cycling from the 5th (MLSS 5-30 ), 10th (MLSS 10-30 ), 15th (MLSS 15-30 ), 20th (MLSS 20-30 ), or 25th (MLSS 25-30 ) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady-state V ˙O2 (MV ˙O2ss ). Results: Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS 15-30 , MLSS 20-30 , and MLSS 25-30 . The PO at MV ˙O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP 2-hyp (244 ± 46 W) and CP lin (248 ± 46 W) and MLSS 15-30 and MLSS 20-30 (both 245 ± 46 W), respectively, displayed, on average, the greatest agreement with MV ˙O2ss . Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV ˙O2ss . Conclusions: Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model estimate should be considered. For MLSS, MLSS 15-30 and MLSS 20-30 demonstrated the highest degree of accuracy.

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W′ Reconstitution Accelerates More with Decreasing Intensity in the Heavy- versus the Moderate-Intensity Domain

Cited by 15 publications

References 39 publications

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

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

Interaction of exercise bioenergetics with pacing behavior predicts track distance running performance

Methodological Reconciliation of CP and MLSS and Their Agreement with the Maximal Metabolic Steady State

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