Evidence map›Paper›PMID 31749980›Full record

ArticleBMJ open sport & exercise medicine2019

Bayesian approach to quantify morphological impact on performance in international elite freestyle swimming.

Robin Pla, Arthur Leroy, Romain Massal, Maxime Bellami, Fatima Kaillani, Philippe Hellard, Jean-François Toussaint, Adrien Sedeaud

Abstract read
In one paragraph

Article in BMJ open sport & exercise medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Robin PlaFrench Swimming Federation, Clichy, France.ORCID 0000-0002-8504-5069
Arthur Leroy'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.ORCID 0000-0003-0806-8934
Romain Massal'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.
Maxime Bellami'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.
Fatima Kaillani'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.
Philippe HellardFrench Swimming Federation, Clichy, France.
Jean-François Toussaint'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.
Adrien Sedeaud'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe purpose of this study was to quantify the impact of morphological characteristics on freestyle swimming performance by event and gender.

designHeight, mass, body mass index (BMI) and speed data were collected for the top 100 international male and female swimmers from 50 to 1500 m freestyle events for the 2000-2014 seasons.

methodsSeveral Bayesian hierarchical regressions were performed on race speed with height, mass and BMI as predictors. Posterior probability distributions were computed using Markov chain Monte Carlo algorithms.

resultsRegression results exhibited relationships between morphology and performance for both genders and all race distances. Height was always positively correlated with speed with a 95% probability. Conversely, mass plays a different role according to the context. Heavier profiles seem favourable on sprint distances, whereas mass becomes a handicap as distance increases. Male and female swimmers present several differences on the influence of morphology on speed, particularly about the mass. Best morphological profiles are associated with a gain of speed of 0.7%-3.0% for men and 1%-6% for women, depending on race distance. BMI has been investigated as a predictor of race speed but appears as weakly informative in this context.

conclusionMorphological indicators such as height and mass strongly contribute to swimming performance from sprint to distance events, and this contribution is quantified for each race distance. These profiles may help swimming federations to detect athletes and drive them to compete in specific distances according to their morphology.

Indexed as

Bayesian regressionmorphologyperformanceswimmingtalent identification

Identifiers

PMID31749980
PMCPMC6830458

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.