Understanding injury causation through injury models

In the introduction to the performance health series we highlighted injuries as a major factor influencing performance. Injuries limit availability, accumulate over time, impact goal achievement, and are the a leading cause of youth athlete drop outs. As a result, we want to avoid injuries as much as we can, whilst also understanding that, in order to improve, athletes have to undertake training loads and modalities that expose them to an increased risk of injury. This is the balancing act that all coaches face, and in order to do our best we need to start off with an understanding of why athletes get injured in the first place. For that, we can lean on injury models.

As the saying goes: all models are wrong, but some are useful. In other words, better models can enhance our understanding even if they aren’t perfect. Fortunately, when it comes to models of injury, there are lots to choose from. Jo Clubb has done a fantastic job of summarizing the majority of injury models here, which I’d strongly encourage you to read, but I’ll pick out some of the key models below. Remember, models can be useful for helping us better understand key aspects, but we shouldn’t just apply them blindly.

Bahr & Krosshaug: Comprehensive model for injury causation

More information: Open access article

Overview: This is arguably the injury model, and is certainly the most well-known. The key flow of the model is that we have an athlete, with their own unique level of injury predisposition, who is exposed to a variety of external factors, which makes them susceptible to an inciting event. Some of these factors, like sex, are fixed; others, like age and anatomy, are reasonably stable and tend not to change quickly. Other risk factors may change fairly rapidly, including many of the external risk factors the athlete is exposed to. This dynamism of injury risk, with subsequent exposure to risk factors feed forward to modify the future risk of injury, was captured in Meeuwisse model detailed below.

Meeuwisse: Recursive model of sport injury

More information: Open access article

Overview: The key part of this model is that athlete predisposition and susceptibility interact to determine that athlete’s response following an “inciting event” (as covered in Bahr and Krosshaug’s model). The susceptible athlete is either injured or uninjured following an exposure, and this in turn leads to either adaptation, a required period of recovery, or no recovery and eventually retirement. The key concept here is that the athlete is a complex dynamic system, and their injury risk is always changing. One of the factors that drives the injury risk at a given point in time is the athletes response, both over the short-term (e.g. the day after a training session) and long-term (e.g. the accumulation of load over a training block).

Windy & Gabbett: Workload-injury aetiology model

More information: Open access article

Overview: As you might expect from Tim Gabbett—who has largely responsible for developing the Acute:Chronic Workload Ratio—this model includes workloads as both a protective (“fitness”) and risk (“fatigue”) factor for injury. Whilst Gabbett’s ACWR is commonly criticized, load, and the athlete’s response to that load, is an important factor in our thinking around injuries. The key take-home from my perspective is that, in general, we need to ensure we have sufficient load to induce positive training effects, but not so much that we have increased fatigue and insufficient recovery periods for the various biological systems within the body. Getting this balance right is a crucial piece of the puzzle.

Junge: Psychological factor model

More information: Open access article

Overview: So far, the models have primarily examined the influence of, and interaction between, physiological and environmental factors in the development of injury risk. However, in the last 25 or so years, we’ve become increasingly aware that psychological and sociological factors, collectively termed psychosocial factors, also influence the risk of injury in athletes. A number of studies have demonstrated this; researchers from the Queensland Academy of Sport, for example, showed the relationship between mood and injury. Similarly, a review paper from 2000 concluded that psychosocial stressors (including negative life events), coping mechanisms, and situation dependent athlete emotion state combine to influence the risk of injury in an athlete, but that there was not an “at-risk” personality profile. This opened the door for various interventions aimed at enhancing the coping resources of the athlete, and influencing their emotional state, in a way that might reduce the risk of future injury, as in the diagram above.

A similar, theoretical model, was proposed in 1988 by Anderson and Williams who proposed that three key psychological factors—personality, history of stressors, and coping resources—combined to modify the individual stress response to athletic situations, which subsequently affected athlete injury risk. Important within this stress response is cognitive appraisal; does the athlete view a particular stimulus as a stressor, and how much weighting do they place on this? As an example, let’s take two sprinters, training partners who are about to undertake a 200m sprint time trial. Athlete A is fully fit, whilst athlete B is just coming back from a big hamstring injury. At around 150m, as muscular pain starts to increase, athlete A will perceive this as normal, whilst athlete B will likely be more anxious; debating internally as to whether this is muscle pain caused by metabolic stress, or injury? If we filter in personality traits, such as levels of anxiety, we can see how this could further alter the cognitive appraisal and hence the potential injury outcome.

Bittencourt: Complex systems model of sports injury

More information: Open access article

Overview: In looking at the models above it is clear that numerous potentially causative factors interact to influence athlete injury risk, and that these factors, and their relative contribution to risk of injury, and ever changing and in flux. This means that sports injury is a complex system, not a linear one, and so we must treat it as one. Complex systems have a number of traits that we can see are relevant to sports injuries; they are dynamic, open (i.e. interacting with the environment), non-linear, recursive (i.e. have a feedback loop), uncertain (i.e. hard to predict), and self-organizing—whereby each individual sub-component interacts to create properties that couldn’t be predicted based on the individual sub-components. This is the focus of the Bittencourt model.

The focus of this model is the web of determinants—underlying factors that influence sports injury. These determinants interact (dotted and solid arrows) to different extents, and have different weightings (thick vs thin outlines). In turn, these determinants produce an athlete profile, which is ever changing, to which the exposure of competition or training load and stress leads to a set outcome. This outcome then feeds back to the underlying determinants; for example, hamstring injury (an emerging pattern) increases the risk of future hamstring injury, and so becomes a determinant in the underlying web itself. The determinants and their weightings differ between sports and contexts for the same injury; for example, knee valgus is the predominant determinant for ACL injury in basketballers, whilst fatigue is the leading determinant for ACL injuries in ballet dancers.

Final thoughts

As you can no doubt see, there are a number of different models that we can use to better understand the causes of injury. The models outlined in this article are not exhaustive; there are many more, some of which are worth your while exploring in detail. For example, in 2018, Edwards proposed a model for overuse injuries in sport and a mechanical fatigue phenomenon (open access). Here, the magnitude of the load, and cumulative exposure to the load over time, interact to drive injury risk. Similarly, a pre-print uploaded in 2019 (open access) and published earlier this year in the Journal of Science and Medicine in Sport provides a detailed model for stress-related, strain-related, and overuse injuries in sport.

Where does this leave us? Sports injuries are complex and multifactorial. There is rarely a linear relationship between a set determinant and an outcome. Even when it comes to eccentric hamstring strength and hamstring injury, there will be players with high levels of eccentric hamstring strength who get a hamstring injury, and players with low levels who do not. By understanding that injury risk and prevalence is complex and multifactorial, we can better understand that we need to spread our net wide when it comes to reducing this risk of injury. Additionally, the dynamic nature of sports injuries demonstrates that, at a given time on a given day, the key factors underpinning an athlete’s risk of injury may be different to the day, week, or month before; so we must keep updating the mental model in our head with the information we’re consistently receiving. In future articles in this series I will look at specific factors in more details to help coaches better assess that information coming in.