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Int J Sports Med 2021; 42(08): 731-739
DOI: 10.1055/a-1300-2703
Training & Testing

Training Load and Injury Risk in Elite Rugby Union: The Largest Investigation to Date

Stephen W. West 
1   Department for Health, University of Bath, Bath, United Kingdom
,
Sean Williams 
1   Department for Health, University of Bath, Bath, United Kingdom
,
Dario Cazzola
1   Department for Health, University of Bath, Bath, United Kingdom
,
Simon Kemp 
2   Medical Services, Rugby Football Union, Twickenham, United Kingdom
3   Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London,United Kingdom
,
Matthew J. Cross
4   Premier Rugby Limited, Twickenham, United Kingdom
,
Keith A. Stokes 
1   Department for Health, University of Bath, Bath, United Kingdom
2   Medical Services, Rugby Football Union, Twickenham, United Kingdom
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Abstract

Training load monitoring has grown in recent years with the acute:chronic workload ratio (ACWR) widely used to aggregate data to inform decision-making on injury risk. Several methods have been described to calculate the ACWR and numerous methodological issues have been raised. Therefore, this study examined the relationship between the ACWR and injury in a sample of 696 players from 13 professional rugby clubs over two seasons for 1718 injuries of all types and a further analysis of 383 soft tissue injuries specifically. Of the 192 comparisons undertaken for both injury groups, 40% (all injury) and 31% (soft tissue injury) were significant. Furthermore, there appeared to be no calculation method that consistently demonstrated a relationship with injury. Some calculation methods supported previous work for a “sweet spot” in injury risk, while a substantial number of methods displayed no such relationship. This study is the largest to date to have investigated the relationship between the ACWR and injury risk and demonstrates that there appears to be no consistent association between the two. This suggests that alternative methods of training load aggregation may provide more useful information, but these should be considered in the wider context of other established risk factors.

