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Is the Acute: Chronic Workload Ratio (ACWR) Associated with Risk of Time-Loss Injury in Professional Team Sports? A Systematic Review of Methodology, Variables and Injury Risk in Practical Situations

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Abstract

Background

The acute: chronic workload ratio (ACWR) is an index of the acute workload relative to the cumulative chronic workloads. The monitoring of physical workloads using the ACWR has emerged and been hypothesized as a useful tool for coaches and athletes to optimize performance while aiming to reduce the risk of potentially preventable load-driven injuries.

Objectives

Our goal was to describe characteristics of the ACWR and investigate the association of the ACWR with the risk of time-loss injuries in adult elite team sport athletes.

Data sources

PubMed, EMBASE and grey literature databases; inception to May 2019.

Eligibility criteria

Longitudinal studies that assess the relationship of the ACWR and time-loss injury risk in adult professional or elite team sports.

Methods

We summarized the population characteristics, workload metrics and ACWR calculation methods. For each workload metric, we plotted the risk estimates for the ACWR in isolation, or when combined with chronic workloads. Methodological quality was assessed using a modified version of the Downs and Black scale.

Results

Twenty studies comprising 2375 injuries from 1234 athletes (all males and mean age of 24 years) from different sports were included. Internal (65%) and external loads (70%) were collected in more than half of the studies and the session-rating of perceived exertion and total distance were the most commonly collected metrics. The ACWR was commonly calculated using the coupled method (95%), 1:4 weekly blocks (95%) and subsequent week injury lag (80%). There were 14 different binning methods with almost none of the studies using the same binning categories.

Conclusion

The majority of studies suggest that athletes are at greater risk of sustaining a time-loss injury when the ACWR is higher relative to a lower or moderate ACWR. The heterogenous methodological approaches not only reflect the wide range of sports studied and the differing demands of these activities, but also limit the strength of recommendations.

PROSPERO registration number

CRD42017067585.

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Data Availability

Additional data can be provided by reasonable request to authors.

References

  1. Bittencourt NFN, Meeuwisse WH, Mendonça LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. 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(21):1309.

    CAS  PubMed  Google Scholar 

  2. Hulme A, Finch CF. From monocausality to systems thinking: a complementary and alternative conceptual approach for better understanding the development and prevention of sports injury. Inj Epidemiol. 2015;2(1):31.

    PubMed  PubMed Central  Google Scholar 

  3. Meeuwisse WH, Tyreman H, Hagel B, Emery C. A dynamic model of etiology in sport injury: the recursive nature of risk and causation. Clin J Sport Med. 2007;17(3):215–9.

    PubMed  Google Scholar 

  4. Gabbett TJ, Whyte DG, Hartwig TB, Wescombe H, Naughton GA. The relationship between workloads, physical performance, injury and illness in adolescent male football players. Sports Med. 2014;44(7):989–1003.

    PubMed  Google Scholar 

  5. Windt J, Gabbett TJ. How do training and competition workloads relate to injury? The workload—injury aetiology model. Br J Sports Med. 2017;51(5):428.

    PubMed  Google Scholar 

  6. Burgess DJ. The research doesn't always apply: practical solutions to evidence-based training-load monitoring in elite team sports. Int J Sports Physiol Perform. 2017;12(Suppl 2):S2136–S21412141.

    PubMed  Google Scholar 

  7. Fox JL, Scanlan AT, Stanton R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res. 2017;31(7):2021–9.

    PubMed  Google Scholar 

  8. 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–S21702170.

    PubMed  Google Scholar 

  9. Quarrie KL, Raftery M, Blackie J, Cook CJ, Fuller CW, Gabbett TJ, et al. Managing player load in professional rugby union: a review of current knowledge and practices. Br J Sports Med. 2017;51(5):421–7.

    PubMed  Google Scholar 

  10. Fox JL, Stanton R, Sargent C, Wintour SA, Scanlan AT. The association between training load and performance in team sports: a systematic review. Sports Med. 2018;48(12):2743–74.

    PubMed  Google Scholar 

  11. Drew MK, Cook J, Finch CF. Sports-related workload and injury risk: simply knowing the risks will not prevent injuries: narrative review. Br J Sports Med. 2016;50(21):1306–8.

