Predicting MAOD Using Only a Supramaximal Exhaustive Test

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.

Validation of the AC[La]+EPOCfast Method in Sprinters and Middle-distance Runners

The anaerobic capacity (AC[La]+EPOCfast) method is of interest as it can differentiate between the contributions of phosphocreatine (PCr) and glycolytic energy, assess the anaerobic metabolism during a single effort, and evaluate various exercise modalities. However, no previous studies have investigated whether the superiority of the PCr and glycolytic systems can be assessed in athletes with high anaerobic capacity. This study aimed to compare the AC[La]+EPOCfast method in seven sprinters (SP) and seven middle-distance runners (MD). The participants underwent a graded exercise test and constant-load exercise at 115% ˙VO2max until exhaustion using a bicycle ergometer. AC[La]+EPOCfast was calculated as the sum of oxygen equivalents from the phosphagen (EPCr) and glycolytic (ELa) pathways. This study reveals that there was no significant difference in AC[La]+EPOCfast or maximal accumulated oxygen deficit (MAOD) between the two groups. Although EPCr was not significantly different between groups, ELa was significantly higher in the SP group than in the MD group. In conclusion, the AC[La]+EPOCfast method can be used to evaluate high and low anaerobic capacities. However, further studies on athletes with high aerobic capacity are required.

Calculation of a conversion factor for estimating the glycolytic contribution in exercise from post-exercise blood lactate concentration

Purpose: Often, the glycolytic contribution in a bout of heavy or severe intensity exercise is estimated by multiplying the increase in blood lactate concentration above resting levels that is engendered by the exercise (in mM) by 3.3 (or 3) mL·kg-1 per mM. Our purpose was to verify the value of this conversion factor, using methods that were completely different from those of the original studies. Methods: Six women (mean ± SD), age, 23 ± 1 year; VO2max, 46 ± 4 mL·kg-1·min-1) and three men (23 ± 0 years; 54 ± 8 mL·kg-1·min-1) completed 6 min of heavy intensity exercise in conditions of normoxia and hypoxia (FIO2, ∼12%). VO2 was measured throughout the exercise and 7 min of recovery. The increase in glycolytic contribution was estimated as the reduction in aerobic contribution in hypoxia, after correction for the effects of hypoxia on the oxygen demand and on the contribution from phosphocreatine. The peak post-exercise blood lactate concentration was measured in fingerstick blood samples. Results: The ratio between the increase in estimated glycolytic contribution (in mL·kg-1) in hypoxia and the increase in peak blood lactate concentration (in mM) yielded an oxygen equivalent of 3.4 ± 0.4 mL·kg-1 per mM (range, 2.6 mL·kg-1 per mM to 4.0 mL·kg-1 per mM) for cycle ergometer exercise. Conclusion: These results generally support the use of a common conversion factor to calculate the glycolytic contribution from post-exercise blood lactate concentrations. However, there is some inter-individual variability in the conversion factor.

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.

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.

High-intensity resistance exercise is not as effective as traditional high-intensity interval exercise for increasing the cardiorespiratory response and energy expenditure in recreationally active subjects

PURPOSE

Traditional high-intensity interval exercise (HIIE) highly stimulates the cardiorespiratory system and increases energy expenditure (EE) during exercise. High-intensity resistance exercise (HIRE) has become more popular in recreationally active subjects. The physiological responses to HIRE performed with light or moderate load is currently largely unknown. Here, we examined the effect of the type of interval exercise [HIRE at 40% (HIRE40) and 60% (HIRE60) 1-RM vs. traditional HIIE] on the cardiorespiratory response and EE during and after exercise.

METHODS

Fifteen recreationally active adults randomly completed traditional HIIE on an ergocyle, HIRE40 and HIRE60. The sessions consisted of two sets of ten 30-s intervals (power at 100% VO_2max during HIIE; maximal number of repetitions for 10 different free-weight exercises during HIRE40 and HIRE60) separated by 30-s active recovery periods. Gas exchange, heart rate (HR) and EE were assessed during and after exercise.

RESULTS

VO_2mean, VO_2peak, HR_mean, the time spent above 90% VO_2max and HR_max, and aerobic EE were lower in both HIRE sessions compared with HIIE (P < 0.05). Anaerobic glycolytic contribution to total exercise EE was higher in HIRE40 and HIRE60 compared with HIIE (P < 0.001). EE from excess post-exercise oxygen consumption (EPOC) was similar after the three sessions. Overall, similar cardiorespiratory responses and EE were found in HIRE40 and HIRE60.

CONCLUSIONS

HIRE is not as effective as HIIE for increasing the cardiorespiratory response and EE during exercise, while EPOC remains similar in HIRE and HIIE. These parameters are not substantially different between HIRE40 and HIRE60.

Relationship Between Heart Rate, Oxygen Consumption, and Energy Expenditure in Futsal

The primary aim of this study was to compare the measured oxygen consumption (Measured-VO₂) in a simulated futsal game (S-Game) with the estimated oxygen consumption (Estimated-VO₂) through a regression equation between heart rate (HR) and oxygen consumption (VO₂) (HR-VO₂) in treadmill running, and a secondary aim was to calculate the total energy expenditure (EE) in S-Game. Ten professional players (22.20 ± 3.22 years) were evaluated. HR-VO₂ was determined individually in the continuous test on the treadmill (Cont_Test). The Measured-VO₂ in S-Game was compared with the Estimated-VO₂ in the Cont_Test. Alactic and lactic pathways were estimated by VO₂. The Estimated-VO₂ presented no statistically significant difference with the Measured-VO₂, using the paired t-test (p = 0.38). However, the correlation between Estimated- and Measured-VO₂ was very weak (r = −0.05), and it presented poor agreement (concordance correlation coefficient = −0.04). In addition, a Bland–Altman plot presented bias of −2.8 ml/kg/min and individual difference as large as 19 ml/kg/min. The HR-VO₂ determined by the Cont_Test was not a good individual predictor of VO₂. The high intensity and intermittent nature of the futsal game possibly caused dissociation in the HR-VO₂ relationship. Cont_Test is not recommended for estimating VO₂ and calculating individual EE in the futsal game. This is recommended only for the group mean. The total EE in S-Game was 13.10 ± 1.25 kcal.min⁻¹ (10.81 ± 1.57 metabolic equivalents). The contributions from the metabolic pathways were as follows: aerobic (93%), alactic (5%), and lactic (2%).

