ECSS Paris 2023: OP-PN22
INTRODUCTION: The hyperbolic relationship between power output and time to exhaustion allows for the estimation of both critical power (CP) and the curvature constant of such a relationship (W’). The latter theoretically represents a fixed amount of work that can be performed above CP, and increasing evidence is highlighting its relevance as a performance determinant in short or intermittent endurance events requiring high anaerobic capacity. However, while previous research has shown relevant links between W’ and several muscle physiological and morphological parameters, a holistic exploration of its relationship with common physiological and functional anaerobic capacity measures remains to be done. Accordingly, this study investigated the relationship between CP and W’ with maximal accumulated oxygen deficit (MAOD), maximal post-exercise lactate concentration and the force-velocity profile (Fmax, Vmax, Vopt, Pmax). METHODS: Eleven healthy trained triathletes (31.2±8.15years, 69.2±5.87kg, 180±5cm, 63.2±3.5ml/kg/min VO2max) performed four separate time-to-exhaustion (TTE) trials. Four different models (2-parameter hyperbolic, 3-parameter hyperbolic, linear, and linear 1/time) were used to compute CP and W. Maximal accumulated oxygen deficit was estimated during a supramaximal TTE trial performed at 100% of peak power output, and blood lactate concentration was assessed 30 minutes post-effort. Fmax, Vmax, Vopt and Pmax were computed from the force-velocity relationship. RESULTS: Moderate-to-large significant Pearson correlation coefficients were observed among W’ linear and 1/Time (16632±4448 and 13810±45 Joules, respectively), MAOD (54.9±11.5 ml/kg), Pmax (1041±183 W), and peak lactate concentration (13.1±3.4 mmol/L; r > 0.63, P < 0.05). Regardless of the model, no significant correlations were observed between these parameters and CP. CONCLUSION: The magnitude of W’ appears to be linked to the anaerobic capacity of moderately trained cyclists, as estimated from MAOD and post-exercise lactate accumulation measures following a supramaximal TTE effort. In line with previous research, the maximal power capabilities estimated from the force-velocity relationship seem also to be linked to W’. These findings provide useful information when it comes to evaluate anaerobic capacity in cycling. Further investigation will help to gain a deeper understanding of these links and their underlying mechanisms.
Read CV Antonio MORALES-ARTACHOECSS Paris 2023: OP-PN22
INTRODUCTION: The apex of the fat oxidation curve (FATmax) as well as maximal fat oxidation rate (MFO) serve as indicators for metabolic health and performance [1]. Among others, peak oxygen uptake (VO2peak) and gross efficiency (GE) also represent key determinants for endurance performance [2]. A relationship of these parameters is debatable and there is a lack of data in elite athletes and therefore, the aim of this work was to assess a potential relationship between parameters of fat oxidation and VO2peak as well as GE in a cohort of elite triathletes. METHODS: Fourteen male elite triathletes (stature 1.83 ± 0.06 m, body mass 71.2 ± 5.5 kg, incremental peak power output: 5.4 ± 0.2 W/kg) were recruited for this study. Participants performed a graded exercise test on a cycle ergometer (Cyclus2, RBM electronics, Germany) where respiratory gases were measured continuously (MetaMax 3B-R2, Cortex Biophysik GmbH, Leipzig, Germany). The test commenced at 80 W and load was increased by 40 W every 4 min. VO2 and carbon dioxide (VCO2) data were used to calculate fat oxidation according to [3] and the highest measured value for fat oxidation was accepted as FATmax [4]. FATmax was expressed relative to VO2peak. GE was calculated using VO2 and VCO2 data obtained from the last minute of the stage that corresponded to FATmax intensity. Pearson moment correlation was used to assess the strength of any relationship between FATmax (%VO2peak) and VO2max (mL/min/kg), between FATmax (%VO2peak) and GE, and between VO2peak (mL/min/kg) and GE. Standard errors of the estimates were calculated for significant correlations and significance was accepted at p < 0.050. RESULTS: Mean ± SD were 62.4 ± 6.5%, 21.4 ± 1.0%, and 68 ± 5 mL/min/kg for