Monitoring Training Load and Injury Reduction

Pre-season training is in full swing, and although we don’t know when exactly the season will start, this is the perfect opportunity to use our time wisely and ensure we are fighting fit for when things kick off again!

Non-contact, soft tissue injuries that commonly occur during pre-season training are more often than not due to a rapid increase in stress or load being placed on the muscles, ligaments and tendons. Such injuries arise when the load is far greater than the tissue is familiar with.

When prescribing pre-season training, it is important to remember that sometimes, less is more. When there is a rapid increase in training load, the tissues don’t have the time or capacity to adapt to the imposed stresses being placed on them. This is turn results in an increased risk of sustaining a soft tissue injury.

How can we ensure this doesn’t happen?

Optimal training refers to training at a level slightly above what the body is accustomed to but not to the point of injury or decreased performance. By stressing the body to the optimal level, we see enhanced performance while at the same time reducing the risk of injury.

How can we ensure we are training at the optimal level?

The key is to have training loads adequate enough to improve fitness without increasing the incidence of injury. To do this, we must monitor training load. One method of monitoring training load is by using the acute:chronic workload ratio (ACWR). This ratio examines the relationship between the training you are doing now (acute workload) and the training you have done (chronic workload). We can use this ratio to calculate our training ‘sweet-spot’. The ‘sweet-spot’ refers to the optimal workload with the lowest relative injury risk. This is where we want to be.

It is important to remember that one rule doesn’t apply to everyone. Every athlete is individual. When monitoring training load it is essential to assess athletes individually and make comparisons between individual longitudinal data as opposed to group averages.