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.

Energetic Profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2

Purpose: To analyze the energetic profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2 (YYIR1 and YYIR2). Methods: Intermittent running distance (IR1D and IR2D), time to exhaustion (IR1T and IR2T), and total recovery time between shuttles (IR1R and IR2R) were measured in 10 well-trained male athletes (age 24.4 [2.0] y, height 182 [1] cm, weight 75.8 [7.9] kg). Respiratory gases and blood lactate (BLC) were obtained preexercise, during exercise, and until 15 min postexercise. Metabolic energy, average metabolic power , and energy share (percentage of aerobic [WAER], anaerobic lactic [WBLC], and anaerobic alactic energy system [WPCr]) were calculated using the PCr-La-O2 method. Results: Peak oxygen consumption was possibly higher in YYIR2 (60.3 [5.1] mL·kg−1·min−1) than in YYIR1 (P = .116, 57.7 [4.5] mL·kg−1·min−1, d = −0.58). IR1D, IR1T, and IR1R were very likely higher than IR2D, IR2T, and IR2R, respectively (P < .001, 1876 [391] vs 672 [132] m, d = −2.83; P < .001, 916 [175] vs 304 [57] s, d = −3.03; and P < .001, 460 [100] vs 150 [40] s, d = −2.83). Metabolic energy was most likely lower in YYIR2 than in YYIR1 (P < .001, 493.5 [118.1] vs 984.8 [171.7] kJ, d = 3.24). Average metabolic power was most likely higher in YYIR2 than in YYIR1 (P < .001, 21.5 [1.7] vs 14.5 [2.2] W·kg−1, d = 3.54). When considering aerobic phosphocreatine restoration during breaks between shuttles, WAER (P = .693, 49% [10%] vs 48% [5%], d = −0.16) was similar, WPCr (P = .165, 47% [11%] vs 42% [6%], d = −0.54) possibly higher, and WBLC (P < .001, 4% [1%] vs 10% [3%], d = 1.95) almost certainly lower in YYIR1 than in YYIR2. Conclusions: WAER and WPCr are predominant in YYIR1 and YYIR2 with almost identical WAER. Higher IR1D and IR1T in YYIR1 result in higher metabolic energy but lower average metabolic power and slightly lower peak oxygen consumption. Higher IR1R allows for higher reliance on WPCr in YYIR1, while YYIR2 requires a higher fraction of WBLC.

Determination of Maximum Accumulated Oxygen Deficit Using Backward Extrapolation

This study aimed to compare the Maximum Accumulated Oxygen Deficit determined by the conventional method (MAOD_C) with that determined by the backward extrapolation technique (MAOD_EXTR) in runners. Fourteen runners underwent a maximal incremental test for determination of iVO_2MAX, ten submaximal efforts (50-95% of iVO_2MAX for 7 min). During the submaximal efforts oxygen consumption (VO₂) values were obtained conventionally and through the backward extrapolation technique (~ 3 s after the end of each effort). A supramaximal effort (110% of iVO_2MAX) (tLim_C) and five supramaximal bouts (tLim_EXTR) were performed. MAOD_C and MAOD_EXTR were determined from the difference between the VO₂ accumulated during tLim_C and tLim_EXTR and the predicted values. The tLim_C was lower than tLim_EXTR (164.06±36.32 s, 200.23±63.78 s, p<0.05). No significant differences were found between absolute and relative MAOD_C and MAOD_EXTR values, however, low intraclass correlations (0.26 and 0.24), high typical errors (2.03 L and 24 mL∙kg^-1) were observed, and coefficients of variation (46 and 48%), respectively. The graphical analysis of the differences showed agreement and correlation between the methods (r=0.86 and 0.85). Thus, it can be concluded that the MAOD_EXTR is not a valid method for estimating the anaerobic capacity of runners, moreover, unreliable.

Beta alanine supplementation effects on metabolic contribution and swimming performance

BACKGROUND

Investigations of β-alanine supplementation shows effects on metabolic (aerobic and anaerobic) participation and performance on swimming by a possible blood acidosis buffering. Considering this background, the objective of the present study was to analyze the effects of β-alanine supplementation on metabolic contribution and performance during 400-m swim.

METHODS

Thirteen competitive swimmers underwent a 6-week, double-blind placebo-controlled study, ingesting 4.8 g.day- 1 of β-alanine or placebo. Before and after the supplementation period, the total anaerobic contribution (TAn) and 30-s all-out tethered swimming effort (30TS) were assessed. Anaerobic alactic (AnAl) and lactic energy (AnLa) was assumed as the fast component of excess post-exercise oxygen consumption and net blood lactate accumulation during exercise (∆[La-]), respectively. Aerobic contribution (Aer) was determined by the difference between total energy demand and TAn. In addition to conventional statistical analysis (Repeated measures ANOVA; p > 0.05), a Bayesian repeated measures ANOVA was used to evidence the effect probability (BFincl).

RESULTS

No differences and effects were found between groups, indicating no supplementation effects. Repeated measures ANOVA, with confirmation of effect, was indicate reduce in ∆Lactate (p: 0.001; BFincl: 25.02); absolute AnLa (p: 0.002; BFincl: 12.61), fatigue index (p > 0.001; BFincl: 63.25) and total anaerobic participation (p: 0.008; BFincl: 4.89).

CONCLUSIONS

Thus, the results demonstrated that all changes presented were evidenced as a result of exposure to the training period and β-alanine supplementation doesn't affect metabolic contribution and performance during 400-m freestyle.

