Eduardo Zapaterra Campos

Objective: Verify the reliability and validity of two submaximal tests – walking-running test (WRT) and self-selected intensity test (SSIT) – to predict V̇O2MAX in healthy adults. Methodology: A total of 20 adults of both sexes were evaluated in three visits. The WRT was initially performed at a speed of 5 km‧h-1 and 4% grade. The grade was increased by 2% until 12%. The speed was increased to 9 km‧h-1 and the grade reduced to 1% if subjects did not reach 65% of the heart rate reserve (HRRES). Once HRRES was attained, the effort was maintained for 6 minutes. The SSIT consisted in 4 minutes of free intensity selection (speed and/or grade) and 6 minutes for HR monitoring. The Student’s t-test, ICC and typical error were used to test reliability. ANOVA and a graphical Bland-Altman analysis were used for validity. Only speed was different in WRT between test-retest. Results: The main results achieved in the research should be commented on. Discussion: Differences were found between the measured V̇O2MAX (49.5 ± 7.9 mL·kg-1·min-1) and predicted V̇O2MAX in WRT (43.2 ± 7.6 mL·kg-1·min-1) and SSIT (40.6 ± 9.8 mL·kg-1·min-1). Conclusions: The two tests proved to be reproducible, especially for the WRT; however, both may present an underestimation of V̇O2MAX.

This study is the first to determine how hypoxia affects human muscle fatigue kinetics and metabolic perturbations during intense dynamic exercise. Using randomized, single-blinded crossover designs, three trials of two-legged knee extensions were performed under hypoxic (HYP, FiO₂ 0.135) and normoxic (NOR) conditions. Trial 1 (n = 8): quadriceps femoris twitch force (F_tw) was measured before, during, and after 4 min intense exercise followed by exhaustive exercise. Maximal voluntary contraction (MVC) was measured pre- and post-exercise. Trial 2 (n = 8): muscle lactate and pH were determined before and after 4 min intense exercise. Trial 3 (n = 6): blood was sampled frequently from the femoral artery and vein during intense exhaustive exercise. Dynamic F_tw decreased more (P < 0.05) in HYP from 60s of exercise and onwards. After 4 min, isometric F_tw decreased more (P < 0.05) in HYP, whereas MVC was similar between conditions. At exhaustion, isometric F_tw and MVC were similar between conditions despite HYP exercise time being 55 ± 17% of NOR (P < 0.01). Muscle lactate and pH in- and decreased more (P < 0.001), respectively, after 4 min in HYP. Exercise-induced blood metabolites disturbances were largely unaffected by hypoxia. Conclusively, moderate hypoxia accelerated muscular fatigue from 60s and onwards. Hypoxia caused higher muscle but not blood lactate and H⁺ accumulation rates.

The present study aimed to verify the effect of acute nitrate supplementation on oxidative, phosphocreatine, and glycolytic energy contribution (C_OXI, C_PCr, and C_LAC^, respectively) during a high-intensity intermittent exercise (HIIE). Fifteen physically active subjects were submitted to incremental running test on a treadmill and two random HIIE (10 × 1 min at maximal aerobic speed with 1 min of passive recovery) in the following conditions: sodium nitrate (SN) or Placebo (PL). Repeated measure ANOVA was used to compare C_OXI, and C_PCr within the 10 efforts. Student t test was used to compare total C_OXI, C_PCr, C_LAC, as well oxidative and non-oxidative contribution. Considering the 10 efforts, C_OXI presented the main effect for condition (F = 16.7; p = .001; SN: 1.9 ± 0.1 L; PL: 1.6 ± 0.1 L), and no interaction effect (F = 0.9; p = .415), while C_PCr presented effect for condition (F = 13.4; p = .003; SN: 1.2 ± 0.1; PL: 1.4 ± 0.8 L), and no interaction (F = 1.2; p = .317). Total oxidative contribution were higher (p < .001) after SN (19.1 ± 3.4 L) than Placebo (16.7 ± 3.1 L), while total C_PCr were lower (p = .002) after SN (12.4 ± 2.5 L) than PL (14.1 ± 2.6 L). Total energy contribution were not different between trials. Acute dose of SN supplementation improved oxidative contribution during HIIE, and reduced C_PCr. Higher C_OXI is likely related to increased oxygen kinetics, while the lower C_PCr might be related to the improved energetic efficiency.

