Infographic: The training–injury prevention paradox: shouldathletes be training smarter and harder?Tim Gabbett

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

1School of Exercise Science,Australian Catholic University,Brisbane, Queensland,Australia2School of Human MovementStudies, University ofQueensland, Brisbane,Queensland, Australia

Correspondence toDr Tim J Gabbett, School ofExercise Science, AustralianCatholic University, 1100Nudgee Road, Brisbane,QLD 4014, Australia;tim_gabbett@yahoo.com.au

Accepted 16 November 2015Published Online First12 January 2016

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

ABSTRACTBackground There is dogma that higher training loadcauses higher injury rates. However, there is alsoevidence that training has a protective effect againstinjury. For example, team sport athletes who performedmore than 18 weeks of training before sustaining theirinitial injuries were at reduced risk of sustaining asubsequent injury, while high chronic workloads havebeen shown to decrease the risk of injury. Second,across a wide range of sports, well-developed physicalqualities are associated with a reduced risk of injury.Clearly, for athletes to develop the physical capacitiesrequired to provide a protective effect against injury, theymust be prepared to train hard. Finally, there is alsoevidence that under-training may increase injury risk.Collectively, these results emphasise that reductions inworkloads may not always be the best approach toprotect against injury.Main thesis This paper describes the ‘Training-InjuryPrevention Paradox’ model; a phenomenon wherebyathletes accustomed to high training loads have fewerinjuries than athletes training at lower workloads. TheModel is based on evidence that non-contact injuries arenot caused by training per se, but more likely by aninappropriate training programme. Excessive and rapidincreases in training loads are likely responsible for alarge proportion of non-contact, soft-tissue injuries. Iftraining load is an important determinant of injury, itmust be accurately measured up to twice daily and overperiods of weeks and months (a season). This paperoutlines 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’straining burden. I describe the critical variable—acute:chronic workload ratio—as a best practice predictor oftraining-related injuries. This provides the foundation forinterventions to reduce players risk, and thus, time-lossinjuries.Summary The appropriately graded prescription ofhigh training loads should improve players’ fitness,which in turn may protect against injury, ultimatelyleading to (1) greater physical outputs and resilience incompetition, and (2) a greater proportion of the squadavailable for selection each week.

TRAINING–PERFORMANCE RELATIONSHIPIn a British Journal of Sports Medicine blog, DrJohn Orchard1 proposed hypothetical relationshipsbetween training (both under-training and over-training), injury, fitness and performance. Hespeculated that both inadequate and excessive train-ing loads would result in increased injuries,reduced fitness and poor team performance (seefigure 1). The relationship between training load,

injury, fitness and performance is critical to sportsmedicine/physiotherapy and sport science practi-tioners. In this paper I use the term ‘practitioners’to refer to the wide gamut of health professionalsand 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 amultidisciplinary one and this paper is relevant tothe field broadly.Injuries impair team performance, but any injur-

ies that could potentially be considered ‘trainingload-related’ are commonly viewed as ‘preventable’,and therefore the domain of the sport science andmedicine team. Sport science (including strengthand conditioning) and sports medicine (includingdoctors and physiotherapists) practitioners share acommon goal of keeping players injury free. Sportscience and strength and conditioning staff aim todevelop resilience through exposing players tophysically intense training to prepare players forthe physical demands of competition, including themost 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 thephysiotherapist or doctor advocating to reducetraining loads so that fewer players will succumb to‘load-related’ (eg, overuse) injuries. However, howmany of the decisions governing players and theirindividual 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 estimatedfrom the difference between a negative function(‘fatigue’) and a positive function (‘fitness’). Theideal training stimulus ‘sweet spot’ is the one thatmaximises net performance potential by having anappropriate training load while limiting the nega-tive consequences of training (ie, injury, illness,fatigue and overtraining).3

Several studies have investigated the influence oftraining volume, intensity and frequency on athleticperformance, with performance generally improvedwith increases in training load.4–10 In individualsports (eg, swimming and running) greater trainingvolume,4 8 and higher training intensity5 6 8

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

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

TRAINING LOADS CAN BE MEASURED IN DIFFERENT WAYSSport scientists typically obtain measurements of a prescribedexternal training load (ie, physical ‘work’), accompanied by aninternal training load (ie, physiological or perceptual‘response’). External training loads may include total distancerun, the weight lifted or the number and intensity of sprints,jumps or collisions (to name a few).16 Internal training loadsinclude 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 theapplied 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 withvastly different individual characteristics; the training stimulusmay be appropriate for one athlete, but inappropriate (eithertoo high or too low) for another. An overweight, middle-aged

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

EXTERNAL TRAINING LOAD—‘TRACKING’ EVERY METRE!Global positioning systems (GPS) have been a ‘game-changer’ inthe monitoring of external loads.17 These devices, which aretypically no larger than a mobile phone, are worn by athletesduring training and match-play activities. GPS provides informa-tion on speed and distances covered, while inertial sensors (ie,accelerometers, gyroscopes) embedded in the devices alsoprovide 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 performancetargets.

