If you are a follower of some of the top researchers and practitioners in the field of strength and conditioning then it is likely that the above image from Gabbett (2016, p.278) has been a regular feature on your social media feeds of late. It is the image that comes to mind when we think of the acute:chronic workload ratio (ACWR); an important recent development in monitoring training load that seems to be the ‘hot topic’ in player monitoring and injury prevention right now. The ACWR takes into account the current training load (acute) and the training load that an athlete has been prepared for (chronic). In essence, it is a model that provides an index of athlete preparedness for training and/or competition. Gabbett suggests that there is a training ‘sweet spot’ that maximises performance potential while simultaneously limiting the negative consequences of training (i.e., injury, illness, fatigue and overtraining)…but is it that simple? In this blog, I hope to demonstrate some of the complexities involved in the calculation of ACWR based on the findings of a systematic review I am currently writing.
The Rolling Average v the Exponentially Weighted Moving Average Model
There are two main models for calculating the ACWR; (1) the Rolling Average model (RA) and (2) the Exponentially Weighted Moving Average model (EWMA). The RA model is calculated by dividing the acute (i.e.: rolling 7-day) workload by the chronic workload (i.e. average 28-day). The EWMA model assigns a decreasing weighting for each older load value in order to give greater weighting to the recent load performed by the athlete. There is much debate as to the best method of calculating ACWR with some suggestions that the RA method fails to account for the decaying nature of fitness and fatigue (Williams et al. 2017; Menaspà, 2016). From my review of the literature, it is evident that there is a clear association between injury and ACWR irrespective of which model is used to calculate ACWR. To date, much of the research has examined the RA model. It would appear however, that the EWMA is the more suitable measure, in part, due to its greater sensitivity.
Internal v External Training Loads
It is evident from my systematic review that ACWR using both internal training load (ITL) and external training load (ETL) are significantly associated with injury risk and coaches can use either in isolation, combination of both. The recent study by Jaspers et al. (2018) is good evidence of this as both ETL (total distance, high-speed distance, acceleration efforts and deceleration efforts) and ITL (sRPE by duration) were examined. A medium ACWR (0.85–1.12) using the ITL measure showed a most likely beneficial effect and a medium ACWR (0.86–1.12) using the ETL measure showed a very likely beneficial effect in terms of injury risk.
Acute and Chronic Time Periods
Regardless of the model used to calculate ACWR, the time period designated as the acute training period and the chronic training period has a significant effect on its relationship to injury. The most commonly investigated ACWR is 7:28 days, however a range of acute and chronic ratios have been examined. From the review of the literature, it appears the most suitable ratio may depend on the specific structure of the given sport. This is supported by Carey et al (2016) who state that the acute training load period has a strong influence over the ACWR’s ability to inform injury risk and windows must be decided on a sport by sport basis. Further research is warranted into the optimal ACWR timeframes across different sports.
The Mathematical Coupling Enigma
Another aspect of ACWR calculation that must be considered is whether it is ‘coupled’ or ‘uncoupled’. Coupled ACWR can be defined as the ratio between the most recent week of work with the average of the most recent 4 weeks and with it comes the possibility of spurious correlations meaning a relationship exists between two variables regardless of any true biological/physiological association between them (Lolli et al. 2017). This can be avoided using an uncoupled ACWR which is the ratio of the most recent week of work with the average of the three preceding weeks. However, the issue with this is that no research has yet examined uncoupled ACWR so future research is required.
Association v Prediction
While the literature supports a significant association between the ACWR and injury, it is important to understand that this does not necessarily translate into the ability to predict injury. Studies by Fanchini et al. (2018) and McCall et al. (2018) showed a significant association exists between ACWR and injury risk in professional soccer players but also found poor prediction ability.
There is support for the association between the ACWR and 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. For practitioners, it is important to understand the intricacies of the ACWR before deciding on the best method of calculation for their desired population and also to be aware of the protective qualities of high chronic loads and the detrimental effects of sudden spikes in acute training loads.
Feature image: See Gabbett, TJ. The training-injury prevention paradox: should athletes be training smarter and harder?. Br J Sports Med. 2016:50(5):273-280. doi:10.1136/bjsports-2015-095788
- 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. doi:10.1136/bjsports-2016-096309
- 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 Football. 2018;2(2):108-14. doi:10.1080/24733938.2018.1429014
- Gabbett, TJ. The training-injury prevention paradox: should athletes be training smarter and harder?. Br J Sports Med. 2016:50(5):273-280. doi:10.1136/bjsports-2015-095788
- Jaspers A, Kuyvenhoven JP, Staes F, Frencken WG, 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. doi:10.1016/j.jsams.2017.10.005
- Lolli L, Batterham AM, Hawkins R, Kelly DM, Strudwick AJ, Thorpe R, Gregson W, Atkinson G. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations. Br J Sports Med. 2017;0(0). doi:10.1136/bjsports-2017-098110
- 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. doi:10.1136/bjsports-2017-098473
- Menaspà P. Are rolling averages a good way to assess training load for injury prevention?. Br J Sports Med. 2017;51(7):618-9. doi:10.1136/bjsports-2016-096131
- 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. doi:10.1136/bjsports-2016-096589
Alan Griffin is a PhD Candidate on the Irish Rugby Injury Surveillance (IRIS) project in the Department of Physical Education and Sport Sciences in the University of Limerick. His primary research interests are in the area of Strength and Conditioning. He is currently undertaking research into the development of injury prevention measures in amateur Rugby Union through the examination of training load. Contact Alan via email at firstname.lastname@example.org or view his profile on LinkedIn.