    PubMed  Google Scholar 

  12. Damsted C, Glad S, Nielsen RO, Sorensen H, Malisoux L. Is there evidence for an association between changes in training load and running-related injuries? A systematic review. Int J Sports Phys Ther. 2018;13(6):931–42.

    PubMed  PubMed Central  Google Scholar 

  13. Griffin A, Kenny IC, Comyns TM, Lyons M. The association between the acute:chronic workload ratio and injury and its application in team sports: a systematic review. Sports Med. 2020;50(3):561–80.

    PubMed  Google Scholar 

  14. 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 

  15. 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 

  16. Maupin D, Schram B, Canetti E, Orr R. The relationship between acute: chronic workload ratios and injury risk in sports: a systematic review. Open Access J Sports Med. 2020;11:51–755.

    PubMed  PubMed Central  Google Scholar 

  17. Gabbett TJ. The training—injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273–80.

    PubMed  PubMed Central  Google Scholar 

  18. 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 

  19. Bowen L, Gross AS, Gimpel M, Bruce-Low S, Li FX. Spikes in acute: chronic workload ratio (ACWR) associated with a 5–7 times greater injury rate in English Premier League football players: a comprehensive 3-year study. Br J Sports Med. 2019. https://doi.org/10.1136/bjsports-2018-099422.

    Article  PubMed  PubMed Central  Google Scholar 

  20. 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 

  21. 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 

  22. 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 

  23. 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 

  24. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.

    PubMed  PubMed Central  Google Scholar 

  25. Dwyer DB, Gabbett TJ. Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes. J Strength Cond Res. 2012;26(3):818–24.

    PubMed  Google Scholar 

  26. 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 

  27. Higgins JP, Green S. Section 13.5. 2.3. Tools for assessing methodological quality or risk of bias in non-randomized studies. Cochrane handbook for systematic reviews of interventions, version 5 1. 2011. https://handbook-5-1.cochrane.org/chapter_13/13_5_2_3_tools_for_assessing_methodological_quality_or_risk_of.htm

  28. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Neiva M. ColorADD: color identification system for color-blind people. In: Espregueira-Mendes J, van Dijk CN, Neyret P, Cohen M, Della Villa S, Pereira H, et al., editors. Injuries and health problems in football: what everyone should know. Berlin: Springer; 2017. p. 303–304.

    Google Scholar 

  30. 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 

  31. 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 

  32. Cummins C, Welch M, Inkster B, Cupples B, Weaving D, Jones B, et al. Modelling the relationships between volume, intensity and injury-risk in professional rugby league players. J Sci Med Sport. 2019;22(6):653–60.

    PubMed  Google Scholar 

  33. 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 

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

    Google Scholar 

  36. 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 

  37. 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 

  38. 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 

  39. 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 

  40. 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 

  41. 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 

  42. 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 

  43. Thornton HR, Delaney JA, Duthie GM, Dascombe BJ. Importance of various training-load measures in injury incidence of professional rugby league athletes. Int J Sports Physiol Perform. 2017;12(6):819–24.

    PubMed  Google Scholar 

  44. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load-injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017;51(8):645–50.

    PubMed  Google Scholar 

  45. Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6(3):311–21.

    PubMed  Google Scholar 

  46. 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 

  47. Wang C, Vargas JT, Stokes T, Steele R, Shrier I. Analyzing activity and injury: lessons learned from the acute: chronic workload ratio. Sports Med. 2020. https://doi.org/10.1007/s40279-020-01280-1.

    Article  PubMed  Google Scholar 

  48. Hunter JS. The exponentially weighted moving average. J Qual Technol. 1986;18(4):203–10.

    Google Scholar 

  49. 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 

  50. 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 

  51. Hulin BT, Gabbett TJ, Pickworth NJ, Johnston RD, Jenkins DG. Relationships among PlayerLoad, high-intensity intermittent running ability, and injury risk in professional rugby league players. Int J Sports Physiol Perform. 2020;15(3):423–9.

    Google Scholar 

  52. Cousins BEW, Morris JG, Sunderland C, Bennett AM, Shahtahmassebi G, Cooper SB. match and training load exposure and time-loss incidence in elite Rugby Union players. Front Physiol. 2019;10:1413.