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. 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). 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). 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.

The aim of the study was to compare maximum oxygen uptake, maximum heart rate, speed corresponding to VO2max, time to exhaustion, and ventilatory thresholds between two maximal test protocols (scaled vs ramp) with equalized metabolic demand in apparently healthy young adults. Twenty-seven subjects of both sexes were analyzed and submitted to two maximum tests: 1) scaled test with 1.2 km∙h-1 every 2 min; 2) ramp test with 0.1 km∙h-1 every 0.18 min; both with 1% slope and maximum total duration of twenty minutes. Respiratory gas exchanges were directly analyzed, and ventilatory thresholds determined through visual inspection. Central tendency and dispersion measures were adopted, as well as paired t-test and effect size. Bland-Altman analysis verified the agreement among variables. Maximum speed showed significance difference (p < 0.01) for the scaled test (14.0 ± 1.9 km∙h-1) and ramp test (14.6 ± 2.3 km∙h-1). Even with small BIAS values, negative trend and high limits of agreement could be observed. Despite the non-difference presented for variables, except for maximum speed, it could be concluded that the scaled test is able to determine the analyzed variables similar to the ramp test, considering the same conditions of the present study.

The purpose of this present study was to develop and validate a prediction equation for body composition assessment using anthropometric measures of elderly women. This is cross-sectional correlational study with 243 older women ± 64.5 years old and body mass index (BMI) ± 28.70 kg/m². For the development of the equation it was utilized the method of hold-out sample validation. The participants were randomly divided into equation development group (96 elderly women) and a group for validation (147 elderly women). Total body mass, height, waist and hip circumferences, ratio waist-hip ratio and BMI were measured. The whole-body dual-energy X-ray absorptiometry (DXA) assessed body composition (percentage of body fat, fat mass, and fat-free mass). The equations were developed using multiple linear regression, with validation by the stepwise method; the comparison of the equations was analyzed by the paired Student's t test and the analysis of residual scores by the method of Blant and Altman. The New Equation presents a strong correlation (R = 0.83) and (R² = 0.69), and a standard error of estimation equals to 3.21% for percentage body fat prediction. The mean difference between the estimations of percentage body fat from DXA and the New Equation was 0.11% (t(0,180); P = 0.850). Therefore, the New Equation had an accuracy of 93.5% and a total error of 1.8%. The body fat estimation in older women using this New Equation based on BMI and age is valid and accurate.

The purpose of the present study is two-fold. First, we evaluated whether 8-weeks of combined training (high-intensity intermittent plus strength training) may change brain derived neurotropic factor (BDNF) and lipid parameters (triacylglycerol, HDL-c and non-HDL) in a fasted state. Second, we investigated the effect of an acute session of high-intensity intermittent exercise followed by strength training on systemic BDNF and lipid parameters pre- and post 8-weeks of training. Twenty-one healthy and physically active men were divided into two groups: high-intensity intermittent exercise combined with strength training (HSG; n = 11) and control (CG; n = 10). The HSG exercised for one minute at 100% of speedVO(2)max (sVO(2)max) interspersed with one minute of passive recovery followed by strength training (8 exercises with 8-12 repetition maximum loads) for 8-weeks. Heart rate variability, blood pressure, lipid profile, and BDNF concentrations were measured in the fasted state pre- and post-exercise and before and after the 8-week training period. After 8-weeks of exercise training, there was an increase in spectral high frequency component (ms(2)) and RR interval (p < 0.05), a decreased spectral low frequency component (nu) and heart rate values (p < 0.05), an increase in HDL-c (p < 0.001), and lower BDNF concentrations (p < 0.001). These results suggest that 8-weeks of high-intensity intermittent exercise combined with strength exercise is an effective protective cardio-metabolic strategy capable of increasing HDL-c and BDNF concentrations after an acute exercise session. In the long-term, the modulation on BDNF and HDL-c concentrations may be a determining factor for protection against neurological and cardiovascular diseases.

