Effects of Plyometric Training on Explosive Power:The Optimal Training Effectiveness Interval
Objective:To explore the optimal training effect intervals of plyometric training on explosive pow-er.Methods:Stata 15.1 and GraphPad Prism 8.0 were used to analyze the effects of training cycle,frequency,vol-ume,rest intervals,and load on 1,435 participants from 81 included studies.The Cochrane risk of bias assessment tool and funnel plot were employed to evaluate the quality of the studies and publication bias.The average effect size(Cohen's d)and 95%confidence intervals(CI)for explosive power indicators were calculated.Results:(1)Training duration:Various explosive power indicators(ie.,jumping,sprinting,throwing ability)showed an up-ward trend with the extension of the training duration.Large effects(ES≥1.2)were observed for unloaded jump-ing,sprinting,and throwing ability after 10 weeks,while loaded jumping and sprinting ability achieved large ef-fects after 8 and 9 weeks.(2)Training inter-set recovery:30-60 seconds was identified as the optimal inter-set rest interval for developing explosive power(moderate effect,ES≥0.6).(3)Training inter-exercise recovery:The optimal inter-exercise rest intervals for developing jumping and throwing ability were 0.5-1 minute(adolescents)and 3-4 minutes(adults),while for sprinting ability,they were 0.5-1 minute(adolescents)and 1-1.5 minutes(adults).(4)Training frequency:No significant difference in average effect size was found between training fre-quencies of≤2 times per week and>2 times per week(P＞0.05).(5)Training volume:The explosive power indi-cators showed a slight overall increase with training volume,though with considerable fluctuation.(6)External training load(%body mass):Jumping and sprinting abilities peaked at 8-10.5%and 2.5%-8%extra load,respec-tively.Conclusion:Plyometric training impacts explosive power differently depending on factors such as training duration,rest intervals,extra load percentage,and training volume.Therefore,selecting the optimal training ef-fect intervals for these variables is crucial for effective training program design.
万方数据
维普
