Anaerobic Capacityestimated in A Single Supramaximal Test in Cycling: Validity and Reliability Analysis

An alternate to accumulated oxygen deficit (AOD) for measuring anaerobic contribution: 'AOD_alt' is valid in normoxia and hypoxia

PURPOSE

The gold standard measure of anaerobic contribution is accumulated oxygen deficit (AOD). The purpose of this study was to investigate the validity of an alternate measure, AOD_alt. AOD_alt is the sum of the phosphocreatine and glycolytic contributions, which are estimated from post-exercise oxygen uptake and blood lactate concentration, respectively.

METHODS

In Study One, six women and three men performed 6-min bouts of heavy intensity cycle ergometer exercise, once in normoxia (FIO2 ~ 21%) and twice under hypoxic conditions (FIO2 ~ 15% and ~ 12%). In Study Two, four women and two men performed severe intensity tests to exhaustion, once in normoxia (~ 10 min) and twice in hypoxia (FIO2 ~ 15% and ~ 10%). Physiological responses were measured during exercise and 7 min of recovery.

RESULTS

In 6 min of heavy exercise, Study One, the alternate and criterion measures of anaerobic contribution (AOD_alt and AOD) were correlated, in normoxia and in hypoxia. In exhaustive severe exercise, Study Two, AOD_alt and AOD were correlated (r = 0.77) and similar, in normoxia and at FIO2 ~ 15%. However, AOD_alt and AOD values were neither correlated (r = 0.27) nor similar (57 ± 5 mL·kg-1 vs 51 ± 7 mL·kg-1) at FIO2 ~ 10%.

CONCLUSION

These results confirm the validity of AOD_alt as a measure of anaerobic capacity in severe intensity exercise, demonstrate its validity in heavy exercise, and assert its validity in conditions of hypoxia (FIO2 ~ 12%).

Reliability of Anaerobic Contributions during a Single Exhaustive Knee-extensor Exercise

The total anaerobic contribution (AC[La-]+PCr) is a valid and reliable methodology. However, the active muscle mass plays an important role in the AC[La-]+PCr determination, which might influence its reliability. Thus, this study aimed to investigate the effects of two exhaustive intensities on the reliability of the AC[La-]+PCr during a one-legged knee extension (1L-KE) exercise. Thirteen physically active males were submitted to a graded exercise to determine the peak power output (PPO) in the 1L-KE. Then, two constant-load exercises were conducted to task failure at 100% (TTF100) and 110% (TTF110) of PPO, and the exercises were repeated on a third day. The blood lactate accumulation and the oxygen uptake after exercise were used to estimate the anaerobic lactic and alactic contributions, respectively. Higher values of AC[La-]+PCr were found after the TTF100 compared to TTF110 (p=0.042). In addition, no significant differences (p=0.432), low systematic error (80.9 mL), and a significant ICC (0.71; p=0.004) were found for AC[La-]+PCr in the TTF100. However, an elevated coefficient of variation was found (13.7%). In conclusion, we suggest the use of the exhaustive efforts performed at 100% of the PPO with the 1L-KE model, but its elevated individual variability must be carefully considered in future studies.

Sodium bicarbonate induces alkalosis, but improves high-intensity cycling performance only when participants expect a beneficial effect: a placebo and nocebo study

The study aimed to investigate the effects of sodium bicarbonate (NaHCO3) intake with divergent verbal and visual information on constant load cycling time-to-task failure, conducted within the severe intensity domain. Fifteen recreational cyclists participated in a randomized double-blind, crossover study, ingesting NaHCO3 or placebo (i.e., dextrose), but with divergent information about its likely influence (i.e., likely to induce ergogenic, inert, or harmful effects). Performance was evaluated using constant load cycling time to task failure trial at 115% of peak power output estimated during a ramp incremental exercise test. Data on blood lactate, blood acid-base balance, muscle electrical activity (EMG) through electromyography signal, and the twitch interpolation technique to assess neuromuscular indices were collected. Despite reduced peak force in the isometric maximal voluntary contraction and post-effort peripheral fatigue in all conditions (P < 0.001), neither time to task failure, EMG nor, blood acid-base balance differed between conditions (P > 0.05). Evaluation of effect sizes of all conditions suggested that informing participants that the supplement would be likely to have a positive effect (NaHCO3/Ergogenic: 0.46; 0.15-0.74; Dextrose/Ergogenic: 0.45; 0.04-0.88) resulted in improved performance compared to control. Thus, NaHCO3 ingestion consistently induced alkalosis, indicating that the physiological conditions to improve performance were present. Despite this, NaHCO3 ingestion did not influence performance or indicators of neuromuscular fatigue. In contrast, effect size estimates indicate that participants performed better when informed that they were ingesting an ergogenic supplement. These findings suggest that the apparently ergogenic effect of NaHCO3 may be due, at least in part, to a placebo effect.