Background: Prescription of exercise training in subjects with chronic obstructive pulmonary disease (COPD) remains a challenge in clinical practice. The heart rate deflection point (HRDP) is an option to reduce costs, reflects the anaerobic threshold and it is noninvasive; however its applicability in COPD is still unknown. Aim: To compare HRDP with gas exchange threshold values (GET) in subjects with COPD. Methods: Thirty-one subjects were evaluated in this cross-sectional study, diagnosed with COPD. The subjects performed an incremental exercise test on a treadmill to determine the GET, peak oxygen consumption (VO_2PEAK), speed corresponding to VO_2PEAK (sVO_2PEAK), and peak values of heart rate (HR_PEAK). To determine HRDP the heart rate values observed were plotted as a function of intensity. Results: The HRDP was visible in 96.77% of subjects. There was no difference between HRDP and GET (p = 0.07) and a moderate intraclass correlation coefficient (ICC): 0.74 [0.52–0.87] and high limits of agreements were presented (±1.1 km·h⁻¹). Conclusion: The HRDP cannot be used to determine the limits between the physiological domains of these subjects. Thus, although significant correlation was found between HRDP and GET, their application for training prescription and monitoring in subjects with COPD proved to be limited.

The purpose of this study was to compare maximal strength gains during strength training (ST) and concurrent training (CT) consisting of high-intensity intermittent training plus strength training over the course of a 12-week intervention. A secondary purpose was to examine the relationship between strength training volume and strength gain in both groups. Nineteen recreationally active males were divided into CT (n = 11) and ST (n = 8) groups. The CT group performed repeated 1 min efforts at 100% of maximal aerobic speed interspersed by 1 min of passive recovery until accumulating a total miming distance of 5km followed by a strength session (consisting of three sets of seven exercises with loads of 8-12 repetition maximum) twice weekly for a period of 12 weeks. The ST group performed only strength training sessions during the same 12-week period. Strength training total volume-load (Sigma repetitions x load) for the upper-and lower-body was computed, while maximal strength (1RM) was evaluated at baseline, week 8, and week 12. Lower-body volume-load over 12 weeks was not different between groups. Absolute 1RM increased in both groups at week 8 and week 12, while 1RM relative to body mass increased in both groups at week 8, but only ST increased relative maximum strength between week 8 and week 12. There was a statistically significant correlation between strength training lower-body volume-load and maximum strength change between baseline and week 8 for the CT group (r = 0.656), while no significant correlations were found for the ST group. In summary, executing high-intensity intermittent exercise twice a week before strength training did not impair maximal strength after 8 weeks, however, only ST demonstrated an increase in relative strength after 12 weeks.

Purpose: The relationship between inflammatory markers and energetic metabolism has been explored. However, the relationship between exercise intensity and fitness status is unclear, and it is necessary to understand this relationship to apply specific exercise guidance. The purpose of the study was to analyze metabolic and inflammatory responses imposed by acute exercise sessions performed at moderate, heavy, and severe intensities and their relationship with the physical fitness status.Methods: Nineteen healthy male volunteers performed three acute exercise sessions until exhaustion or up to 60 minutes on a cycle ergometer at moderate (90% of first ventilatory threshold [VT1]), heavy (midpoint between VT1 and second ventilatory threshold [VT2]), and severe (midpoint between VT2 and maximal aerobic power) intensities. Blood lactate, glucose, NEFA, endotoxin, and cytokines were determined for each exercise session. Peripheral and lipopolysaccharide (LPS)‐stimulated release of tumor necrosis factor alpha (TNF‐α), interleukin (IL)‐6, and IL‐10 was analyzed before, after, and 60 minutes after sessions.Results: In peripheral blood, severe intensity increased lactate, endotoxin, and TNF‐α immediately after exercise and glucose at 60 min after exercise. There was a trend for IL‐10 increase at 60 minutes after exercise in peripheral blood. Immediately after exercise, LPS‐stimulated TNF‐α, IL‐6, IL‐6/IL‐10 ratio, and lactate levels were higher in the severe intensity while nonester fatty acid levels decreased at this time. At 60 minute after exercise, higher concentrations of glucose and a trend for increased IL‐10 were observed in severe intensity. A positive correlation was observed between maximal aerobic power and IL‐10 ( r = 0.513; P = 0.042), and negative correlations between maximal aerobic power and endotoxin ( r = −0.531; P = 0.034) and lactate ( r = −0.538; P = 0.031) in heavy intensity.Conclusion: Our data show a novel finding that higher cytokine responses occur at higher intensities, mainly in severe intensity. However, the anti‐inflammatory (IL‐10) response was physical fitness–dependent.