    PubMed  PubMed Central  Google Scholar 

  53. Tysoe A, Moore IS, Ranson C, McCaig S, Williams S. Bowling loads and injury risk in male first class county cricket: is ‘differential load’ an alternative to the acute-to-chronic workload ratio? J Sci Med Sport. 2020. https://doi.org/10.1016/j.jsams.2020.01.004.

    Article  PubMed  Google Scholar 

  54. Sampson JA, Fullagar HH, Murray A. Evidence is needed to determine if there is a better way to determine the acute: chronic workload. Br J Sports Med. 2017;51(7):621–2.

    CAS  PubMed  Google Scholar 

  55. 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(9):597–600.

    PubMed  Google Scholar 

  56. 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 

  57. 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 

  58. Coyne JOC, Nimphius S, Newton RU, Haff GG. Does mathematical coupling matter to the acute to chronic workload ratio? A case study from elite sport. Int J Sports Physiol Perform. 2019. https://doi.org/10.1123/ijspp.2018-0874.

    Article  PubMed  Google Scholar 

  59. Windt J, Ardern CL, Gabbett TJ, Khan KM, Cook CE, Sporer BC, et al. Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies. BMJ Open. 2018;8(10):e022626.

    PubMed  PubMed Central  Google Scholar 

  60. Orchard JW, Blanch P, Paoloni J, Kountouris A, Sims K, Orchard JJ, et al. Cricket fast bowling workload patterns as risk factors for tendon, muscle, bone and joint injuries. Br J Sports Med. 2015;49(16):1064–8.

    PubMed  Google Scholar 

  61. Carey DL, Crossley KM, Whiteley R, Mosler A, Ong KL, Crow J, et al. Modeling training loads and injuries: the dangers of discretization. Med Sci Sports Exerc. 2018;50(11):2267–76.

    PubMed  Google Scholar 

  62. Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012;12:21.

    PubMed  PubMed Central  Google Scholar 

  63. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080.

    PubMed  PubMed Central  Google Scholar 

  64. Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66(3):411–21.

    PubMed  Google Scholar 

  65. Nielsen RO, Bertelsen ML, Ramskov D, Møller M, Hulme A, Theisen D, et al. Time-to-event analysis for sports injury research part 1: time-varying exposures. Br J Sports Med. 2019;53(1):61–8.

    PubMed  Google Scholar 

  66. Nielsen RO, Bertelsen ML, Ramskov D, Møller M, Hulme A, Theisen D, et al. Time-to-event analysis for sports injury research part 2: time-varying outcomes. Br J Sports Med. 2019;53(1):70–8.

    PubMed  Google Scholar 

  67. Hulme A, Thompson J, Nielsen RO, Read GJM, Salmon PM. Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. Br J Sports Med. 2019;53(9):560–9.

    PubMed  Google Scholar 

  68. Gabbett TJ, Nielsen RO, Bertelsen ML, Bittencourt NFN, Fonseca ST, Malone S, et al. In pursuit of the ‘unbreakable’ athlete: what is the role of moderating factors and circular causation? Br J Sports Med. 2019;53(7):394–5.

    PubMed  Google Scholar 

  69. Mørtvedt AI, Krosshaug T, Bahr R, Petushek E. I spy with my little eye … a knee about to go ‘pop’? Can coaches and sports medicine professionals predict who is at greater risk of ACL rupture? Br J Sports Med. 2020;54(3):154–8.

    PubMed  Google Scholar 

  70. 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 

  71. Verhagen E, van Dyk N, Clark N, Shrier I. Do not throw the baby out with the bathwater; screening can identify meaningful risk factors for sports injuries. Br J Sports Med. 2018;52(19):1223–4.

    PubMed  Google Scholar 

  72. 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 

  73. Impellizzeri FM, McCall A, Meyer T. Registered reports coming soon: our contribution to better science in football research. Sci Med Footb. 2019;3(2):87–8.

    Google Scholar 

  74. Windt J, Zumbo BD, Sporer B, MacDonald K, Gabbett TJ. Why do workload spikes cause injuries, and which athletes are at higher risk? Mediators and moderators in workload-injury investigations. Br J Sports Med. 2017;51(13):993–4.

    PubMed  Google Scholar 

  75. Verhagen E, Gabbett T. Load, capacity and health: critical pieces of the holistic performance puzzle. Br J Sports Med. 2019;53(1):5–6.