Assessment of two methods to determine the relative contributions of the aerobic and anaerobic energy systems in racehorses

A prospective, randomized, controlled study was designed to determine relative aerobic and anaerobic (lactic and alactic) contributions at supramaximal exercise intensities using two different methods. Thoroughbred racehorses (n = 5) performed a maximal rate of oxygen consumption (V̇o2max) test and three supramaximal treadmill runs (105, 115, and 125% V̇o2max). Blood lactate concentration (BL) was measured at rest, every 15 s during runs, and 2, 5, 10, 20, 30, 40, 50, and 60 min postexercise. In method 1, oxygen demand was calculated for each supramaximal intensity based on the V̇o2max test, and relative aerobic and anaerobic contributions were calculated from measured V̇o2 and the accumulated oxygen deficit. In method 2, aerobic contribution was calculated using the trapezoidal method to determine V̇o2 during exercise. A monoexponential model was fitted to the postexercise V̇o2 curve. Alactic contribution was calculated using the coefficients of this model. Lactate anaerobic contribution was calculated by multiplying the peak to resting change in BL by 3. Linear mixed-effects models were used to examine the effects of exercise intensity and method (as fixed effects) on measured outcomes (P ≤ 0.05). Relative aerobic and anaerobic contributions were not different between methods (P = 0.20). Horses' mean contributions were 81.4, 77.6, and 72.5% (aerobic), and 18.5, 22.3, and 27.4% (anaerobic) at 105, 115, and 125% V̇o2max, respectively. Individual alactic anaerobic energy was not different between supramaximal exercise intensities (P = 0.43) and was negligible, contributing a mean of 0.11% of the total energy. Relative energy contributions can be calculated using measured V̇o2 and BL in situations where the exercise intensity is unknown. Understanding relative metabolic demands could help develop tailored training programs. NEW & NOTEWORTHY Relative energy contributions of horses can be calculated using measured V̇o2 and BL in situations where the exercise intensity is unknown. Horses' mean contributions were 81.4, 77.6, and 72.5% (aerobic), and 18.5, 22.3, and 27.4% (anaerobic) at 105, 115, and 125% of V̇o2max, respectively. Individual alactic capacity was unaltered between supramaximal exercise intensities and accounted for a mean contribution of 0.11% of energy use.

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.

Relationships Between Aerobic and Anaerobic Parameters With Game Technical Performance in Elite Goalball Athletes

Our aims were to compare physiological parameters from the laboratory environment (LaB) and simulated goalball games (GaM), test relationships between physiological parameters in the laboratory and game technical performance (GTP), and examine the associations between physiological and technical responses during games. Seven elite athletes from the Brazilian National Team performed in LaB environment; (i) an incremental test to determine peak oxygen consumption (O_2PEAK), its corresponding speed, and peak blood lactate concentration and (ii) submaximal and supramaximal efforts to estimate maximal anaerobic contribution (AnC). In GaM condition, simulated games were also performed to determine physiological responses throughout the game, and to analyze the GTP (number of throws, defenses, recovery, and density of actions). No correlations (unclear) were found between laboratory and games analyses for O_2PEAK [47.3 (17.2) vs. 25.8 (18.2) mL⋅Kg^-1⋅min^-1], peak blood lactate concentrations [10.2 (5.4) vs. 2.0 (0.7) mM], and total AnC [21.0 (14.0) vs. 4.8 (6.1) mL Kg^-1]. O_2PEAK in the laboratory condition presented very likely correlations with throw and recovery frequency in games (r = -0.87 and confidence interval [CI] = 0.41; r = -0.90 and CI = 0.35; respectively). Oxygen consumption remained above baseline while blood lactate concentration remained unchanged during the games. The very likely correlation between anaerobic alactic contribution and action density (r = 0.95 and CI = 0.25) highlights the importance of the alactic metabolism. In general, our study demonstrates that goalball can be characterized as a high-intensity intermittent effort, where athlete performance is based on aerobic metabolism predominance while determinant actions are supplied by the anaerobic alactic metabolism. Specifically, higher values of LaB vs. GaM highlighted the need for standardization of specific protocols for goalball evaluation, mainly for the reproduction of ecologically valid values. In addition, O_2PEAK correlated with recovery frequency in the LaB condition, demonstrating that passive or low-intensity recovery between actions is fundamental to maintain performance.

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.

Caffeine Increases Work Done above Critical Power, but Not Anaerobic Work

PURPOSE

The assumption that the curvature constant (W') of the power-duration relationship represents anaerobic work capacity is a controversial, unresolved question. We investigated if caffeine ingestion could increase total work done above critical power (CP), and if this would be accompanied by greater anaerobic energy expenditure and by an enhanced maintenance of maximal oxidative metabolic rate.

METHODS

Nine men (26.6 ± 5.3 yr, V˙O2max 40.6 ± 5.8 mL·kg·min) cycled until exhaustion at different exercise intensities on different days to determine the CP and W'. On separated days, participants cycled until exhaustion in the severe-intensity domain (136% ± 7% of CP) after ingesting either caffeine (5 mg·kg body mass) or a placebo.

RESULTS

Time to exhaustion was 34% longer with caffeine compared with placebo, and this was accompanied by a greater work done above CP (23.7 ± 5.7 vs 17.5 ± 3.6 kJ; 130% ± 30% vs 95% ± 14% of W', P < 0.01). Caffeine increased the aerobic energy expenditure (296.4 ± 91.0 vs 210.2 ± 71.9 kJ, P < 0.01), but not anaerobic lactic, anaerobic alactic, and total anaerobic (lactic + alactic) energy expenditure. The end values of heart rate and ventilation were higher with caffeine, but the V˙O2 end was similar between conditions and was not different from V˙O2max. Caffeine did not change time to reach V˙O2max but increased time maintained at V˙O2max (199.3 ± 105.9 vs 111.9 ± 87.1 s, P < 0.05).

CONCLUSIONS

Caffeine increased total work done above CP, but this was not associated with greater anaerobic work. Rather, this was associated with a higher tolerance to maintain exercise at maximal oxidative metabolic rate.

Arterialized and venous blood lactate concentration difference during different exercise intensities

Objective

The purpose of this study was to investigate the difference between arterialized and venous blood lactate concentrations [La] during constant-load exercises at different intensities.

