With sleep being such a hot topic in sports, what are the latest topics in sports that correlate to the assigned readings for this module? 2. Ho

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The training—injury prevention paradox: should athletes be training smarter and harder? Tim J Gabbett1,2

1School of Exercise Science, Australian Catholic University, Brisbane, Queensland, Australia 2School of Human Movement Studies, University of Queensland, Brisbane, Queensland, Australia

Correspondence to Dr Tim J Gabbett, School of Exercise Science, Australian Catholic University, 1100 Nudgee Road, Brisbane, QLD 4014, Australia; [email protected]

Accepted 16 November 2015 Published Online First 12 January 2016

To cite: Gabbett TJ. Br J Sports Med 2016;50: 273–280.

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.

TRAINING–PERFORMANCE RELATIONSHIP In a British Journal of Sports Medicine blog, Dr John Orchard1 proposed hypothetical relationships between training (both under-training and over- training), injury, fitness and performance. He speculated that both inadequate and excessive train- ing loads would result in increased injuries, reduced fitness and poor team performance (see figure 1). The relationship between training load,

injury, fitness and performance is critical to sports medicine/physiotherapy and sport science practi- tioners. In this paper I use the term ‘practitioners’ to refer to the wide gamut of health professionals and also sport scientists who work with athletes/ teams (ie, strength and conditioning coaches, certi- fied personal trainers, etc). Our field—sports per- formance and sports injury prevention is a multidisciplinary one and this paper is relevant to the field broadly. Injuries impair team performance, but any injur-

ies that could potentially be considered ‘training load-related’ are commonly viewed as ‘preventable’, and therefore the domain of the sport science and medicine team. Sport science (including strength and conditioning) and sports medicine (including doctors and physiotherapists) practitioners share a common goal of keeping players injury free. Sport science and strength and conditioning staff aim to develop resilience through exposing players to physically intense training to prepare players for the physical demands of competition, including the most demanding passages of play. On the other hand, doctors and physiotherapists

are often viewed as the staff responsible for ‘man- aging players away from injury’. A stereotype is the physiotherapist or doctor advocating to reduce training loads so that fewer players will succumb to ‘load-related’ (eg, overuse) injuries. However, how many of the decisions governing players and their individual training loads are based on empirical evi- dence or the practitioners’ ‘expert’ intuition (ie, ‘gut feel’)? Banister et al2 proposed that the performance of

an athlete in response to training can be estimated from the difference between a negative function (‘fatigue’) and a positive function (‘fitness’). The ideal training stimulus ‘sweet spot’ is the one that maximises net performance potential by having an appropriate training load while limiting the nega- tive consequences of training (ie, injury, illness, fatigue and overtraining).3

Several studies have investigated the influence of training volume, intensity and frequency on athletic performance, with performance generally improved with increases in training load.4–10 In individual sports (eg, swimming and running) greater training volume,4 8 and higher training intensity5 6 8

improved performance. In a study of 56 runners, cyclists and speed skaters undertaking 12 weeks of training, a 10-fold increase in training load was associated with an approximately 10% improve- ment in performance.10 In competitive swimmers, significant associations were found between greater training volume (r=0.50–0.80) and higher training intensity (r=0.60–0.70) and improved

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performance.9 However, adverse events of exercise training are also dose related, with the highest incidence of illness and injury occurring when training loads were highest.10–15

TRAINING LOADS CAN BE MEASURED IN DIFFERENT WAYS Sport scientists typically obtain measurements of a prescribed external training load (ie, physical ‘work’), accompanied by an internal training load (ie, physiological or perceptual ‘response’). External training loads may include total distance run, the weight lifted or the number and intensity of sprints, jumps or collisions (to name a few).16 Internal training loads include ratings of perceived exertion and heart rate. The indi- vidual characteristics of the athlete (eg, chronological age, train- ing age, injury history and physical capacity) combined with the applied external and internal training loads determine the train- ing outcome.16

For example, identical external training loads could elicit con- siderably different internal training loads in two athletes with vastly different individual characteristics; the training stimulus may be appropriate for one athlete, but inappropriate (either too high or too low) for another. An overweight, middle-aged

male will have very different physiological and perceptual responses to an 800 m effort than a trained runner. Although the external training load is identical, the internal training load will be much higher in the older, unfit individual! As the dose– response to training varies between individuals, training should be prescribed on an individual basis.

