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The Association Between the Acute:Chronic Workload Ratio and Injury and its Application in Team Sports: A Systematic Review

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Abstract

Background

There has been a recent increase in research examining training load as a method of mitigating injury risk due to its known detrimental effects on player welfare and team performance. The acute:chronic workload ratio (ACWR) takes into account the current training load (acute) and the training load that an athlete has been prepared for (chronic). The ACWR can be calculated using; (1) the rolling average model (RA) and (2) the exponentially weighted moving average model (EWMA).

Objective

The primary aim of this systematic review was to investigate the literature examining the association between the occurrence of injury and the ACWR and to investigate if sufficient evidence exists to determine the best method of application of the ACWR in team sports.

Methods

Studies were identified through a comprehensive search of the following databases: EMBASE, Medline, SPORTDiscus, SCOPUS, AMED and CINAHL. Extensive data extraction was performed. The methodological quality of the included studies was assessed according to the Newcastle–Ottawa Scale (NOS) for Cohort Studies.

Results

A total of 22 articles met the inclusion criteria. The assessment of article quality had an overall median NOS score of 8 (range 5–9). The findings of this review support the association between the ACWR and non-contact injuries and its use as a valuable tool for monitoring training load as part of a larger scale multifaceted monitoring system that includes other proven methods. There is support for both models, but the EWMA is the more suitable measure, in part due to its greater sensitivity. The most appropriate acute and chronic time periods, and training load variables, may be dependent on the specific sport and its structure.

Conclusions

For practitioners, it is the important to understand the intricacies of the ACWR before deciding the best method of calculation. Future research needs to focus on the more sensitive EWMA model, for both sexes, across a larger range of sports and time frames and also combinations with other injury risk factors.

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References

  1. Rice SM, Purcell R, De Silva S, Mawren D, McGorry PD, Parker AG. The mental health of elite athletes: a narrative systematic review. Sports Med. 2016;46(9):1333–53.

    PubMed  PubMed Central  Google Scholar 

  2. Gouttebarge V, Tol JL, Kerkhoffs GM. Epidemiology of symptoms of common mental disorders among elite Gaelic athletes: a prospective cohort study. Phys Sportsmed. 2016;44(3):283–9.

    PubMed  Google Scholar 

  3. Manley G, Gardner AJ, Schneider KJ, Guskiewicz KM, Bailes J, Cantu RC, et al. A systematic review of potential long-term effects of sport-related concussion. Br J Sports Med. 2017;51(12):969–77.

    PubMed  PubMed Central  Google Scholar 

  4. McCrory P, Meeuwisse W, Dvořák J, Aubry M, Bailes J, Broglio S, et al. Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med. 2017;51(11):838–47.

    PubMed  Google Scholar 

  5. Gabbett TJ. Severity and cost of injuries in amateur rugby league: a case study. J Sports Sci. 2001;19(5):341–7.

    CAS  PubMed  Google Scholar 

  6. Drew MK, Raysmith BP, Charlton PC. Injuries impair the chance of successful performance by sportspeople: a systematic review. Br J Sports Med. 2017;51(16):1209–14.

    PubMed  Google Scholar 

  7. Eckard TG, Padua DA, Hearn DW, Pexa BS, Frank BS. The relationship between training load and injury in athletes: a systematic review. Sports Med. 2018;48(8):1929–61.

    PubMed  Google Scholar 

  8. Drew MK, Finch CF. The relationship between training load and injury, illness and soreness: a systematic and literature review. Sports Med. 2016;46(6):861–83.

    PubMed  Google Scholar 

  9. Meeuwisse WH. Assessing causation in sport injury: a multifactorial model. Clin J Sport Med. 1994;4(3):166–70.

    Google Scholar 

  10. Bahr R, Krosshaug T. Understanding injury mechanisms: a key component of preventing injuries in sport. Br J Sports Med. 2005;39(6):324–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Rogalski B, Dawson B, Heasman J, Gabbett TJ. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport. 2013;16(6):499–503.

