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Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages
  1. Nicholas B Murray1,
  2. Tim J Gabbett2,
  3. Andrew D Townshend1,
  4. Peter Blanch3,4
  1. 1School of Exercise Science, Australian Catholic University, Brisbane, Queensland, Australia
  2. 2Gabbett Performance Solutions, Brisbane, Queensland, Australia
  3. 3Brisbane Lions Australian Football Club, Brisbane, Queensland, Australia
  4. 4School of Allied Health Sciences, Griffith University, Gold Coast, Queensland, Australia
  1. Correspondence to Nick B Murray, School of Exercise Science, Australian Catholic University, Brisbane, QLD 4014, Australia; nbmurr001{at}myacu.edu.au

Abstract

Objective To determine if any differences exist between the rolling averages and exponentially weighted moving averages (EWMA) models of acute:chronic workload ratio (ACWR) calculation and subsequent injury risk.

Methods A cohort of 59 elite Australian football players from 1 club participated in this 2-year study. Global positioning system (GPS) technology was used to quantify external workloads of players, and non-contact ‘time-loss’ injuries were recorded. The ACWR were calculated for a range of variables using 2 models: (1) rolling averages, and (2) EWMA. Logistic regression models were used to assess both the likelihood of sustaining an injury and the difference in injury likelihood between models.

Results There were significant differences in the ACWR values between models for moderate (ACWR 1.0–1.49; p=0.021), high (ACWR 1.50–1.99; p=0.012) and very high (ACWR >2.0; p=0.001) ACWR ranges. Although both models demonstrated significant (p<0.05) associations between a very high ACWR (ie, >2.0) and an increase in injury risk for total distance ((relative risk, RR)=6.52–21.28) and high-speed distance (RR=5.87–13.43), the EWMA model was more sensitive for detecting this increased risk. The variance (R2) in injury explained by each ACWR model was significantly (p<0.05) greater using the EWMA model.

Conclusions These findings demonstrate that large spikes in workload are associated with an increased injury risk using both models, although the EWMA model is more sensitive to detect increases in injury risk with higher ACWR.

  • Training load
  • Global positioning system

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Footnotes

  • Contributors NBM was primarily responsible for the collection and analysis of the study data. All authors were responsible for the study concept and design, and contributed to the writing and critical revision of the manuscript.

  • Competing interests None declared.

  • Ethics approval Approval was granted by the Australian Catholic University Human Research Ethics Committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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