EXTERNAL TRAINING LOAD—‘TRACKING’ EVERY METRE! Global positioning systems (GPS) have been a ‘game-changer’ in the monitoring of external loads.17 These devices, which are typically no larger than a mobile phone, are worn by athletes during training and match-play activities. GPS provides informa- tion on speed and distances covered, while inertial sensors (ie, accelerometers, gyroscopes) embedded in the devices also provide information on non-locomotor sport-specific activities (eg, jumps in volleyball, collisions in rugby and strokes in swim- ming).18 Importantly, most of this data can be obtained in ‘real- time’ to ensure athletes are meeting planned performance targets.

INTERNAL TRAINING LOAD—THE ATHLETE’S PERCEPTION OF EFFORT The session-rating of perceived exertion (RPE) has been used to quantify the internal training loads of athletes. At the comple- tion of each training session, athletes provide a 1–10 ‘rating’ on the intensity of the session. The intensity of the session is multi- plied by the session duration to provide training load. The units are ‘RPE units×minutes’ and in football codes generally range between 300 and 500 units for lower-intensity sessions and 700–1000 units for higher-intensity sessions. For ease, we have referred to them as ‘arbitrary units’ in previous work. A more accurate term might be ‘exertional minutes’. The value of session-RPE will depend on the goal of those measuring it and that topic is beyond the scope of this paper.

MONITORING INDIVIDUAL ATHLETE WELL-BEING Monitoring athlete well-being is common practice in high per- formance sport.19–21 A wide range of subjective questionnaires are used with many of them employing a simple 5, 7 or 10-point Likert scale.19–23 Longer, more time consuming surveys are also employed.24 25

Figure 1 Hypothetical relationship between training loads, fitness, injuries and performance. Redrawn from Orchard.1

Table 1 Relationship between external workloads and risk of injury in elite rugby league players

Risk factors

Relative risk (95% CI)

Transient Time lost Missed matches

Injury history in the previous season (no vs yes) 1.4 (0.6 to 2.8) 0.7 (0.4 to 1.4) 0.9 (0.2 to 4.1) Total distance (≤3910 vs >3910 m) 0.6 (0.3 to 1.4) 0.5 (0.2 to 1.1) 1.1 (0.2 to 6.0) Very low intensity (≤542 vs >542 m) 0.6 (0.2 to 1.3) 0.4 (0.2 to 0.9)* 0.4 (0.1 to 2.8) Low intensity (≤2342 vs >2342 m) 0.5 (0.2 to 1.1) 0.5 (0.2 to 0.9)* 1.2 (0.2 to 5.5) Moderate intensity (≤782 vs >782 m) 0.4 (0.2 to 1.1) 0.5 (0.2 to 1.0) 0.5 (0.1 to 2.3) High intensity (≤175 vs >175 m) 0.8 (0.2 to 3.1) 0.9 (0.3 to 3.4) 2.9 (0.1 to 16.5) Very high intensity (≤9 vs >9 m) 2.7 (1.2 to 6.5)* 0.7 (0.3 to 1.6) 0.6 (0.1 to 3.1) Total high intensity (≤190 vs >190 m) 0.5 (0.1 to 2.1) 1.8 (0.4 to 7.4) 0.7 (0.1 to 30.6) Mild acceleration (≤186 vs >186 m) 0.2 (0.1 to 0.4)† 0.5 (0.2 to 1.1) 1.5 (0.3 to 8.6) Moderate acceleration (≤217 vs >217 m) 0.3 (0.1 to 0.6)† 0.4 (0.2 to 0.9)* 1.4 (0.3 to 7.5) Maximum acceleration (≤143 vs >143 m) 0.4 (0.2 to 0.8)* 0.5 (0.2 to 0.9)* 1.8 (0.4 to 8.8) Repeated high-intensity effort bouts (≤3 vs >3 bouts) 0.9 (0.4 to 2.0) 1.6 (0.8 to 3.3) 1.0 (0.2 to 4.4)