    PubMed  Google Scholar 

  12. Fuller CW, Taylor AE, Brooks JH, Kemp SP. Changes in the stature, body mass and age of English professional rugby players: a 10-year review. J Sports Sci. 2013;31(7):795–802.

    PubMed  Google Scholar 

  13. Phibbs PJ, Jones B, Read DB, Roe GAB, Darrall-Jones J, Weakley JJS, et al. The appropriateness of training exposures for match-play preparation in adolescent schoolboy and academy rugby union players. J Sports Sci. 2018;36(6):704–9.

    PubMed  Google Scholar 

  14. Ball S, Halaki M, Sharp T, Orr R. Injury patterns, physiological profile, and performance in university rugby union. Int J Sports Physiol Perform. 2018;13(1):69–74.

    PubMed  Google Scholar 

  15. Morton RH. Modelling training and overtraining. J Sports Sci. 1997;15(3):335–40.

    CAS  PubMed  Google Scholar 

  16. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12(Suppl 2):S2161–70.

    PubMed  Google Scholar 

  17. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(Suppl 2):S139–47.

    PubMed  Google Scholar 

  18. Impellizzeri FM, Marcora SM, Coutts AJ. Internal and external training load: 15 years on. Int J Sports Physiol Perform. 2019;14(2):270–3.

    PubMed  Google Scholar 

  19. Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281–91.

    PubMed  Google Scholar 

  20. Soligard T, Schwellnus M, Alonso JM, Bahr R, Clarsen B, Dijkstra HP, et al. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med. 2016;50(17):1030–41.

    PubMed  Google Scholar 

  21. Banister EW, Calvert T, Savage M, Bach T. A systems model of training for athletic performance. Aust J Sports Med. 1975;3:57–61.

    Google Scholar 

  22. Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48(8):708–12.

    PubMed  Google Scholar 

  23. Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ. The acute:chonic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport. 2017;20(6):561–5.

    PubMed  Google Scholar 

  24. Williams S, West S, Cross MJ, Stokes KA. Better way to determine the acute:chronic workload ratio? Br J Sports Med. 2017;51(3):209–10.

    PubMed  Google Scholar 

  25. Menaspà P. Are rolling averages a good way to assess training load for injury prevention? Br J Sports Med. 2017;51(7):618–9.

    PubMed  Google Scholar 

  26. Murray NB, Gabbett TJ, Townshend AD, Blanch P. 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(9):749–54.

    PubMed  Google Scholar 

  27. Lolli L, Batterham AM, Hawkins R, Kelly DM, Strudwick AJ, Thorpe R, et al. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations. Br J Sports Med. 2019;53(15):921–2.

    PubMed  Google Scholar 

  28. Jones CM, Griffiths PC, Mellalieu SD. Training load and fatigue marker associations with injury and illness: a systematic review of longitudinal studies. Sports Med. 2017;47(5):943–74.

    PubMed  Google Scholar 

  29. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6(7):e1000100.

    PubMed  PubMed Central  Google Scholar 

  30. Ryan R, Hill S, Prictor M, McKenzie J. Cochrane consumers and communication review group. Study quality guide. 2013. http://cccrg.cochrane.org/authorresources. Accessed 10 Jan 2019.

  31. Wells G, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P. Newcastle-Ottawa quality assessment scale cohort studies. 2014. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.2014. Accessed 10 Jan 2019.

  32. Carey DL, Blanch P, Ong KL, Crossley KM, Crow J, Morris ME. Training loads and injury risk in Australian football-differing acute: chronic workload ratios influence match injury risk. Br J Sports Med. 2017;51(16):1215–20.

    PubMed  Google Scholar 

  33. Colby MJ, Dawson B, Peeling P, Heasman J, Rogalski B, Drew MK, 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(12):1068–74.

    PubMed  Google Scholar 

  34. Esmaeili A, Hopkins WG, Stewart AM, Elias GP, Lazarus BH, Aughey RJ. The individual and combined effects of multiple factors on the risk of soft tissue non-contact injuries in elite team sport athletes. Front Physiol. 2018;9:1280.