Methods

Fifteen physically active men cycled for 30 minutes (or until exhaustion) at the first lactate threshold (LT₁), at 50% of the difference between the first and second lactate threshold (TT_50%), at the second lactate threshold (LT₂), and at 25% of the difference between LT₂ and maximal aerobic power output (TW_25%). Samples of both arterialized and venous blood were collected simultaneously at rest and every 5 minutes during the exercise.

Results

The arterialized blood [La] was higher at minute 5 than venous blood [La] for all exercise intensities (p < 0.05). After this period, the arterialized and venous [La] samples became similar until the end of the exercise (p > 0.05). The arterialized-venous difference during the first 10 minutes was greater for the two highest exercise intensities (LT₂ and TW_25%) compared with the two lowest (LT₁ and TT_50%, p < 0.05). Thereafter, arterialized-venous difference decreased progressively, reaching values close to zero for all exercise intensities (p > 0.05).

Conclusion

These results suggest a delayed lactate appearance in the venous blood, which is accentuated at higher exercise intensities. The lactate measured in arterialized and venous blood is interchangeable only when blood samples are collected at least 10 minutes after the exercise starts.

Taurine supplementation can increase lipolysis and affect the contribution of energy systems during front crawl maximal effort

Taurine can affect the energy system metabolism, specifically the lipid metabolism, since an increase in lipid oxidation may promote carbohydrate savings. We hypothesized that taurine supplementation associated with high-intensity exercise could increase levels of lipolysis, benefiting swimmer performance. Nine male competitive swimmers performed two 400-m front crawl maximal efforts with a 1-week washout, and the athletes received 6 g of taurine (TAU) or placebo (PLA) supplementation 120 min before performing the effort. Oxygen consumption and the contribution of the energy systems were analyzed post effort using a Quark CPET gas analyzer. Blood samples were collected before, and 5 min post the effort for taurine and glycerol analysis. Immediately before and 3, 5, and 7 min post the effort, blood samples from the earlobe were collected to determine lactate levels. An increase of 159% was observed in taurine plasma levels 120 min post ingestion. Glycerol levels were higher in both groups post effort; however, the TAU condition promoted an 8% higher increase than the PLA. No changes were observed in swimmer performance or lactate levels; however, the percentage change in lactate levels (∆[La^-]) was different (TAU: 9.36 ± 2.78 mmol L^-1; PLA: 11.52 ± 2.19 mmol L^-1, p = 0.04). Acute taurine supplementation 120 min before performing a maximal effort did not improve swimmer performance; however, it increased glycerol plasma levels and reduced both the ∆[La^-] and lactic anaerobic system contribution.

Anaerobic Contribution Determined in Swimming Distances: Relation with Performance

Total anaerobic contribution (TAn) can be assessed by accumulated oxygen deficit, and through sum of glycolytic and phosphagen contribution which enable the evaluation of TAn without influences on mechanical parameters. However, little is known about the difference of TAn within swimming distances. Therefore, the objectives of the present study were to determine and compare the TAn in different performances using the backward extrapolation technique and amount of lactate accumulated during exercise, and relate it with swimming performance. Fourteen competitive swimmers performed five maximal front crawl swims of 50, 100, 200, 400, and 800 m. The total phosphagen (AnAl) and glycolytic (AnLa) contributions were assumed as the fast component of post-exercise oxygen consumption (EPOC_FAST) and amount of blood lactate accumulated during exercise, respectively. TAn was the sum of AnAl and AnLa. Significantly lower values of AnLa were observed in the 800 m (p < 0.01) than other distances. For AnAl, the 50 m performance presented the lowest values, followed by 100 and 800 m (p < 0.01). The highest values of AnAl were observed in the 200 and 400 m (p > 0.13). The TAn was significantly higher in the 200 and 400 m performances than observed at 50 and 800 m (p < 0.01). Anaerobic contributions were correlated with 50, 100, 200, and 400 m performances (p < 0.01). The AnAl contribution was not correlated with 400 m performance. Anaerobic parameters were not correlated with 800 m performance. In conclusion, the highest values of anaerobic contribution were observed in the 200 and 400 m distances. Moreover, TAn is important to performances below 400 m, and may be used in training routines.

Effect of caffeine ingestion on anaerobic capacity quantified by different methods

We investigated whether caffeine ingestion before submaximal exercise bouts would affect supramaximal oxygen demand and maximal accumulated oxygen deficit (MAOD), and if caffeine-induced improvement on the anaerobic capacity (AC) could be detected by different methods. Nine men took part in several submaximal and supramaximal exercise bouts one hour after ingesting caffeine (5 mg·kg-1) or placebo. The AC was estimated by MAOD, alternative MAOD, critical power, and gross efficiency methods. Caffeine had no effect on exercise endurance during the supramaximal bout (caffeine: 131.3 ± 21.9 and placebo: 130.8 ± 20.8 s, P = 0.80). Caffeine ingestion before submaximal trials did not affect supramaximal oxygen demand and MAOD compared to placebo (7.88 ± 1.56 L and 65.80 ± 16.06 kJ vs. 7.89 ± 1.30 L and 62.85 ± 13.67 kJ, P = 0.99). Additionally, MAOD was similar between caffeine and placebo when supramaximal oxygen demand was estimated without caffeine effects during submaximal bouts (67.02 ± 16.36 and 62.85 ± 13.67 kJ, P = 0.41) or when estimated by alternative MAOD (56.61 ± 8.49 and 56.87 ± 9.76 kJ, P = 0.91). The AC estimated by gross efficiency was also similar between caffeine and placebo (21.80 ± 3.09 and 20.94 ± 2.67 kJ, P = 0.15), but was lower in caffeine when estimated by critical power method (16.2 ± 2.6 vs. 19.3 ± 3.5 kJ, P = 0.03). In conclusion, caffeine ingestion before submaximal bouts did not affect supramaximal oxygen demand and consequently MAOD. Otherwise, caffeine seems to have no clear positive effect on AC.

