Sudden Increases or Decreases in Loading Can Increase Injury Risk

John De Witt, Ph.D., C.S.C.S
Head Sports Performance Coach, Houston Dynamo Academy
Assistant Coach, Houston Dash
Assistant Coach, Afghanistan Women’s National Team

Article Objectives

  • Coaches often use preseason as a period for high loading to prepare players for the upcoming season.
  • Research shows that sudden changes in typical loading can increase injury risk.
  • Coaches should consider planning training intensity in a progressive manner.

There has been much written, including by myself, about training loading and the importance of consideration of loads by the coaching staff. By loading, I mean the intensity, duration, and types of training that is completed by players. When planning training sessions, in addition to training objectives, level of training load should be taken into account.

There are many methods to quantify training load. Because physiological loading is difficult to summarize with a single number, there is not one right way to measure load. Load could be quantified by volume, such as total distance covered during a session. However, because there are many ways to complete the same distance, the total alone does not tell a complete story. For this reason, other metrics might be used, but unfortunately they all suffer from the same weakness.

Total metabolic load can be summarized using an index, like a Bannister or Edward’s TRIMP (TRIMP is short for the term 'training impulse') score. These measures essentially sum the time that a players’ heart rate is in specific zones of intensity. Once again, however, it could be possible to have the same TRIMP score for very different conditions. For example, An accumulated heart rate score for a long, continuous run at a speed below the maximal aerobic speed might be the same as a shorter, high intensity interval session. Same score, but entirely different training effects.  

Another method to quantify load is to ask for players to report their perceived exertion on a scale of 1-10 (10 is most intense), and multiply the score by the training duration. For example, a 90 minute session with a rating of 7 would have a training load of 90 x 7 = 630. Although this method is based on the subjective ratings of the players, research has shown (including unpublished analyses I have completed on my current teams) that player reported training loads correlate well with quantitative measures completed using GPS, accelerometers, and heart rate monitors.

This brings us to the the topic of this article, acute and chronic training loadings. Coaches measure training loads in order to optimize training. The definition of optimize depends on the coach, but for me, optimize means to maximize the intended adaptations from the training while reducing injury risk.

A popular subject that has been recently emphasized is the concept of over-training or functional overreaching. While there are scientific definitions to these terms, in simplicity, they mean training too hard. Essentially, we can stress the body (and mind) to induce a positive adaptation, but we can also stress the body too hard, resulting in less than optimal adaptation, or worse, injury.

The trick is that we never know if an injury would have occurred unless it occurs. This may sound silly, but consider this: if a player does not train, then they cannot get injured, right? So if a coach holds a player out of training, there is no chance for injury. However, there is also no chance for improvement. There has to be a balance.

When it comes to using training loads for determining if a player is over, under, or optimally trained, my experience says that this has to be completed on an individual basis. It is not prudent to establish a value of training load applied across a team that defines too high or too low. In the earlier example, a training load of 630 might be very high for one player and low for another. Making a decision that a training load of 630 is the goal could be a very bad decision depending upon the player. Any time a coach states that a training load is too high or too low leads me to the immediate questions of

  1. How do you know the load is too high or too low; and
  2. Too high or too low compared to what?

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