Can Hypoxia Alter the Anaerobic Capacity Measured by a Single Exhaustive Exercise?

The present study aimed to compare the MAODALT in situations of hypoxia and normoxia to confirm the method validity. Seventeen healthy and physically active men participated in this study, aged 25.2±3.2 years. All participants underwent four days of evaluation. The first day was performed a body composition test, an incremental test to exhaustion to determine the maximum oxygen uptake, familiarizing the hypoxia (H) and normoxia (N) situation and the equipment used. On the second, third and fourth days, supramaximal efforts were performed until exhaustion at 110% of maximum oxygen uptake, in a situation of hypoxia (FIO2=14.0%) and normoxia (FIO2=20.9%). The anaerobic capacity was considered the sum of energy supply of the alactic and lactic systens. The absolute or relative anaerobic capacity values were not different (H=3.9±1.1 L, N=3.8±0.9 L, p=0.69), similarly no differences were found for the alactic contribution (H=1.7±0.5 L, N=1.5±0.5 L, p=0.30) and lactic contribution (H=2.3±0.9 L, N=2.3±0.7 L, p=0.85). It can be concluded that the anaerobic capacity measured by a single exhaustive effort is not altered by hypoxia.

Mechanical energy on anaerobic capacity during a supramaximal treadmill running in men: Is there influence between runners and active individuals?

This study verified whether mechanical variables influence the anaerobic capacity outcome on treadmill running and whether these likely influences were dependent of running experience. Seventeen physical active and 18 amateur runners, males, performed a graded exercise test and constant load exhaustive running efforts at 115% of intensity associated to maximal oxygen consumption. During the constant load were determined the metabolic responses (i.e., gas exchange and blood lactate) to estimate the energetic contribution and anaerobic capacity as well as kinematic responses. The runners showed higher anaerobic capacity (16.6%; p = 0.005), but lesser time to exercise failure (-18.8%; p = 0.03) than active subjects. In addition, the stride length (21.4%; p = 0.00001), contact phase duration (-11.3%; p = 0.005), and vertical work (-29.9%; p = 0.015). For actives, the anaerobic capacity did not correlate significantly with any physiologic, kinematic, and mechanical variables and no regression model was fitted using the stepwise multiple regression, while to runners the anaerobic capacity was significantly correlated with phosphagen energetic contribution (r = 0.47; p = 0.047), external power (r = -0.51; p = 0.031), total work (r = -0.54; p = 0.020), external work (r = -0.62; p = 0.006), vertical work (r = -0.63; p = 0.008), and horizontal work (r = -0.61; p = 0.008), and the vertical work and phosphagen energetic contribution presented a coefficient of determination of 62% (p = 0.001). Based on findings, it is possible to assume that for active subjects, the mechanical variables have no influence over the anaerobic capacity, however, for experienced runners, the vertical work and phosphagen energetic contribution have relevant effect over anaerobic capacity output.

Energy System Contributions during Olympic Combat Sports: A Narrative Review

This narrative review focuses on the studies that estimate the energy systems' contributions during match simulations of striking (boxing, karate, and taekwondo), grappling (judo), and weapon-based (fencing) Olympic combat sports. The purpose is to provide insights into the metabolism of these athletes. In striking Olympic combat sports, the oxidative contribution varied from 62% (in karate and taekwondo) to 86% (in boxing), the ATP-PCr system contribution varied from 10% (in boxing) to 31% (in taekwondo), and the glycolytic contribution was between 3% (in the third round of taekwondo) and 21% (in karate). In grappling combat sports, only judo was studied, and for a 4 min match, the oxidative contribution was 79%, followed by 14% ATP-PCr system contribution and 7% contribution from the glycolytic system. In fencing, the only weapon-based Olympic combat sport, the oxidative contribution varied from 81% (in the first bout) to 90% (in the second bout), followed by 9% (bout 2) to 12% (bout 1) contribution from the ATP-PCr system, and 0.6% to 7% contribution from the glycolytic system during 3 × 3 min bouts of épée match simulation. Hence, Olympic combat sports are primarily powered by the oxidative system, but the key scoring actions are likely fueled by anaerobic pathways.

