Training Load and Its Role in Injury Prevention, Part I: Back to the Future

Abstract

The purpose of this 2-part commentary series is† to explain why we believe our ability to control injury risk by manipulating training load (TL) in its current state is an illusion and why the foundations of this illusion are weak and unreliable. In part 1, we introduce the training process framework and contextualize the role of TL monitoring in the injury-prevention paradigm. In part 2, we describe the conceptual and methodologic pitfalls of previous authors who associated TL and injury in ways that limited their suitability for the derivation of practical recommendations. The first important step in the training process is developing the training program: the practitioner develops a strategy based on available evidence, professional knowledge, and experience. For decades, exercise strategies have been based on the fundamental training principles of overload and progression. Training-load monitoring allows the practitioner to determine whether athletes have completed training as planned and how they have coped with the physical stress. Training load and its associated metrics cannot provide a quantitative indication of whether particular load progressions will increase or decrease the injury risk, given the nature of previous studies (descriptive and at best predictive) and their methodologic weaknesses. The overreliance on TL has moved the attention away from the multifactorial nature of injury and the roles of other important contextual factors. We argue that no evidence supports the quantitative use of TL data to manipulate future training with the purpose of preventing injury. Therefore, determining “how much is too much” and how to properly manipulate and progress TL are currently subjective decisions based on generic training principles and our experience of adjusting training according to an individual athlete's response. Our message to practitioners is to stop seeking overly simplistic solutions to complex problems and instead embrace the risks and uncertainty inherent in the training process and injury prevention.

Assessment of External Load during Matches in Two Consecutive Seasons Using the Mediacoach® Video Analysis System in a Spanish Professional Soccer Team: Implications for Injury Prevention

“…Based on the data obtained through video analysis systems, controlled training sessions can be programmed to expose players to demands that are comparable to those of a match, thus trying to reduce the risk of injury [43]. As Impellizzeri et al [44] stated, athletic performance and injury prevention are dependent, and, for this reason, the communication between medical and technical staff should be highly coordinated to improve performance while trying to minimise the risk of injury.…”

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

“…The scientic challenge in practice with this framework has been the proper parametrisation of 1) the training load [40,55,654,883]; 2) the recovery state and preparedness to train [322,394,537]; 3) injury risk prediction [356,357,601,755,881]. Out of these three goals, training load and recovery state are within practical reach at current technological maturity level, whereas injury prediction seems overly challenging [315,356,357,385,386,599,678] 1 , mainly due to inherent low prevalence of injuries in athletic population [101,105,344,377,397,627,686,743,878], which subsequently leads to injury prediction models with poor predictive power [348,504].…”

“…Some of the training is quantied for example in a form of training log with subjective rating of perceived eort (RPE) or reps in reserve (RIR, and mathematical models can be developed from them. The dose-response to training prescription depends on the athlete's internal load [280,324,327,355,507], that is not often captured quantitatively by the existing frameworks such as acute:chronic workload ration (ACWR) [357]. [383,854] and soft robots applications [738] are enabling the design of future non-invasive and cost-eective continuous performance monitoring solutions for strength training.…”

The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach

“…Measuring the training load consists of recording physiological and psychological requirements during exercise training and competition periods in order to maximize training adaptation and minimize overtraining and the injury risk [1][2][3]. Training load is a construct comprising two components: the external and the internal training load.…”