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Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players?
  1. Johann Windt1,2,3,
  2. Tim J Gabbett4,5,
  3. Daniel Ferris6,
  4. Karim M Khan1,2,3
  1. 1Experimental Medicine Program, University of British Columbia, Vancouver, British Columbia, Canada
  2. 2Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada
  3. 3Department of Family Practice, University of British Columbia, Vancouver, British Columbia, Canada
  4. 4School of Human Movement Studies, The University of Queensland, Brisbane, Queensland, Australia
  5. 5School of Exercise Science, Australian Catholic University, Brisbane, Queensland, Australia
  6. 6High Performance Unit, Manly Sea Eagles, Sydney, New South Wales, Australia
  1. Correspondence to Johann Windt, Experimental Medicine Program University of British Columbia, 2635 Laurel Street, Vancouver, British Columbia, Canada; V5Z 1M9; johannwindt{at}gmail.com

Abstract

Aim To determine whether players who completed a greater number of planned preseason training sessions were more or less likely to be injured during the competitive season.

Methods A cohort of 30 elite rugby league players was prospectively studied during their 17-week preseason and 26-round competitive season. Injuries were recorded using a match time loss definition. Preseason participation was quantified as the number of ‘full’ training sessions that players completed, excluding modified, rehabilitation or missed sessions. In-season training load variables, collected using global positioning system (GPS) data, included distance covered (m), high-speed distance covered (m) and the percentage of distance covered at high speeds (%). Multilevel logistic regression models were used to determine injury likelihood in the current and subsequent week, with random intercepts for each player. Odds ratios (OR) were used as effect size measures to determine the changes in injury likelihood with (1) a 10-session increase in preseason training participation or (2) standardised changes in training load variables.

Results Controlling for training load in a given week, completing 10 additional preseason sessions was associated with a 17% reduction in the odds of injury in the subsequent week (OR=0.83, 95% CI=0.70 to 0.99). Increased preseason participation was associated with a lower percentage of games missed due to injury (r=−0.40, p<0.05), with 10 preseason sessions predicting a 5% reduction in the percentage of games missed.

Conclusions Maximising participation in preseason training may protect elite rugby league players against in-season injury.

  • Athlete
  • Rugby
  • Training load
  • Sporting injuries

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Introduction

Athletic injuries are common in team sports,1 ,2 compromising team success3–5 and posing a significant financial burden to organisations.6 High training loads and substantial spikes (rapid increases) in training volume have been associated with increased injury rates.3 ,7 ,8 External (work completed)9–11 and internal (physiological response such as perceived exertion or heart rate)12–15 training load measures have been used to identify the association between workloads and injury risk.

Traditionally, workload-injury investigations focused on absolute workloads and injury,14 ,16 and higher workloads were associated with greater rates of injuries.16 However, high training loads are necessary for beneficial physiological adaptation such as increased aerobic capacity, strength and repeat sprint ability, along with optimal body composition,17 ,18 many of which are associated with decreased injury risks.11 ,19 ,20

Recently, load-injury investigations have highlighted that the relationship between acute 1-week and chronic (rolling 4-week total averaged to 1-week) workloads, termed the acute:chronic workload ratio, may better predict injury risk than total workloads.8 ,9 Moreover, Hulin et al8 demonstrated that as long as players' acute:chronic workload ratios were kept within a moderate level (0.85–1.35), high chronic workloads may reduce injury risk in rugby league players—the training load--injury paradox.21

Preseason training provides several physical benefits for sporting teams. It allows players to reach high chronic workloads,8 as well as to develop the physical capacities associated with reduced injury risks.11 ,19 ,20 Indeed, preseason periods often include higher training loads than in-season periods.3 ,15 Theoretically, players who have a more ‘successful’ preseason may be more resilient to injury when faced with the demands of the competitive season.

