The ability to accurately measure the internal training load (ITL) of an athlete is essential when trying to optimize athletic performance 1 and to prevent adverse training outcomes such as injury or overtraining. 2 This is important for coaches and practitioners who prescribe training loads for
Robert H. Mann, Craig A. Williams, Bryan C. Clift and Alan R. Barker
Durva Vahia, Adam Kelly, Harry Knapman and Craig A. Williams
, weather, and nutrition, cause different adaptations among players ( 19 ). For the same ETL (training drills), players experience different internal training loads (ITLs; response to ETL). Players with higher levels of fitness often do not receive sufficient stimulus to improve their fitness levels during
Corrado Lupo, Alexandru Nicolae Ungureanu, Riccardo Frati, Matteo Panichi, Simone Grillo and Paolo Riccardo Brustio
Although internal training load (ITL) has been successfully monitored in several sport conditions, 1 – 3 the complexity of team sports can make this procedure more difficult, especially because of the presence of different goals and workouts, even within a single training session. 4 For instance
Anthony N. Turner, Conor Buttigieg, Geoff Marshall, Angelo Noto, James Phillips and Liam Kilduff
Session rating of perceived exertion (sRPE) is known to significantly relate to heart-rate (HR) -based methods of quantifying internal training load (TL) in a variety of sports. However, to date this has not been investigated in fencing and was therefore the aim of this study. TL was calculated by multiplying the sRPE with exercise duration and through HR-based methods calculated using Banister and Edwards TRIMP. Seven male elite foil fencers (mean ± SD age 22.3 ± 1.6 y, height 181.3 ± 6.5 cm, body mass 77.7 ± 7.6 kg) were monitored over the period of 1 competitive season. The sRPE and HR of 67 training sessions and 3 competitions (87 poule bouts and 12 knockout rounds) were recorded and analyzed. Correlation analysis was used to determine any relationships between sRPE- and HR-based methods, accounting for individual variation, mode of training (footwork drills vs sparring sessions), and stage of competition (poules vs knockouts). Across 2 footwork sessions, sRPE and Banister and Edwards TRIMP were found to be reliable, with coefficient of variation values of 6.0%, 5.2%, and 4.5%, respectively. Significant correlations with sRPE for individual fencers (r = .84–.98) and across mode of exercise (r = .73–.85) and competition stages (r = .82–.92) were found with HR-based measures. sRPE is a simple and valuable tool coaches can use to quantify TL in fencing.
Helen Alexiou and Aaron J. Coutts
The purpose of this study was to compare the session-RPE method for quantifying internal training load (TL) with various HR-based TL quantification methods in a variety of training modes with women soccer players.
Fifteen elite women soccer players took part in the study (age: 19.3 ± 2.0 y and VO2max: 50.8 ± 2.7 mL·kg−1·min−1). Session-RPE, heart rate, and duration were recorded for 735 individual training sessions and matches over a period of 16 wk. Correlation analysis was used to compare session-RPE TLs with three commonly used HR-based methods for assessing TL.
The mean correlation for session-RPE TL with Banister’s TRIMP, LTzone TL and Edwards’s TL were (r = 0.84, 0.83, and 0.85, all P < .01, respectively). Correlations for session-RPE TL and three HR-based methods separated by session type were all significant (all P < .05). The strongest correlations were reported for technical (r = 0.68 to 0.82), conditioning (r = 0.60 to 0.79), and speed sessions (r = 0.61 to 0.79).
The session-RPE TL showed a significant correlation with all training types common to soccer. Higher correlations were found with less intermittent, aerobic-based training sessions and suggest that HR-based TLs relate better to session-RPE TLs in less intermittent training activities. These results support previous findings showing that the session-RPE TL compares favorably with HR-based methods for quantifying internal TL in a variety of soccer training activities.
Luis Suarez-Arrones, Javier Núñez, Eduardo Sáez de Villareal, Javier Gálvez, Gabriel Suarez-Sanchez and Diego Munguía-Izquierdo
To describe the repeated-high-intensity activity and internal training load of rugby sevens players during international matches and to compare the differences between the 1st and 2nd halves.
Twelve international-level male rugby sevens players were monitored during international competitive matches (n = 30 match files) using global positioning system technology and heart-rate monitoring.
The relative total distance covered by the players throughout the match was 112.1 ± 8.4 m/min. As a percentage of total distance, 35.0% (39.2 ± 9.0 m/min) was covered at medium speed and 17.1% (19.2 ± 6.8 m/min) at high speed. A substantial decrease in the distance covered at >14.0 km/h and >18.0 km/h, the number of accelerations of >2.78 m/s and >4.0 m/s, repeated-sprint sequences interspersed with ≤60 s rest, and repeated-acceleration sequences interspersed with ≤30 s or ≤60 s rest was observed in the 2nd half compared with the 1st half. A substantial increase in the mean heart rate (HR), HRmax, percentage of time at >80% HRmax and at >90% HRmax, and Edwards training load was observed in the 2nd half compared with the 1st half.
