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 [EPCr] 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 EPCr 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.

Factors determining 800-m running performance in young male athletes.

BACKGROUNDː The aim of the present study was to identify determinant variables on 800-m running performance in young male athletes derived from field tests and biological maturity. METHODSː A total of 89 athletes, aged between 13 and 15 years old, performed a 800-m running trial and a battery of tests which involved anthropometric measurements, a running anaerobic test (RAST), a flexibility test (sit-and-reach), a counter movement jump test and progressive test for aerobic fitness evaluation. A stepwise multiple regression model selected three independent variables to explain the variance in 800-m running performance trial: peak of aerobic speed (PAS), total time of all sprints (RAST) and predicted mature stature (PMS). RESULTSː The PAS speed explained 73.6% (P<0.01) of the variance, whereas the RAST and PMS accounted for additional 7.7% (P<0.01) and 0.9% (P<0.05), respectively. Taken together, these variables explained 82.2% of the overall 800-m running performance. CONCLUSIONSː The results indicate that the 800-m running performance might be explained mainly by aerobic (PAS) and anaerobic metabolisms (RAST). Beyond the traditional variables of performance in 800-m running trial, the biological maturity must be considered in regard of endurance performance of young athletes.

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 = A(1;) (mL . s(-1)) x t(1;) (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.

Ventilation behavior during upper-body incremental exercise.

This study tested the ventilation (VE) behavior during upper-body incremental exercise by mathematical models that calculate 1 or 2 thresholds and compared the thresholds identified by mathematical models with V-slope, ventilatory equivalent for oxygen uptake (VE/V(O2)), and ventilatory equivalent for carbon dioxide uptake (VE/V(CO2)). Fourteen rock climbers underwent an upper-body incremental test on a cycle ergometer with increases of approximately 20 W · min(-1) until exhaustion at a cranking frequency of approximately 90 rpm. The VE data were smoothed to 10-second averages for VE time plotting. The bisegmental and the 3-segmental linear regression models were calculated from 1 or 2 intercepts that best shared the VE curve in 2 or 3 linear segments. The ventilatory threshold(s) was determined mathematically by the intercept(s) obtained by bisegmental and 3-segmental models, by V-slope model, or visually by VE/V(O2) and VE/V(CO2). There was no difference between bisegmental (mean square error [MSE] = 35.3 ± 32.7 l · min(-1)) and 3-segmental (MSE = 44.9 ± 47.8 l · min(-1)) models in fitted data. There was no difference between ventilatory threshold identified by the bisegmental (28.2 ± 6.8 ml · kg(-1) · min(-1)) and second ventilatory threshold identified by the 3-segmental (30.0 ± 5.1 ml · kg(-1) · min(-1)), VE/V(O2) (28.8 ± 5.5 ml · kg(-1) · min(-1)), or V-slope (28.5 ± 5.6 ml · kg(-1) . min(-1)). However, the first ventilatory threshold identified by 3-segmental (23.1 ± 4.9 ml · kg(-1) · min(-1)) or by VE/V(O)2 (24.9 ± 4.4 ml · kg(-1) · min(-1)) was different from these 4. The VE behavior during upper-body exercise tends to show only 1 ventilatory threshold. These findings have practical implications because this point is frequently used for aerobic training prescription in healthy subjects, athletes, and in elderly or diseased populations. The ventilatory threshold identified by VE curve should be used for aerobic training prescription in healthy subjects and athletes.

Low carbohydrate diet affects the oxygen uptake on-kinetics and rating of perceived exertion in high intensity exercise.

The aim of this study was to determine if the carbohydrate (CHO) availability alters the rate of increase in the rating of perceived exertion (RPE) during high intensity exercise and whether this would be associated with physiological changes. Six males performed high intensity exercise after 48 h of controlled, high CHO (80%) and low CHO (10%) diets. Time to exhaustion was lower in the low compared to high CHO diet. The rate of increase in RPE was greater and the VO2 slow component was lower in the low CHO diet than in the control. There was no significant condition effect for cortisol, insulin, pH, plasma glucose, potassium, or lactate concentrations. Multiple linear regression indicated that the total amplitude of VO2 and perceived muscle strain accounted for the greatest variance in the rate of increase in RPE. These results suggest that cardiorespiratory variables and muscle strain are important afferent signals from the periphery for the RPE calculations.

Is acute static stretching able to reduce the time to exhaustion at power output corresponding to maximal oxygen uptake?

