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Typical Model Studies01:30

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Shallow Water (Paddling) Variants of Water Maze Tests in Mice
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Published on: June 3, 2013

Prediction of flatwater kayaking performance.

Ken A van Someren1, Glyn Howatson

  • 1English Institute of Sport, St Marys High Performance Centre, Twickenham, UK.

International Journal of Sports Physiology and Performance
|February 12, 2009
PubMed
Summary

Physiological attributes and upper body dimensions are key predictors of flatwater kayaking performance across 200m, 500m, and 1000m events. These factors help predict race times with high accuracy for competitive male kayakers.

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Area of Science:

  • Sports Science
  • Exercise Physiology
  • Biomechanics

Background:

  • Flatwater kayaking performance is influenced by a complex interplay of physiological and anthropometric factors.
  • Understanding these relationships is crucial for optimizing training and athlete selection.

Purpose of the Study:

  • To investigate the relative importance of anthropometric and physiological attributes for performance in 1000m, 500m, and 200m flatwater kayaking.
  • To identify key predictors for each race distance.

Main Methods:

  • Eighteen competitive male kayakers participated in the study.
  • Athletes completed performance trials for 1000m, 500m, and 200m flatwater kayaking.
  • A comprehensive battery of anthropometric and physiological tests was administered.

Main Results:

  • Performance across all distances correlated with several physiological parameters.
  • 500m and 200m performance showed correlations with upper body dimensions.
  • Predictive models identified specific physiological and anthropometric variables for each race distance, achieving high R-squared values.

Conclusions:

  • Physiological variables significantly correlate with flatwater kayaking performance across all tested distances.
  • Specific anthropometric and physiological attributes are important predictors for different race distances.
  • The study successfully predicted race times with low standard errors, highlighting the validity of the identified predictors.