All injuries were classified as a transient (no training missed), time loss (any injury resulting in missed training) or a missed match (any injury resulting in a subsequent missed match) injury. *p<0.05; †p<0.01. Reproduced from Gabbett and Ullah.26

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These questionnaires are used to determine the readiness of team sport athletes to train. Typically players report their mood, stress level, energy, sleep and diet, along with their feelings of soreness in the upper-body, quadriceps, hamstring, groin and calf. The sum of the questions indicates the athlete’s well-being. Practitioners can then adapt the training prescription for players on an individual basis (eg, continue regular training, investigate training loads or modify training programme).

RELATIONSHIP BETWEEN TRAINING LOADS AND INJURY Training load monitoring is increasingly popular in high per- formance sport to ensure athletes achieve an adequate training stimulus and to minimise the negative consequences of training (injury risk, overtraining). In the following section, I discuss the relationship between training loads (both internal and external loads) and injury in team sport athletes.

EXTERNAL WORKLOADS AND INJURY In elite rugby league, players who performed greater amounts (>9 m) of very high-speed (>7 m/s) running per session were 2.7 times more likely to sustain a non-contact, soft-tissue injury than players who performed less very high-speed running per session (table 1).26 This ‘threshold’ of 9 m of very high-speed running is lower than what would typically be performed in other team sport training sessions (eg, soccer and Australian football),27 and likely reflects the greater contact and repeated-effort demands, and lower running demands of rugby league.27 For example, in studies of Australian football players, higher 3-weekly total distance (73 721–86 662 m, OR=5.5) and 3-weekly sprint distance (>1453 m, OR=3.7) were associated with a greater risk of injury.13

Although external loads are commonly measured using GPS devices, some team sports expose athletes to physically demand- ing external loads that require very little high-speed running (eg, baseball pitching, cricket fast bowling). Of the studies that have been performed in baseball,28–30 greater pitch counts were

associated with greater injury rates. Youth pitchers who threw over 100 innings in a season, had 3.5 times greater injury risk than players who pitched fewer than 100 pitches.30

Similar findings have been observed in cricket players; fast bowlers who bowled more than 50 overs in a match were at increased risk of injury for up to 28 days (OR=1.62).31

Furthermore, bowlers who bowled more deliveries in a week (>188 deliveries, relative risk=1.4) and had less recovery between sessions (<2 days, relative risk=2.4) were at greater injury risk than those who bowled between 123 and 188 deliv- eries per week and had 3–3.99 days recovery between sessions. Complicating this issue is that bowlers who bowled fewer deliv- eries each week (<123 deliveries, relative risk=1.4) and had greater recovery (>5 days, relative risk=1.8) were also at increased risk of injury.32

INTERNAL WORKLOADS AND INJURY These findings on external loads are consistent with results from studies on internal loads; higher training loads were associated with greater injury rates.11 15 33–36 In early work11 a strong rela- tionship (r=0.86) was reported between training loads (derived from the session-RPE) and training injury rates across a playing season in semiprofessional rugby league players (figure 2). Furthermore, over a 3-year period, reduced training loads mark- edly reduced injury rates in the same cohort of players (figure 3).37 It is likely that excessive training loads performed early in the study, led to overtraining, resulting in a spike in injury rates. However, it should be noted that this study was published over 10 years ago, and no subsequent study has replicated these results.

In professional rugby union players, higher 1-week (>1245 arbitrary units) and 4-week cumulative loads (>8651 arbitrary units) were associated with a higher risk of injury.14 In profes- sional rugby league players, training load was associated with overall injury (r=0.82), non-contact field injury (r=0.82), and contact field injury (r=0.80) rates.35 Significant relationships were also observed between the field training load and overall field injury (r=0.68), non-contact field injury (r=0.65), and contact field injury (r=0.63) rates. Strength and power training loads were significantly related to the incidence of strength and power injuries (r=0.63). There was no significant relationship between field training loads and the incidence of strength and power injuries. However, strength and power training loads were significantly associated with the incidence of contact (r=0.75) and non-contact (r=0.87) field training injuries. Collectively, these findings suggest that (1) the harder rugby league players train, the more injuries they will sustain and (2) high strength and power training loads may contribute indirectly to field injuries. Monitoring of training loads and careful sched- uling of field and gymnasium sessions to avoid residual fatigue is warranted to minimise the effect of training-related injuries on professional rugby league players.