This study analyzed the effect of an acute static stretching bout on the time to exhaustion (Tlim) at power output corresponding to VO2max. Eleven physically active male subjects (age 22.3+/-2.8 years, VO2max 2.7+/-0.5 L.min) completed an incremental cycle ergometer test, 2 muscle strength tests, and 2 maximal tests to exhaustion at power output corresponding to VO2max with and without a previous static stretching bout. The Tlim was not significantly affected by the static stretching (164+/-28 vs. 150+/-26 seconds with and without stretching, respectively, p=0.09), but the time to reach VO2max (118+/-22 vs. 102+/-25 seconds), blood-lactate accumulation immediately after exercise (10.7+/-2.9 vs. 8.0+/-1.7 mmol.L), and oxygen deficit (2.4+/-0.9 vs. 2.1+/-0.7 L) were significantly reduced (p

A low carbohydrate diet affects autonomic modulation during heavy but not moderate exercise.

The aim of this study was to examine the effects of low carbohydrate (CHO) availability on heart rate variability (HRV) responses during moderate and severe exercise intensities until exhaustion. Six healthy males (age, 26.5 +/- 6.7 years; body mass, 78.4 +/- 7.7 kg; body fat %, 11.3 +/- 4.5%; V(O)(2)(max) 39.5 +/- 6.6 mL kg(-1) min(-1)) volunteered for this study. All tests were performed in the morning, after 8-12 h overnight fasting, at a moderate intensity corresponding to 50% of the difference between the first (LT(1)) and second (LT(2)) lactate breakpoints and at a severe intensity corresponding to 25% of the difference between the maximal power output and LT(2). Forty-eight hours before each experimental session, the subjects performed a 90-min cycling exercise followed by 5-min rest periods and subsequent 1-min cycling bouts at 125% V(O)(2)(max) (with 1-min rest periods) until exhaustion, in order to deplete muscle glycogen. A diet providing 10% (CHO(low)) or 65% (CHO(control)) of energy as carbohydrates was consumed for the following 2 days until the experimental test. The Poicaré plots (standard deviations 1 and 2: SD1 and SD2, respectively) and spectral autoregressive model (low frequency LF, and high frequency HF) were applied to obtain HRV parameters. The CHO availability had no effect on the HRV parameters or ventilation during moderate-intensity exercise. However, the SD1 and SD2 parameters were significantly higher in CHO(low) than in CHO(control), as taken at exhaustion during the severe-intensity exercise (P < 0.05). The HF and LF frequencies (ms(2)) were also significantly higher in CHO(low) than in CHO(control) (P < 0.05). In addition, ventilation measured at the 5 and 10-min was higher in CHO(low) (62.5 +/- 4.4 and 74.8 +/- 6.5 L min(-1), respectively, P < 0.05) than in CHO(control) (70.0 +/- 3.6 and 79.6 +/- 5.1 L min(-1), respectively; P < 0.05) during the severe-intensity exercise. These results suggest that the CHO availability alters the HRV parameters during severe-, but not moderate-, intensity exercise, and this was associated with an increase in ventilation volume.

Relationship between training status and maximal fat oxidation rate.

This study aimed to compare maximal fat oxidation rate parameters between moderate- and low-performance runners. Eighteen runners performed an incremental treadmill test to estimate individual maximal fat oxidation rate (Fatmax) based on gases measures and a 10,000-m run on a track. The subjects were then divided into a low and moderate performance group using two different criteria: 10,000-m time and VO2max values. When groups were divided using 10,000-m time, there was no significant difference in Fatmax (0.41 ± 0.16 and 0.27 ± 0.12 g.min(-1), p = 0.07) or in the exercise intensity that elicited Fatmax (59.9 ± 16.5 and 68.7 ± 10.3 % O2max, p = 0.23) between the moderate and low performance groups, respectively (p > 0.05). When groups were divided using VO2max values, Fatmax was significantly lower in the low VO2max group than in the high VO2max group (0. 29 ± 0.10 and 0.47 ± 0.17 g.min(-1), respectively, p < 0.05) but the intensity that elicited Fatmax did not differ between groups (64.4 ± 14.9 and 61.6 ± 15.4 %VO2max). Fatmax or %VO2max that elicited Fatmax was not associated with 10,000 m time. The only variable associated with 10,000-m running performance was %VO2max used during the run (p < 0.01). In conclusion, the criteria used for the division of groups according to training status might influence the identification of differences in Fatmax or in the intensity that elicits Fatmax. Key pointsThe results of the present study suggest that the criteria used to categorize aerobic training status of subjects can influence the magnitude of differences in Fatmax.The Fatmax is similar between groups with similar 10,000-m running performance.The 10,000-m running performance seems to be associated with an increased ability to oxidize carbohydrate.

Effect of performance level on pacing strategy during a 10-km running race.

