Finding optimal training load is like Goldilocks searching for the porridge that’s ‘just right.’ Too high loading, or too significant increments in acute loading, can increase your chance of injury. The same can also be applied when considering too small, or not enough progression in loading, leaving you underprepared for life and competition demands.
Both overtraining and undertraining can be problematic. But how do we determine our optimal training load and progression? Emerging evidence indicates that training volume may be the most important factor when it comes to injury prevention. Although there seems to be a growing body of literature on this subject, evidence-based application of this research is often poorly implemented. This article covers some of the common myths surrounding training load and guidance on calculating your ideal training load.
Why is training load important?
We need sufficient training load to elicit adaptation (i.e. improved performance). Although the exact nature and process of adaptation is complex, generally speaking our body’s response to a training stimulus is to repair and rebuild stronger than before, so that it may handle increased stress in the future. There is however a limit to how much and how quickly the body can adapt, from both a short and long-term perspective. It’s important to pay attention to both acute and chronic training load to maximize adaptation and performance improvements, while avoiding pitfalls associated with overtraining.
The 10% ‘Rule’
You may have heard that training load should not exceed 10% each week. Although rapid changes in training load correspond with increased risk of injury, this 10% rule should be interpreted as more of a guideline. There are a number of factors to take into consideration, including the type of training performed (endurance, strength, plyometric etc.), your current level (new to training vs athlete), and your emotional or lifestyle stressors.
It's important to use some common sense when making training progressions. For example, if you have a low chronic load (e.g. you run 1km per week), your body would most likely tolerate larger increases in loading, at least in the short-term.
A degree of fatigue is necessary for training adaptations to occur. It's hard to improve if the training stimulus is inadequate. When increasing training load, 10% increases per week can be used as a guide, but context (e.g. chronic load, training phase, load tolerance) is still key!
Acute Chronic Workload Ratio (ACWR) - Is it all about the ratio?
One common way of measuring training load is to look at the current week’s training load (acute load) in relation to the long-term training load (chronic load). This helps us determine the ACWR, or training-stress balance.
In terms of injury risk, an ACWR within the range of 0.8–1.3 could be considered the training ‘sweet spot’, while ACWR >1.5 typically represents the ‘danger zone’. Therefore to reduce the risk of injury, it would be preferable to maintain an ACWR between 0.8 and 1.3, while avoiding excessive spikes or troughs in short-term training load.
Using ACWR is a relatively simple method for tracking training load. As such, it provides a good starting framework for tracking training load over time (however is not a comprehensive predictor for injury risk). When considering reliability as a tool for predicting injury, ACWR fails to take into account non-training related factors such as sleep, stress and emotional state. In addition, the rolling average fails to account for fatigue effects over time.
Calculating your ACWR
Depending on the type of sport or training, it can be difficult to calculate ACWR. When it comes to CrossFit, one method is to use your rate of perceived exertion (RPE). First, rank how challenging or taxing you found the workout, from 1 (“I could do it with my eyes closed”) to 10 (“I’m going to die”). You can then multiply this RPE by the length of the workout to get your day’s workload. You can then repeat this activity for the week, to calculate the week’s RPE based workload.
For the example below, the week’s RPE based workload would be 1500. To reduce injury risk, you would then want the following week’s total workload to fall between 1200 and 1950.
TABLE: Sample training week, calculating RPE based workload
The body’s not a machine - Including human factors
Calculating your ACWR provides a great guideline for reducing injury risk. If the body were a machine we could stop here, however other human factors should also be considered when determining training load.
Your ‘response’ to training is important. For example, if you are feeling excessive fatigue, interrupted sleep, mood disturbances, or soreness that presents in the morning and persists at night, then your training load may be too high (more than is tolerable). On the other hand, if you are tolerating increased load with ease, it may be appropriate to increase load further.
So, if you find yourself within your training ‘sweet spot’ yet feel tired, stressed or just underprepared for the day’s training, it would be beneficial to take it a little easier. This could involve taking an extra rest day, or just lowering the intensity of your workout for the day. Looking at these human factors is also important to remember when calculating training load, as they can have a huge impact on your risk of injury. For example, athletes sleeping less than 8 hours per night have 1.7 times greater risk of injury.
So be sure to listen to your body and adjust daily training load accordingly. Remember, it’s important to look at more than just the numbers!
References:
Gabbett, T. J. (2018). Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners. Br J Sports Med, bjsports-2018.
Education for Movement Professionals - When Progressing Training, Not All Load is Created Equally, Tim Gabbett, link: https://southcoastseminars.com/blog/2018/10/14/when-progressing-training-not-all-load-is-created-equally
Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., ... & Cable, N. T. (2017). Monitoring athlete training loads: consensus statement. International journal of sports physiology and performance, 12(Suppl 2), S2-161.
