Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers’ Speed

in International Journal of Sports Physiology and Performance

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Jorge E. Morais Department of Sport Sciences, Instituto Politécnico de Bragança, Bragança, Portugal
Research Center in Sports Health and Human Development (CIDESD), Covilhã, Portugal

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Tiago M. Barbosa Department of Sport Sciences, Instituto Politécnico de Bragança, Bragança, Portugal
Research Center in Sports Health and Human Development (CIDESD), Covilhã, Portugal

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José A. Bragada Department of Sport Sciences, Instituto Politécnico de Bragança, Bragança, Portugal
Research Center in Sports Health and Human Development (CIDESD), Covilhã, Portugal

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Rodrigo Ramirez-Campillo Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, Chile

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Daniel A. Marinho Research Center in Sports Health and Human Development (CIDESD), Covilhã, Portugal
Department of Sport Sciences, University of Beira Interior, Covilhã, Portugal

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Purpose: The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke. Design: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed. Methods: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed. Results: The final model identified time (estimate = −0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = −0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = −0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers. Conclusions: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.

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