    PubMed  PubMed Central  Google Scholar 

  35. Murray NB, Gabbett TJ, Townshend AD, Hulin BT, McLellan CP. Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers. Scand J Med Sci Sports. 2017;27(9):990–8.

    CAS  PubMed  Google Scholar 

  36. Murray NB, Gabbett TJ, Townshend AD. The use of relative speed zones in australian football: are we really measuring what we think we are? Int J Sports Physiol Perform. 2018;13(4):442–51.

    PubMed  Google Scholar 

  37. Stares J, Dawson B, Peeling P, Heasman J, Rogalski B, Drew M, et al. Identifying high risk loading conditions for in-season injury in elite Australian football players. J Sci Med Sport. 2018;21(1):46–51.

    PubMed  Google Scholar 

  38. Delecroix B, McCall A, Dawson B, Berthoin S, Dupont G. Workload and non-contact injury incidence in elite football players competing in European leagues. Eur J Sport Sci. 2018;18(9):1280–7.

    PubMed  Google Scholar 

  39. Fanchini M, Rampinini E, Riggio M, Coutts A, Pecci C, McCall A. Despite association, the acute:chronic work load ratio does not predict non-contact injury in elite footballers. Sci Med Football. 2018;2(2):108–14.

    Google Scholar 

  40. Jaspers A, Kuyvenhoven JP, Staes F, Frencken WGP, Helsen WF, Brink MS. Examination of the external and internal load indicators’ association with overuse injuries in professional soccer players. J Sci Med Sport. 2018;21(6):579–85.

    PubMed  Google Scholar 

  41. Malone S, Owen A, Mendes B, Hughes B, Collins K, Gabbett TJ. High-speed running and sprinting as an injury risk factor in soccer: Can well-developed physical qualities reduce the risk? J Sci Med Sport. 2018;21(3):257–62.

    PubMed  Google Scholar 

  42. McCall A, Dupont G, Ekstrand J. Internal workload and non-contact injury: a one-season study of five teams from the UEFA Elite Club Injury Study. Br J Sports Med. 2018;52(23):1517–22.

    PubMed  Google Scholar 

  43. Hulin BT, Gabbett TJ, Caputi P, Lawson DW, Sampson JA. Low chronic workload and the acute:chronic workload ratio are more predictive of injury than between-match recovery time: a two-season prospective cohort study in elite rugby league players. Br J Sports Med. 2016;50(16):1008–12.

    PubMed  Google Scholar 

  44. Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med. 2016;50(4):231–6.

    PubMed  Google Scholar 

  45. Cross MJ, Williams S, Trewartha G, Kemp SP, Stokes KA. The influence of in-season training loads on injury risk in professional rugby union. Int J Sports Physiol Perform. 2016;11(3):350–5.

    PubMed  Google Scholar 

  46. Malone S, Roe M, Doran DA, Gabbett TJ, Collins KD. Protection against spikes in workload with aerobic fitness and playing experience: the role of the acute:chronic workload ratio on injury risk in elite gaelic football. Int J Sports Physiol Perform. 2017;12(3):393–401.

    PubMed  Google Scholar 

  47. Malone S, Hughes B, Doran DA, Collins K, Gabbett TJ. Can the workload-injury relationship be moderated by improved strength, speed and repeated-sprint qualities? J Sci Med Sport. 2019;22(1):29–34.

    PubMed  Google Scholar 

  48. Sampson JA, Murray A, Williams S, Halseth T, Hanisch J, Golden G, et al. Injury risk-workload associations in NCAA American college football. J Sci Med Sport. 2018;21(12):1215–20.

    CAS  PubMed  Google Scholar 

  49. Timoteo TF, Debien PB, Miloski B, Werneck FZ, Gabbett T, Bara Filho MG. Influence of workload and recovery on injuries in elite male volleyball players. J Strength Cond Res. 2018. https://doi.org/10.1519/JSC.0000000000002754.

    Article  PubMed  Google Scholar 

  50. Weiss KJ, Allen SV, McGuigan MR, Whatman CS. The relationship between training load and injury in men’s professional basketball. Int J Sports Physiol Perform. 2017;12(9):1238–42.