Anaerobic Contribution Determined in Free-Swimming: Sensitivity to Maturation Stages and Validity

Evaluation of anaerobic contribution is important under swimming settings (training and modification through ages), therefore, it is expected to change during maturation. The accumulated oxygen deficit (AOD) method can be used to determine the contribution of nonoxidative energy during swimming; however, it requires several days of evaluation. An alternative method to estimate anaerobic contribution evaluation (AC_ALT), which can also be evaluated without snorkel (i.e., free-swimming, AC_FS), has been proposed; however, these methods have never been compared. Thus, this study (i) analyzed the effect of maturation stage on AC_FS during maximal 400 m swimming (Part I), and (ii) compared AOD with AC_ALT and AC_FS, determined in a maximal 400 m effort (Part II). In Part I, 34 swimmers were divided into three groups, according to maturation stages (early-pubertal, middle-pubertal, and pubertal), and subjected to a maximal 400 m free-swimming to determine AC_FS. In Part II, six swimmers were subjected to one 400 m maximal effort, and four submaximal constant efforts. The AOD was determined by the difference between the estimated demand and accumulated oxygen during the entire effort. The AC_ALT and AC_FS (for Part I as well) was assumed as the sum of lactic and alactic anaerobic contributions. AC_FS was higher in pubertal (3.8 ± 1.1 L) than early (2.1 ± 0.9 L) and middle pubertal group (2.4 ± 1.1 L). No difference was observed among absolute AOD (3.2 ± 1.3 L), AC_ALT (3.2 ± 1.5 L), and AC_FS (4.0 ± 0.9 L) (F = 3.6; p = 0.06). Relative AOD (51.8 ± 12.2 mL·kg⁻¹), AC_ALT (50.5 ± 14.3 mL·kg⁻¹), and AC_FS (65.2 ± 8.8 mL·kg⁻¹) presented main effect (F = 4.49; p = 0.04), without posthoc difference. The bias of AOD vs. AC_ALT was 0.04 L, and AOD vs. AC_FS was −0.74 L. The limits of agreement between AOD and AC_ALT were +0.9 L and −0.8 L, and between AOD and AC_FS were +0.7 L and −2.7 L. It can be concluded that AC_FS determination is a feasible tool to determine anaerobic contribution in young swimmers, and it changes during maturation stages. Also, AC_FS might be useful to measure anaerobic contribution in swimmers, especially because it allows greater speeds.

Anaerobic Contributions Are Influenced by Active Muscle Mass and The Applied Methodology in Well-Controlled Muscle Group

The anaerobic metabolism determination is complex and the applied methodologies present limitations. Thus, the purpose of this study was to investigate the effects of different calculations (MAOD vs. AOD) on the anaerobic contribution using the dynamic knee extension. Twenty-four male were recruited [Mean (SD); age 27 (1) years, body mass 90 (3) kg, height 181 (2) cm]. This study was divided into two independent experiments (EXP_1: one-legged; EXP₂: two-legged). In both experiments, it was performed a graded exercise test to determine maximal power (MP-GXT); 2-4 submaximal efforts (VO₂-intensity relationship); and an exhaustive effort. The theoretical energy demand for the exhaustive effort (TED_ex) was constructed from the submaximal efforts. Therefore, MAOD was assumed as the difference between the TED_ex and the accumulated VO₂ (AVO₂). In contrast, the energy demand for AOD was calculated as the product between VO₂ at the end of exercise and time to exhaustion (TED_aod). Thus, AOD was assumed as the difference between TED_aod and AVO₂. Bayesian paired t-test was used to compare the differences between the applied methods. Also, correlations between the anaerobic indices and performance were verified. In EXP₁, AOD was higher than MAOD [1855 (741) vs. 434 (245); BF₁₀ = 2925; ES = 2.5]. In contrast, in EXP₂, MAOD was higher than AOD [2832 (959) vs. 1636 (549); BF₁₀ = 3.33; ES = 1.4]. Also, AOD was correlated to performance (r = .59; BF₁₀ = 4.38). We concluded that MAOD and AOD are a distinct phenomenon and must be utilized according to the exercise model.

