Our new open access editorial discusses the challenges of creating training load guidelines and mandates.
I am excited to share a new open-access editorial in the International Journal of Sports Physiology and Performance (IJSPP). Led by Dr Stephen West, this discussion also brought together the thoughts of Ian Shrier, Franco Impellizzeri, Patrick Ward, Garrett Bullock, and myself on generating training load interventions, guidelines, and mandates.
You can access the full text open-access in IJSPP here.
Training load management has become a central topic in sports science, given its role in balancing performance optimisation and injury prevention. Our recent journal publication delves into the intricate challenges of load management, emphasising the necessity for robust data-informed guidelines while warning against the risks of arbitrary mandates.
The Complexity of Load Management
Training load management involves the careful monitoring, prescription, and adjustment of an athlete's physical activity to improve performance and reduce injury risks. This concept has gained significant attention due to advancements in sports technologies that allow for measurement of internal and external load variables.
Coaches, athletes, and medical teams must balance training volume and intensity with injury prevention. Factors like environmental conditions, athlete health, and team requirements all influence decisions.
Load management, however, isn't a new idea. It stems from long-standing practices like periodisation, which adjusts training intensities and volumes throughout the season to peak athletes’ performance at the right time.
But the tools available, such as Global Positioning System (GPS) devices and accelerometers, are often limited in their accuracy and reliability. Relying too heavily on these technologies can be problematic, especially if the data is misinterpreted or lacks context.
Training Load Guidelines vs. Mandates
One of the paper’s key distinctions is between guidelines and mandates. Guidelines offer principles for managing training load but allow flexibility depending on the context. Mandates, by contrast, are non-negotiable rules aimed at reducing injury risk, such as contact limitations in American football or the NBA’s load management policies.
While these guidelines and mandates seem logical, they may not always align with the latest scientific understanding of injury mechanisms. The issue lies in the fact that many training load guidelines are based on statistical associations rather than causal evidence.
In some cases, mandates or guidelines designed to limit player load might reduce physical preparedness, leaving athletes more vulnerable to injury, particularly in high-intensity competition. For example, limiting contact during training may hinder the development of robustness in players, preventing them from acclimating to the stresses they will face in games.
The Faulty Logic of Load Reduction
Another point of concern is the assumption that reducing training load will automatically reduce injury risk. While reducing load may indeed decrease exposure, it doesn’t necessarily decrease the rate of injury per minute of play.
A common metric used in sports science, the acute chronic workload ratio (ACWR), is often cited in research to predict injury risk. However, many of these studies fail to account for the multivariate nature of injury causality. Without understanding the causal mechanisms at play, interventions based on ACWR may have limited effectiveness. Indeed, a recent randomised controlled trial in elite youth football found that following ACWR guidelines did not result in fewer injuries (Dalen-Lorentsen et al., 2021).
Caution should also be used given the limitations with technology capturing such data. While tools like GPS and accelerometry provide a wealth of data, their ability to precisely measure the mechanical load experienced at specific joints or muscle groups remains questionable.
For instance, accelerometers do not capture ground reaction forces accurately, a key component in injury mechanisms such as overuse injuries in the lower extremities. Such proxy measurements, while useful in broader contexts, may not provide the level of detail required for pinpointing injury risk. This is something I've previously discussed with Loughborough University Biomechanics lecturer Dr Stuart McErlain-Naylor:
Context is Key
The issue becomes further complicated when considering that no one-size-fits-all guideline can account for individual variability. Each athlete responds differently to training loads, based on a wide range of factors, including genetics, previous injury history, and even psychological stressors. Hence, broad guidelines based on population averages can misrepresent the needs of individual athletes.
We put forward a hypothetical example of an athlete suffering a grade 2 medial cruciate ligament (MCL) knee sprain. If they're a young amateur athlete at the beginning of the season, they might be willing to accept less risk compared to a professional athlete during the playoffs. Such decisions will be unique to each club, situation, and their context, making general recommendations challenging given that the risk tolerance is more fluid than a fixed decision (“go/no-go”) threshold.
Practical Solutions: Strengthening Research and Practice
To move the field forward, we emphasise the need for better research design. Collaborating with statisticians and epidemiologists can help ensure training load studies are grounded in valid research methods, improving the quality of guidelines and mandates. Open science practices—where researchers share data and methods—can also accelerate knowledge development and reduce the reliance on questionable guidelines.
In practice, practitioners should be cautious about applying broad guidelines to individual athletes. Training load should be combined with data from multiple streams, such as heart rate, neuromuscular response, and self-reported outcomes, to get a more comprehensive view of each athlete’s response to training. Rather than relying solely on numbers generated from single devices, practitioners should be critical of their data, incorporating multiple metrics to make informed decisions.
"Without precise scientific inquiries and accounting for the unique risk and cost–benefit of different scenarios or player capacities, applying training-load interventions or guidelines based on an “average player” can have unintended consequences and implications for injury, performance, and athlete development." - West et al., (2024)
Final Thoughts
While current training load guidelines often come from good intentions, they can lead to unintended consequences without a strong scientific foundation. Arbitrary guidelines, faulty assumptions, and over-cautious restrictions could negatively affect performance and injury risk. Moving forward, the sports science community must prioritise high-quality research, use individual data more effectively, and embrace open science to improve training load management.
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