The aim of this study was to examine the influence of the performance level of athletes on pacing strategy during a simulated 10-km running race, and the relationship between physiological variables and pacing strategy. Twenty-four male runners performed an incremental exercise test on a treadmill, three 6-min bouts of running at 9, 12 and 15 km h(-1), and a self-paced, 10-km running performance trial; at least 48 h separated each test. Based on 10-km running performance, subjects were divided into terziles, with the lower terzile designated the low-performing (LP) and the upper terzile designated the high-performing (HP) group. For the HP group, the velocity peaked at 18.8 +/- 1.4 km h(-1) in the first 400 m and was higher than the average race velocity (P < 0.05). The velocity then decreased gradually until 2,000 m (P < 0.05), remaining constant until 9,600 m, when it increased again (P < 0.05). The LP group ran the first 400 m at a significantly lower velocity than the HP group (15.6 +/- 1.6 km h(-1); P > 0.05) and this initial velocity was not different from LP average racing velocity (14.5 +/- 0.7 km h(-1)). The velocity then decreased non-significantly until 9,600 m (P > 0.05), followed by an increase at the end (P < 0.05). The peak treadmill running velocity (PV), running economy (RE), lactate threshold (LT) and net blood lactate accumulation at 15 km h(-1) were significantly correlated with the start, middle, last and average velocities during the 10-km race. These results demonstrate that high and low performance runners adopt different pacing strategies during a 10-km race. Furthermore, it appears that important determinants of the chosen pacing strategy include PV, LT and RE.

Construct and concurrent validation of OMNI-Kayak rating of Perceived Exertion Scale.

This study tested the concurrent and construct validity of a newly developed OMNI-Kayak Scale, testing 8 male kayakers who performed a flatwater load-incremented "shuttle" test over a 500-m course and 3 estimation-production trials over a 1,000-m course. Velocity, blood lactate concentration, heart rate, and rating of perceived exertion (RPE), using the OMNI-Kayak RPE Scale and the Borg 6-20 Scale were recorded. OMNI-Kayak Scale RPE was highly correlated with velocity, the Borg 6-20 Scale RPE, blood lactate, and heart rate for both load-incremented test (rs = .87-.96), and estimation trials (rs = .75-.90). There were no significant differences among velocities, heart rate and blood lactate concentration between estimation and production trials. The OMNI-Kayak RPE Scale showed concurrent and construct validity in assessing perception of effort in flatwater kayaking and is a valid tool for self-regulation of exercise intensity.

Acute high-intensity exercise with low energy expenditure reduced LDL-c and total cholesterol in men.

A reduction in LDL cholesterol and an increase in HDL cholesterol levels are clinically relevant parameters for the treatment of dyslipidaemia, and exercise is often recommended as an intervention. This study aimed to examine the effects of acute, high-intensity exercise ( approximately 90% VO(2max)) and varying carbohydrate levels (control, low and high) on the blood lipid profile. Six male subjects were distributed randomly into exercise groups, based on the carbohydrate diets (control, low and high) to which the subjects were restricted before each exercise session. The lipid profile (triglycerides, VLDL, HDL cholesterol, LDL cholesterol and total cholesterol) was determined at rest, and immediately and 1 h after exercise bouts. There were no changes in the time exhaustion (8.00 +/- 1.83; 7.82 +/- 2.66; and 9.09 +/- 3.51 min) and energy expenditure (496.0 +/- 224.8; 411.5 +/- 223.1; and 592.1 +/- 369.9 kJ) parameters with the three varying carbohydrate intake (control, low and high). Glucose and insulin levels did not show time-dependent changes under the different conditions (P > 0.05). Total cholesterol and LDL cholesterol were reduced after the exhaustion and 1 h recovery periods when compared with rest periods only in the control carbohydrate intake group (P < 0.05), although this relation failed when the diet was manipulated. These results indicate that acute, high-intensity exercise with low energy expenditure induces changes in the cholesterol profile, and that influences of carbohydrate level corresponding to these modifications fail when carbohydrate (low and high) intake is manipulated.

Association between neuromuscular tests and kumite performance on the brazilian karate national team.

The aim of this study was to verify the relationship of strength and power with performance on an international level karate team during official kumite simulations. Fourteen male black belt karate athletes were submitted to anthropometric data collection and then performed the following tests on two different days: vertical jump test, bench press and squat maximum dynamic strength (1RM) tests. We also tested power production for both exercises at 30 and 60%1RM and performed a kumite match simulation. Blood samples were obtained at rest and immediately after the kumite matches to measure blood lactate concentration. Karate players were separated by performance (winners vs. defeated) on the kumite matches. We found no significant differences between winners and defeated for strength, vertical jump height, anthropometric data and blood lactate concentration. Interestingly, winners were more powerful in the bench press and squat exercises at 30% 1RM. Maximum strength was correlated with absolute (30% 1RM r = 0.92; 60% 1RM r = 0.63) and relative power (30% 1RM r = 0.74; 60% 1RM r = 0.11, p > 0.05) for the bench press exercise. We concluded that international level karate players' kumite match performance are influenced by higher levels of upper and lower limbs power production. Key PointsMuscle power at low workloads seems to be a reasonable predictor of karate performance.There are differences in neuromuscular characteristics between winners and defeated karate players among an international level karate team.Karate players rely more on muscle power, rather than on muscle strength.