Impacts of high-intensity exercise on the metabolomics profile of human skeletal muscle tissue

The study aimed to identify and quantify the metabolites profile and metabolic pathways in human muscle tissue engaged during exhaustive high-intensity cycling exercise. Seven healthy physically active men performed a graded exercise test and an exhaustive supramaximal effort at 115% of maximal aerobic power with muscles biopsies performed in rest and immediately after exhaustion for quantifying of muscle metabolites changes by ¹ H-NMR spectroscopy. The time until exhaustion (tlim) recorded was 224.7 ± 35.5 s whereas the muscle pH at exhaustion was 6.48 ± 0.05. A total of 54 metabolites were identified and quantified. The most enriched and impacted pathways included: beta oxidation of very long chain fatty acids, mitochondrial electron transport chain, alanine aspartate, and glutamate metabolism, citric acid cycle, arginine biosynthesis, propanoate metabolism, threonine and 2-oxobutanoate degradation and pyruvate metabolism. In addition, the muscle concentrations in Post exercise, compared to Pre increased significantly (p < 0.0398) for fumarate (42.0%), succinate (101.2%), glucose (249.7%), lactate (122.8%), O-acetylcarnitine (164.7%), glycerol (79.3%), AMP (288.2%), 2-oxobutyrate (121.0%), and methanol (58.5%), whereas decreased significantly (p < 0.010) for creatine phosphate (-70.2%), ADP (-56.5%), carnitine (-33.5%), and glutamate (-42.3%). Only the succinate was significantly correlated with tlim (r = -0.76; p = 0.0497). Besides the classical expected contribution of glycolytic and phosphagen energetic pathways, it was demonstrated that the high-intensity exercise is also associated with pathways indicatives of amino acid and fatty acid oxidation metabolisms, highlighting the inverse relation between changes in the intramuscular succinate levels and tlim.

Anaerobic Capacity is Associated with Metabolic Contribution and Mechanical Output Measured During the Wingate Test

The study aimed to investigate the relationship between anaerobic capacity, mechanical and anaerobic contribution during the 30-s Wingate Anaerobic Test (30sWAnT). After familiarization, fifteen, male recreational mountain biking practitioners underwent the following sequence of tests: 1) a graded exercise test to determine maximal oxygen uptake and associated intensity i V ˙ O 2 m a x ; 2 and 3) supramaximal exhaustive effort at 115% of iVO_2max and 30sWAnT, performed randomly. The glycolytic and phosphagen pathways measured during the supramaximal effort were significantly correlated with peak power (r = 0.85; p < 0.01 and r = 0.57; p = 0.02, respectively), mean power (r = 0.78; p < 0.01 and r = 0.69; p < 0.01, respectively), and total work (r = 0.78; p < 0.01 and r = 0.69; p< 0.02, respectively) measured during the 30sWAnT. A significant correlation was also found between anaerobic capacity and peak power (r = 0.88; p < 0.01), mean power (r = 0.89; p < 0.01), and total work (r = 0.89; p < 0.01). Additionally, anaerobic capacity estimated during the supramaximal effort and the anaerobic contribution measured during the 30sWAnT were not different (p = 0.44) and presented significant good reliability and association (ICC = 0.84; p = 0.001) and good agreement, evidenced by the mean of differences and 95% limits of agreement near to zero (mean bias = 0.11). The results suggest that glycolytic and phosphagen capacity were associated with mechanical performance in the 30sWAnT. In addition, anaerobic contribution during the 30sWAnT seems to be valid for estimating anaerobic capacity in recreational mountain bike cyclists, as well as to estimate the glycolytic and phosphagen contributions.