INTERNAL TRAINING LOAD—THE ATHLETE’S PERCEPTIONOF EFFORTThe session-rating of perceived exertion (RPE) has been used toquantify the internal training loads of athletes. At the comple-tion of each training session, athletes provide a 1–10 ‘rating’ onthe intensity of the session. The intensity of the session is multi-plied by the session duration to provide training load. The unitsare ‘RPE units×minutes’ and in football codes generally rangebetween 300 and 500 units for lower-intensity sessions and700–1000 units for higher-intensity sessions. For ease, we havereferred to them as ‘arbitrary units’ in previous work. A moreaccurate term might be ‘exertional minutes’. The value ofsession-RPE will depend on the goal of those measuring it andthat topic is beyond the scope of this paper.

MONITORING INDIVIDUAL ATHLETE WELL-BEINGMonitoring athlete well-being is common practice in high per-formance sport.19–21 A wide range of subjective questionnairesare used with many of them employing a simple 5, 7 or10-point Likert scale.19–23 Longer, more time consumingsurveys 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 ofteam sport athletes to train. Typically players report their mood,stress level, energy, sleep and diet, along with their feelings ofsoreness in the upper-body, quadriceps, hamstring, groin andcalf. The sum of the questions indicates the athlete’s well-being.Practitioners can then adapt the training prescription for playerson an individual basis (eg, continue regular training, investigatetraining loads or modify training programme).

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

EXTERNAL WORKLOADS AND INJURYIn elite rugby league, players who performed greater amounts(>9 m) of very high-speed (>7 m/s) running per session were2.7 times more likely to sustain a non-contact, soft-tissue injurythan players who performed less very high-speed running persession (table 1).26 This ‘threshold’ of 9 m of very high-speedrunning is lower than what would typically be performed inother team sport training sessions (eg, soccer and Australianfootball),27 and likely reflects the greater contact andrepeated-effort demands, and lower running demands of rugbyleague.27 For example, in studies of Australian football players,higher 3-weekly total distance (73 721–86 662 m, OR=5.5) and3-weekly sprint distance (>1453 m, OR=3.7) were associatedwith a greater risk of injury.13

Although external loads are commonly measured using GPSdevices, 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 thathave been performed in baseball,28–30 greater pitch counts were

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

Similar findings have been observed in cricket players; fastbowlers who bowled more than 50 overs in a match were atincreased 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 recoverybetween sessions (<2 days, relative risk=2.4) were at greaterinjury 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 hadgreater recovery (>5 days, relative risk=1.8) were also atincreased risk of injury.32

INTERNAL WORKLOADS AND INJURYThese findings on external loads are consistent with results fromstudies on internal loads; higher training loads were associatedwith greater injury rates.11 15 33–36 In early work11 a strong rela-tionship (r=0.86) was reported between training loads (derivedfrom the session-RPE) and training injury rates across a playingseason 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 (figure3).37 It is likely that excessive training loads performed early inthe study, led to overtraining, resulting in a spike in injury rates.However, it should be noted that this study was published over10 years ago, and no subsequent study has replicated theseresults.

In professional rugby union players, higher 1-week (>1245arbitrary units) and 4-week cumulative loads (>8651 arbitraryunits) were associated with a higher risk of injury.14 In profes-sional rugby league players, training load was associated withoverall injury (r=0.82), non-contact field injury (r=0.82), andcontact field injury (r=0.80) rates.35 Significant relationshipswere also observed between the field training load and overallfield injury (r=0.68), non-contact field injury (r=0.65), andcontact field injury (r=0.63) rates. Strength and power trainingloads were significantly related to the incidence of strength andpower injuries (r=0.63). There was no significant relationshipbetween field training loads and the incidence of strength andpower injuries. However, strength and power training loadswere 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 rugbyleague players train, the more injuries they will sustain and (2)high strength and power training loads may contribute indirectlyto field injuries. Monitoring of training loads and careful sched-uling of field and gymnasium sessions to avoid residual fatigue iswarranted to minimise the effect of training-related injuries onprofessional rugby league players.