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

The aim was to verify the validity (i.e., study A) and reliability (i.e., study B) of the alternative maximal accumulated oxygen deficit determined using onlya supramaximal effort (MAOD_ALT)to estimate anaerobic capacity [i.e., estimated by the gold standard maximal accumulated oxygen deficit method (MAOD)] during cycling. In study A, the effects of supramaximal intensities on MAOD_ALT and the comparison with the MAOD were investigated in fourteen active subjects (26 ± 6 years). In study B, the test-retest reliability was investigated, where fourteen male amateur cyclists (29 ± 5 years) performed the MAOD_ALT twice at 115% of the intensity associated to maximal oxygen uptake (). MAOD_ALT determined at 130 and 150% of was lower than MAOD (p ≤ 0.048), but no differences between MAOD_ALT determined at 100, 105, 110, 115, 120 and 140% of (3.58 ± 0.53L; 3.58 ± 0.59L; 3.53 ± 0.52L; 3.48 ± 0.72L; 3.52 ± 0.61L and 3.46 ± 0.69L, respectively) with MAOD (3.99 ± 0.64L). The MAOD_ALT determined from the intensities between 110 and 120% of presented the better agreement and concordance with MAOD. In the test-retest, the MAOD_ALT was not different (p > 0.05), showed high reproducibility when expressed in absolute values (ICC = 0.96, p < 0.01), and a good level of agreement in the Bland-Altman plot analysis (mean differences ± CI95%:-0.16 ± 0.53L). Thus, the MAOD_ALT seems to be valid and reliable to assess anaerobic capacity in cycling.

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.

The sensitivity of the alternative maximal accumulated oxygen deficit method to discriminate training status

The purpose of the study was to investigate the sensitivity of an alternative maximal accumulated oxygen deficit (MAOD_ALT) method to discriminate the "anaerobic" capacity while comparing: least trained (LT) participants (n = 12), moderately trained (MT) participants (n = 12), endurance trained (ET) participants (n = 16), and rugby (RG) players (n = 11). Participants underwent a graded exercise test on a treadmill and a supramaximal effort for assessing MAOD_ALT. MAOD_ALT was calculated as the sum of oxygen equivalents from the phosphagen and glycolytic metabolic pathways. MAOD_ALT was significantly higher (P < 0.05) in RG (64.4 ± 12.1 mL · kg^-1) than in ET (56.8 ± 5.4 mL · kg^-1; effect size [ES] = 0.77; +13.5%), MT (53.8 ± 5.3 mL · kg^-1; ES = 1.08; +19.8%), and LT (49.9 ± 4.5 mL · kg^-1; ES = 1.50; +36.4%). In addition, the magnitude-based inference analysis revealed that MAOD_ALT was likely (LT vs. MT), very likely (MT vs. RG, and ET vs. RG) and most likely (LT vs. ET, and LT vs. RG) different between all groups, except for MT and ET, which presented an unclear difference. In conclusion, MAOD_ALT was sensitive enough to distinguish the "anaerobic" capacity in individuals with different training status, especially for RG players compared with LT participants and MT participants.

Effects of carbohydrate intake on time to exhaustion and anaerobic contribution during supramaximal exercise

ABSTRACT Objective: This study evaluated the effect of carbohydrate intake on time to exhaustion and anaerobic contribution during supramaximal exercise on a cycle ergometer. Methods: The sample comprised ten participants with a mean age of 23.9±2.5 years, mean body mass of 75.1±12.3 kg, mean height of 170.0±1.0 cm, and mean body fat of 11.3±5.2%. The participants underwent an incremental test to determine maximal oxygen uptake and maximum power output, and two supramaximal tests with a constant load of 110% of the maximum power output to exhaustion. Thirty minutes before the supramaximal tests the participants consumed carbohydrates (2 g.kg-1) or placebo. Results: The times to exhaustion of carbohydrate and placebo did not differ (carbohydrate: 170.7±44.6s; placebo: 156.1±26.7s, p=0.17; effect size=0.39). Similarly, the anaerobic contributions of the two treatments did not differ (carbohydrate: 3.0±0.9 L; placebo: 2.7±1.1 L, p=0.23; effect size=0.29). Conclusion: Carbohydrate intake was not capable of increasing time to exhaustion and anaerobic contribution in physically active men cycling at 110% of maximum power output.

Acute LED irradiation does not change the anaerobic capacity and time to exhaustion during a high-intensity running effort: a double-blind, crossover, and placebo-controlled study : Effects of LED irradiation on anaerobic capacity and performance in running

The purpose of this study was to investigate the acute effects of photobiomodulation therapy using cluster light-emitting diodes (LEDT; 104 diodes) (wavelength 660 and 850 nm; energy density 1.5 and 4.5 J/cm(2); energy 60 J at each point; total energy delivered 600 J) on alternative maximal accumulated oxygen deficit (MAODALT) and time to exhaustion, during a high-intensity running effort. Fifteen moderately active and healthy males (age 25.1 ± 4.4 years) underwent a graded exercise test and two supramaximal exhaustive efforts at 115 % of the intensity associated with maximal oxygen uptake performed after acute LEDT or placebo irradiation in a double-blind, crossover, and placebo-controlled study design. The MAODALT was assumed as the sum of both oxygen equivalents estimated from the glycolytic and phosphagen metabolism pathways during each supramaximal effort. For the statistical analysis, a paired t test was used to determine differences between the treatments. The significance level was assumed as 95 %. In addition, a qualitative analysis was used to determine the magnitude of differences between groups. No significant differences were found for the values of oxygen equivalents from each energetic metabolism (P ≥ 0.28), for MAODALT values between the LEDT and placebo conditions (P ≥ 0.27), or for time to exhaustion (P = 0.80), except for the respiratory exchange ratio (P = 0.01). The magnitude-based inference of effect size reported only a possibly negative effect of photobiomodulation on MAODALT when expressed in units relative to body mass and on the glycolysis pathway (26 %). In summary, LEDT after a high-intensity running effort did not alter the MAODALT, metabolic energy pathways, or high-intensity running performance.