    PubMed  Google Scholar 

  51. Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Scand J Med Sci Sports. 2006;16(2):83–92.

    CAS  PubMed  Google Scholar 

  52. Brooks JH, Fuller CW, Kemp SP, Reddin DBCP. Epidemiology of injuries in English professional rugby union: part 1 match injuries. Br J Sports Med. 2005;39(10):757–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Fuller CW, Molloy MG, Bagate C, Bahr R, Brooks JH, Donson H, et al. Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Br J Sports Med. 2007;41(5):328–31.

    PubMed  PubMed Central  Google Scholar 

  54. King DA, Hume PA, Milburn PD, Guttenbeil D. Match and training injuries in rugby league: a review of published studies. Sports Med. 2010;40(2):163–78.

    PubMed  Google Scholar 

  55. Orchard J, Hoskins W. For debate: consensus injury definitions in team sports should focus on missed playing time. Clin J Sport Med. 2007;17(3):192–6.

    PubMed  Google Scholar 

  56. Brooks JH, Fuller CW. The influence of methodological issues on the results and conclusions from epidemiological studies of sports injuries: illustrative examples. Sports Med. 2006;36(6):459–72.

    PubMed  Google Scholar 

  57. Colby MJ, Dawson B, Heasman J, Rogalski B, Rosenberg M, Lester L, et al. Preseason workload volume and high-risk periods for noncontact injury across multiple australian football league seasons. J Strength Cond Res. 2017;31(7):1821–9.

    PubMed  Google Scholar 

  58. Gabbett TJ. Influence of training and match intensity on injuries in rugby league. J Sports Sci. 2004;22(5):409–17.

    PubMed  Google Scholar 

  59. Hägglund M, Waldén M, Magnusson H, Kristenson K, Bengtsson H, Ekstrand J. Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study. Br J Sports Med. 2013;47(12):738–42.

    PubMed  Google Scholar 

  60. Owen AL, Wong PL. In-season weekly high-intensity training volume among professional English soccer players: a 20-week study. Soccer J. 2009;4:28–32.

    Google Scholar 

  61. Timpka T, Jacobsson J, Ekberg J, Finch CF, Bichenbach J, Edouard P, et al. Meta-narrative analysis of sports injury reporting practices based on the Injury Definitions Concept Framework (IDCF): a review of consensus statements and epidemiological studies in athletics (track and field). J Sci Med Sport. 2015;18(6):643–50.

    PubMed  Google Scholar 

  62. Engebretsen L, Soligard T, Steffen K, Alonso JM, Aubry M, Budgett R, et al. Sports injuries and illnesses during the London Summer Olympic Games 2012. Br J Sports Med. 2013;47(7):407–14.

    PubMed  Google Scholar 

  63. Clarsen B, Myklebust G, Bahr R. Development and validation of a new method for the registration of overuse injuries in sports injury epidemiology: the Oslo Sports Trauma Research Centre (OSTRC) overuse injury questionnaire. Br J Sports Med. 2013;47(8):495–502.

    PubMed  Google Scholar 

  64. Hägglund M, Waldén M, Bahr R, Ekstrand J. Methods for epidemiological study of injuries to professional football players: developing the UEFA model. Br J Sports Med. 2005;39(6):340–6.

    PubMed  PubMed Central  Google Scholar 

  65. Hulin BT. The never-ending search for the perfect acute:chronic workload ratio: what role injury definition? Br J Sports Med. 2017;51(13):991–2.

    PubMed  Google Scholar 

  66. Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond Res. 2010;24(10):2593–603.

    PubMed  Google Scholar 

  67. Yeomans C, Kenny IC, Cahalan R, Warrington GD, Harrison AJ, Hayes K, et al. The incidence of injury in amateur male rugby union: a systematic review and meta-analysis. Sports Med. 2018;48(4):837–48.

