Methodological Approaches and Related Challenges Associated With the Determination of Critical Power and Curvature Constant

Muniz‐Pumares, Daniel; Karsten, Bettina; Triska, Christoph; Glaister, Mark

doi:10.1519/jsc.0000000000002977

Cited by 68 publications

(76 citation statements)

References 113 publications

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“…Other authors however, define a "heavy" intensity zone as being between two key physiological thresholds; the gas exchange threshold (GET) at the lower end, and the critical power (CP; for cycling or critical speed [CS] for running) at the higher end; thereby accounting for metabolic perturbations at the said intensity [30,31]. Although the intensity zone defined by Poole and Jones [30] and Rossiter [31] is arguably more valid and rigorous, a legitimate critique is that it is reliant on substantially more sophisticated equipment and multiple testing bouts that may be beyond the reach of most individuals [25].…”

Section: Introductionmentioning

confidence: 99%

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High-Intensity Interval Training Prescribed Within the Secondary Severe-Intensity Domain Improves Critical Speed But Not Finite Distance Capacity

Thomas

Pettitt

Kramer

2020

J. of SCI. IN SPORT AND EXERCISE

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Purpose Improvements in D′ (the fatigability constant for running) subsequent to training interventions remain elusive. High-intensity interval training (HIIT) within the severe intensity domain for short durations (< 2-min) have been theorized to improve D′. The purpose of the present study was to assess this in a group of moderately trained individuals. Methods Eighteen participants completed graded exercise testing (GXT), 40-m sprint testing and a 3-min all-out test (3MT) for running to determine key mechanistic and physiological parameters. Participants were randomly assigned into one of two groups based on intensity prescription (G 140% = 140% of critical speed [CS]), or time intervals (G 90-s = 90-s) to complete a twice-weekly training intervention for 6-weeks followed by re-assessment. Results No between-group differences were present either prior to or following the intervention. Substantial and meaningful improvements were detected during the post-intervention period for both groups for VO 2max 0.62 m/s; G 90-s : M = 0.46 m/s), D′ (G 140% : M = − 56.34 m; G 90-s : M = − 18.36 m), FI% (G 140% M = − 6.75%; G 90-s : M = − 4.38%) and maximal distance (G 140% : M = 49.67 m; G 90-s : M = 58.38 m).Conclusions The prescribed intensities and durations were insufficient to elicit improvements in D′. Improvements in D′ may be dependent on very short-duration intervals (i.e. < 60 to 90-s) at speeds exceeding 140% CS but below maximal sprint speed.

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Section: Introductionmentioning

confidence: 99%

“…In lieu of the substantial methodological variability of the traditional method of deriving CS and D′ [25], the 3-min all-out test (3MT) for running may potentially be consigned as the most viable contender for deriving CS and D [27]. Optimal HIIT prescriptions have been observed when accounting for both CS and D′ [7].…”

Section: Introductionmentioning

confidence: 99%

High-Intensity Interval Training Prescribed Within the Secondary Severe-Intensity Domain Improves Critical Speed But Not Finite Distance Capacity

Thomas

Pettitt

Kramer

2020

J. of SCI. IN SPORT AND EXERCISE

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“…CP estimates from different models are reported to be in close agreement with each other in [34,55,56]. However, as illustrated in Table 1, the estimation of W′ remains elusive as the same data can yield different estimates depending on the model used even though CP estimates are comparable [34,38,[55][56][57][58][59][60][61]. The two-parameter model, though having limitations (P = ∞ at t = 0 and CP lasting indefinitely), owing to its simplicity, can potentially be used to optimize performance as well as determining strategies by estimating time-to-exhaustion [16,17,62].…”

Section: Main Textmentioning

confidence: 64%

“…Furthermore, CP estimates from the CWR protocol at 60 rpm have been found to be significantly greater than those at 100 rpm [76]. Considering these limitations, Muniz-Pumares and colleagues [61] suggest the use of the two-parameter hyperbolic model with at least three CWR trials of durations > 2 min and < 15 min and freely chosen cadence to arrive at reliable estimates.…”

Section: Limitations Of the Protocols Used To Determine Cp And W′mentioning

confidence: 99%

“…4 Schematic representation of a 3-min all-out test to determine critical power (CP) and the curvature constant (W′). The average power of the last 30s yields CP and the area below the curve and above CP yields W′ lead to a better understanding of W′, which has been shown to be ambiguous and significantly dependent on the mathematical model used [34,[55][56][57][59][60][61].…”

Section: Limitations Of the Protocols Used To Determine Cp And W′mentioning

confidence: 99%

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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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Critical speed and finite distance capacity: norms for athletic and non-athletic groups

2020

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Purpose Two parameters in particular span both health and performance; critical speed (CS) and finite distance capacity (D′). The purpose of the present study was to: (1) classify performance norms, (2) distinguish athletic from non-athletic individuals using the 3-min all-out test (3MT) for running, and (3) introduce a deterministic model highlighting the relationship between variables of the 3MT. Methods Athletic (n = 43) and non-athletic (n = 25) individuals participated in the study. All participants completed a treadmill graded exercise test (GXT) with verification bout and a 3MT on an outdoor sprinting track. Results Meaningful differences between non-athletic and athletic individuals (denoted by mean difference scores, p value and Cohen's d with 95% confidence intervals) were evident for CS (− 0.74 m s −1 , p < 0.001, d = − 1.41 [1.97, − 0.87]), exponential growth time constant ( g ; 2.75 s, p < 0.001, d = − 1.29 [− 1.45, − 0.42]), time to maximal speed ( t max ; − 2.80 s, p < 0.001, d = − 0.98 [− 1.51, − 0.47]), maximal speed ( S max ; − 1.36 m s −1 , p < 0.001, d = − 1.56 [− 2.13, − 1.01]), gas exchange threshold (GET; − 5.62 ml kg −1 min −1 , p < 0.001, d = − 0.97 [− 1.50, − 0.45]), distance covered in the first minute (1st min; − 81.69 m, p < 0.001, d = − 1.91 [− 2.52, − 1.33]), distance covered in the second minute (2nd min; − 52.02 m, p < 0.001, d = − 1.71 [− 2.30, − 1.15]) and maximal distance (− 153.78 m, p < 0.001, d = − 1.27 [− 1.82, − 0.74]). The correlation coefficient between key physiological and performance variables are shown in the form of a deterministic model created from the data derived from the 3MT. Conclusions Coaches and clinicians may benefit from the use of normative data to potentially identify exceptional or irregular occurrences in 3MT performances.

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Methodological Approaches and Related Challenges Associated With the Determination of Critical Power and Curvature Constant

Abstract: Methodological approaches and related challenges associated with the determination of critical power and W'

Cited by 68 publications

References 113 publications

High-Intensity Interval Training Prescribed Within the Secondary Severe-Intensity Domain Improves Critical Speed But Not Finite Distance Capacity

High-Intensity Interval Training Prescribed Within the Secondary Severe-Intensity Domain Improves Critical Speed But Not Finite Distance Capacity

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

Critical speed and finite distance capacity: norms for athletic and non-athletic groups

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