ECSS Paris 2023: CP-AP28
INTRODUCTION: Training improves performance through progressive overload, delicately balancing stress and recovery. Hence, an objective fatigue-recovery marker would gravely benefit athletes seeking to sustainably improve performance. Interestingly, a non-linear heart rate variability (HRV) index (DFA a1) holds promise as a marker of exercise intensity as well as one’s physiological status of fatigue during endurance training. Specifically, DFA a1 has gained attention for its potential to demarcate intensity thresholds as it’s value decreases with increasing intensity and hereby correlates with aerobic and anaerobic thresholds. Moreover, it could be applied to monitor fatigue as it shows suppressed values during prolonged exercises. However, responsiveness of DFA a1 to training sessions of different intensities with identical workload remains unexplored. METHODS: Eighteen participants (18-35yrs, BMI<25, ≥5h endurance training/week) performed three training sessions (light, moderate, high-intensity) separated by at least one week. Training sessions were matched for workload (joules) by adapting training time, calculated using the formula 𝐸=𝑃×Δ𝑡. The high-intensity training was done first and determined the duration of the light and moderate training. The order of the light and moderate training was randomized and was performed as a continuous training at the first and second lactate threshold (i.e LT1 and LT2) power, respectively, based on the subject’s individual maximal incremental exercise test results. The initial high-intensity training followed a 10x1-minute high-intensity interval protocol at 110% VO₂max power alternated with 1 minute rest at 80% LT1 power. Each training started and ended with a standardized 10-min warming-up (WU) and 10-min cooling-down (CD) at LT1 power. Mean DFA a1 and HR were calculated during the final two minutes of WU and CD to assess intensity-specific physiological changes. RESULTS: Mean differences in DFA a1 and HR (beats/min) values between the last two minutes of WU vs. CD were 0.051 (±0.160), p=0.66 and 6.50 (±3.67) beats/min, p=0.27 for light-intensity, 0.285 (±0.140), p=0.006 and 8.28 (±3.48) beats/min, p=0.14 for moderate-intensity, and 0.321 (±0.222), p=0.009 and 9.39 (±5.71) beats/min, p=0.11 for HIIT. Repeated measures ANOVA of DFA a1 during WU vs. CD revealed significant differences for light vs. moderate (0.234, p<0.001) and light vs. HIIT (0.269, p<0.001). However, no significant difference was observed for moderate vs. HIIT (0.035, p=0.486). Importantly, HR values obtained during WU vs. CD were not significantly different across training intensities, indicating that DFA a1 seems to be a more sensitive marker of acute fatigue. CONCLUSION: The responsiveness of DFA a1 to training intensity, especially compared to unchanged HR values, highlights its potential as a sensitive marker of fatigue. This responsiveness suggests DFA a1 could serve as a practical tool for monitoring physiological stress and personalizing exercise and recovery programs.
Read CV Toon de BeukelaarECSS Paris 2023: CP-AP28
INTRODUCTION: High-intensity interval training (HIIT) in hyperoxia is proposed to enhance exercise performance and aerobic capacity, but its potential efficacy and underlying mechanisms remain unclear. Therefore, the aim of this study was to determine the effect of breathing hyperoxic gas during HIIT on post-exercise mRNA responses in equine skeletal muscle. METHODS: In a randomized crossover study design, seven trained Thoroughbred horses (3 geldings and 4 mares) performed four work-matched treadmill exercise protocols: 1) moderate-intensity continuous exercise in normoxia (fraction of inspired oxygen [FIO2] = 0.21, 6 min at 70% VO2max; N-MICT), 2) high-intensity interval exercise in normoxia (FIO2 = 0.21, 6 × 30 s at 100% VO2max with 30 s recovery; N-HIIT), 3) moderate-intensity continuous exercise in hyperoxia (FIO2 = 0.30, 