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 |
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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. |
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ISSN: | 0905-7188 1600-0838 |
DOI: | 10.1111/sms.14578 |