Metabolic Profile and Performance Responses During Two Consecutive Sessions of Sprint Interval Training

Malta, ES, Brisola, GMP, de Poli, RAB, Dutra, YM, Franchini, E, and Zagatto, AM. Metabolic profile and performance responses during two consecutive sessions of sprint interval training. J Strength Cond Res 34(4): 1078-1085, 2020-The study aimed to (a) typify the cardiorespiratory, metabolic, and performance responses during a single sprint interval training (SIT) session, (b) investigate the interference of 2 subsequent sessions on cardiorespiratory, metabolic, and performance responses, and (c) verify the relationships of SIT total work performed with aerobic fitness indices. Thirty-six untrained men performed 2 SIT sessions (SIT1 and SIT2) separated by 24 hours of recovery. Each session was composed of 4 Wingate bouts interspersed by 4 minutes. Within SIT sessions, bout work, peak power, and mean power of each Wingate bout decreased significantly, while the fatigue index increased over time (p < 0.001). The SIT elicited lower acute responses of oxygen uptake and heart rate than maximal values (≈67 and ≈79%, respectively) (p < 0.05) as well as a short time spent at high cardiorespiratory demand. For SIT outcomes, no interactions were verified between Wingate bout performance, average heart rate, and average oxygen uptake. In addition, the oxygen uptake integral (SIT1:300.5 ± 38.6 ml·kg; SIT2:306.9 ± 41.1 ml·kg) and total work (SIT1:54.6 ± 10.4 kJ; SIT2:54.9 ± 10.6 kJ) did not differ between SIT sessions (p > 0.05). Furthermore, significant moderate to strong correlations were found between SIT1 and SIT2 total work and peak oxygen uptake (r = 0.48; r = 0.52, respectively), maximal aerobic power (r = 0.89; r = 0.89, respectively), and respiratory compensation point (r = 0.80; r = 0.78, respectively). In summary, an SIT session elicited a short time spent at high cardiorespiratory demand, while the SIT total work was significantly correlated with aerobic fitness indices. In addition, 2 consecutive SIT sessions interspaced by 24 hours did not affect performance outcomes, or cardiorespiratory and blood responses.

Determining MAOD Using a Single Exhaustive Severe Intensity Test

Maximal accumulated oxygen deficit (MAOD) provides a measure of anaerobic capacity. However, its measurement is a time-consuming process. The purpose of this study was to evaluate a measure of anaerobic capacity that avoids contentious assumptions and demands of the MAOD method. Twelve women and eight men volunteered for the study and completed cycle ergometer tests that resulted in exhaustion after ~4 min and ~8 min. In each test, anaerobic capacity was determined as (i) the MAOD and (ii) the sum of the phosphocreatine and glycolytic contributions (PCr+glycolysis). MAOD was determined by subtraction of the accumulated oxygen uptake from the total oxygen cost. Phosphocreatine and glycolytic contributions were calculated from post-exercise VO2 and blood lactate responses. MAOD in the 4-min and 8-min tests (79.1 ± 7.6 mL·kg-1 and 79.6 ± 7.4 mL·kg-1) and PCr+glycolysis in these tests (80.0 ± 7.3 mL·kg-1 and 79.0 ± 6.9 mL·kg-1) were correlated (r ≥ 0.91) and not significantly different. These results support the use of postexercise measures to quantify the phosphocreatine and glycolytic contributions and to provide an alternative to MAOD for measurement of anaerobic capacity.

Cycling Performance Enhancement After Drop Jumps May Be Attributed to Postactivation Potentiation and Increased Anaerobic Capacity

de Poli, RAB, Boullosa, DA, Malta, ES, Behm, D, Lopes, VHF, Barbieri, FA, and Zagatto, AM. Cycling performance enhancement after drop jumps may be attributed to postactivation potentiation and increased anaerobic capacity. J Strength Cond Res 34(9): 2465-2475, 2020-The study aimed to investigate the effects of drop jumps (DJs) on supramaximal cycling performance, anaerobic capacity (AC), electromyography, and fatigue. Thirty-eight recreational cyclists participated into 3 independent studies. In study 1 (n = 14), neuromuscular fatigue was assessed with the twitch interpolation technique. In study 2 (n = 16), the AC and metabolic contributions were measured with the maximal accumulated oxygen deficit method and the sum of the glycolytic and phosphagen pathways. In study 3 (n = 8), postactivation potentiation (PAP) induced by repeated DJs was evaluated. The DJ protocol was effective for significantly improving cycling performance by +9.8 and +7.4% in studies 1 and 2, respectively (p ≤ 0.05). No differences were observed in electromyography between conditions (p = 0.70); however, the force evoked by a doublet at low (10 Hz) and high frequencies (100 Hz) declined for control (-16.4 and -23.9%) and DJ protocols (-18.6 and -26.9%) (p < 0.01). Force decline was greater in the DJ condition (p < 0.03). Anaerobic capacity and glycolytic pathway contributions were +7.7 and +9.1% higher after DJ protocol (p = 0.01). Peak force during maximal voluntary contraction (+5.6%) and doublet evoked force at 100 Hz (+5.0%) were higher after DJs. The DJ protocol induced PAP, improved supramaximal cycling performance, and increased AC despite higher peripheral fatigue.