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.11

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.37

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DIFFERENCES IN TRAINING ADAPTATIONS BETWEEN YOUNGER AND OLDER ATHLETES The age of the athlete influences adaptations to training.38 39

Gabbett38 investigated training loads, injury rates and physical performance changes associated with a 14-week field condition- ing programme in junior (approximately 17 years) and senior (approximately 25 years) rugby league players. Training improved muscular power and maximal aerobic power in the junior and senior players, however the improvement in muscular power and maximal aerobic power were greatest in the junior players. Training loads and injury rates were higher in the senior players. Thus, junior and senior rugby league players may adapt

differently to a given training stimulus, suggesting that training programmes should be modified to accommodate differences in training age.

Rogalski et al39 also showed that at a given training load, older and more experienced (7+ years’ experience in the Australian Football League competition) players were at greater risk of injury than less experienced, younger (1–3 years’ experi- ence in the Australian Football League competition) players. It is likely that the higher training injury risk in the more experi- enced players is confounded by previous injury which is a major risk factor for a new injury.40 Older players likely had experi- enced a greater number of injuries across the course of their careers than the less experienced first to third year players. Clearly, further research investigating the dose–response rela- tionship between training and injury in athletes of different ages and genders is warranted.41

MODELLING THE TRAINING LOAD–INJURY RELATIONSHIP AND USING IT TO PREDICT INJURY This section focuses on the use of training monitoring to model the relationship between load and injury risk.

Over a 2-year period, Gabbett42 used the session-RPE to model the relationship between training loads and the likelihood of injury in elite rugby league players. Training load and injury data were modelled using a logistic regression model with a binomial distribution (injury vs no injury) and logit link func- tion, with data divided into preseason, early competition and late competition phases.

Players were 50–80% likely to sustain a preseason injury within the weekly training load range of 3000 to 5000 arbitrary minutes (RPE×minutes, as above). These training load ‘thresh- olds’ for injury were considerably lower (1700–3000 session-RPE units/week) in the competitive phase of the season. Importantly, on the steep portion of the sigmoidal training load-injury curve, very small changes in training load resulted in very large changes in injury risk (figure 4).

Training load and injury data were prospectively recorded over a further two competitive seasons in those elite rugby league players. An injury prediction model based on planned and actual training loads was developed and implemented to determine if non-contact, soft-tissue injuries could be predicted. One-hundred and fifty-nine non-contact, soft-tissue injuries were sustained over those two seasons. The percentage of true-

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.42

Table 2 Accuracy of model for predicting non-contact, soft-tissue injuries

Actual status Injured Not injured

Predicted status

Predicted injury True positive False positive Positive predictive value N=121 N=20 85.8%

Predicted no injury False negative True negative Negative predictive value N=18 N=1589 98.9%

Sensitivity Specificity 87.1 (80.5 to 91.7)% 98.8 (98.1 to 99.2)%

Likelihood ratio positive 70.0 (45.1 to 108.8)

Likelihood ratio negative 0.1 (0.1 to 0.2)

‘True Positive’—predicted injury and player sustained injury; ‘False Positive’—predicted injury but player did not sustain injury; ‘False Negative’—no injury predicted but player sustained injury; ‘True Negative’—no injury predicted and player did not sustain injury. ‘Sensitivity’—proportion of injured players who were predicted to be injured; Specificity—proportion of uninjured players who were predicted to remain injury-free. ‘Likelihood ratio positive’—sensitivity/(1−specificity); ‘Likelihood ratio negative’—(1−sensitivity)/specificity. While there were 91 players in the sample, injury predictions based on the training loads performed by individual players were made on a weekly basis, so that within the total cohort, there was a total number of true positive and negative predictions, and a total number of false positive and negative predictions. Sensitivity and specificity data, and positive and negative likelihood ratios are expressed as rates (and 95% CIs). Reproduced from Gabbett.42

