TY - JOUR
T1 - Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers' Speed
AU - Morais, Jorge E.
AU - Barbosa, Tiago M.
AU - Bragada, José A.
AU - Ramirez-Campillo, Rodrigo
AU - Marinho, Daniel A.
N1 - Publisher Copyright:
© 2023 Human Kinetics Publishers Inc.. All rights reserved.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - 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.
AB - 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.
KW - human physical conditioning
KW - modeling
KW - physical education and training
KW - speed determinants
KW - swimming
UR - http://www.scopus.com/inward/record.url?scp=85166363545&partnerID=8YFLogxK
U2 - 10.1123/ijspp.2022-0430
DO - 10.1123/ijspp.2022-0430
M3 - Article
C2 - 37268299
AN - SCOPUS:85166363545
SN - 1555-0265
VL - 18
SP - 833
EP - 839
JO - International Journal of Sports Physiology and Performance
JF - International Journal of Sports Physiology and Performance
IS - 8
ER -