6 min at 70% VO2max; H-MICT), and 4) high-intensity interval exercise in hyperoxia (FIO2 = 0.30, 6 × 30 s at 100% VO2max with 30 s recovery; H-HIIT). Arterial blood samples were collected during exercise to measure blood gases and plasma lactate concentrations. Biopsy samples from the middle gluteal muscle were collected pre-exercise and 4 hours post-exercise for real-time PCR analysis of gene expression. Data are expressed as mean ± standard error (SE), and the effects of time, protocol and their interaction (time x protocol) were analyzed using mixed models (P < 0.05). RESULTS: At the end of exercise, arterial O2 saturation (SaO2) was higher in H-MICT (95.6 ± 0.1%), H-HIIT (95.1 ± 0.2%) and N-MICT (92.4 ± 0.7%) compared to N-HIIT (86.5 ± 1.6%) (P < 0.001). Plasma lactate concentrations were higher in N-HIIT (23.6 ± 2.6 mmol/L) and H-HIIT (19.5 ± 2.6 mmol/L) compared to N-MICT (9.5 ± 2.1 mmol/L) and H-MICT (12.0 ± 2.5 mmol/L) (P = 0.001). After 4 hours of exercise, HIF-1α mRNA increased 4.6-fold in N-HIIT (P= 0.046), VEGF mRNA increased 2.0-fold in H-HIIT (P = 0.032), and HSP70 mRNA increased 6.2-fold in N-HIIT (P = 0.001) and 2.9-fold in H-HIIT (P = 0.036). PGC-1α mRNA increased by 4.2- to 5.4-fold (P < 0.001 to P = 0.0033), and PDK4 mRNA increased by 7.9- to 10.1-fold (P < 0.001 to P = 0.019) in all groups. CONCLUSION: Plasma lactate concentration was unaffected by FIO2, whereas SaO2 was maintained during HIIT when breathing hyperoxic gas. These results suggest that H-HIIT can enhance whole-body O2 delivery to skeletal muscle, which may reduce cellular adaptation to hypoxia but enhance angiogenic responses. However, H-HIIT had no additional effects on heat shock response, mitochondrial biogenesis, or lipid metabolism compared to N-HIIT.
Read CV Kazutaka MukaiECSS Paris 2023: CP-AP28
INTRODUCTION: Agility is an individual’s ability to maintain and control body posture while running and changing direction quickly[1]. During childhood, routine play activities often involve speed, twists, and turns, making this period particularly sensitive for agility development[2]. The Illinois Agility Test and the T-Agility Test have been validated in adults and athletes[3], yet their reliability and validity in Chinese children remain underexplored. This study aims to evaluate the test–retest reliability and validity of the Illinois Agility Test and the T-Agility Test in Chinese children. METHODS: For test–retest reliability, 90 participants (41 males and 49 females; age: 11.6 ± 0.6 years; height: 148.3 ± 7.9 cm; weight: 43.1 ± 12.6 kg; BMI: 19.4 ± 4.6) completed the Illinois and T-Agility Tests twice, with a two-week interval between assessments. To assess validity, 189 participants (86 males and 103 females; age: 11.6 ± 0.5 years; height: 148.9 ± 7.7 cm; weight: 42.4 ± 11.3 kg; BMI: 18.9 ± 4.0) were evaluated using both agility tests alongside the 20-meter dash and the standing long jump (SLJ). Data were analyzed using Spearman correlation. Significance was set at p<0.05. RESULTS: The intraclass correlation coefficient (ICC) for test–retest reliability of the Illinois Agility Test was 0.778 (p < 0.001), while the ICC for the T-Agility Test was 0.771 (p < 0.001). For validity, the Spearman correlation coefficients between the 20-meter dash and the Illinois and T-Agility Tests were 0.540 (p < 0.001) and 0.581 (p < 0.001), respectively. The Spearman correlation coefficients between the SLJ and the Illinois and T-Agility Tests were -0.611 (p < 0.001) and -0.537 (p < 0.001), respectively. CONCLUSION: This study demonstrates that both the Illinois Agility Test and the T-Agility Test are reliable and valid measures of agility in children. Among Chinese children, the Illinois Agility Test exhibits superior reliability and validity compared to the T-Agility Test. 1.Sheppard J.M & Young W.B (2006) 2. Thieschafer L & Busch D (2022) 3.Nimphius S et al. (2018)
Read CV yu hongjunECSS Paris 2023: CP-AP28