    PubMed  Google Scholar 

  76. Charest J, Grandner MA. Sleep and athletic performance: impacts on physical performance, mental performance, injury risk and recovery, and mental health. Sleep Med Clin. 2020;15(1):41–57.

    PubMed  Google Scholar 

  77. Galambos SA, Terry PC, Moyle GM, Locke SA, Lane AM. Psychological predictors of injury among elite athletes. Br J Sports Med. 2005;39(6):351–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lathlean TJH, Gastin PB, Newstead SV, Finch CF. A prospective cohort study of load and wellness (sleep, fatigue, soreness, stress, and mood) in elite junior australian football players. Int J Sports Physiol Perform. 2019;14(6):829–40.

    PubMed  Google Scholar 

  79. Enright K, Green M, Hay G, Malone JJ. Workload and injury in professional soccer players: role of injury tissue type and injury severity. Int J Sports Med. 2020;41(2):89–97.

    PubMed  Google Scholar 

  80. Nielsen RO, Bertelsen ML, Moller M, Hulme A, Windt J, Verhagen E, et al. Training load and structure-specific load: applications for sport injury causality and data analyses. Br J Sports Med. 2018;52(16):1016–7.

    PubMed  Google Scholar 

  81. Verheul J, Nedergaard NJ, Vanrenterghem J, Robinson MA. Measuring biomechanical loads in team sports—from lab to field. Sci Med Footb. 2020. https://doi.org/10.1080/24733938.2019.1709654.

    Article  Google Scholar 

  82. Stovitz SD, Johnson RJ. “Underuse” as a cause for musculoskeletal injuries: is it time that we started reframing our message? Br J Sports Med. 2006;40(9):738–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Clarsen B, Bahr R. Matching the choice of injury/illness definition to study setting, purpose and design: one size does not fit all! Br J Sports Med. 2014;48(7):510–2.

    PubMed  Google Scholar 

  84. 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 

  85. Büttner F, Winters M, Delahunt E, Elbers R, Lura CB, Khan KM, et al. Identifying the ‘incredible’! Part 1: assessing the risk of bias in outcomes included in systematic reviews. Br J Sports Med. 2019. https://doi.org/10.1136/bjsports-2019-100806.

    Article  PubMed  Google Scholar 

  86. Büttner F, Winters M, Delahunt E, Elbers R, Lura CB, Khan KM, et al. Identifying the ‘incredible’! Part 2: spot the difference—a rigorous risk of bias assessment can alter the main findings of a systematic review. Br J Sports Med. 2019. https://doi.org/10.1136/bjsports-2019-101675.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like thank Dr. for their courtesy in authorizing the use and implementation of the ColorADD® colour system for our figures. The authors would also like to thank Cristina Valente for her valuable help in building and finetuning all the figures in this article.

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Contributions

RA and ARM performed the database searches. RA and EHW performed the data extraction, methodological quality assessment and initial interpretation of results. TG provided advice throughout the interpretation of data and manuscript drafting. RA was responsible for initial drafting of the article, which was reviewed and edited by all authors. All authors were involved in the conception, design and interpretation of data. All authors read and reviewed the manuscript critically for important intellectual content and approved the final version to be submitted.

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Correspondence to Renato Andrade.

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No sources of funding were used to assist in the preparation of this article.

Conflict of interest

Tim Gabbett works as a consultant to several high-performance organisations, including sporting teams, industry, military and higher education institutions. He also conducts training load workshops for health practitioners—in these workshops, among other topics, the strengths and limitations of the acute:chronic workload ratio are discussed. Peter Blanch is currently employed by a sporting organization which has been involved in the production of ACWR research. Renato Andrade, Eirik Halvorsen Wik, Alexandre Rebelo-Marques, Rodney Whiteley and João Espregueira-Mendes declare that they have no conflicts of interest relevant to the content of this review.

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Andrade, R., Wik, E.H., Rebelo-Marques, A. et al. Is the Acute: Chronic Workload Ratio (ACWR) Associated with Risk of Time-Loss Injury in Professional Team Sports? A Systematic Review of Methodology, Variables and Injury Risk in Practical Situations. Sports Med 50, 1613–1635 (2020). https://doi.org/10.1007/s40279-020-01308-6

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