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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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Kobe Vermeire, Michael Ghijs, Jan G. Bourgois, and Jan Boone

Purpose: The purpose of this commentary is to outline some of the pitfalls when using the fitness–fatigue model to unravel the interaction between training load and performance. By doing so, we encourage sport scientists and coaches to interpret the parameters from the model with some extra caution. Conclusions: Caution is needed when interpreting the fitness–fatigue model since the parameter values are influenced by the starting parameter values, the modeling technique, and the input of the model. Also, the use of general constants should be avoided since they do not account for interindividual differences and differences between training-load methods. Therefore, we advise sport scientists and coaches to use the model as a way to work more data-informed rather than working data-driven.

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Kobe M. Vermeire, Kevin Caen, Jan G. Bourgois, and Jan Boone

Purpose: To examine the differences in training load (TL) metrics when quantifying training sessions differing in intensity and duration. The relationship between the TL metrics and the acute performance decrement measured immediately after the sessions was also assessed. Methods: Eleven male recreational cyclists performed 4 training sessions in a random order, immediately followed by a 3-km time trial (TT). Before this period, participants performed the time TT in order to obtain a baseline performance. The difference in the average power output for the TTs following the training sessions was then expressed relative to the best baseline performance. The training sessions were quantified using 7 different TL metrics, 4 using heart rate as input, 2 using power output, and 1 using the rating of perceived exertion. Results: The load of the sessions was estimated differently depending on the TL metrics used. Also, within the metrics using the same input (heart rate and power), differences were found. TL using the rating of perceived exertion was the only metric showing a response that was consistent with the acute performance decrements found for the different training sessions. The Training Stress Score and the individualized training impulse demonstrated similar patterns but overexpressed the intensity of the training sessions. The total work done resulted in an overrepresentation of the duration of training. Conclusion: TL metrics provide dissimilar results as to which training sessions have higher loads. The load based on TL using the rating of perceived exertion was the only one in line with the acute performance decrements found in this study.

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Jan Boone, Kevin Caen, Maarten Lievens, Gil Bourgois, Alessandro L. Colosio, and Jan G. Bourgois

Purpose: To analyze the physical profile and training program of a world-class lightweight double sculls rowing crew toward the Tokyo 2020 Olympics. Method: A case study in which both rowers performed physical testing in November 2020 and April 2021 (anthropometrics, incremental rowing test, and power profiling). The training program (38 wk) in the buildup to the Olympics was analyzed, providing insight into training characteristics (volume; contribution of rowing, alternative, and strength training; prescribed and recorded [heart rate] training-intensity distribution). The entire period was split into 3 phases: preparation period (8 wk), competition period 1 (11 wk), and competition period 2 (9 wk), and training characteristics were compared. Results: In the April 2021 testing, rower A (1.89 m, 74.6 kg, 4.4% body fat) had a peak oxygen uptake of 5.8 L·min−1 (77.8 mL·min−1·kg−1) and a peak power output of 491 W. Rower B (1.82 m, 70.6 kg, 7.8% body fat) had a peak oxygen uptake of 5.5 L·min−1 (77.9 mL·min−1·kg−1) and a peak power output of 482 W. The mean weekly training volume was 14 hours 47 minutes (4 h 5 min), of which 58.5% (14.6%) consisted of rowing, 13.4% (6.8%) strength training, and 28.1% (2.6%) alternative training. Heart-rate training-intensity distribution was 77.8% (4.2%) in zone 1, 16.6% (3.7%) in zone 2, and 5.6% (2.8%) in zone 3 with a lower contribution of zone 1 in competition period 1 (P = .029) and competition period 2 (P = .023) compared with the preparation period, and a higher contribution of zone 3 in competition period 1 (P = .018) and competition period 2 (P = .011) compared with the preparation period. Conclusion: The crew combined a high volume of rowing, alternative, and strength training in a pyramidal heart-rate training-intensity distribution throughout the year.

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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 [221] m; zone 6: 274 [105] 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.

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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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Jasmien Dumortier, An Mariman, Jan Boone, Liesbeth Delesie, Els Tobback, Dirk Vogelaers, and Jan G. Bourgois

Purpose : This study aimed to determine the influencing factors of potential differences in sleep architecture between elite (EG) and nonelite (NEG) female artistic gymnasts. Methods : Twelve EG (15.1 [1.5] y old) and 10 NEG (15.3 [1.8] y old) underwent a nocturnal polysomnography after a regular training day (5.8 [0.8] h vs 2.6 [0.7] h), and, on a separate test day, they performed an incremental treadmill test after a rest day in order to determine physical fitness status. A multiple linear regression assessed the predictive value of training and fitness parameters toward the different sleep phases. Total sleep time and sleep efficiency (proportion of time effectively asleep to time in bed), as well as percentage of nonrapid eye movement sleep phase 1 (NREM1) and 2 (NREM2), slow wave sleep (SWS), and rapid eye movement sleep (REM), during a single night were compared between EG and NEG using an independent-samples t test. Results : Peak oxygen uptake influenced NREM1 (β = 1.035, P = .033), while amount of weekly training hours predicted SWS (β = 1.897, P = .032). No differences were documented between EG and NEG in total sleep time and sleep efficiency. SWS was higher in EG (36.9% [11.4%]) compared with NEG (25.9% [8.3%], P = .020), compensated by a lower proportion of NREM2 (38.7% [10.2%] vs 48.4% [6.5%], P = .017), without differences in NREM1 and REM. Conclusions : The proportion of SWS was only predicted by weekly training hours and not by training hours the day of the polysomnography or physical fitness, while NREM1 was linked with fitness level. Sleep efficiency did not differ between EG and NEG, but in EG, more SWS and less NREM2 were identified.

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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 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). 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.

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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 ΔV̇O2f 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.

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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.