To the best of our knowledge, no study has investigated whether preseason training provides a foundation which decreases in-season injury risk in elite team sport athletes. Therefore, we investigated whether elite rugby league players who participated in a greater number of preseason sessions were more or less likely to miss games due to injury throughout the competitive season, while accounting for their external training loads during the competitive season.

Methods

Study design

We prospectively followed 30 rugby league players (mean±SD age, 25±3 years) from one elite rugby league club throughout their 17-week preseason period and 26-round competitive season. All participants provided informed written consent and received a clear explanation of the study. All experimental procedures were approved by the Institutional Review Board for Human Investigation at Australian Catholic University.

Measures

For the purpose of this study, we collected time-varying and time-invariant variables. Time-varying variables were summarised weekly and included injury status and daily training load variables. Time-invariant variables included the number of preseason sessions completed, player position and age at the start of the preseason. The competitive season was divided into three time periods for descriptive purposes, around the representative (ie, ‘State of Origin’ interstate series) period: ‘pre-origin’ (weeks 1–8), ‘origin’ (weeks 9–17) and ‘post-origin’ (weeks 18–26).

Injury status

The team's medical staff (including physician and physiotherapist) diagnosed injuries, while the team physiotherapist updated and maintained the injury reports. For the purpose of this investigation, an injury was defined as any injury that resulted in a loss of match time—‘match time loss only’.22 Injury incidence was calculated as the number of injuries per 1000 participation hours.

Preseason attendance

For each training day throughout the preseason and competitive season, players' participation in training was recorded as ‘full’, ‘modified’, ‘rehab’ or ‘away’. Players' individual preseason participation levels were quantified as the number of ‘full’ preseason sessions they completed.

Quantifying in-season training loads

External workloads were obtained using global positioning system (GPS) devices (GPSports, SPI-HPU 5 Hz (interpolated 15 Hz), Canberra, Australia). Load variables collected included total distance, high-speed (>5 m/s) distance covered and the percentage of total distance completed at high speeds. Our analysis included all field training sessions and National Rugby League matches throughout the 2015 season.

Data collection and analysis procedures

Data were categorised into weekly blocks from Monday to Sunday throughout the 26-week season. If GPS data were missing for players who were recorded as attending the ‘full’ training session, load data were estimated by calculating the average workload for players of the same position who participated in the full session. Since models were fitted to determine the likelihood of sustaining a time-loss injury in a given week or subsequent week, players' data for a given week were excluded if they were already injured, suspended or released from the team.

Statistical analysis

All data were analysed in the open-source statistical software, R (V.3.2.2). Independent random effect (multilevel) logistic regression models were fitted for each independent variable using the R's lme4 package, with the likelihood of sustaining a time-loss injury as the outcome variable, and random intercepts for each player. These models were used to determine which variables were associated with an increased or decreased risk for injury throughout the season, not controlling for other covariates. Random-effect models were chosen for their ability to handle unbalanced data with varying number of follow-up observations, their capacity to generate individual-specific predictions and for their recommended use in analysing repeated-measures designs with correlated data.

In fitting the regression models, all training load variables were standardised owing to the different scales of the measures and subsequent failure of the models to converge in the statistical software with unadjusted predictor variables. Odds ratios (OR) were calculated to determine the effect size associated with a 1 SD increase in training load variables. For preseason participation, ORs were calculated to examine the effect sizes associated with an increase of 10 ‘full’ preseason sessions. Statistical significance was set at p<0.05 for all analyses, and ORs were calculated as an effect size for all models.

Two separate multilevel logistic regression models were fit to determine the effect of preseason participation on injury likelihood, controlling for training loads. One model was fit to determine the likelihood of injury in the current week. A second model was fit to determine the likelihood of injury in the subsequent week. The final models were first fitted by including variables shown to be significant predictors from univariate models. From here, all other training load variables, as well as time-invariant covariates (age, position, season period), and interaction terms were added to the model to optimise model fit. Model fit was assessed by minimising the model deviance, the values of model diagnostics criteria (Akaike information criterion (AIC)/Bayesian information criterion (BIC)) and the SD of the random intercepts. Variables that did not improve the model fit were excluded from the final models.

