Development and field validation of an omni-domain power-duration model

Puchowicz, Michael J.; Baker, Jonathan; Clarke, David

doi:10.1080/02640414.2020.1735609

Cited by 8 publications

(31 citation statements)

References 39 publications

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“…A major limitation of the approaches discussed thus far is that they fail to describe power outputs for individual efforts. To do this the mean maximal power output (MMP) approach can be used (van Erp and Sanders 2020 ; Puchowicz et al 2020 ; Quod et al 2010 ; Vogt et al 2007b ). MMP values represent the highest average power that was recorded for a given (arbitrary) duration, during an event.…”

Section: Methodological Approaches To Power Profilingmentioning

confidence: 99%

“…However, this is clearly not the case for real-world performances where exercise at the CP is limited to 20–40 min (Poole et al 2016 ). For this reason, previous research (Peronnet and Thibault 1989 ; Puchowicz et al 2020 ) has suggested an exponential decay term below the CP to predict power outputs in the heavy exercise intensity domain (see Fig. 2 and Eqs.…”

Section: Deriving a Power-duration Relationshipmentioning

confidence: 99%

“…Alongside the APR model, power output in the extreme exercise intensity domain can also be predicted using the three-parameter critical power (3-P CP) (Morton 1996 ), the Peronnet and Thibault model (P&T) ( 1989 ) and Puchowicz’s omni power duration model (OmPD) (Puchowicz et al 2020 ). It should be noted that in the P&T model, P max is provided as a parameter estimate, whereas in the APR model, 3-P CP model and the OmPD model P max is required as an input parameter.…”

Section: Deriving a Power-duration Relationshipmentioning

confidence: 99%

“…While the power-duration relationship in the severe exercise intensity domain has been well investigated based on the CP concept (Jones et al 2010 ; Poole et al 2016 ), limited research exists on deriving the power-duration relationship in the moderate and heavy exercise intensity domains (Black et al 2017 ). Hence Puchowicz et al ( 2020 ) and Peronnet and Thibault ( 1989 ) proposed mathematical models with an aerobic decay term, but limited research exists to assess if these concepts have a high predictive ability for the power-duration relationship in the moderate and heavy exercise intensity domains in relation to the muscle bioenergetic system (Korzeniewski 2019 ; Korzeniewski and Rossiter 2020 , 2021 ; Vanhatalo et al 2016 ).…”

Section: Future Directionsmentioning

confidence: 99%

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Power profiling and the power-duration relationship in cycling: a narrative review

et al. 2021

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Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W′/t + CP (W′—work capacity above CP; t—time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.

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Section: Methodological Approaches To Power Profilingmentioning

confidence: 99%

Section: Deriving a Power-duration Relationshipmentioning

confidence: 99%

Section: Deriving a Power-duration Relationshipmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

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Power profiling and the power-duration relationship in cycling: a narrative review

et al. 2021

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“…In cycling, the availability of bike-mounted power meters enables the power-duration relationship to be computed as best-power-for-duration efforts, which may also be extracted from the maximal mean power profile. 1 Various mathematical models have been proposed to express the power-duration relationship in terms of physiologically interpretable parameters. 2 One such construct is the 2-parameter CP (critical power) model, which describes the capacity of an individual to sustain particular work rates as a function of time.…”

mentioning

confidence: 99%

The W′ Balance Model: Mathematical and Methodological Considerations

Skiba¹,

Clarke²

2021

International Journal of Sports Physiology and Performance

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Since its publication in 2012, the W′ balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model’s intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W′ balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W′ during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W′ balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.

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Modelling human endurance: power laws vs critical power

2023

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The power–duration relationship describes the time to exhaustion for exercise at different intensities. It is believed to be a “fundamental bioenergetic property of living systems” that this relationship is hyperbolic. Indeed, the hyperbolic (a.k.a. critical-power) model which formalises this belief is the dominant tool for describing and predicting high-intensity exercise performance, e.g. in cycling, running, rowing or swimming. However, the hyperbolic model is now the focus of a heated debate in the literature because it unrealistically represents efforts that are short (< 2 min) or long (> 15 min). We contribute to this debate by demonstrating that the power–duration relationship is more adequately represented by an alternative, power-law model. In particular, we show that the often-observed good fit of the hyperbolic model between 2 and 15 min should not be taken as proof that the power–duration relationship is hyperbolic. Rather, in this range, a hyperbolic function just happens to approximate a power law fairly well. We also prove mathematical results which suggest that the power-law model is a safer tool for pace selection than the hyperbolic model and that the former more naturally models fatigue than the latter.

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Development and field validation of an omni-domain power-duration model

Cited by 8 publications

References 39 publications

Power profiling and the power-duration relationship in cycling: a narrative review

Power profiling and the power-duration relationship in cycling: a narrative review

The W′ Balance Model: Mathematical and Methodological Considerations

Modelling human endurance: power laws vs critical power

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