Metformin improves performance in high-intensity exercise, but not anaerobic capacity in healthy male subjects

The aim of this study was to determine the ergogenic effects of metformin in high-intensity exercise, as well as its effects on anaerobic capacity, in healthy and physically active men. Ten subjects (mean (± standard deviation) maximal oxygen uptake (V˙O2max ) 38.6 ± 4.5 mL/kg per min) performed the following tests in a cycle ergometer: (i) an incremental test; (ii) six submaximal constant workload tests at 40%-90% (V˙O2max ); and (iii) two supramaximal tests (110% (V˙O2max ). Metformin (500 mg) or placebo was ingested 60 min before the supramaximal test. There were no significant differences between the placebo and metformin groups in terms of maximum accumulated oxygen deficit (2.8 ± 0.6 vs 3.0 ± 0.8 L, respectively; P = 0.08), lactate concentrations (7.8 ± 2.6 vs 7.5 ± 3.0 mmol/L, respectively; P = 0.75) or O2 consumed in either the last 30 s of exercise (40.4 ± 4.4 vs 39.9 ± 4.0 mL/kg per min, respectively; P = 0.35) or the first 110 s of exercise (29.0 ± 2.5 vs 29.5 ± 3.0 mL/kg per min, respectively; P = 0.42). Time to exhaustion was significantly higher after metformin than placebo ingestion (191 ± 33 vs 167 ± 32 s, respectively; P = 0.001). The fast component of V˙O2 recovery was higher in the metformin than placebo group (12.71 vs 12.18 mL/kg per min, respectively; P = 0.025). Metformin improved performance and anaerobic alactic contribution during high-intensity exercise, but had no effect on overall anaerobic capacity in healthy subjects.

Acute administration of high doses of taurine does not substantially improve high-intensity running performance and the effect on maximal accumulated oxygen deficit is unclear

The aim of the present study was to investigate the effects of acute administration of taurine overload on time to exhaustion (TTE) of high-intensity running performance and alternative maximal accumulated oxygen deficit (MAODALT). The study design was a randomized, placebo-controlled, crossover design. Seventeen healthy male volunteers (age: 25 ± 6 years; maximal oxygen uptake: 50.5 ± 7.6 mL·kg−1·min−1) performed an incremental treadmill-running test until voluntary exhaustion to determine maximal oxygen uptake and exercise intensity at maximal oxygen uptake. Subsequently, participants completed randomly 2 bouts of supramaximal treadmill-running at 110% exercise intensity at maximal oxygen uptake until exhaustion (placebo (6 g dextrose) or taurine (6 g) supplementation), separated by 1 week. MAODALT was determined using a single supramaximal effort by summating the contribution of the phosphagen and glycolytic pathways. When comparing the results of the supramaximal trials (i.e., placebo and taurine conditions) no differences were observed for high-intensity running TTE (237.70 ± 66.00 and 277.30 ± 40.64 s; p = 0.44) and MAODALT (55.77 ± 8.22 and 55.06 ± 7.89 mL·kg−1; p = 0.61), which seem to indicate trivial and unclear differences using the magnitude-based inferences approach, respectively. In conclusion, acute 6 g taurine supplementation before exercise did not substantially improve high-intensity running performance and showed an unclear effect on MAODALT.

GEDAE-LaB: A Free Software to Calculate the Energy System Contributions during Exercise

Purpose

The aim of the current study is to describe the functionality of free software developed for energy system contributions and energy expenditure calculation during exercise, namely GEDAE-LaB.

Methods

Eleven participants performed the following tests: 1) a maximal cycling incremental test to measure the ventilatory threshold and maximal oxygen uptake (V˙O₂max); 2) a cycling workload constant test at moderate domain (90% ventilatory threshold); 3) a cycling workload constant test at severe domain (110% V˙O₂max). Oxygen uptake and plasma lactate were measured during the tests. The contributions of the aerobic (A_MET), anaerobic lactic (LA_MET), and anaerobic alactic (AL_MET) systems were calculated based on the oxygen uptake during exercise, the oxygen energy equivalents provided by lactate accumulation, and the fast component of excess post-exercise oxygen consumption, respectively. In order to assess the intra-investigator variation, four different investigators performed the analyses independently using GEDAE-LaB. A direct comparison with commercial software was also provided.

Results

All subjects completed 10 min of exercise at moderate domain, while the time to exhaustion at severe domain was 144 ± 65 s. The A_MET, LA_MET, and AL_MET contributions during moderate domain were about 93, 2, and 5%, respectively. The A_MET, LA_MET, and AL_MET contributions during severe domain were about 66, 21, and 13%, respectively. No statistical differences were found between the energy system contributions and energy expenditure obtained by GEDAE-LaB and commercial software for both moderate and severe domains (P > 0.05). The ICC revealed that these estimates were highly reliable among the four investigators for both moderate and severe domains (all ICC ≥ 0.94).

Conclusion

These findings suggest that GEDAE-LaB is a free software easily comprehended by users minimally familiarized with adopted procedures for calculations of energetic profile using oxygen uptake and lactate accumulation during exercise. By providing availability of the software and its source code we hope to facilitate future related research.

Relationship of aerobic and anaerobic parameters with 400 m front crawl swimming performance

The aims of the present study were to investigate the relationship of aerobic and anaerobic parameters with 400 m performance, and establish which variable better explains long distance performance in swimming. Twenty-two swimmers (19.1±1.5 years, height 173.9±10.0 cm, body mass 71.2±10.2 kg; 76.6±5.3% of 400 m world record) underwent a lactate minimum test to determine lactate minimum speed (LMS) (i.e., aerobic capacity index). Moreover, the swimmers performed a 400 m maximal effort to determine mean speed (S400m), peak oxygen uptake (V.O2PEAK) and total anaerobic contribution (C_ANA). The C_ANA was assumed as the sum of alactic and lactic contributions. Physiological parameters of 400 m were determined using the backward extrapolation technique (V.O2PEAK and alactic contributions of C_ANA) and blood lactate concentration analysis (lactic anaerobic contributions of C_ANA). The Pearson correlation test and backward multiple regression analysis were used to verify the possible correlations between the physiological indices (predictor factors) and S400m (independent variable) (p < 0.05). Values are presented as mean ± standard deviation. Significant correlations were observed between S400m (1.4±0.1 m·s^-1) and LMS (1.3±0.1 m·s^-1; r = 0.80), V.O2PEAK (4.5±3.9 L·min^-1; r = 0.72) and C_ANA (4.7±1.5 L·O₂; r= 0.44). The best model constructed using multiple regression analysis demonstrated that LMS and V.O2PEAK explained 85% of the 400 m performance variance. When backward multiple regression analysis was performed, C_ANA lost significance. Thus, the results demonstrated that both aerobic parameters (capacity and power) can be used to predict 400 m swimming performance.