Results

Injuries

A total of 40 injuries were sustained during the competitive season (29.0/1000 h). These led to 241 total matches missed. There were no significant differences in injury likelihood when comparing positions (p=0.73) or season period (p=0.46).

Preseason participation

During the preseason period (3 November 2014–27 February 2015), the team had 87 preseason training sessions. Players completed an average of 64±19 ‘full’ preseason sessions (range 12–86).

Preseason loads and injury risk

There was a significant correlation between the number of full preseason training sessions that players completed and the number of full in-season sessions completed (r=0.59, p<0.001). Further, there was a significant association (r=−0.40, p<0.05) between the number of preseason sessions players completed and the percentage of games they missed due to injury (figure 1). Without adjusting for training loads, greater preseason participation was associated with a decreased likelihood of injury throughout the competitive season during the current (OR=0.82, 95% CI 0.69 to 0.97) and subsequent week (OR=0.80, 95% CI 0.68 to 0.94).

Figure 1

The association of preseason participation with games missed due to injury (r=−0.40, p<0.05). The linear regression slope shows that for every 10 additional preseason sessions, the predicted percentage of games missed decreases by 5%. Percentage of games missed due to injury was calculated by dividing the number of games missed due to injury by the number of games that players were eligible to play. Games were excluded from calculation if players were ineligible due to suspension or being traded during the season and therefore not eligible to play.

In-season training loads and injury risk

Training load measures collected during the competitive season are summarised in table 1. The average distance and injury incidence for each week of the competitive season are displayed in figure 2.

Table 1

Descriptive statistics for players' average workload and injuries over the duration of the study

Figure 2

Average weekly distance per player and total team injury incidence during each week of the competitive season.

Higher acute 1-week distances were associated with lower injury likelihoods in the current week (OR=0.64, 95% CI 0.46 to 0.90) but not in the subsequent week. A greater percentage of total distance completed at high speeds was associated with an increased likelihood of injury both in the current (OR=1.34, 95% CI 1.03 to 1.73) and subsequent week (OR=1.07, 95% CI 1.06 to 1.08). Absolute high-speed running distance was not associated with injury likelihood in either the current or subsequent week.

Chronic workloads were not significantly associated with injury risk in either the current or subsequent week. Similarly, acute:chronic workload ratios for all training load variables were not associated with significant changes in injury likelihood. Table 2 summarises all models of single training load variables and their associated effects on injury risk.

Table 2

Association of training load variables with injury likelihood in the current and subsequent week

Full injury prediction models

Two multivariate injury prediction models quantified the effect of preseason participation on injury risk, controlling for training loads (table 3). Training load variables included in the final models were those that had a significant association with injury risk in independent univariate models, specifically 1-week total distance and the 1-week proportion of distance performed at high speeds. The fit of these final models was not improved with the addition of any other variable, nor with the addition of a random slope to the model, so none were included.

Table 3

Effect of preseason participation on injury likelihood in current and subsequent week, controlling for training load variables

Model 1 predicts the likelihood of injury in the current week. Controlling for training load, increased preseason participation was still associated with a reduced odds of injury, though this was no longer statistically significant (OR=0.85, 95% CI 0.70 to 1.02). Similarly, a greater percentage of distance run at high speeds appeared to be associated with an increased injury risk, but the effect was no longer significant when controlling for preseason participation and acute distance (OR=1.27, 95% CI 0.99 to 1.63). Finally, as with univariate models, greater acute distance was associated with a significantly reduced likelihood of injury (OR=0.56, 95% CI 0.36 to 0.87).

