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Lorenzo Lolli, Alan M. Batterham, Gregory MacMillan, Warren Gregson, and Greg Atkinson
Pitre C. Bourdon, Marco Cardinale, Warren Gregson, and N. Timothy Cable
Robin T. Thorpe, Greg Atkinson, Barry Drust, and Warren Gregson
The increase in competition demands in elite team sports over recent years has prompted much attention from researchers and practitioners to the monitoring of adaptation and fatigue in athletes. Monitoring fatigue and gaining an understanding of athlete status may also provide insights and beneficial information pertaining to player availability, injury, and illness risk. Traditional methods used to quantify recovery and fatigue in team sports, such as maximal physical-performance assessments, may not be feasible to detect variations in fatigue status throughout competitive periods. Faster, simpler, and nonexhaustive tests such as athlete self-report measures, autonomic nervous system response via heart-rate-derived indices, and to a lesser extent, jump protocols may serve as promising tools to quantify and establish fatigue status in elite team-sport athletes. The robust rationalization and precise detection of a meaningful fluctuation in these measures are of paramount importance for practitioners working alongside athletes and coaches on a daily basis. There are various methods for arriving at a minimal clinically important difference, but these have been rarely adopted by sport scientists and practitioners. The implementation of appropriate, reliable, and sensitive measures of fatigue can provide important information to key stakeholders in team-sport environments. Future research is required to investigate the sensitivity of these tools to fundamental indicators such as performance, injury, and illness.
Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson
Purpose:
To quantify the mean daily changes in training and match load and any parallel changes in indicators of morningmeasured fatigue across in-season training weeks in elite soccer players.
Methods:
After each training session and match (TL), session ratings of perceived exertion (s-RPE) were recorded to calculate overall session load (RPE-TL) in 29 English Premier League players from the same team. Morning ratings of fatigue, sleep quality, and delayed-onset muscle soreness (DOMS), as well as submaximal exercise heart rate (HRex), postexercise heart-rate recovery (HRR%), and heart-rate variability (HRV) were recorded before match day and 1, 2, and 4 d postmatch. Data were collected for a median duration of 3 wk (range 1–13) and reduced to a typical weekly cycle including no midweek match and a weekend match day. Data were analyzed using withinsubject linear mixed models.
Results:
RPE-TL was approximately 600 arbitrary units (AU) (95% confidence interval 546–644) higher on match day than following day (P < .001). RPE-TL progressively decreased by »60 AU per day over the 3 days before a match (P < .05). Morning-measured fatigue, sleep quality, and DOMS tracked the changes in RPE-TL, being 35–40% worse on postmatch day vs prematch day (P < .001). Perceived fatigue, sleep quality, and DOMS improved by 17–26% from postmatch day to 3 d postmatch, with further smaller (7%–14%) improvements occurring between 4 d postmatch and prematch day (P < .01). There were no substantial or statistically significant changes in HRex, HRR%, or HRV over the weekly cycle (P > .05).
Conclusions:
Morning-measured ratings of fatigue, sleep quality, and DOMS are clearly more sensitive than HR-derived indices to the daily fluctuations in session load experienced by elite soccer players in a standard in-season week.
Matthew Weston, Warren Gregson, Carlo Castagna, Simon Breivik, Franco M. Impellizzeri, and Ric J. Lovell
Athlete case studies have often focused on the training outcome and not the training process. Consequently, there is a dearth of information detailing longitudinal training protocols, yet it is the combined assessment of both outcome and process that enhances the interpretation of physical test data. We were provided with a unique opportunity to assess the training load, physical match performance, and physiological fitness of an elite soccer referee from the referee’s final season before attaining full-time, professional status (2002) until the season when he refereed the 2010 UEFA Champions League and FIFA World Cup finals. An increased focus on on-field speed and gym-based strength training was observed toward the end of the study period and longitudinal match data showed a tendency for decreased total distances but an increased intensity of movements. Laboratory assessments demonstrated that VO2max remained stable (52.3 vs 50.8 mL-kg–1-min–1), whereas running speed at the lactate threshold (14.0 vs 12.0 km-h-1) and running economy (37.3 vs 43.4 mLkg–1min–1) both improved in 2010 compared with 2002.
Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson
Purpose:
To quantify the relationship between daily training load and a range of potential measures of fatigue in elite soccer players during an in-season competitive phase (17 d).
