Literature DB >> 10189080

Prediction of elite schoolboy 2000m rowing ergometer performance from metabolic, anthropometric and strength variables.

A P Russell1, P F Le Rossignol, W A Sparrow.   

Abstract

In 19 elite schoolboy rowers, the relationships between anthropometric characteristics, metabolic parameters, strength variables and 2000-m rowing ergometer performance time were analysed to test the hypothesis that a combination of these variables would predict performance better than either individual variables or one category of variables. Anthropometric characteristics, maximal oxygen uptake (VO2max), accumulated oxygen deficit, net efficiency, leg strength and 2000-m rowing ergometer time were measured. Body mass, VO2max and knee extension correlated with 2000-m performance time (r= -0.41, -0.43 and -0.40, respectively; P< 0.05), while net efficiency and accumulated oxygen deficit did not. Multiple-regression analyses indicated that the prediction model using anthropometric variables alone best predicts performance (R = 0.82), followed by the equation comprising body mass, VO2max and skinfolds (R = 0.80). Although the regression equations increased the predictive power from that obtained using single variables, the hypothesis that a prediction model consisting of variables from different physiological categories would predict performance better than variables from one physiological category was not supported.

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Mesh:

Year:  1998        PMID: 10189080     DOI: 10.1080/026404198366380

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  16 in total

Review 1.  Towards an ideal rowing technique for performance : the contributions from biomechanics.

Authors:  Clara Soper; Patria Anne Hume
Journal:  Sports Med       Date:  2004       Impact factor: 11.136

Review 2.  Measures of rowing performance.

Authors:  T Brett Smith; Will G Hopkins
Journal:  Sports Med       Date:  2012-04-01       Impact factor: 11.136

3.  Physique traits of lightweight rowers and their relationship to competitive success.

Authors:  G J Slater; A J Rice; I Mujika; A G Hahn; K Sharpe; D G Jenkins
Journal:  Br J Sports Med       Date:  2005-10       Impact factor: 13.800

Review 4.  Monitoring of performance and training in rowing.

Authors:  Jarek Mäestu; Jaak Jürimäe; Toivo Jürimäe
Journal:  Sports Med       Date:  2005       Impact factor: 11.136

5.  Energy systems contributions in 2,000 m race simulation: a comparison among rowing ergometers and water.

Authors:  Fernando de Campos Mello; Rômulo Cássio de Moraes Bertuzzi; Patricia Moreno Grangeiro; Emerson Franchini
Journal:  Eur J Appl Physiol       Date:  2009-08-26       Impact factor: 3.078

6.  Physiological factors to predict on traditional rowing performance.

Authors:  Mikel Izquierdo-Gabarren; Rafael González de Txabarri Expósito; Eduardo Sáez Sáez de Villarreal; Mikel Izquierdo
Journal:  Eur J Appl Physiol       Date:  2009-09-16       Impact factor: 3.078

Review 7.  Strength testing and training of rowers: a review.

Authors:  Trent W Lawton; John B Cronin; Michael R McGuigan
Journal:  Sports Med       Date:  2011-05-01       Impact factor: 11.136

8.  Prediction of rowing ergometer performance from functional anaerobic power, strength and anthropometric components.

Authors:  Fırat Akça
Journal:  J Hum Kinet       Date:  2014-07-08       Impact factor: 2.193

9.  Anthropometric determinants of rowing ergometer performance in physically inactive collegiate females.

Authors:  R Podstawski; Dj Choszcz; S Konopka; J Klimczak; M Starczewski
Journal:  Biol Sport       Date:  2014-10-16       Impact factor: 2.806

10.  Energy systems efficiency influences the results of 2,000 m race simulation among elite rowers.

Authors:  Stefan Adrian Martin; Valeriu Tomescu
Journal:  Clujul Med       Date:  2017-01-15
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