Optimized training for jumping performance using the force-velocity imbalance: Individual adaptation kinetics

利用力-速度不平衡优化跳跃表现训练:个体适应动力学

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Abstract

AIMS: We analysed the changes in force-velocity-power variables and jump performance in response to an individualized training program based on the force-velocity imbalance (FVimb). In particular, we investigated (i) the individual adaptation kinetics to reach the optimal profile and (ii) de-training kinetics over the three weeks following the end of the training program. METHODS: Sixty subjects were assigned to four sub-groups according to their initial FVimb: high or low force-deficit (FD) and high or low velocity-deficit (VD). The duration of training intervention was set so that each individual reached their "Optimal force-velocity (F-v) profile". Mechanical and performance variables were measured every 3 weeks during the program, and every week after the end of the individualized program. RESULTS: All subjects in the FD sub-groups showed extremely large increases in maximal theoretical force output (+30±16.6% Mean±SD; ES = 2.23±0.28), FVimb reduction (-74.3±54.7%; ES = 2.17±0.27) and large increases in jump height (+12.4±7.6%; ES = 1.45±0.23). For the VD sub-groups, we observed moderate to extremely large increases in maximal theoretical velocity (+15.8±5.1%; ES = 2.72±0.29), FVimb reduction (-19.2±6.9%; ES = 2.36±0.35) and increases in jump height (+10.1±2.7%; ES = 0.93±0.09). The number of weeks needed to reach the optimal F-v profile (12.6 ± 4.6) was correlated to the magnitude of initial FVimb (r = 0.82, p<0.01) for all participants regardless of their initial subgroup. No significant change in mechanical variables or jump performance was observed over the 3-week de-training period. CONCLUSIONS: Collectively, these results provide useful insights into a more specific, individualized (i.e. based on the type and magnitude of FVimb) and accurate training prescription for jumping performance. Considering both training content and training duration together with FVimb may enable more individualized, specific and effective training monitoring and periodization.

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