Guilherme Passos Ramos

Physical performance and environmental conditions: 2014 World Soccer Cup and 2016 Summer Olympics in Brazil

ABSTRACT This editorial is for the special issue “Temperature sciences in Brazil” of the journal Temperature. It focuses on the physical performance and environmental conditions during the 2014 World Cup and the coming 2016 Summer Olympics. It emphasizes that a hot and humid environment imposes a great challenge to the human thermoregulation system, can lead to performance decrements, and increases the risk of developing hyperthermia. Adequate hydration, acclimatization, and body cooling strategies are effective interventions to minimize the risks associated with exercise in the heat.

Effects of Precooling on 30-km Cycling Performance and Pacing in Hot and Temperate Environments

This study examined the effects of precooling on performance and pacing during 30-km cycling exercise in hot and temperate environments. 8 trained male cyclists performed 4 trials involving either cooling (PRECTEMP and PRECHOT) or no-cooling interventions (TEMP and HOT) prior to a 30-km self-paced cycling exercise in either a hot (35°C, 68% relative humidity) or temperate environment (24°C, 68% relative humidity). Exercise time was longer in HOT (60.62±3.47 min) than in TEMP (58.28±3.30 min; P<0.001), and precooling attenuated this thermal strain performance impairment (PRECHOT 58.28±3.30 min; P=0.048), but it was still impaired compared with TEMP (P=0.02). Exercise performance in PRECTEMP (54.58±4.35 min) was no different from TEMP. Initial power output was sustained until the end of the exercise in both TEMP and PRECTEMP, but was reduced from the 12th km until the end of the trial in HOT (P<0.05). This reduction was delayed by precooling because power output was reduced only after the 20th km during PRECHOT (P<0.05). Heart rate was similar in all conditions throughout almost the entire exercise, suggesting the maintenance of similar relative intensities. In conclusion, precooling was effective in attenuating, but not completely reversing thermal strain performance impairment and offered no ergogenic effect in the temperate environment.

Metabolic Demand and Internal Training Load in Technical-Tactical Training Sessions of Professional Futsal Players.

Abstract Wilke, CF, Ramos, GP, Pacheco, DAS, Santos, WHM, Diniz, MSL, Gonçalves, GGP, Marins, JCB, Wanner, SP, and Silami-Garcia, E. Metabolic demand and internal training load in technical-tactical training sessions of professional futsal players. J Strength Cond Res 30(8): 2330–2340, 2016—The aim of the study was to characterize aspects of technical-tactical training sessions of a professional futsal team. We addressed 4 specific aims: characterize the metabolic demands and intensity of these training sessions, compare the training intensity among players of different positions, compare the intensity of different futsal-specific activities (4 × 4, 6 × 4, and match simulation), and investigate the association between an objective (training impulse; TRIMP) and a subjective method (session rating of perceived exertion; sRPE) of measuring a players internal training load. Twelve top-level futsal players performed an incremental exercise to determine their maximal oxygen consumption, maximal heart rate (HRmax), ventilatory threshold (VT), and respiratory compensation point (RCP). Each players HR and RPE were measured and used to calculate energy expenditure, TRIMP, and sRPE during 37 training sessions over 8 weeks. The average intensity was 74 ± 4% of HRmax, which corresponded to 9.3 kcal·min−1. The players trained at intensities above the RCP, between the RCP and VT and below the VT for 20 ± 8%, 28 ± 6%, and 51 ± 10% of the session duration, respectively. Wingers, defenders, and pivots exercised at a similar average intensity but with different intensity distributions. No difference in intensity was found between the 3 typical activities. A strong correlation between the average daily TRIMP and sRPE was observed; however, this relationship was significant for only 4 of 12 players, indicating that sRPE is a useful tool for monitoring training loads but that it should be interpreted for each player individually rather than collectively.

Potencia máxima estima el MLSS antes y después del entrenamiento aerobio

The purpose of this study is to present an equation to predict the maximal lactate steady state (MLSS) through a VO2peak incremental protocol. Twenty-six physically active men were divided in two groups (G1 and G2). They performed one maximal incremental test to determine their VO2peak and maximal power output (Wpeak), and also several constant intensity tests to determine MLSS intensity (MLSSw) on a cycle ergometer. Group G2 underwent six weeks of aerobic training at MLSSw. A regression equation was created using G1 subjects Wpeak and MLSSw to estimate the MLSS intensity (MLSSweq) before and after training for G2 (MLSSweq = 0.866 x Wpeak-41.734). The mean values were not different (150±27W vs 148±27W, before training / 171±26W vs 177±24W, after training) and significant correlations were found between the measured and the estimated MLSSw before (r²=0.49) and after training (r²=0.62) in G2. The proposed equation was effective to estimate the MLSS intensity before and after aerobic training.

Maximal power output estimates the MLSS before and after aerobic training

The purpose of this study is to present an equation to predict the maximal lactate steady state (MLSS) through a VO2peak incremental protocol. Twenty-six physically active men were divided in two groups (G1 and G2). They performed one maximal incremental test to determine their VO2peak and maximal power output (Wpeak), and also several constant intensity tests to determine MLSS intensity (MLSSw) on a cycle ergometer. Group G2 underwent six weeks of aerobic training at MLSSw. A regression equation was created using G1 subjects Wpeak and MLSSw to estimate the MLSS intensity (MLSSweq) before and after training for G2 (MLSSweq = 0.866 x Wpeak-41.734). The mean values were not different (150±27W vs 148±27W, before training / 171±26W vs 177±24W, after training) and significant correlations were found between the measured and the estimated MLSSw before (r²=0.49) and after training (r²=0.62) in G2. The proposed equation was effective to estimate the MLSS intensity before and after aerobic training.