Although reliability correlations of traditional power indices of the Wingate test have been well documented, no study has analyzed new generation power indices based on milliseconds obtained from a Peak Bike. The purpose of this study was to investigate the retest reliability of new generation power indices. Thirty-two well-trained male athletes who were specialized in basketball, football, tennis, or track and field volunteered to take part in the study (age: 24.3 ± 2.2 years; body mass: 77 ± 8.3 kg; height: 180.3 ± 6.3 cm). Participants performed two Wingate all-out sessions on two separate days. Intra-class correlation coefficient (ICC), standard error measurement (SEM), smallest real differences (SRD) and coefficient of variation (CV) scores were analyzed based on the test and retest data. Reliability results of traditional power indices calculated based on 5-s means such as peak power, average power, power drop, and fatigue index ratio were similar with the previous findings in literature (ICC ≥ 0.94; CV ≤ 2.8%; SEM ≤ 12.28; SRD% ≤ 7.7%). New generation power indices such as peak power, average power, lowest power, power drop, fatigue index, power decline, maximum speed as rpm, and amount of total energy expenditure demonstrated high reliability (ICC ≥ 0.94; CV ≤ 4.3%; SEM ≤ 10.36; SRD% ≤ 8.8%). Time to peak power, time at maximum speed, and power at maximum speed showed a moderate level of reliability (ICC ≥ 0.73; CV ≤ 8.9%; SEM ≤ 63.01; SRD% ≤ 22.4%). The results of this study indicate that reliability correlations and SRD% of new generation power and fatigue-related indices are similar with traditional 5-s means. However, new time-related indices are very sensitive and moderately reliable.

In this study, thermal behaviours of the athletes were investigated with respect to thermal comfort and exercise intensity. The relationship between an index for analysing thermal comfort (Predicted Mean Vote: PMV) and Rating of Perceived Exertion (RPE) which shows exercise intensity and exhaustion level was evaluated. Eleven moderately trained male athletes ( $\dot{\mathrm{V}}$ O_2max 54 ± 9.9 mL∙min⁻¹∙kg⁻¹) had volunteered for the study (age: 22.2 ± 3.7 years; body mass: 73.8 ± 6.9 kg; height: 181 ± 6.3 cm; Body surface area (BSA): 1.93 ± 0.1 m²; body fat: 12.6% ± 4.2%; $\dot{\mathrm{V}}$ O_2max: 54 ± 9.9 mL∙min⁻¹∙kg⁻¹). Experiments were carried out by using a cycle ergometer in an air-conditioned test chamber which provided fresh air and had the ability to control the temperature and relative humidity. The study cohort was divided into two groups according to maximal oxygen consumption levels of the participants. Statistical analyses were conducted with the whole study cohort as well as the two separated groups. There was a moderate correlation between PMV and RPE for whole cohort (r: −0.51). When the whole cohort divided as low and high aerobic power groups, an average correlation coefficient at high oxygen consumption cohort decreased to r: −0.21, while the average correlation coefficient at low oxygen consumption cohort increased to r: −0.77. In conclusion, PMV and RPE have a high correlation in less trained participants, but not in the more trained ones. The case may bring to mind that thermal distribution may be better in high aerobic power group in spite of high RPE and thus the relation between PMV and RPE is affected by exercise performance status.

The aim of this study was to classify potential sub-zones within the extreme exercise domain. Eight well-trained male cyclists participated in this study. The upper boundary of the severe exercise domain (P_upper-bound) was estimated by constant-work-rate tests. Then three further extreme-work-rate tests were performed in discrete regions within the extreme domain: extreme-1) at a work-rate greater than the P_upper-bound providing an 80-110-s time to task failure; extreme-2) a 30-s maximal sprint; and extreme-3) a 4-s maximal sprint. Different functions were used to describe the behaviour of the V˙O2 kinetics over time. V˙O2 on-kinetics during extreme-1 exercise was best described by a single-exponential model (R² ≥ 0.97; SEE ≤ 0.10; p < 0.001), and recovery V˙O2 decreased immediately after the termination of exercise. In contrast, V˙O2 on-kinetics during extreme-2 exercise was best fitted by a linear function (R² ≥ 0.96; SEE ≤ 0.16; p < 0.001), and V˙O2 responses continued to increase during the first 10-20 s of recovery. During the extreme-3 exercise, V˙O2 could not be modelled due to inadequate data, and there was an M-shape recovery V˙O2 response with an exponential decay at the end. The V˙O2 response to exercise across the extreme exercise domain has distinct features and must therefore be characterised with different fitting strategies in order to describe the responses accurately.