Energy system contribution in a maximal incremental test: correlations with pacing and overall performance in a 10-km running trial

This study aimed to verify the association between the contribution of energy systems during an incremental exercise test (IET), pacing, and performance during a 10-km running time trial. Thirteen male recreational runners completed an incremental exercise test on a treadmill to determine the respiratory compensation point (RCP), maximal oxygen uptake (V˙O2max), peak treadmill speed (PTS), and energy systems contribution; and a 10-km running time trial (T10-km) to determine endurance performance. The fractions of the aerobic (WAER) and glycolytic (WGLYCOL) contributions were calculated for each stage based on the oxygen uptake and the oxygen energy equivalents derived by blood lactate accumulation, respectively. Total metabolic demand (WTOTAL) was the sum of these two energy systems. Endurance performance during the T10-km was moderately correlated with RCP, V˙O2max and PTS (P<@0.05), and moderate-to-highly correlated with WAER, WGLYCOL, and WTOTAL (P<0.05). In addition, WAER, WGLYCOL, and WTOTAL were also significantly correlated with running speed in the middle (P<0.01) and final (P<0.01) sections of the T10-km. These findings suggest that the assessment of energy contribution during IET is potentially useful as an alternative variable in the evaluation of endurance runners, especially because of its relationship with specific parts of a long-distance race.

Sodium bicarbonate supplementation improved MAOD but is not correlated with 200- and 400-m running performances: a double-blind, crossover, and placebo-controlled study

The aim of the study was to investigate the effects of acute supplementation of sodium bicarbonate (NaHCO3) on maximal accumulated oxygen deficit (MAOD) determined by a single supramaximal effort (MAODALT) in running and the correlation with 200- and 400-m running performances. Fifteen healthy men (age, 23 ± 4 years; maximal oxygen uptake, 50.6 ± 6.1 mL·kg−1·min−1) underwent a maximal incremental exercise test and 2 supramaximal efforts at 110% of the intensity associated with maximal oxygen uptake, which was carried out after ingesting either 0.3 g·kg−1 body weight NaHCO3 or a placebo (dextrose) and completing 200- and 400-m performance tests. The study design was double-blind, crossover, and placebo-controlled. Significant differences were found between the NaHCO3 and placebo conditions for MAODALT (p = 0.01) and the qualitative inference for substantial changes showed a very likely positive effect (98%). The lactic anaerobic contribution in the NaHCO3 ingestion condition was significantly higher (p < 0.01) and showed a very likely positive effect (99% chance), similar to that verified for peak blood lactate concentration (p < 0.01). No difference was found for time until exhaustion (p = 0.19) or alactic anaerobic contribution (p = 0.81). No significant correlations were observed between MAODALT and 200- and 400-m running performance tests. Therefore, we can conclude that both MAODALT and the anaerobic lactic metabolism are modified after acute NaHCO3 ingestion, but it is not correlated with running performance.

Association between anaerobic components of the maximal accumulated oxygen deficit and 30-second Wingate test

The purpose of this study was to analyze the relationship between the anaerobic components of the maximal accumulated oxygen deficit (MAOD) and of the 30-second Wingate anaerobic test (30-WAnT). Nine male physical education students performed: a) a maximal incremental exercise test; b) a supramaximal constant workload test to determine the anaerobic components of the MAOD; and c) a 30-WAnT to measure the peak power (PP) and mean power (MP). The fast component of the excess post-exercise oxygen consumption and blood lactate accumulation were measured after the supramaximal constant workload test in order to determine the contributions made by alactic (AL_MET) and lactic (LA_MET) metabolism. Significant correlations were found between PP and AL_MET (r=0.71; P=0.033) and between MP and LA_MET (r=0.72; P=0.030). The study results suggested that the anaerobic components of the MAOD and of the 30-WAnT are similarly applicable in the assessment of AL_MET and LA_MET during high-intensity exercise.

Mathematical simulation of energy expenditure and recovery during sprint cross-country skiing

Purpose

A cross-country sprint competition relies on maximal effort durations of 3–4 minutes. Significant anaerobic energy contribution is expected. Anaerobic energy contribution has been estimated in different sports to date from the accumulated O₂ deficit. However, the O₂-deficit model can be questioned. We investigate anaerobic energy contribution by applying other methods than the O₂ deficit.

Methods

Theoretical model development.

Results

For sprint cross-country competitions, the anaerobic energy contribution was 20%–25% independent of the employed mathematical model. Recovery times of a minimum 20 minutes were found to be required after sprint races to be sure that the performance in subsequent heats was not influenced.

Conclusion

The O₂-deficit model gave anaerobic energy results in agreement with other models from the literature. Recovery times of a minimum 20 minutes were found to be required after sprint races to be sure that the performance in subsequent heats was not influenced.

