Document Type : Research Paper I Open Access I Released under CC BY-NC 4.0 license
Authors
1 Azad University of Neyshabour
2 Gilan University
Abstract
This study aimed to compare the effects of high intensity and high volume training on lactate accumulation, time performance and VO2peak in 10-14 years old distance runners. 20 male endurance runners aged 12.5±2.32 years old and with a height of 154.62±3.27 cm, weight of 34.5±5.12 kg, maximum heart rate of 207.5±3.1 and VO2max of 36.45±3.22 ml /kg/min took part in the experiment. The experiment was undertaken randomly in a crossover form between two modes and two groups of runners (Group A and B), and two intermittent exercise models. The exercise models included: (i) a high intensity and low volume (HT) model (60 seconds exercise with 90% VO2 peak and 60 seconds rest with 30% VO2 peak) within 30 minutes activity and (ii) a low intensity and high volume (VT) model (60 seconds exercise with 65% VO2 peak and 60 seconds rest with 30% VO2 peak) within 60 minutes activity. The experiments were performed during two 6-week periods with a 5-week rest in between.
Respiratory Exchange Ratio (RER), Time performance in 1500 meters (T1500m) and Lactate maximum (Lacmax) were measured throughout the exercises. Repeated-measures ANOVA and Bonferroni post hoc tests were used to determine any significant difference between the groups and exercises. The results showed that there was a significant difference in the value of T1500m between pre and post HT and VT models (P<0.05).With regard to the maximum amount of RER and lactate, there was only a significant difference between pre and post HT exercise model (P<0.05). The results revealed that both HT and VT models had a significant difference in VO2peak between pre and post exercise (P<0.05). Overall, the findings of this study showed that the HT exercise model compared to the VT exercise model had higher performance on the RER, Lacmax, T1500m andVO2peak parameters. Where time limitation is an issue for athletes, the HT exercise model may therefore be a good substitution for long and boring training models.
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