Key words

workload - injury - prevention - athlete management

Supplementary Material



Publication History

Received: 23 June 2020

Accepted: 21 October 2020

Article published online:
08 December 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Gabbett TJ. The training-injury prevention paradox: Should athletes be training smarter and harder?. Br J Sports Med 2016; 50: 273-280
    CrossrefPubMedGoogle Scholar
  • 2 Griffin A, Kenny IC, Comyns TM. et al. The association between the acute:chronic workload ratio and injury and its application in team sport: A systematic review. Sports Med 2020; 50: 561-580
    CrossrefPubMedGoogle Scholar
  • 3 Andrade R, Halvorsen Wik E, Rebelo-Marques A. et al. Is the acute:chronic workload ratio (acwr) associated with risk of time-loss injury in professional sports teams? A systematic review of methodology, variables and injury risk in practical situations. Sports Med 2020; 50: 1613-1635
    CrossrefPubMedGoogle Scholar
  • 4 Hulin BT, Gabbett TJ, Blanch P. et al. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med 2014; 48: 708-712
    CrossrefPubMedGoogle Scholar
  • 5 Menaspa P. Are rolling averages a good way to assess training load for injury prevention?. Br J Sports Med 2017; 51: 618-619
    CrossrefPubMedGoogle Scholar
  • 6 Carey DL, Blanch P, Ong KL. et al. Training loads and injury risk in Australian football-differing acute:chronic workload ratios influence match injury risk. Br J Sports Med 2017; 51: 1215-1220
    CrossrefPubMedGoogle Scholar
  • 7 Lolli L, Batterham AM, Hawkins R. et al. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations. Br J Sports Med 2019; 53: 921-922
    CrossrefPubMedGoogle Scholar
  • 8 Wang C, Vargas JT, Stokes T. et al. Analyzing activity and injury: lessons learned from the acute:chronic workload ratio. Sports Med 2020; 50: 1243-1254
    CrossrefPubMedGoogle Scholar
  • 9 Lolli L, Batterham AM, Hawkins R. et al. The acute-to-chronic workload ratio: an inaccurate scaling index for an unnecessary normalisation process?. Br J Sports Med 2018; 53: 1510-1512
    CrossrefPubMedGoogle Scholar
  • 10 Impellizzeri FM, Woodcock S, McCall A. et al. The acute-chronic workload ratio-injury figure and its 'sweet spot' are flawed. SportRxiv 2019; DOI: 10.31236/osf.io/gs8yu.
    CrossrefPubMedGoogle Scholar
  • 11 Wang C, Vargas JT, Stokes T. et al. The acute:chronic workload ratio: challenges and prospects for improvement. arXiv 2019; 1907.05326
    PubMedGoogle Scholar
  • 12 Impellizzeri FM, Woodcock S, Coutts AJ. et al. Acute to random workload ratio is 'as' associated with injury as acute to actual chronic workload ratio: time to dismiss ACWR and its components. Front Physiol 2020; 11: 1034
    CrossrefPubMedGoogle Scholar
  • 13 Dalen-Lorentsen T, Anderson TE, Bjorneboe J. et al. A cherry tree ripe for picking: The relationship between the acute:chronic workload ratio and health problems. SportRxiv 2020; DOI: 10.31236/osf.io/nhqbx.
    CrossrefPubMedGoogle Scholar
  • 14 Williams S, West S, Cross MJ. et al. Better way to determine the acute:chronic workload ratio?. Br J Sports Med 2017; 51: 209-210
    CrossrefPubMedGoogle Scholar
  • 15 Stares J, Dawson B, Peeling P. et al. Identifying high risk loading conditions for in-season injury in elite Australian football players. J Sci Med Sport 2018; 21: 46-51
    CrossrefPubMedGoogle Scholar
  • 16 Williams S, Trewartha G, Kemp S. et al. A meta-analysis of injuries in senior men’s professional Rugby Union. Sports Med 2013; 43: 1043-1055
    CrossrefPubMedGoogle Scholar
  • 17 Cross MJ, Williams S, Trewartha G. et al. The influence of in-season training loads on injury risk in professional rugby union. Int J Sports Physiol Perform 2016; 11: 350-355
    CrossrefPubMedGoogle Scholar
  • 18 Cousins BEW, Morris JG, Sunderland C. et al. Match and training load exposure and time-loss incidence in elite rugby union players. Front Physiol 2019; 10: 1-11
    CrossrefPubMedGoogle Scholar
  • 19 Meeuwisse WH, Tyreman H, Hagel B. et al. A dynamic model of etiology in sport injury: the recursive nature of risk and causation. Clin J Sports Med 2007; 17: 215-219
    CrossrefPubMedGoogle Scholar
  • 20 Williams S, Trewartha G, Kemp SPT. et al. How much rugby is too much? A seven-season prospective cohort study of match exposure and injury risk in professional rugby union players. Sports Med 2017; 47: 2395-2402
    CrossrefPubMedGoogle Scholar
  • 21 Cross MJ, Kemp SPT, Smith A. et al. Professional Rugby Union players have a 60% greater risk of time loss injury after concussion: a 2-season prospective study of clinical outcomes. Br J Sports Med 2015; 50: 926-931
    CrossrefPubMedGoogle Scholar
  • 22 Brooks JHM, Fuller CW, Kemp SPT. et al. Epidemiology of injuries in English professional rugby union:match injuries. Br J Sports Med 2005; 39: 757-766
    CrossrefPubMedGoogle Scholar
  • 23 Chalmers DJ, Samaranayaka A, Gulliver P. et al. Risk factors for injury in rugby union football in New Zealand: A cohort study. Br J Sports Med 2012; 46: 95-102
    CrossrefPubMedGoogle Scholar
  • 24 Harriss DJ, Macsween A, Atkinson G. Ethical standards in sport and exercise science research: 2020 update. Int J Sports Med 2019; 40: 813-817
    Article in Thieme ConnectPubMedGoogle Scholar