Caffeine alters anaerobic distribution and pacing during a 4000-m cycling time trial

The purpose of the present study was to investigate the effects of caffeine ingestion on pacing strategy and energy expenditure during a 4000-m cycling time-trial (TT). Eight recreationally-trained male cyclists volunteered and performed a maximal incremental test and a familiarization test on their first and second visits, respectively. On the third and fourth visits, the participants performed a 4000-m cycling TT after ingesting capsules containing either caffeine (5 mg.kg⁻¹ of body weight, CAF) or cellulose (PLA). The tests were applied in a double-blind, randomized, repeated-measures, cross-over design. When compared to PLA, CAF ingestion increased mean power output [219.1±18.6 vs. 232.8±21.4 W; effect size (ES)  = 0.60 (95% CI = 0.05 to 1.16), p = 0.034] and reduced the total time [419±13 vs. 409±12 s; ES = −0.71 (95% CI = −0.09 to −1.13), p = 0.026]. Furthermore, anaerobic contribution during the 2200-, 2400-, and 2600-m intervals was significantly greater in CAF than in PLA (p<0.05). However, the mean anaerobic [64.9±20.1 vs. 57.3±17.5 W] and aerobic [167.9±4.3 vs. 161.8±11.2 W] contributions were similar between conditions (p>0.05). Similarly, there were no significant differences between CAF and PLA for anaerobic work (26363±7361 vs. 23888±6795 J), aerobic work (68709±2118 vs. 67739±3912 J), or total work (95245±8593 vs. 91789±7709 J), respectively. There was no difference for integrated electromyography, blood lactate concentration, heart rate, and ratings of perceived exertion between the conditions. These results suggest that caffeine increases the anaerobic contribution in the middle of the time trial, resulting in enhanced overall performance.

Effects of a low- or a high-carbohydrate diet on performance, energy system contribution, and metabolic responses during supramaximal exercise

The purpose of the present study was to examine the effects of a high- or low-carbohydrate (CHO) diet on performance, aerobic and anaerobic contribution, and metabolic responses during supramaximal exercise. Six physically-active men first performed a cycling exercise bout at 115% maximal oxygen uptake to exhaustion after following their normal diet for 48 h (∼50% of CHO, control test). Seventy-two hours after, participants performed a muscle glycogen depletion exercise protocol, followed by either a high- or low-CHO diet (∼70 and 25% of CHO, respectively) for 48 h, in a random, counterbalanced order. After the assigned diet period (48 h), the supramaximal cycling exercise bout (115% maximal oxygen consumption) to exhaustion was repeated. The low-CHO diet reduced time to exhaustion when compared with both the control and the high-CHO diet (−19 and −32%, respectively, p < 0.05). The reduced time to exhaustion following the low-CHO diet was accompanied by a lower total aerobic energy contribution (−39%) compared with the high-CHO diet (p < 0.05). However, the aerobic and anaerobic energy contribution at the shortest time to exhaustion (isotime) was similar among conditions (p > 0.05). The low-CHO diet was associated with a lower blood lactate concentration (p < 0.05), with no effect on the plasma concentration of insulin, glucose and K+ (p > 0.05). In conclusion, a low-CHO diet reduces both performance and total aerobic energy provision during supramaximal exercise. As peak K+ concentration was similar, but time to exhaustion shorter, the low-CHO diet was associated with an earlier attainment of peak plasma K+ concentration.

On the kinetics of anaerobic power

BACKGROUND

This study investigated two different mathematical models for the kinetics of anaerobic power. Model 1 assumes that the work power is linear with the work rate, while Model 2 assumes a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power. In order to test these models, a cross country skier ran with poles on a treadmill at different exercise intensities. The aerobic power, based on the measured oxygen uptake, was used as input to the models, whereas the simulated blood lactate concentration was compared with experimental results. Thereafter, the metabolic rate from phosphocreatine break down was calculated theoretically. Finally, the models were used to compare phosphocreatine break down during continuous and interval exercises.

RESULTS

Good similarity was found between experimental and simulated blood lactate concentration during steady state exercise intensities. The measured blood lactate concentrations were lower than simulated for intensities above the lactate threshold, but higher than simulated during recovery after high intensity exercise when the simulated lactate concentration was averaged over the whole lactate space. This fit was improved when the simulated lactate concentration was separated into two compartments; muscles + internal organs and blood. Model 2 gave a better behavior of alactic energy than Model 1 when compared against invasive measurements presented in the literature. During continuous exercise, Model 2 showed that the alactic energy storage decreased with time, whereas Model 1 showed a minimum value when steady state aerobic conditions were achieved. During interval exercise the two models showed similar patterns of alactic energy.

CONCLUSIONS

The current study provides useful insight on the kinetics of anaerobic power. Overall, our data indicate that blood lactate levels can be accurately modeled during steady state, and suggests a linear relationship between the alactic anaerobic power and the rate of change of the aerobic power.

Determining the contribution of the energy systems during exercise

One of the most important aspects of the metabolic demand is the relative contribution of the energy systems to the total energy required for a given physical activity. Although some sports are relatively easy to be reproduced in a laboratory (e.g., running and cycling), a number of sports are much more difficult to be reproduced and studied in controlled situations. This method presents how to assess the differential contribution of the energy systems in sports that are difficult to mimic in controlled laboratory conditions. The concepts shown here can be adapted to virtually any sport.

The following physiologic variables will be needed: rest oxygen consumption, exercise oxygen consumption, post-exercise oxygen consumption, rest plasma lactate concentration and post-exercise plasma peak lactate. To calculate the contribution of the aerobic metabolism, you will need the oxygen consumption at rest and during the exercise. By using the trapezoidal method, calculate the area under the curve of oxygen consumption during exercise, subtracting the area corresponding to the rest oxygen consumption. To calculate the contribution of the alactic anaerobic metabolism, the post-exercise oxygen consumption curve has to be adjusted to a mono or a bi-exponential model (chosen by the one that best fits). Then, use the terms of the fitted equation to calculate anaerobic alactic metabolism, as follows: ATP-CP metabolism = A₁ (mL . s^-1) x t₁ (s). Finally, to calculate the contribution of the lactic anaerobic system, multiply peak plasma lactate by 3 and by the athlete’s body mass (the result in mL is then converted to L and into kJ).

The method can be used for both continuous and intermittent exercise. This is a very interesting approach as it can be adapted to exercises and sports that are difficult to be mimicked in controlled environments. Also, this is the only available method capable of distinguishing the contribution of three different energy systems. Thus, the method allows the study of sports with great similarity to real situations, providing desirable ecological validity to the study.