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The Critical Power Model as a Potential Tool for Anti-doping

Overview of attention for article published in Frontiers in Physiology, June 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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73 X users
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Title
The Critical Power Model as a Potential Tool for Anti-doping
Published in
Frontiers in Physiology, June 2018
DOI 10.3389/fphys.2018.00643
Pubmed ID
Authors

Michael J. Puchowicz, Eliran Mizelman, Assaf Yogev, Michael S. Koehle, Nathan E. Townsend, David C. Clarke

Abstract

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W'. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 73 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 116 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 16%
Student > Ph. D. Student 18 16%
Researcher 12 10%
Student > Bachelor 12 10%
Other 6 5%
Other 20 17%
Unknown 29 25%
Readers by discipline Count As %
Sports and Recreations 51 44%
Biochemistry, Genetics and Molecular Biology 7 6%
Medicine and Dentistry 5 4%
Agricultural and Biological Sciences 4 3%
Engineering 4 3%
Other 15 13%
Unknown 30 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 49. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 October 2023.
All research outputs
#855,668
of 25,381,151 outputs
Outputs from Frontiers in Physiology
#477
of 15,594 outputs
Outputs of similar age
#18,663
of 336,395 outputs
Outputs of similar age from Frontiers in Physiology
#37
of 497 outputs
Altmetric has tracked 25,381,151 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,594 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,395 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 497 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.