Creatine Supplementation Improves Phosphagen Energy Pathway During Supramaximal Effort, but Does Not Improve Anaerobic Capacity or Performance

This study aimed to investigate the effects of short-duration creatine monohydrate supplementation on anaerobic capacity (AC), anaerobic energy pathways, and time-to-exhaustion during high-intensity running. Fourteen healthy men underwent a graded exercise test (GXT) followed by a O_2max confirmation test, 5 submaximal efforts, and 4 supramaximal running bouts at 115% of V˙O_2max intensity (the first two supramaximal sessions were applied as familiarization trials) to measure the AC using two procedures; the maximum accumulated oxygen deficit (MAOD) and non-oxidative pathways energetics sum (AC_{[La-]+EPOCfast}). The investigation was conducted in a single-blind and placebo-controlled manner, with participants performing the efforts first after being supplemented with a placebo (dextrose 20 g⋅day^-1 for 5 days), and then, after a 7 day “placebo” washout period, they started the same procedure under creatine supplementation (20 g⋅day^-1 for 5 days. This order was chosen due to the prolonged washout of creatine. MAOD was not different between placebo (3.35 ± 0.65 L) and creatine conditions (3.39 ± 0.79 L; P = 0.58) and presented a negligible effect [effect size (ES) = 0.08], similar to, AC_{[La-]+EPOCfast} (placebo condition (3.66 ± 0.79 Land under creatine ingestion 3.82 ± 0.85 L; P = 0.07) presenting a small effect (ES = 0.20). The energetics from the phosphagen pathway increased significantly after creatine supplementation (1.66 ± 0.40 L) compared to the placebo condition (1.55 ± 0.42 L; P = 0.03). However, the glycolytic and oxidative pathways were not different between conditions. Furthermore, time to exhaustion did not differ between placebo (160.79 ± 37.76 s) and creatine conditions (163.64 ± 38.72; P = 0.49). Therefore, we can conclude that creatine supplementation improves the phosphagen energy contribution, but with no statistical effect on AC or time to exhaustion in supramaximal running.

Prediction of the Wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach

The Wingate Anaerobic Test (WAnT) is a short-term maximal intensity cycle ergometer test, which provides anaerobic mechanical power output variables. Despite the physiological significance of the variables extracted from the WAnT, the test is very intense, and generally applies for athletes. Our goal, in this paper, was to develop a new approach to predict the anaerobic mechanical power outputs using maximal incremental cardiopulmonary exercise stress test (CPET). We hypothesized that maximal incremental exercise stress test hold hidden information about the anaerobic components, which can be directly translated into mechanical power outputs. We therefore designed a computational model that included aerobic variables (features), and used a new computational \ predictive algorithm, which enabled the prediction of the anaerobic mechanical power outputs. We analyzed the chosen predicted features using clustering on a network. For peak power (PP) and mean power (MP) outputs, the equations included six features and four features, respectively. The combination of these features produced a prediction model of r = 0.94 and r = 0.9, respectively, on the validation set between the real and predicted PP/MP values (P< 0.001). The newly predictive model allows the accurate prediction of the anaerobic mechanical power outputs at high accuracy. The assessment of additional tests is desired for the development of a robust application for athletes, older individuals, and/or non-healthy populations.