Figure 2 Relationship between training load and injury rate in teamsport athletes. Training loads were measured using the session-ratingof perceived exertion method. Redrawn from Gabbett.11

Figure 3 Influence of reductions inpreseason training loads on injuryrates and changes in aerobic fitness inteam sport athletes. Training loadswere measured using thesession-rating of perceived exertionmethod. Redrawn from Gabbett.37

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DIFFERENCES IN TRAINING ADAPTATIONS BETWEENYOUNGER AND OLDER ATHLETESThe age of the athlete influences adaptations to training.38 39

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

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

Rogalski et al39 also showed that at a given training load,older and more experienced (7+ years’ experience in theAustralian Football League competition) players were at greaterrisk of injury than less experienced, younger (1–3 years’ experi-ence in the Australian Football League competition) players. It islikely that the higher training injury risk in the more experi-enced players is confounded by previous injury which is a majorrisk factor for a new injury.40 Older players likely had experi-enced a greater number of injuries across the course of theircareers 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 agesand genders is warranted.41

MODELLING THE TRAINING LOAD–INJURY RELATIONSHIPAND USING IT TO PREDICT INJURYThis section focuses on the use of training monitoring to modelthe relationship between load and injury risk.

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

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

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

Figure 4 Relationships between training load, training phase, andlikelihood of injury in elite team sport athletes. Training loads weremeasured using the session-rating of perceived exertion method.Players were 50–80% likely to sustain a preseason injury within thetraining load range of 3000–5000 arbitrary units. These training load‘thresholds’ were considerably reduced (1700–3000 arbitrary units) inthe competitive phase of the season (indicated by the arrow and shiftof the curve to the left). On the steep portion of the preseason trainingload-injury curve (indicated by the grey-shaded area), very smallchanges in training load result in very large changes in injury risk.Pre-Season Model: Likelihood of Injury=0.909327/(1+exp(−(TrainingLoad−2814.85)/609.951)). Early Competition Model: Likelihood ofInjury=0.713272×(1−exp(−0.00038318×Training Load)). LateCompetition Model: Likelihood of Injury=0.943609/(1+exp(−(TrainingLoad−1647.36)/485.813)). Redrawn from Gabbett.42

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

Actual statusInjured Not injured

Predicted status

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

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

Sensitivity Specificity87.1 (80.5 to 91.7)% 98.8 (98.1 to 99.2)%

Likelihood ratio positive70.0 (45.1 to 108.8)

Likelihood ratio negative0.1 (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 sustainedinjury; ‘True Negative’—no injury predicted and player did not sustain injury. ‘Sensitivity’—proportion of injured players who were predicted to be injured; Specificity—proportion ofuninjured 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 andnegative 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-positiveand false-negative predictions were 13% (N=20) and 11%(N=18), respectively. Players who exceeded the weekly trainingload threshold were 70 times more likely to test positive fornon-contact, soft-tissue injury, while players who did not exceedthe 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 predictiveratios obtained from the model. The prevalence of injury in thissample of professional rugby league players was 8.6%. If thepredictive equation was positive for a given player, the likeli-hood of injury increased from 8.6% to 86%, and if the resultsof the test were negative, the likelihood of injury decreasedfrom 8.6% to 0.1%. Furthermore, 87% (121 from 139 injuries)of the 8.6% of players who sustained an injury were correctlyidentified by the injury prediction model.

Although several commercially available software programsclaim to predict training load-related injuries, to date, this isthe 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 predictsinjury is best suited to the population from which it isderived. Caution should be applied when extrapolating theseresults to other sports and populations. Despite this potentiallimitation, these findings provide information on the trainingdose–response relationship in elite rugby league players, and ascientific method of monitoring and regulating trainingload in these athletes. Importantly, in a team environment,this approach allows players to be managed on an individualbasis.

THE CRITICAL ELEMENT OF WEEK-TO-WEEK CHANGE(USUALLY INCREASES!) IN TRAINING LOADAccepting that high absolute training loads are associated withgreater injury risk,42 strength and conditioning practitioners mustalso consider how week-to-week changes in training load inde-pendently influence injury risk (aside from total training load). Ina study of Australian football players, Piggott et al34 showed that40% of injuries were associated with a rapid change (>10%) inweekly training load in the preceding week. Rogalski et al39 alsoshowed that larger 1-weekly (>1750 arbitrary units, OR=2.44–3.38), 2-weekly (>4000 arbitrary units, OR=4.74) and previousto current week changes in internal load (>1250 arbitrary units,OR=2.58) were related to a greater risk of injury. Largeweek-to-week changes in training load (1069 arbitrary units) alsoincreased the risk of injury in professional rugby union players.14

We have also modelled the relationship between changes in weeklytraining 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% riskof injury (figure 5). However, when training load was increased by≥15% above the previous week’s load, injury risk escalated tobetween 21% and 49%. To minimise the risk of injury, practi-tioners should limit weekly training load increases to <10%.