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positive predictions was 62% (N=121) and the false-positive and false-negative predictions were 13% (N=20) and 11% (N=18), respectively. Players who exceeded the weekly training load threshold were 70 times more likely to test positive for non-contact, soft-tissue injury, while players who did not exceed the training load threshold were injured 1/10 as often (table 2). Furthermore, following the introduction of this model, the inci- dence of non-contact, soft-tissue injuries was halved.42

We also analysed the prevalence of injury and the predictive ratios obtained from the model. The prevalence of injury in this sample of professional rugby league players was 8.6%. If the predictive equation was positive for a given player, the likeli- hood of injury increased from 8.6% to 86%, and if the results of the test were negative, the likelihood of injury decreased from 8.6% to 0.1%. Furthermore, 87% (121 from 139 injuries) of the 8.6% of players who sustained an injury were correctly identified by the injury prediction model.

Although several commercially available software programs claim to predict training load-related injuries, to date, this is the only study to predict injury based on training load data, apply that model in a high performance sporting environ- ment, and then report the results in a peer-reviewed journal. We acknowledge that any regression model that predicts injury is best suited to the population from which it is derived. Caution should be applied when extrapolating these results to other sports and populations. Despite this potential limitation, these findings provide information on the training dose–response relationship in elite rugby league players, and a scientific method of monitoring and regulating training load in these athletes. Importantly, in a team environment, this approach allows players to be managed on an individual basis.

THE CRITICAL ELEMENT OF WEEK-TO-WEEK CHANGE (USUALLY INCREASES!) IN TRAINING LOAD Accepting that high absolute training loads are associated with greater injury risk,42 strength and conditioning practitioners must also consider how week-to-week changes in training load inde- pendently influence injury risk (aside from total training load). In a study of Australian football players, Piggott et al34 showed that 40% of injuries were associated with a rapid change (>10%) in weekly training load in the preceding week. Rogalski et al39 also showed that larger 1-weekly (>1750 arbitrary units, OR=2.44– 3.38), 2-weekly (>4000 arbitrary units, OR=4.74) and previous to current week changes in internal load (>1250 arbitrary units, OR=2.58) were related to a greater risk of injury. Large week-to-week changes in training load (1069 arbitrary units) also increased the risk of injury in professional rugby union players.14

We have also modelled the relationship between changes in weekly training load (reported as a percentage of the previous weeks’ training load) and the likelihood of injury (unpublished observa- tions). When training load was fairly constant (ranging from 5% less to 10% more than the previous week) players had <10% risk of injury (figure 5). However, when training load was increased by ≥15% above the previous week’s load, injury risk escalated to between 21% and 49%. To minimise the risk of injury, practi- tioners should limit weekly training load increases to <10%.

CONSIDERING BOTH ACUTE AND CHRONIC TRAINING LOAD: A BETTER WAY TO MODEL THE TRAINING–INJURY RELATIONSHIP? Is there a benefit in modelling the training–injury relationship using a combination of both acute and chronic training loads? Acute training loads can be as short as one session, but in team sports, 1 week of training appears to be a logical and convenient unit. Chronic training loads represent the rolling average of the most recent 3–6 weeks of training. In this respect, chronic train- ing loads are analogous to a state of ‘fitness’ and acute training loads are analogous to a state of ‘fatigue’.2

Comparing the acute training load to the chronic training load as a ratio provides an index of athlete preparedness. If the acute training load is low (ie, the athlete is experiencing minimal ‘fatigue’) and the rolling average chronic training load is high (ie, the athlete has developed ‘fitness’), then the athlete will be in a well-prepared state. The ratio of acute:chronic workload will be around 1 or less. Conversely, if the acute load is high (ie, training loads have been rapidly increased resulting in ‘fatigue’) and the rolling average chronic training load is low (ie, the athlete has performed inadequate training to develop ‘fitness’), then the athlete will be in a fatigued state. In this case the ratio of the acute:chronic workload will exceed 1. The use of the acute: chronic workload ratio emphasises both the positive and negative consequences of training. More importantly, this ratio considers the training load that the athlete has performed relative to the training load that he or she has been prepared for.43

The first study to investigate the relationship between the acute:

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