Effects of Caffeine Ingestion on Anaerobic Capacity in a Single Supramaximal Cycling Test

The aim of this study was to verify the effects of caffeine on anaerobic capacity estimated by the sum of the estimated glycolytic [E_[La]] and phosphagen [E_PCr] metabolism based on blood lactate and excess post-oxygen consumption responses (AC_{[La-]+EPOCfast}). Fourteen male cyclists were submitted to a graded exercise test to determine the maximal oxygen uptake ( V ° O 2 m a x ) and intensity associated with   V ° O 2 m a x (i V ° O 2 m a x ). Subsequently, the participants performed two supramaximal efforts at 115% of i V ° O 2 m a x to determine the AC_{[La-]+EPOCfast}, after previous supplementation with caffeine (6 mg·kg^-1) or a placebo (dextrose), in a cross over, randomized, double blind, and placebo-controlled design. The time to exhaustion was higher in the caffeine (186.6 ± 29.8 s) than in the placebo condition (173.3 ± 25.3 s) (p = 0.006) and a significant correlation was found between them (r = 0.86; P = 0.00008). Significant differences were not found between AC_{[La-]+EPOCfast} values from the placebo (4.06 ± 0.83 L and 55.2 ± 5.7 mL·kg^-1) and caffeine condition (4.00 ± 0.76 L and 54.6 ± 5.4 mL·kg^-1); however, a significant correlation was observed only for AC_{[La-]+EPOCfast} expressed in absolute values (r = 0.74; p < 0.002). The E_[La] and E_PCr also presented no significant differences and they were significantly correlated (r = 0.82 and r = 0.55, respectively; p < 0.05). We conclude based on the overall comparison of mean values between two treatments that acute caffeine ingestion improves the time to exhaustion but does not affect anaerobic capacity estimation.

Anaerobic capacity estimated by the sum of both oxygen equivalents from the glycolytic and phosphagen pathways is dependent on exercise mode: Running versus cycling

The purpose of this study was to verify whether the exercise modality (i.e., running and cycling) alters the magnitude of “anaerobic” capacity estimated by a single supramaximal effort (AC_{[La]+EPOCfast}). Fourteen healthy men (age: 26±9 years) underwent a maximum incremental test and a supramaximal effort to exhaustion at 115% of the intensity associated with maximal oxygen uptake to determine the AC_{[La]+EPOCfast} (i.e., the sum of both oxygen equivalents from the glycolytic and phosphagen pathways), performed on both a treadmill and cycle ergometer. The maximal oxygen uptake during running was higher (p = 0.001; large effect size) vs. cycling (48.9±3.9mL·kg^-1·min^-1 vs. 44.8±5.5mL·kg^-1·min^-1 respectively). Contrarily, the oxygen equivalent from the glycolytic metabolism was not different between exercise modalities (p = 0.133; small effect size; running = 2.35±0.48 L and cycling = 2.18±0.58 L). Furthermore, the “anaerobic” capacity was likely meaning fully (3.65±0.70 L) and very likely meaningfully (949.1±5.7 mL·kg^-1) greater in running than cycling (3.81±0.71 L and 52.0±8.1 mL·kg^-1). Additionally, the contribution of the phosphagen metabolism was higher (p = 0.001; large effect size) for running compared to cycling (1.6±0.3 L vs.1.3±0.3 L respectively). Therefore, the “anaerobic” capacity estimated by the sum of both oxygen equivalents from the glycolytic and phosphagen pathways during a supramaximal effort is influenced by exercise modality and is able to identify the difference in phosphagen metabolic contribution, based on the methodological conditions of this study.

Is Oxygen Uptake Measurement Enough to Estimate Energy Expenditure During High-Intensity Intermittent Exercise? Quantification of Anaerobic Contribution by Different Methods

Purpose: The aim of the present study was to compare the contributions of the anaerobic pathway as determined by two different methods and energy expenditure during a typical high-intensity intermittent exercise (HIIE) protocol.

Methods: A descriptive research design was utilized in which thirteen physically active men performed six experimental sessions consisting of an incremental test (session 1), submaximal tests at 40, 50, 60, 70, 75, 80, 85, 90% of velocity associated with maximum oxygen uptake (vV˙O_2max) with two intensities per session (sessions 2–5), and the HIIE protocol (session 6; 10 efforts of 1 min at vV˙O_2max interspersed by 1 min of passive recovery). The estimation of anaerobic energy system contribution was calculated by: (a) the excess post-exercise oxygen consumption plus delta lactate method and (b) the accumulated oxygen deficit method using the difference between predicted oxygen demand from the submaximal tests of varying intensities and accumulated oxygen uptake during HIIE. Estimation of aerobic energy system contribution was calculated through the measurement of oxygen consumption during activity. Total EE during the entire HIIE protocol (efforts + recovery) and for the efforts only were calculated from each method.