Methods:
Total high-intensity-running (THIR) distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), postexercise heart-rate recovery (HRR), and heart-rate variability (Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of daily fluctuation in THIR distance on potential fatigue variables.
Results:
Fluctuations in fatigue (r = −.51, large, P < .001), Ln rMSSD (r = −.24, small, P = .04), and CMJ (r = .23, small, P = .04) were significantly correlated with fluctuations in THIR distance. Correlations between variability in muscle soreness, sleep quality, and HRR and THIR distance were negligible and not statistically significant.
Conclusions:
Perceived ratings of fatigue and Ln rMSSD were sensitive to daily fluctuations in THIR distance in a sample of elite soccer players. Therefore, these particular markers show promise as simple, noninvasive assessments of fatigue status in elite soccer players during a short in-season competitive phase.
Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson
Purpose:
To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous 2, 3, and 4 d during a short in-season competitive period in elite senior soccer players (N = 10).
Methods:
Total highspeed-running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), submaximal heart rate (HRex), postexercise heart-rate recovery (HRR), and heart-rate variability (HRV: Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of 2-, 3-, and 4-d total high-speed-running-distance accumulation on fatigue measures.
Results:
Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed-running-distance accumulation covered on the previous 2 d (r = –.31; small), 3 d (r = –.42; moderate), and 4 d (r = –.28; small) (P < .05). Changes in HRex (r = .28; small; P = .02) were correlated with changes in 4-d total high-speed-running-distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR%, and HRV and total high-speed-running distance were negligible and not statistically significant for all accumulation training loads.
Conclusions:
Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed-running-distance accumulation, although sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous day’s training.
Lorenzo Lolli, Amanda Johnson, Mauricio Monaco, Valter Di Salvo, Greg Atkinson, and Warren Gregson
Purpose: To assess conventional assumptions that underpin the percentage of mature height index as the simple ratio of screening height (numerator) divided by actual or predicted adult height (denominator). Methods: We examined cross-sectional data from 99 academy youth soccer players (chronological age range, 11.5 to 17.7 y) skeletally immature at the screening time and with adult height measurements available at follow-up. Results: The y-intercept value of −60 cm (95% confidence interval, −115 to −6 cm) from linear regression between screening height and adult height indicated the failure to meet the zero y-intercept assumption. The correlation coefficient between present height and adult height of .64 (95% confidence interval, .50 to .74) was not equal to the ratio of coefficient of variations between these variables (CV x /CV y = 0.46) suggesting Tanner’s special circumstance was violated. The non-zero correlation between the ratio and the denominator of .21 (95% confidence interval, .01 to .39) indicated that the percentage of mature height was biased low for players with generally shorter adult height, and vice versa. Conclusion: For the first time, we have demonstrated that the percentage of mature height is an inconsistent statistic for determining the extent of completed growth, leading to potentially biased inferences for research and applied purposes.
Paolo Gaudino, F. Marcello Iaia, Anthony J. Strudwick, Richard D. Hawkins, Giampietro Alberti, Greg Atkinson, and Warren Gregson
Purpose:
The aim of the current study was to identify the external-training-load markers that are most influential on session rating of perceived exertion (RPE) of training load (RPE-TL) during elite soccer training.
Methods:
Twenty-two elite players competing in the English Premier League were monitored. Training-load data (RPE and 10-Hz GPS integrated with a 100-Hz accelerometer) were collected during 1892 individual training sessions over an entire in-season competitive period. Expert knowledge and a collinearity r < .5 were used initially to select the external training variables for the final analysis. A multivariateadjusted within-subjects model was employed to quantify the correlations of RPE and RPE-TL (RPE × duration) with various measures of external training intensity and training load.
Results:
Total high-speed-running (HSR; >14.4 km/h) distance and number of impacts and accelerations >3 m/s2 remained in the final multivariate model (P < .001). The adjusted correlations with RPE were r = .14, r = .09, and r = .25 for HSR, impacts, and accelerations, respectively. For RPE-TL, the correlations were r = .11, r = .45, and r = .37, respectively.
Conclusions:
The external-load measures that were found to be moderately predictive of RPE-TL in soccer training were HSR distance and the number of impacts and accelerations. These findings provide new evidence to support the use of RPE-TL as a global measure of training load in elite soccer. Furthermore, understanding the influence of characteristics affecting RPE-TL may help coaches and practitioners enhance training prescription and athlete monitoring.
Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson, and N. Timothy Cable
Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.
