Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path‐analysis modeling

The aim of this study was to understand the interactions between anthropomet- ric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in...

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Veröffentlicht in:Scandinavian journal of medicine & science in sports 2024-02, Vol.34 (2), p.e14578-n/a
Hauptverfasser: Morais, J. E., Barbosa, Tiago M., Gomeñuka, Natalia A., Marinho, Daniel A.
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Sprache:eng
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Zusammenfassung:The aim of this study was to understand the interactions between anthropomet- ric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was cho- sen as the variable to be predicted. A series of anthropometric (hand surface area– HSA), kinetic (thrust and active drag coefficient (CDA)), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were meas- ured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust—SL. After deleting this non- significant path (thrust—SL) and recalculating, the model goodness-of- fit im- proved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance. This work is supported by national funds (FCT-Portuguese Foundation for Science and Technology) under the project UIDB/DTP/04045/2020.
ISSN:0905-7188
1600-0838
DOI:10.1111/sms.14578