FATmax (%VO2peak), GE, and VO2peak, respectively. Significant correlations were found between FATmax and VO2max (p = 0.016; r = -0.631, SEE = ±5.2%) and VO2peak and GE (p = 0.010; r = -0.664; SEE = ±0.8%), but not between FATmax and GE (p = 0.106; r = 0.451). CONCLUSION: Present results indicate a strong negative relationship between FATmax intensity and VO2peak, but merely a moderate and non-significant relationship between FATmax intensity and GE. This suggests that a higher aerobic power is associated with a lower relative intensity of maximal fat oxidation, but no association exists between GE and FATmax. Data therefore confirms an inverse relation between VO2peak and the intensity where the highest fat oxidation occurs. This inverse relationship, however, questions the notion of the non-trainability of FATmax. In accordance with previous published work in cyclists a strong negative relationship was found between GE and VO2peak in a cohort of elite triathletes [5]. References: [1] Jeukendrup & Achten, 2004, Nutrition [2] Joyner & Coyle, 2008, J Physiol [3] Jeukendrup & Wallis, 2005, Int J Sports Med [4] Achten et al., 2002, Med Sci Sport Exer [5] Lucia et al., 2002, Med Sci Sport Exer
Read CV Christoph TriskaECSS Paris 2023: OP-PN22
INTRODUCTION: Combining training sets of various intensities in the same session is a common practise in swimming. Previous studies have examined the effect of set sequence in sessions that combine aerobic-dominated training sets and sprint intervals [1,2]. However, training sets planned to enhance anaerobic power may also be applied following aerobic training sets and vice versa. The aim of the study was to investigate the effect of the applied sequence of anaerobic power and threshold training sets on performance, metabolic responses and training load. METHODS: Twelve male swimmers (19±3 years) performed 25 m swimming at maximal effort to evaluate maximal speed. On a subsequent day they completed an incremental intermittent protocol (5x200 m) to calculate the speed corresponding to the lactate threshold (sLT). Based on the preliminary tests, training sets of 8x200 m at sLT separated by 30 s of recovery (set A) and 8x25 m at maximal speed separated by 2 min of recovery (set B) were planned. In two randomized order sessions all swimmers completed the following set sequences: i) set A followed by set B (A-B), ii) set B followed by set A (B-A) with 10 min of passive recovery between sets. Time to complete each repetition was recorded continuously. Blood lactate (BL) was determined at the start, middle and the end of each set. The area under the BL curve was calculated. Blood pH, base excess (BE) and bicarbonate (HCO3) were determined before and after each set. Session-RPE was recorded 30 min after the completion of each session and was used to calculate training load (TL). RESULTS: Performance in each set separately was not different between sessions regardless of sets sequence (p>0.05). Mean BL in set A was higher in B-A session compared to the reverse order (A-B: 3.5±1.6, B-A: 6.8±3.1 mmol/L, p<0.05). However, BL in set B was similar between sessions (p>0.05). In B-A session, mean BL was higher compared to A-B (BL: A-B: 5.3±3.7, B-A: 6.3±3.6 mmol/L, p<0.05). The area under the BL curve was higher in B-A session (A-B: 182.3±79.0, B-A: 284.0±108.3 mmol/L×min, p<0.05). pH and BE of the entire A-B session was not different compared to B-A session (pH: A-B: 7.34±0.08, B-A: 7.33±0.08, BE: A-B: -5.4±6.4, B-A: -6.9±6.1 mmol/L, p>0.05) while HCO3 was lower in B-A session compared to the reverse sequence (A-B: 20.4±5.2, B-A:19.0±4.9 mmol/L, p<0.05). Acid-base parameters in each set separately did not change irrespective to the set sequence (p>0.05). TL was higher in B-A session (A-B: 224.3±61.3, B-A: 258.3±44.3 a.u., p<0.05). CONCLUSION: Training sets sequence does not affect performance in each set separately but may influence the entire session metabolic responses. The longer time spent with high metabolic disturbance may cause higher training load induced when anaerobic power training set in preceded. 1. Nikitakis et al., 2023, Sports, 11(12):240. 2. Nikitakis et al., 2023, Int J Sports Physiol Perf, 19(1):53-61.
Read CV Argyris ToubekisECSS Paris 2023: OP-PN22