Purpose: The highest work-rate that provides maximal oxygen uptake ( $\dot{V}{O}_{2max}$) may be one of the best exercise stimuli to yield both $\dot{V}{O_2}_{max}$ and lactate accumulation. The aim of this study was to analyze physiological and metabolic acute responses of an exercise modality performed at the upper boundary of the severe exercise domain, and compare those responses with exercise modalities applied within the severe exercise domain. Method: Ten trained male cyclists participated in this study. The $\dot{V}{O}_{2max}$, corresponding power output (PO_VO2max), and the highest work-rate that provides the $\dot{V}{O}_{2max}$ (I_HIGH) were determined by constant work-rate exercises. Cyclists performed three high-intensity interval training (HIIT) strategies as follows; HIIT-1: 4–6 × 3-min at 95% of PO_VO2max with 1:1 (workout/rest ratio); HIIT-2: 16–18 × 1-min at 105% of PO_VO2max with 1:1; HIIT-3: 4–7 × 1-2-min at the I_HIGH with 1:2. Capillary blood samples were analyzed before, immediately after HIIT sessions, and at the first, third, and fifth minutes of recovery periods. Lactate difference between the highest lactate response and resting status was considered as the peak lactate response for each HIIT modality. Results: Time spent at $\dot{V}{O}_{2max}$ was greater at HIIT-1 and HIIT-3 (272 ± 127 and 208 ± 111 seconds, respectively; p = 0.155; effect size = 0.43) when compared to the HIIT-2 (~26 seconds; p < 0.001), while there was a greater lactate accumulation at HIIT-3 (~16 mmol·L⁻¹) when compared to HIIT-1 and HIIT-2 (12 and 14 mmol·L⁻¹, respectively; p < 0.001). Conclusions: In conclusion, HIIT-3 performed at I_HIGH was successful to provide time spent at $\dot{V}{O}_{2max}$ with a greater lactate accumulation in a single session.

Purpose: To estimate the highest power output at which predominant energy contribution is derived from the aerobic system (aerobic limit power: ALP) and to compare ALP with the upper boundary of the severe intensity exercise domain. Methods: Fifteen male individuals participated in this study. The upper boundary was estimated using i) linear relationship between time to achieve $$\dot{\text{V}}$$V ˙ O_2max and time to task failure (P_UPPERBOUND), ii) hyperbolic relationships between time to achieve $$\dot{\text{V}}$$V ˙ O_2max vs. power output, and time to task failure vs. power output (P_UPPERBOUND´), and iii) precalculated $$\dot{\text{V}}$$V ˙ O_2max demand (I_HIGH). ALP was estimated by aerobic, lactic, and phospholytic energy contributions using $$\dot{\text{V}}$$V ˙ O₂ response, blood [lactate] response, and fast component of recovery $$\dot{\text{V}}$$V ˙ O₂ kinetics, respectively. Results: ALP was determined as the highest power output providing predominant aerobic contribution; however, anaerobic pathways became the predominant energy source when ALP was exceeded by 5% (ALP + 5%) (from 46 to 52%; p = 0.003; ES:0.69). The $$\dot{\text{V}}$$V ˙ O₂ during exercise at ALP was not statistically different from $$\dot{\text{V}}$$V ˙ O_2max (p > 0.05), but $$\dot{\text{V}}$$V ˙ O_2max could not be attained at ALP + 5% (p < 0.01; ES:0.63). ALP was similar to P_UPPERBOUND and P_UPPERBOUND´ (383 vs. 379 and 384 W; p > 0.05). There was a close agreement between ALP and P_UPPERBOUND (r: 0.99; Bias: − 3 W; SEE: 6 W; TE: 8 W; LoA: − 17 to 10 W) and P_UPPERBOUND´ (r: 0.98; Bias: 1 W; SEE: 8 W; TE: 8 W; LoA: − 15 to 17 W). ALP, P_UPPERBOUND, and P_UPPERBOUND´ were greater than I_HIGH (339 ± 53 W; p < 0.001). Conclusion: ALP may provide a new perspective to intensity domain framework.