In last month's article on running mileage I mentioned Acute:Chronic Workload Ratio (ACWR) can be used as a more accurate method to monitor training load than weekly mileage. However, it can be a confusing concept so in this post I will provide a simple explanation on what it is, the research behind it, and how you can use it for your running training.
What is the Acute:Chronic Workload Ratio?
The ACWR is the ratio between two different time periods of training. The ‘acute’ side is the amount of training you have done in the previous 1 week. The Chronic side is the amount of training you have done in the previous 4 weeks. Therefore, the ratio is comparing the difference between the two. This shows you whether the training you have done this week is more or less than the average of the last 4 weeks. This is important as it tells you whether you have had a ‘spike’ in training or whether you have had a ‘dip’ in training.
For example if the last 4 weeks you have been running an average of 20 miles a week, and then in the last week of that 4 weeks you went up to 40 miles, the ratio would be higher and it would show you that you have had a ‘spike’ in your training. The ratio would be 2:1, or 2.0
If you have a ‘dip’ in training, say in the final week you did 10miles compared to the 20 (ratio 0.5) then this could also have a small increase risk of injury, as shown in fig 1. The researchers suggest that there is an ACWR ‘sweet spot’ ranging from 0.8 and 1.3 where athletes are at a much lower risk of injury.
Figure 1: Taken from https://bjsm.bmj.com/content/50/5/273
At this point you may be thinking “how can a dip in training increase injury risk?”. A dip in training can increase your chance of injury due to your body deconditioning, and once you try to increase your training again, your body may not be able to handle the load.
Research behind the ratio
The relationship between acute load (1-week) and chronic load (4-weeks) was first explored by researchers in Brisbane, Australia back in 2013, who looked at reducing injury rates in cricket fast-bowlers. The risk of injury increased threefold when acute bowling workloads were double chronic bowling workloads (a ratio ≥2) (1) . The ACWR model was then produced in 2016 by the same authors. (2). The model was then used to see if it could reproduce the same results in other sports. Almost identical results have been found in elite rugby league (3) and soccer (4) players. An ACWR exceeding 1.5 were associated with an increased risk of injury. There has been minimal research using this model in distance runners.
However, as sports teams started using this model flaws started appearing:
It is difficult to calculate the workload from different forms of training; running, gym
Teams were not able to input data for players on international duty.
It does not account for changes in fitness and fatigue over a longer period of 4 weeks
The acute side of the ratio also influences the chronic side of the ratio, meaning there is a mathematical flaw.
For runners it also means that if you have reduced your training for a race, or miss a week due to illness, your ratio will be really low that week, and then the following 3 weeks it will be high. In reality, missing 1 week of training isn’t going to have much effect on your injury risk, but this model will say otherwise!
On the flip side to this last point, the model may not be sensitive enough to an increase in mileage over 4 weeks. If you have 4 weeks of; 40miles, 50miles, 55miles, 60miles:
Acute Load: 60miles
Chronic load: (40+50+55+60)/4 = 51.25miles
Acute:Chronic Workload ratio would be: 60/51.25 = 1.17
1.17 would be deemed safe, but I certainly wouldn’t jump from 40miles to 60miles in 4 weeks. The way to overcome could be to use the RPE to measure load.
One method proposed to overcome these issues is The Exponentially Weighted Moving Average (EWMA) Model. It uses an algorithm that essentially makes the most recent 2 weeks the most influential weeks in the calculation. This means if you have one week where there is a massive change in load, the effect isn’t as exaggerated, and it also doesn’t affect the next 3 weeks. This can be shown in Fig 2 below.
Figure 2: Taken from https://bjsm.bmj.com/content/51/3/209
The maths behind this equation goes over my head and would be hard to use for someone that isn’t a statistician. Fortunately, one of the researchers of the paper this graph is from has put a free copy of an excel sheet on his website that allows you to input data and it works it out for you! This can be found here: https://adam-sullivan.com/free-downloads/
How can you use it in your training?
You can use the traditional Acute:Chronic Ratio very simply by setting up an excel sheet with one row for acute load, one row for chronic load, and then another row with the difference. You could then put this into a graph to see the changes over time. If you were to use this method, it would be sensible to use RPE rather than mileage as RPE includes internal factors to load and is more accurate to the load put on your body. This was mentioned in the article last month (hyperlink).
However, as mentioned before ACWR may not accurately reflect your risk of injury as suggested in the literature. One rest week (or one intense week) will show an injury risk which probably isn’t true. I feel this method uses a very precise and complex formula but the data it is using (RPE, HR or mileage) is very loosely correlated to injury risk due to how many other factors are at play such as, transitioning from grass sessions to track sessions at the end of winter, low sleep levels, life stresses etc.
If you were to ask how I would use this in my training, then I would use the free download to record my training. However, I would use it as a rough guide and its only purpose would be to identify extremely large changes in load that could be missed. I would also consider other factors of my life when deciding to stay in the zone or not, for an example if I recently lost my job, I would stay in the zone. That is if I could motivate myself to run at all!
Thanks for reading
Sport and Exercise Health Sciences BSc
1. Hulin BT, Gabbett TJ, Blanch P, et al. “Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers” British Journal of Sports Medicine 2014; 48: 708-712.
2. Gabbett TJ “The training—injury prevention paradox: should athletes be training smarter and harder?” British Journal of Sports Medicine 2016; 50: 273-280.
3. Hulin BT, Gabbett TJ, Lawson DW, et al “The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players” British Journal of Sports Medicine 2016; 50: 231-236.
4. Ehrmann, FE et al “GPS and Injury Prevention in Professional Soccer”, Journal of Strength and Conditioning Research: 2016; 30:2, 360-367