This study provides evidence of a pronounced reduction in high-intensity and repeated-highintensity activities and increases in internal training load in rugby sevens players during the 2nd half of international matches.
Miguel Angel Campos-Vazquez, Alberto Mendez-Villanueva, Jose Antonio Gonzalez-Jurado, Juan Antonio León-Prados, Alfredo Santalla and Luis Suarez-Arrones
To describe the internal training load (ITL) of common training sessions performed during a typical week and to determine the relationships between different indicators of ITL commonly employed in professional football (soccer).
Session-rating-of-perceived-exertion TL (sRPE-TL) and heart-rate- (HR) derived measurements of ITL as Edwards TL and Stagno training impulses (TRIMPMOD) were used in 9 players during 3 periods of the season. The relationships between them were analyzed in different training sessions during a typical week: skill drills/circuit training + small-sided games (SCT+SSGs), ball-possession games + technical-tactical exercises (BPG+TTE), tactical training (TT), and prematch activation (PMa).
HR values obtained during SCT+SSGs and BPG+TTE were substantially greater than those in the other 2 sessions, all the ITL markers and session duration were substantially greater in SCT+SSGs than in any other session, and all ITL measures in BPG+TTE were substantially greater than in TT and PMa sessions. Large relationships were found between HR >80% HRmax and HR >90% HRmax vs sRPE-TL during BPG+TTE and TT sessions (r = .61−.68). Very large relationships were found between Edwards TL and sRPE-TL and between TRIMPMOD and sRPE-TL in sessions with BPG+TTE and TT (r = .73−.87). Correlations between the different HR-based methods were always extremely large (r = .92−.98), and unclear correlations were observed for other relationships between variables.
sRPE-TL provided variable-magnitude within-individual correlations with HR-derived measures of training intensity and load during different types of training sessions typically performed during a week in professional soccer. Caution should be applied when using RPE- or HR-derived measures of exercise intensity/load in soccer training interchangeably.
Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis and Werner F. Helsen
Purpose: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Methods: Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models’ performance on predicting the reported RPE values for future training sessions was compared with the naive baseline’s performance. Results: Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Conclusions: Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.
Carlo Minganti, Laura Capranica, Romain Meeusen and Maria Francesca Piacentini
The aim of the present study was to assess the effectiveness of perceived exertion (session-RPE) in quantifying internal training load in divers.
Six elite divers, three males (age, 25.7 ± 6.1 y; stature, 1.71 ± 0.06 m; body mass, 66.7 ± 1.2 kg) and three females (age, 25.3 ± 0.6 y; stature, 1.63 ± 0.05 m; body mass, 58.3 ± 4.0 kg) were monitored during six training sessions within a week, which included 1 m and 3 m springboard dives. The Edwards summated heart rate zone method was used as a reference measure; the session-RPE rating was obtained using the CR-10 Borg scale modified by Foster and the 100 mm visual analog scale (VAS).
Significant correlations were found between CR-10 and VAS session-RPE and the Edwards summated heart rate zone method for training sessions (r range: 0.71–0.96; R 2 range: 0.50–0.92; P < 0.01) and for divers (r range: 0.67–0.96; R 2 range: 0.44–0.92; P < 0.01).
These findings suggest that session-RPE can be useful for monitoring internal training load in divers.
Stuart R. Graham, Stuart Cormack, Gaynor Parfitt and Roger Eston
Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting preseason variation of match-play exercise intensity (MEI sim/min) using a variable dose–response model. Methods: A total of 21 professional male AF players completed an 18-wk preseason macrocycle. Preseason internal training load was quntified using the session rating-of-perceived-exertion method (sRPE) and external load from satellite (as distance [Dist] and high-speed distance [HS Dist]) and accelerometer (Player Load [PL]) data. Using a training-impulse (TRIMPs) calculation, external load expressed in arbitrary units was represented as TRIMPsDist, TRIMPsHSDist, and TRIMPsPL. Preseason training load and MEI sim/min data were applied to a variable dose–response model, which provided estimates of MEI sim/min. Model estimates of MEI sim/min were correlated with actual measures from each match-play drill performed during the preseason macrocycle. Magnitude-based inferences (effect size [90% confidence interval]) were calculated to determine practical differences in the precision of MEI sim/min estimates using each of the internal- and external-load inputs. Results: Estimates of MEI sim/min demonstrated very large and large associations with actual MEI sim/min with models constructed from external and internal training inputs (r [90% confidence interval]; TRIMPsDist .73 [.72–.74], TRIMPsPL .72 [.71–.73], and sRPESkills .67 [.56–.78]). There were trivial differences in the precision of MEI sim/min estimates between models constructed from TRIMPsDist and TRIMPsPL and between internal input methods. Conclusions: Variable dose-response models from multiple training-load inputs can predict the within-individual variation of MEI sim/min across an entire preseason macrocycle. Models informed by external training inputs (TRIMPsDist and TRIMPsPL) exhibited predictive power comparable to those of sRPESkills models.