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Jan Boone and Jan Bourgois

Purpose:

The current study aimed to gain insight into the physiological profile of elite basketball players in Belgium in relation to their position on the field.

Methods:

The group consisted of 144 players, divided into 5 groups according to position (point guards [PGs], shooting guards [SGs], small forwards [SFs], power forwards [PFs], and centers [Cs]). The anthropometrics were measured and the subjects underwent fitness tests (incremental running test, 10-m sprint, 5 ×-10 m, squat and countermovement jump, isokinetic test) to obtain insight into endurance, speed, agility, and power. The parameters of these tests were compared among the different positions by means of 1-way variance analysis (MANOVA). Tukey post hoc tests were performed in case of a significant MANOVA.

Results:

It was observed that Cs were taller and heavier and had a higher percentage body fat than PGs and SGs. For the anaerobic sprint test Cs were slower than the other positions. For the 5 × 10-m the PGs and SGs were faster than SFs and PFs. For the jump test Cs displayed a significantly lower absolute performance than the other positions. PGs and SGs had a higher VO2peak and speed at the anaerobic threshold than PFs and Cs. The isokinetic strength test showed that the quadriceps muscle group of Cs could exert a higher torque during knee extension than the other positions.

Conclusions:

The current study showed that the physiological profile of elite players in the Belgian first division differs by player position. More specifically, guards were characterized by high endurance, speed, and agility, whereas centers and power forwards had higher muscle strength than the other positions.

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Jan Bourgois, Adelheid Steyaert and Jan Boone

Purpose:

In this case study, a world-class rower was followed over a period of 15 y in which he evolved from junior to professional athlete.

Methods:

An incremental exercise test and a 2000-m ergometer test were performed each year in the peak period of the season starting at the age of 16 y. In addition, the training logs of 1 y each as a junior and a senior rower were recorded and analyzed.

Results:

Maximal oxygen uptake (VO2max), maximal power output (Pmax), and power output at 4 mmol/L blood lactate concentration increased until the age of 27 and then stabilized at 30 y at 6.0 ± 0.2 L/min, 536 ± 15 W, and 404 ± 22 W, respectively. At the age of 27–28 y the rower also had a career-best 2000-m ergometer test (5′58″) and on-water performance with a 4th place at the Olympic Games (2008) in Beijing and World Championships (2009). At the age of 23 y, the rower trained a total of 6091 km in 48 wk. Of the total training time, 15.4% consisted of general training practices, 23.4% resistance training, and 61.2% specific rowing training.

Conclusion:

The on-water performance in the World Championships and Olympic Games corresponded closely to the evolution in the rower’s physiological profile and 2000-m ergometer performance. The long-term build-up program resulted in an increase in the physiological parameters up to the age of 27 y and resulted in a 4th position at the 2008 Olympic Games at a body mass of only 86 kg.

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Jan G. Bourgois, Gil Bourgois and Jan Boone

Training-intensity distribution (TID), or the intensity of training and its distribution over time, has been considered an important determinant of the outcome of a training program in elite endurance athletes. The polarized and pyramidal TID, both characterized by a high amount of low-intensity training (below the first lactate or ventilatory threshold), but with different contributions of threshold training (between the first and second lactate or ventilatory threshold) and high-intensity training (above the second lactate or ventilatory threshold), have been reported most frequently in elite endurance athletes. However, the choice between these 2 TIDs is not straightforward. This article describes the historical, evolutionary, and physiological perspectives of the success of the polarized and pyramidal TID and proposes determinants that should be taken into account when choosing the most appropriate TID.

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Margot Callewaert, Jan Boone, Bert Celie, Dirk De Clercq and Jan G. Bourgois

The aim of this work was to gain more insight into the cardiorespiratory and muscular (m. vastus lateralis) responses to simulated upwind sailing exercise in 10 high-level male and female Optimist sailors (10.8–14.4 years old). Hiking strap load (HSL) and cardiorespiratory variables were measured while exercising on a specially developed Optimist sailing ergometer. Electromyography (EMG) was used to determine mean power frequency (MPF) and root mean square (RMS). Near-infrared spectroscopy was used to measure deoxygenated Hemoglobin and Myoglobin concentration (deoxy[Hb+Mb]) and re-oxygenation. Results indicated that HSL and integrated EMG of the vastus lateralis muscle changed in accordance with the hiking intensity. Cardiorespiratory response demonstrated an initial significant increase and subsequently steady state in oxygen uptake (VO2), ventilation (VE), and heart rate (HR) up to circa 40% VO2peak, 30% VEpeak and 70% HRpeak respectively. At muscle level, results showed that highly trained Optimist sailors manage to stabilize the muscular demand and fatigue development during upwind sailing (after an initial increase). However, approaching the end of the hiking exercise, the MPF decrease, RMS increase, and deoxy[Hb+Mb] increase possibly indicate the onset of muscle fatigue.

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Margot Callewaert, Stefan Geerts, Evert Lataire, Jan Boone, Marc Vantorre and Jan Bourgois

Purpose:

To develop a sailing ergometer that accurately simulates upwind sailing exercise.

Methods:

A sailing ergometer that measures roll moment accompanied by a biofeedback system that allows imposing a certain quasi-isometric upwind sailing protocol (ie, 18 bouts of 90-s hiking at constantly varying hiking intensity interspersed with 10 s to tack) was developed. Ten male high-level Laser sailors performed an incremental cycling test (ICT; ie, step protocol at 80 W + 40 W/3 min) and an upwind sailing test (UST). During both, heart rate (HR), oxygen uptake (VO2), ventilation (VE), respiratory-exchange ratio, and rating of perceived exertion were measured. During UST, also the difference between the required and produced hiking moment (HM) was calculated as error score (ES). HR, VO2, and VE were calculated relative to their peak values determined during ICT. After UST, the subjects were questioned about their opinion on the resemblance between this UST and real-time upwind sailing.

Results:

An average HM of 89.0% ± 2.2% HMmax and an average ES of 4.1% ± 1.8% HMmax were found. Mean HR, VO2, and VE were, respectively, 80% ± 4% HRpeak, 39.5% ± 4.5% VO2peak, and 30.3% ± 3.7% VEpeak. Both HM and cardiorespiratory values appear to be largely comparable to literature reports during on-water upwind sailing. Moreover, the subjects gave the upwind sailing ergometer a positive resemblance score.

Conclusions:

Results suggest that this ergometer accurately simulates on-water upwind sailing exercise. As such, this ergometer could be a great help in performance diagnostics and training follow-up.

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Youri Geurkink, Gilles Vandewiele, Maarten Lievens, Filip de Turck, Femke Ongenae, Stijn P.J. Matthys, Jan Boone and Jan G. Bourgois

Purpose: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. Methods: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. Results: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). Conclusions: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.