Results: For efforts + recovery and efforts only, anaerobic contribution was similar for both methods, and consequently total EE was also equivalent (p = 0.230 for both comparisons). During efforts + recovery, aerobic:anaerobic energy system contribution was (68 ± 4%: 32 ± 4%), while efforts only was (54 ± 5%: 46 ± 5%) with both situations demonstrating greater aerobic than anaerobic contribution (p < 0.001 for both).

Conclusion: Anaerobic contribution seems to be relevant during HIIE and must to be taken into account during total EE estimation; however, the type of method employed did not change the anaerobic contribution or total EE estimates.

Validity and Reliability of the 30-s Continuous Jump for Anaerobic Power and Capacity Assessment in Combat Sport

Cycling test such Wingate anaerobic test (WAnT) is used to measure anaerobic power (AP), but not anaerobic capacity (AC, i.e., the metabolic energy demand). However, in sports that do not involve cycling movements (Karate), the continuous jump for 30 s (vertical jumps for 30 s) has been extensively used to measure anaerobic performance in all young athletes. Limited information’s are available concerning its validity and reliability especially in children. As such, the current study aimed to test validity and reliability of a continuous jumps test (the CJ30s), using WAnT as a reference. Thirteen female Karate kids (age: 11.07 ± 1.32 years; mass: 41.76 ± 15.32 kg; height: 152 ± 11.52 cm; training experience: 4.38 ± 2.14 years) were tested on three separate sessions. The first and second sessions were used to assess the reliability using Intra-class correlation coefficient (ICC) of CJ30s, whereas on the third session WAnT was administered. Following CJ30s and WAnT, we assessed AP (1/CJ30s, as jump height [JH], fatigue index [FI], and blood lactate [BL]; 2/WAnT, as mechanical power [P], FI, and BL) and AC as the excess post-exercise oxygen consumption (EPOC). Large/highly significant correlations were found between CJ30s and WAnT EPOCs (r = 0.730, P = 0.003), and BLs (r = 0.713, P = 0.009). Moderate/significant correlations were found between CJ30s and WAnT FIs (r = 0.640, P = 0.014), CJ30s first four jumps mean JH and WAnT peak P (r = 0.572, P = 0.032), and CJ30s mean JH and WAnT mean P (r = 0.589, P = 0.021). CJ30s showed excellent and moderate reliability (ICC) for AP (maximal JH 0.884, mean JH 0.742, FI 0.657, BL 0.653) and AC (EPOC 0.788), respectively. Correlations observed especially in terms of AC between CJ30s and WAnT provide evidence that former may adequately assess anaerobic performance for the young combat athlete. CJ30 is a reliable test and allow an easy assessment of AP and AC in karate children.

Relationship between anaerobic capacity estimated using a single effort and 30-s tethered running outcomes

The purpose of the current study was to investigate the relationship between alternative anaerobic capacity method (MAOD_ALT) and a 30-s all-out tethered running test. Fourteen male recreational endurance runners underwent a graded exercise test, a supramaximal exhaustive effort and a 30-s all-out test on different days, interspaced by 48h. After verification of data normality (Shapiro-Wilk test), the Pearson’s correlation test was used to verify the association between the anaerobic estimates from the MAOD_ALT and the 30-s all-out tethered running outputs. Absolute MAOD_ALT was correlated with mean power (r = 0.58; P = 0.03), total work (r = 0.57; P = 0.03), and mean force (r = 0.79; P = 0.001). In addition, energy from the glycolytic pathway (E_[La^-_]) was correlated with mean power (r = 0.58; P = 0.03). Significant correlations were also found at each 5s interval between absolute MAOD_ALT and force values (r between 0.75 and 0.84), and between force values and E_[La^-_] (r between 0.73 to 0.80). In conclusion, the associations between absolute MAOD_ALT and the mechanical outputs from the 30-s all-out tethered running test evidenced the importance of the anaerobic capacity for maintaining force during the course of time in short efforts.