Model 2 predicts the likelihood of injury in the subsequent week with a given preseason participation, acute distance and acute percentage of distance run at high speeds. In this model, when controlling for distance and percentage of distance at high speed, increased preseason participation was associated with a reduced likelihood of injury (OR=0.83, 95% CI 0.70 to 0.99). Within this model, neither acute distance nor percentage of distance run at high speeds was significantly associated with injury risk in the subsequent week (figure 3).

Figure 3

Predicted injury probabilities from model 2 (Table 3), based on high-speed running percentage and preseason participation. The model predicts the probability that a player will sustain a match time-loss injury in the subsequent week, controlling for pre-season participation, as well as the total distance and the percentage of total distance run at high speeds in the current week. Preseason participation has been divided into three equal tertiles, such that ‘low’ equals <59 sessions (n=10), ‘moderate’ equals 59–75 sessions (n=10) and ‘high’ equals >75 sessions (n=10).

Discussion

In this sample of rugby league players, greater preseason participation was associated with a decreased injury risk during the competitive season. Players who participated in a greater number of full preseason sessions had a reduced likelihood of injury throughout the competitive season, completed more in-season training sessions and missed fewer games due to injury. This reduced injury likelihood in the subsequent week was maintained even when controlling for training load variables (acute distance and acute percentage of total distance completed at high speeds).

Training loads and injury likelihood

In contrast to recent studies,3 ,8 neither acute:chronic workload ratios nor chronic workloads in isolation were significantly associated with injury. It may be that the current sample size was too small to detect these effects. However, two separate measures of acute (1-week) training loads, (1) distance covered and (2) percentage of total distance covered at high speeds, were significantly associated with changes in injury likelihood.

Higher 1-week total distances were associated with a reduction in injury likelihood in the current week but not in the subsequent week. Similarly, previous data in rugby league players showed that greater distances completed at lower intensities were associated with a reduction in injury risk.11 However, it should be noted that the reduced likelihood of injury associated with higher distances in the current week may be partly attributable to players sustaining an injury earlier in the week. In this case, it may be that increased distances are not preventing injury but are accumulated by players who are healthy.

In contrast to total distance, the percentage of total weekly distance performed at high speeds was associated with an increased risk of injury in the current and subsequent week. This increased injury risk with greater high-speed running loads has been previously seen in team sport athletes.10 ,11 Notably, when controlling for players' preseason attendance, the percentage of running performed at high speeds was no longer significantly associated with injury. This may indicate that players who had accumulated more of the benefits of a successful preseason were better able to tolerate the stress of the competitive season. Similarly, it has previously been shown that team sport athletes who performed >18 weeks of training before sustaining initial injuries were at a reduced risk of sustaining a subsequent injury.23 Collectively, the present and previous23 results demonstrate the protective effect of preseason training in team sport athletes.

Preseason participation and in-season injury: providing protection or revealing underlying differences (ie, identifying the robust players)?

We speculate that there may be two potential mechanisms responsible for the associated reduction in injury likelihood with increased preseason participation. From a physical standpoint, preseason participation may be protective by allowing players to accumulate high chronic workloads8 and develop greater strength and aerobic capacity.23 Further, players who participate in a greater proportion of preseason training sessions may also be better prepared mentally and tactically within the team environment.

On the other hand, increased preseason participation may merely identify players who are inherently more robust to injury and therefore more likely to handle the preseason training loads and the rigours of the competitive season. Thus, the association between preseason training and in-season injury risk may stem from protective and revelatory effects of the preseason.

Preparation through preseason participation: practical implications

Our findings do not necessarily indicate a need for high training loads during the preseason. Rather, preseason training should be conducted so that players are able to participate in the highest proportion of team sessions possible. This is especially pertinent given that the majority of training-related rugby league injuries occur during the preseason periods.24 Even though 24% of a rugby union team's annual training occurred during the preseason period, 34% of training-related injuries occur during this time.25 High injury incidence during the preseason period may be partly attributable to training loads, which are generally higher than in-season periods.3 ,23 Moreover, a reduction in preseason training loads significantly reduced injury incidence in rugby league players.24 From our experience, those who manage team training loads should aim to design preseason training periods which induce positive physiological adaptations while minimising injury risk and maximising player availability. Accurate season-by-season records of injury and training loads will help teams find their ‘sweet spot’.

