背景:各种研究表明,强度度量的类型会影响训练强度分布(TID)计算。这些结论来自提供中环和大环数据的研究,而微循环,例如,到目前为止,高强度间歇训练电击微循环(HIIT-SM)已被忽略。以前的文献表明,在高强度区花费的时间,即,区域3(Z3)或“红色区域”,在HIIT期间对于实现耐久性性能参数的改善可能是重要的。因此,这项随机对照试验旨在比较基于跑步速度的TID(TIDV),7天HIIT-SM期间的运行功率(TIDP)和心率(TIDHR)。29名耐力训练的参与者被分配到HIIT-SM,由10个HIIT课程组成,没有(HSM,n=9)或具有(HSM+LIT,n=9)额外的低强度训练或对照组(n=11)。此外,我们探索了由运行速度(Z3V)确定的在Z3中花费的时间之间的关系,运行功率(Z3P),心率(Z3HR),摄氧量(Z3V•O2)和耐力表现的变化。
结果:两个干预组都显示出TIDV的极化模式(HSM:Z1:38±17,Z2:16±17,Z3:46±2%;HSMLIT:Z1:59±18,Z2:14±18,Z3:27±2%)和TIDP(Z1:50±8,Z2:14±11,Z3:62而TIDHR(Z1:48±13,Z2:26±11,Z3:26±7%;Z1:65±17,Z2:22±18,Z3:13±4%)显示出锥体模式。在两个干预组中,Z3HR的时间均明显少于Z3V和Z3P(均p<0.01)。在HSM+LIT的10个HIIT会话中,Z3中存在时间X强度测量与时间的相互作用(p<0.001,pη2=0.30)。对于两个干预组,每个HIIT会话中的Z3V和Z3P时间在训练期间保持稳定。从第一次(47%)到最后一次(28%),Z3HR的时间在HSM中有所下降,这在HSM+LIT中更为明显(45%至16%)。Z3V中的时间和峰值功率输出(rs=0.52,p=0.028)以及时间试验性能(rs=-0.47,p=0.049)的变化存在中等剂量反应关系,而Z3P中的时间没有这种关联,Z3HR,和Z3V•O2。
结论:本研究表明,强度测量的类型强烈影响HIIT-SM期间的TID计算。由于心率倾向于低估HIIT-SM期间的强度,基于心率的训练决策应谨慎。此外,Z3V的时间与耐力表现的变化最密切相关。因此,为了评估HIIT-SM,我们建议整合一套全面的强度措施。试用注册试用注册:Clinicaltrials.gov,注册号:NCT05067426。
BACKGROUND: Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the \"red zone\", during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TIDV), running power (TIDP) and heart rate (TIDHR) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n = 9) or with (HSM + LIT, n = 9) additional low-intensity training or a control group (n = 11). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3V), running power (Z3P), heart rate (Z3HR), oxygen uptake ( Z 3 V ˙ O 2 ) and changes in endurance performance.
RESULTS: Both intervention groups revealed a polarized pattern for TIDV (HSM: Z1: 38 ± 17, Z2: 16 ± 17, Z3: 46 ± 2%; HSM + LIT: Z1: 59 ± 18, Z2: 14 ± 18, Z3: 27 ± 2%) and TIDP (Z1: 50 ± 8, Z2: 14 ± 11, Z3: 36 ± 7%; Z1: 62 ± 15, Z2: 12 ± 16, Z3: 26 ± 2%), while TIDHR (Z1: 48 ± 13, Z2: 26 ± 11, Z3: 26 ± 7%; Z1: 65 ± 17, Z2: 22 ± 18, Z3: 13 ± 4%) showed a pyramidal pattern. Time in Z3HR was significantly less compared to Z3V and Z3P in both intervention groups (all p < 0.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM + LIT (p < 0.001, pη2 = 0.30). Time in Z3V and Z3P within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3HR declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM + LIT (45% to 16%). A moderate dose-response relationship was found for time in Z3V and changes in peak power output (rs = 0.52, p = 0.028) as well as time trial performance (rs = - 0.47, p = 0.049) with no such associations regarding time in Z3P, Z3HR, and Z 3 V ˙ O 2 .
CONCLUSIONS: The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3V was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures. Trial Registration Trial register: Clinicaltrials.gov, registration number: NCT05067426.