The Reconstitution of W′ Depends on Both Work and Recovery Characteristics

Caen, Kevin; Bourgois, Jan; Bourgois, Gil; Stede, Thibaux Van der; Vermeire, Kobe; Boone, Jan

doi:10.1249/mss.0000000000001968

Cited by 34 publications

(94 citation statements)

References 24 publications

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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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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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show abstract

“…During intermittent exercise the balance of W' remaining has been suggested to predict an athlete's interval training capacity, accounting for both the work and recovery elements of a given training prescription. However, the robustness of W BAL has been questioned 16 . Near infrared spectroscopy (NIRS), is a well-known non-invasive method used to measure muscle oxygenation, which reflects the ratio of oxygen (O 2 ) delivery to the working muscle and muscle oxygen uptake in the capillary beds 17 .…”

mentioning

confidence: 99%

The Acute Physiological and Perceptual Effects of Individualizing the Recovery Interval Duration Based Upon the Resolution of Muscle Oxygen Consumption During Cycling Exercise

Fennell¹,

Hopker²

2021

International Journal of Sports Physiology and Performance

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Purpose: There has been paucity in research investigating the individualization of recovery interval duration during cycling-based high-intensity interval training (HIIT). The main aim of the study was to investigate whether individualizing the duration of the recovery interval based upon the resolution of muscle oxygen consumption would improve the performance during work intervals and the acute physiological response of the HIIT session, when compared with a standardized (2:1 work recovery ratio) approach. Methods: A total of 16 well-trained cyclists (maximal oxygen consumption: 60 [7] mL·kg−1·min−1) completed 6 laboratory visits: (Visit 1) incremental exercise test, (Visit 2) determination of the individualized (IND) recovery duration, using the individuals’ muscle oxygen consumption recovery duration to baseline from a 4- and 8-minute work interval, (Visits 3–6) participants completed a 6 × 4- and a 3 × 8-minute HIIT session twice, using the IND and standardized recovery intervals. Results: Recovery duration had no effect on the percentage of the work intervals spent at >90% and >95% of maximal oxygen consumption, maximal minute power output, and maximal heart rate, during the 6 × 4- and 3 × 8-minute HIIT sessions. Recovery duration had no effect on mean work interval power output, heart rate, oxygen consumption, blood lactate, and rating of perceived exertion. There were no differences in reported session RPE between recovery durations for the 6 × 4- and 3 × 8-minute HIIT sessions. Conclusion: Individualizing HIIT recovery duration based upon the resolution of muscle oxygen consumption to baseline levels does not improve the performance of the work intervals or the acute physiological response of the HIIT session, when compared with standardized recovery duration.

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“…Though the model was validated using data from eight triathletes [84], it may not be able to predict the recovery of W′ for athletes of higher or lower caliber. This is illustrated by Caen and colleagues [94] where faster recovery of W′ was observed. Skiba's second model (also mono-exponential) [85], derived from first principles with valid assumptions, addresses some limitations of the earlier version.…”

Section: Applications Of a Combined Expenditure-recovery Model Of W′mentioning

confidence: 78%

A survey of mathematical models of human performance using power and energy

2019

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The ability to predict the systematic decrease of power during physical exertion gives valuable insights into health, performance, and injury. This review surveys the research of power-based models of fatigue and recovery within the area of human performance. Upon a thorough review of available literature, it is observed that the twoparameter critical power model is most popular due to its simplicity. This two-parameter model is a hyperbolic relationship between power and time with critical power as the power-asymptote and the curvature constant denoted by W′. Critical power (CP) is a theoretical power output that can be sustained indefinitely by an individual, and the curvature constant (W′) represents the amount of work that can be done above CP. Different methods and models have been validated to determine CP and W′, most of which are algebraic manipulations of the twoparameter model. The models yield different CP and W′ estimates for the same data depending on the regression fit and rounding off approximations. These estimates, at the subject level, have an inherent day-today variability called intra-individual variability (IIV) associated with them, which is not captured by any of the existing methods. This calls for a need for new methods to arrive at the IIV associated with CP and W′. Furthermore, existing models focus on the expenditure of W′ for efforts above CP and do not model its recovery in the sub-CP domain. Thus, there is a need for methods and models that account for (i) the IIV to measure the effectiveness of individual training prescriptions and (ii) the recovery of W′ to aid human performance optimization.

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The Reconstitution of W′ Depends on Both Work and Recovery Characteristics

Cited by 34 publications

References 24 publications

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

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

The Acute Physiological and Perceptual Effects of Individualizing the Recovery Interval Duration Based Upon the Resolution of Muscle Oxygen Consumption During Cycling Exercise

A survey of mathematical models of human performance using power and energy

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