Potential limitations

The present study included a sample size (30 players), and total injury occurrences (40), which limits the number of variables that could be included in the injury prediction models and reduces the sensitivity of the models. Owing to the limited availability of GPS devices, the number of players who could be monitored was restricted to 30, as opposed to all members of the rugby league club. Although more injuries would have been captured with a broader definition of injury, ‘match time loss only’ is accepted as an accurate and reliable definition used in team sport contexts.22

Although we used GPS-derived total and high-speed running distances, the inclusion of other external load variables (eg, accelerations, decelerations, collisions) would likely add value to any investigation of the relationship between preseason training load and in-season injury risk in rugby league players.26 ,27 However, as discussed in recent load injury investigations,8 ,28 the ability of GPSports technology to accurately measure these variables is limited. Further, internal load measures (eg, session rating of perceived exertion (RPE) or heart rate) may also be useful to investigate preseason and in-season training loads. Finally, while we quantified preseason participation in sessions, future investigations may quantify preseason training loads to further distinguish characteristics of preseason training that facilitate the development of athlete resilience.

Summary and conclusions

In this first study to investigate the association of preseason training participation and injury likelihood during the competitive season, players who completed a greater number of preseason sessions were less likely to be injured during the competitive season, even when controlling for their external training loads. Total distance covered was associated with a decreased likelihood of injury in the current week, while players who completed a greater percentage of their total distance at high speeds were at increased risk of injury in the current and subsequent week.

What are the findings?

  • Players who participated in a greater number of preseason sessions had a lower likelihood of injury throughout the competitive season. Ten additional preseason sessions reduced the odds of injury by at least 17% in the current and subsequent week. The association between preseason training participation and risk of injury in the subsequent week remained statistically significant even when controlling for in-season training load variables.

  • Running a higher percentage of total distance at high speeds was associated with an increased injury risk, both in the current and subsequent week. For example, a 3.7% increase in the percentage of distance run at high speeds in a given week increased the odds of injury by 34%. However, when controlling for preseason participation, this association was no longer significant.

How might it impact on clinical practice in the future?

  • In addition to its role in preparing players for the performance demands of competition, preseason training may prevent injuries during the competitive season.

  • Future investigations may examine strategies to minimise injury risk during the preseason period so that player availability is maximised during this period.

  • These findings might contribute to a paradigm shift where clinicians may appreciate that total external training load (distance covered) is not necessarily associated with increased injury risk and may in fact decrease risk. However, greater percentages of time spent at high speeds in a given training week may increase injury risk in the current or subsequent week, especially when preseason participation is low.

Acknowledgments

The authors would like to acknowledge the players who participated in this study, as well as Angela Yao for her statistical expertise.

References

View Abstract

Footnotes

  • Twitter Johann Windt at @JohannWindt and Tim Gabbett at @TimGabbett

  • Contributors JW was primarily responsible for the analysis of the study data. DF and TJG were responsible for the data collection. All authors were responsible for study concept and design, and contributed to writing and critical revision of the manuscript.

  • Funding JW is a Vanier Scholar funded by the Canadian Institutes of Health Research.

  • Competing interests Karim Khan is editor-in-chief of BJSM and was not involved in the peer-review. He is blinded to this paper in the ScholarOne manuscript system. His collaboration with Tim J Gabbett has been documented with the BJSM Publisher.

  • Patient consent Obtained.

  • Ethics approval Approval was sought and subsequently granted by the Australian Catholic University.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement All data relevant to the study have been included in the manuscript. In accordance with the original ethics approval, data may not be shared outside this study and team's staff.

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