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.
Jan G. Bourgois, Gil Bourgois, and Jan Boone
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.
Kobe M. Vermeire, Freek Van de Casteele, Maxim Gosseries, Jan G. Bourgois, Michael Ghijs, and Jan Boone
Purpose: Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model. Methods: During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods. Results: The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 , Lucia TRIMP: 625 , Edwards TRIMP: 643 , training stress score: 639 , session rating of perceived exertion: 558 , and kilojoules: 596 ). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6 d). Conclusions: The authors concluded that TL methods cannot be used interchangeably because they evolve differently.
Jonne A. Kapteijns, Kevin Caen, Maarten Lievens, Jan G. Bourgois, and Jan Boone
Purpose: To determine if there is a link between the demands of competitive game activity and performance profiles of elite female field hockey players. Methods: Global positioning systems (GPS) were used to quantify running performance of elite female field hockey players (N = 20) during 26 competitive games. Performance profiles were assessed at 2 time points (preseason and midseason) for 2 competitive seasons. A battery of anthropometric and performance field-based tests (30–15 intermittent fitness test, incremental run test, 10–30-m speed test, T test, and vertical jump test) were used to determine the performance profiles of the players. Results: Players covered a mean total distance of 5384 (835) m, of which 19% was spent at high intensities (zone 5: 796  m; zone 6: 274  m). Forwards covered the lowest mean total distance (estimated marginal means 4586 m; 95% confidence interval, 4275–4897), whereas work rate was higher in forwards compared with midfielders (P = .006, d = 0.43) and central defenders (P = .001, d = 1.41). Players showed an improvement in body composition and anaerobic performance from preseason to midseason. Aerobic performance capacity (maximal oxygen uptake and speed at the 4-mM lactate threshold) was positively correlated with high-intensity activities. Conclusions: There is a clear relationship between running performance and aerobic performance profiles in elite female hockey players. These results highlight the importance of a well-developed aerobic performance capacity in order to maintain a high performance level during hockey games.
Santiago Lopez, Jan G. Bourgois, Enrico Tam, Paolo Bruseghini, and Carlo Capelli
To explore the cardiovascular and metabolic responses of 9 Optimist sailors (12.7 ± 0.8 y, 153 ± 9 cm, 41 ± 6 kg, sailing career 6.2 ± 1 y, peak oxygen uptake [V̇O2peak] 50.5 ± 4.5 mL · min−1 · kg−1) during on-water upwind sailing with various wind intensities (W).
In a laboratory session, peak V̇O2, beat-by-beat cardiac output (Q̇), mean arterial blood pressure (MAP), and heart rate (f H) were measured using a progressive cycle ramp protocol. Steady-state V̇O2, Q̇, MAP, and f H at 4 submaximal workloads were also determined. During 2 on-water upwind sailing tests (constant course and with tacks), W, Q̇, MAP, and f H were measured for 15 min. On-water V̇O2 was estimated on the basis of steady-state f H measured on water and of the individual ΔV̇O2/Δf H relationship obtained in the laboratory.
V̇O2, f H, and Q̇ expressed as percentage of the corresponding peak values were linearly related with W; exercise intensity during on-water sailing corresponded to 46–48% of V̇O2peak. MAP and total vascular peripheral resistance (TPR = MAP/Q̇) were larger (P < .005) during on-water tests (+39% and +50%, respectively) than during cycling, and they were correlated with W. These responses were responsible for larger values of the double (DP) and triple (TP) products of the heart during sailing than during cycling (P < .005) (+37% and +32%, respectively).
These data indicate that the cardiovascular system was particularly stressed during upwind sailing even though the exercise intensity of this activity was not particularly high.
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.