The concepts of internal and external training load were first presented at the Eighth Annual Congress of the European College of Sport Science in Salzburg, Austria (2003) 1 at an invited session and symposium organized by Tom Reilly. The content of this presentation was included in 2 follow
Franco M. Impellizzeri, Samuele M. Marcora and Aaron J. Coutts
Ibrahim Akubat, Steve Barrett and Grant Abt
This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL).
Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]).
The results from the LT test were used to determine velocity at lactate threshold (vLT), velocity at onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (VO2max), and the heart rate–blood lactate profile for calculation of internal TL (individualized training impulse, or iTRIMP). The total distance (TD) and high intensity distance (HID) covered during the BEAST90mod were measured using GPS technology that allowed measurement of performance and external TL. The internal TL was divided by the external TL to form TD:iTRIMP and HID:iTRIMP ratios. Correlation analyses assessed the relationships between fitness measures and the ratios to performance in the BEAST90mod.
vLT, vOBLA, and VO2max showed no significant relationship to TD or HID. HID:iTRIMP significantly correlated with vOBLA (r = .65, P = .04; large), and TD:iTRIMP showed a significant correlation with vLT (r = .69, P = .03; large).
The results suggest that the integrated use of ratios may help in the assessment of fitness, as performance alone showed no significant relationships with fitness.
Dan Weaving, Phil Marshall, Keith Earle, Alan Nevill and Grant Abt
This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.
Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.
Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.
In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.
Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda and Samuel J. Robertson
The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.
TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.
Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.
This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.
Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning
Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.
Stuart R. Graham, Stuart Cormack, Gaynor Parfitt and Roger Eston
enhanced the predictive power of actual MEI/min, equaling the predictive accuracy of all the external training load–MEI/min models. Collectively, these results suggest that the in-season skills and conditioning load is more important to MEI/min performance than other training modalities during an in
Stuart R. Graham, Stuart Cormack, Gaynor Parfitt and Roger Eston
participant using in-season TRIMPs Dist and MEI sim/min data. Table 2 shows the precision of actual MEI sim/min using the different internal and external training input methods. Accuracy of MEI sim/min estimates was greater for external training-load inputs compared with each of the internal inputs
Luka Svilar, Julen Castellano, Igor Jukic and David Casamichana
training periodization. To understand the relationship between the training “dose” and “response,” complementary use of external and internal load 6 is necessary to choose the best approach to optimally improve performance. 7 While external training load (eTL) represents the dose (activities) performed
Thomas W.J. Lovell, Anita C. Sirotic, Franco M. Impellizzeri and Aaron J. Coutts
The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league.
Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training.
There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance − 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = −0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min).
A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.
Alireza Esmaeili, Andrew M. Stewart, William G. Hopkins, George P. Elias and Robert J. Aughey
Detrimental changes in tendon structure increase the risk of tendinopathies. The aim of this study was to investigate the influence of individual internal and external training loads and leg dominance on changes in the Achilles and patellar tendon structure.
The internal structure of the Achilles and patellar tendons of both limbs of 26 elite Australian footballers was assessed using ultrasound tissue characterization at the beginning and the end of an 18-wk preseason. Linear-regression analysis was used to estimate the effects of training load on changes in the proportion of aligned and intact tendon bundles for each side. Standardization and magnitude-based inferences were used to interpret the findings.
Possibly to very likely small increases in the proportion of aligned and intact tendon bundles occurred in the dominant Achilles (initial value 81.1%; change, ±90% confidence limits 1.6%, ±1.0%), nondominant Achilles (80.8%; 0.9%, ±1.0%), dominant patellar (75.8%; 1.5%, ±1.5%), and nondominant patellar (76.8%; 2.7%, ±1.4%) tendons. Measures of training load had inconsistent effects on changes in tendon structure; eg, there were possibly to likely small positive effects on the structure of the nondominant Achilles tendon, likely small negative effects on the dominant Achilles tendon, and predominantly no clear effects on the patellar tendons.
The small and inconsistent effects of training load are indicative of the role of recovery between tendon-overloading (training) sessions and the multivariate nature of the tendon response to load, with leg dominance a possible influencing factor.