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How to assess performance in cycling: the multivariate nature of influencing factors and related indicators

Overview of attention for article published in Frontiers in Physiology, January 2013
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

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6 news outlets
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9 X users
facebook
1 Facebook page
reddit
1 Redditor

Citations

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33 Dimensions

Readers on

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170 Mendeley
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Title
How to assess performance in cycling: the multivariate nature of influencing factors and related indicators
Published in
Frontiers in Physiology, January 2013
DOI 10.3389/fphys.2013.00116
Pubmed ID
Authors

A. Margherita Castronovo, Silvia Conforto, Maurizio Schmid, Daniele Bibbo, Tommaso D'Alessio

Abstract

Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (EffMet), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (EffMech). All these indicators quantify the variations of different factors [i.e., muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue (MFat), environmental variables, ergogenic aids, psychological traits (PsychTr)], which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both EffMet and EffMech.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 169 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 26 15%
Student > Master 25 15%
Student > Ph. D. Student 22 13%
Researcher 15 9%
Student > Doctoral Student 10 6%
Other 35 21%
Unknown 37 22%
Readers by discipline Count As %
Sports and Recreations 68 40%
Medicine and Dentistry 19 11%
Engineering 10 6%
Agricultural and Biological Sciences 6 4%
Nursing and Health Professions 5 3%
Other 19 11%
Unknown 43 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 52. 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 09 August 2022.
All research outputs
#759,352
of 24,241,559 outputs
Outputs from Frontiers in Physiology
#410
of 14,854 outputs
Outputs of similar age
#5,925
of 288,984 outputs
Outputs of similar age from Frontiers in Physiology
#11
of 398 outputs
Altmetric has tracked 24,241,559 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 14,854 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 97% 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 288,984 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 97% of its contemporaries.
We're also able to compare this research output to 398 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 97% of its contemporaries.