  • 25 Fuller CW, Molloy MG, Bagate C. et al. Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Clin J Sports Med 2007; 17: 177-181
    CrossrefPubMedGoogle Scholar
  • 26 Foster C, Florhaug JA, Franklin J. et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15: 109-115
    PubMedGoogle Scholar
  • 27 Sweet TW, Foster C, McGuigan MR. et al. Quantification of resistance training using the session rating of perceived exertion method. J Strength Cond Res 2004; 18: 796-802
    PubMedGoogle Scholar
  • 28 Comyns T, Hannon A. Strength and conditioning coaches' application of the session rating of perceived exertion method of monitoring within professional rugby union. J Hum Kinet 2018; 23: 155-166
    CrossrefPubMedGoogle Scholar
  • 29 Borg G, Hassmen P, Lagerstrom M. Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol 1987; 56: 679-685
    CrossrefPubMedGoogle Scholar
  • 30 Carey DL, Crossley KM, Whiteley R. et al. Modelling training loads and injuries: the dangers of discretization. Med Sci Sports Exerc 2018; 50: 2267-2276
    CrossrefPubMedGoogle Scholar
  • 31 Thornton HR, Delaney JA, Duthie GM. et al. Importance of various training-load measures in injury incidence of professional rugby league athletes. Int J Sports Physiol Perform 2017; 12: 819-824
    CrossrefPubMedGoogle Scholar
  • 32 Esmaeili A, Hopkins WG, Stewart AM. et al. The individual and combined effects of multiple factors on the risk of soft tissue non-contact injuries in elite team sports athletes. Front Physiol 2018; 9: 1280
    CrossrefPubMedGoogle Scholar
  • 33 Newbold T. Package "StatisticalModels". In. github.com. 2019;
    PubMed
  • 34 Williams S, Trewartha G, Cross MJ. et al. Monitoring what matters: A systematic process for selecting training-load measures. Int J Sports Physiol Perform 2017; 12: 101-106
    CrossrefPubMedGoogle Scholar
  • 35 Kutner MH, Nachtsheim C, Neter J. Applied Linear Regression Models. New York, USA: 2004
    Google Scholar
  • 36 Windt J, Gabbett TJ. Is it all for naught? What does mathematical coupling mean for acute:chronic workload ratios?. Br J Sports Med 2018; 53: 988-990
    CrossrefPubMedGoogle Scholar
  • 37 Bates D, Bolker B, Walker S. et al. Package “lme4”. Bolker B. CRAN. 2018
    Google Scholar
  • 38 Colby MJ, Dawson B, Peeling P. et al. Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers. J Sci Med Sport 2017; 20: 1068-1074
    CrossrefPubMedGoogle Scholar
  • 39 Hopkins WG, Marshall SW, Batterham AM. et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41: 3-12
    CrossrefPubMedGoogle Scholar
  • 40 Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 2006; 1: 50-57
    CrossrefPubMedGoogle Scholar
  • 41 Murray NB, Gabbett TJ, Townshend AD. et al. Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med 2017; 51: 749-754
    CrossrefPubMedGoogle Scholar
  • 42 Malone S, Owen A, Newton M. et al. The acute:chronic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport 2017; 20: 561-565
    CrossrefPubMedGoogle Scholar
  • 43 Weiss KJ, Allen SV, McGuigan MR. et al. The relationship between training load and injury in men's professional basketball. Int J Sports Physiol Perform 2017; 12: 1238-1242
    CrossrefPubMedGoogle Scholar
  • 44 Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd. Edition. New Jersey, United States of America: John Wiley & Sons, Inc; 2013
    CrossrefGoogle Scholar
  • 45 Kumar S. Theories of musculoskeletal injury causation. Ergonomics 2001; 44: 17-47
    CrossrefPubMedGoogle Scholar
  • 46 Bittencourt NFN, Meeuwisse WH, Mendonca LD. et al. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept. Br J Sports Med 2016; 50: 1309-1314
    CrossrefPubMedGoogle Scholar
  • 47 Bornn L, Ward P, Norman D. Training Schedule Confounds the Relationship between Acute:Chronic Workload Ratio and Injury: A Casual Analysis in Professional Soccer and American Football. 13th Annual MIT Sloan Sports Analytics Conference. Boston, MA: 2019
    Google Scholar
  • 48 Haddad M, Stylianides G, Djaoui L. et al. Session-RPE method for training load monitoring: Validity, ecological usefulness, and influencing factors. Front Neurosci 2017; 11: 612
    CrossrefPubMedGoogle Scholar
  • 49 Boullosa D, Casado A, Claudino JG. et al. Do you play or do you train? Insights from individual sports for training load and injury risk management in team sports based on individualization. Front Physiol 2020; 11: 995
    CrossrefPubMedGoogle Scholar
  • 50 West SW, Clubb J, Torres-Ronda L. et al. More than a metric: How training load is used in elite sport for athlete management. Int J Sports Med 2020; DOI: 10.1055/a-1268-8791.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 51 Orchard JW, James T, Portus M. et al. Fast bowlers in cricket demonstrate up to 3- to 4-week delay between high workloads and increased risk of injury. Am J Sports Med 2009; 37: 1186-1192
    CrossrefPubMedGoogle Scholar
  • 52 Drew MK, Finch CF. The relationship between training load and injury, illness and soreness: A systematic and literature review. Sports Med 2016; 46: 861-883
    CrossrefPubMedGoogle Scholar
  • 53 Dalen-Lorentsen. Bjornboe T, Clarsen J. et al. Does load management using the acute:chronic workload ratio prevent health problems? A cluster randomised trial of 482 elite youth footballers of both sexes. Br J Sports Med 2020; DOI: 10.1136/bjsports-2020-103003.
    CrossrefPubMedGoogle Scholar