    PubMed  PubMed Central  Google Scholar 

  68. Palmer-Green DS, Stokes KA, Fuller CW, England M, Kemp SP, Trewartha G. Match injuries in English youth academy and schools rugby union: an epidemiological study. Am J Sports Med. 2013;41(4):749–55.

    PubMed  Google Scholar 

  69. Feeley BT, Kennelly S, Barnes RP, Muller MS, Kelly BT, Rodeo SA, et al. Epidemiology of National Football League training camp injuries from 1998 to 2007. Am J Sports Med. 2008;36(8):1597–603.

    PubMed  Google Scholar 

  70. Saw R, Finch CF, Samra D, Baquie P, Cardoso T, Hope D, et al. Injuries in Australian rules football: an overview of injury rates, patterns, and mechanisms across all levels of play. Sports Health. 2018;10(3):208–16.

    PubMed  Google Scholar 

  71. Wong P, Hong YCP. Soccer injury in the lower extremities. Br J Sports Med. 2005;39(8):473–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Brooks JH, Fuller CW, Kemp SP, Reddin DB. An assessment of training volume in professional rugby union and its impact on the incidence, severity, and nature of match and training injuries. J Sports Sci. 2008;26(8):863–73.

    PubMed  Google Scholar 

  73. Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute: chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2017;51(5):452–9.

    PubMed  Google Scholar 

  74. Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109–15.

    CAS  PubMed  Google Scholar 

  75. Pearson JE. The definition and measurement of social support. J Couns Dev. 1986;64(6):390–5.

    Google Scholar 

  76. Gabbett TJ, Hulin B, Blanch P, Chapman P, Bailey D. To couple or not to couple? For acute: chronic workload ratios and injury risk, does it really matter? Int J Sports Med. 2019;40(09):597–600.

    PubMed  Google Scholar 

  77. Windt J, Gabbett TJ. Is it all for naught? What does mathematical coupling mean for acute:chronic workload ratios? Br J Sports Med. 2019;53(16):988–90.

    PubMed  Google Scholar 

  78. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90.

    PubMed  Google Scholar 

  79. Shmueli G. To explain or to predict? Stat Sci. 2010;25(3):289–310.

    Google Scholar 

  80. Bahr R. Why screening tests to predict injury do not work-and probably never will…: a critical review. Br J Sports Med. 2016;50(13):776–80.

    PubMed  Google Scholar 

  81. Hulin BT, Gabbett TJ. Indeed association does not equal prediction: the never-ending search for the perfect acute:chronic workload ratio. Br J Sports Med. 2019;53(3):144–5.

    PubMed  Google Scholar 

  82. Hartling L, Milne A, Hamm MP, Vandermeer B, Ansari M, Tsertsvadze A, et al. Testing the Newcastle Ottawa Scale showed low reliability between individual reviewers. J Clin Epidemiol. 2013;66(9):982–93.

    PubMed  Google Scholar 

  83. Murphy DF, Connolly DA, Beynnon BDCP. Risk factors for lower extremity injury: a review of the literature. Br J Sports Med. 2003;37(1):13–29.

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to acknowledge with considerable gratitude the members of the Irish Rugby Injury Surveillance (IRIS) Research team and the Irish Rugby Football Union (IRFU) for their help throughout the study period.

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Authors and Affiliations

  1. Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland

    Alan Griffin, Ian C. Kenny, Thomas M. Comyns & Mark Lyons

  2. Health Research Institute, University of Limerick, Limerick, Ireland

    Ian C. Kenny & Thomas M. Comyns

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  1. Alan Griffin
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Correspondence to Alan Griffin.

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Funding

Funding for this review was provided by the Irish Research Council and the University of Limerick, Ireland.

Conflict of interest

Alan Griffin, Ian C. Kenny, Thomas M. Comyns and Mark Lyons declare that they have no potential conflicts of interest that are directly relevant to the content of this review.

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The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Griffin, A., Kenny, I.C., Comyns, T.M. et al. The Association Between the Acute:Chronic Workload Ratio and Injury and its Application in Team Sports: A Systematic Review. Sports Med 50, 561–580 (2020). https://doi.org/10.1007/s40279-019-01218-2

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