The training-injury prevention paradox: should athletes be training smarter and harder?

Abstract

Background: There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury.

Main thesis: This paper describes the 'Training-Injury Prevention Paradox' model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load ('internal' and 'external' loads) and suggests capturing both recent ('acute') training loads and more medium-term ('chronic') training loads to best capture the player's training burden. I describe the critical variable-acute:chronic workload ratio-as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries.

Summary: The appropriately graded prescription of high training loads should improve players' fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.

Figure 1

Hypothetical relationship between training loads, fitness, injuries and performance. Redrawn from Orchard.

Figure 2

Relationship between training load and injury rate in team sport athletes. Training loads were measured using the session-rating of perceived exertion method. Redrawn from Gabbett.

Figure 3

Influence of reductions in preseason training loads on injury rates and changes in aerobic fitness in team sport athletes. Training loads were measured using the session-rating of perceived exertion method. Redrawn from Gabbett.

Figure 4

Relationships between training load, training phase, and likelihood of injury in elite team sport athletes. Training loads were measured using the session-rating of perceived exertion method. Players were 50–80% likely to sustain a preseason injury within the training load range of 3000–5000 arbitrary units. These training load ‘thresholds’ were considerably reduced (1700–3000 arbitrary units) in the competitive phase of the season (indicated by the arrow and shift of the curve to the left). On the steep portion of the preseason training load-injury curve (indicated by the grey-shaded area), very small changes in training load result in very large changes in injury risk. Pre-Season Model: Likelihood of Injury=0.909327/(1+exp(−(Training Load−2814.85)/609.951)). Early Competition Model: Likelihood of Injury=0.713272×(1−exp(−0.00038318×Training Load)). Late Competition Model: Likelihood of Injury=0.943609/(1+exp(−(Training Load−1647.36)/485.813)). Redrawn from Gabbett.

Figure 5

Likelihood of injury with different changes in training load. Unpublished data collected from professional rugby league players over three preseason preparation periods. Training loads were measured using the session-rating of perceived exertion method. Training loads were progressively increased in the general preparatory phase of the preseason (ie, November through January) and then reduced during the specific preparatory phase of the preseason (ie, February). The training programme progressed from higher volume-lower intensity activities in the general preparatory phase to lower volume-higher intensity activities in the specific preparatory phase. Each player participated in up to five organised field training sessions and four gymnasium-based strength and power sessions per week. Over the three preseasons, 148 injuries were sustained. Data are reported as likelihoods ±95% CIs.

Figure 6

Guide to interpreting and applying acute:chronic workload ratio data. The green-shaded area (‘sweet spot’) represents acute:chronic workload ratios where injury risk is low. The red-shaded area (‘danger zone’) represents acute:chronic workload ratios where injury risk is high. To minimise injury risk, practitioners should aim to maintain the acute:chronic workload ratio within a range of approximately 0.8–1.3. Redrawn from Blanch and Gabbett.

Figure 7

Relationship between physical qualities, training load, and injury risk in team sport athletes.