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Asociación entre la composición corporal total y segmentaria y el rendimiento anaeróbico en atletas de Crossfit®: diferencias entre sexos y predicción del rendimiento
The main purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81...
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Published in: | Retos (Madrid) 2025-01, Vol.62, p.543 |
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Main Authors: | , , , , |
Format: | Article |
Language: | eng ; spa |
Subjects: | |
Online Access: | Get full text |
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Summary: | The main purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p < 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p < 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. The studied body composition and performance variables found significant differences between males and females. The findings demonstrate that body composition variables are crucial indicators of anaerobic performance in CF athletes. In this regard, it may be advisable for sports performance professionals to consider this information when monitoring athletes throughout the season or designing specific training programs. Similarly, the use of predictive equations could be a useful tool for estimating peak and mean power values. |
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ISSN: | 1579-1726 1988-2041 |
DOI: | 10.47197/retos.v62.109115 |