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

Comparing the acute training load to the chronic training loadas a ratio provides an index of athlete preparedness. If the acutetraining 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 awell-prepared state. The ratio of acute:chronic workload will bearound 1 or less. Conversely, if the acute load is high (ie, trainingloads have been rapidly increased resulting in ‘fatigue’) and therolling average chronic training load is low (ie, the athlete hasperformed inadequate training to develop ‘fitness’), then theathlete will be in a fatigued state. In this case the ratio of theacute:chronic workload will exceed 1. The use of the acute:chronic workload ratio emphasises both the positive and negativeconsequences of training. More importantly, this ratio considersthe training load that the athlete has performed relative to thetraining load that he or she has been prepared for.43

The first study to investigate the relationship between theacute:chronic workload ratio and injury risk was performed onelite cricket fast bowlers.43 Training loads were estimated fromboth session-RPE and balls bowled. When acute workload wassimilar to, or lower than the chronic workload (ie, acute:chronic workload ratio ≤0.99) the likelihood of injury for fastbowlers in the next 7 days was approximately 4%. However,when the acute:chronic workload ratio was ≥1.5 (ie, the work-load in the current week was 1.5 times greater than what thebowler was prepared for), the risk of injury was 2–4 timesgreater in the subsequent 7 days.43

Figure 5 Likelihood of injury with different changes in training load.Unpublished data collected from professional rugby league players overthree preseason preparation periods. Training loads were measuredusing the session-rating of perceived exertion method. Training loadswere progressively increased in the general preparatory phase of thepreseason (ie, November through January) and then reduced during thespecific preparatory phase of the preseason (ie, February). The trainingprogramme progressed from higher volume-lower intensity activities inthe general preparatory phase to lower volume-higher intensityactivities in the specific preparatory phase. Each player participated inup to five organised field training sessions and four gymnasium-basedstrength and power sessions per week. Over the three preseasons, 148injuries were sustained. Data are reported as likelihoods ±95% CIs.

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Using total weekly distance as a predictor variable, almostidentical results have been found in elite rugby league44 andsoccer45 players; ‘spikes’ in acute load relative to chronic load(ie, when the acute:chronic workload ratio exceeded 1.5) wereassociated with an increased risk of injury.

Taken from three different sports (cricket, Australian footballand rugby league), a guide to interpreting and applying acute:chronic workload ratio data is shown in figure 6.46 In terms ofinjury risk, acute:chronic workload ratios within the range of0.8–1.3 could be considered the training ‘sweet spot’, whileacute:chronic workload ratios ≥1.5 represent the ‘danger zone’.To minimise injury risk, practitioners should aim to maintainthe acute:chronic workload ratio within a range ofapproximately 0.8–1.3. It is possible that different sports willhave different training load–injury relationships; until more datais available, applying these recommendations to individual sportathletes should be performed with caution.

THE BALANCE BETWEEN INJURY PREVENTION AND HIGHPERFORMANCE: TRAINING TOO MUCH OR NOT TRAININGENOUGHSuccessful sporting teams report lower injury rates and greaterplayer availability than unsuccessful teams.47–49 Although theevidence linking greater training loads with high injury rates iscompelling, focusing on the negative aspects of training detractsfrom the many positive adaptations that arise from the trainingprocess. In addition, there are several reasons why the resultslinking high training loads to injury should be taken in contextwith the wide range of performance issues relevant to sport.Wrapping players in cotton wool will not bring on-field success.How can practitioners help coaches train players at the ideallevel (maximising performance while also maintaining a low riskof non-contact soft-tissue injuries)?

DOES THIS MEAN ATHLETES SHOULD STOP TRAINING?!Although studies have shown a positive relationship betweentraining load and injury, there is also evidence demonstrating

that training has a protective effect against injury. The results ofthese studies should be considered when evaluating the influ-ence of high training workloads on injury risk:1. Team sport athletes who performed greater than 18 weeks of

training before sustaining their initial injuries were atreduced risk of sustaining a subsequent injury.50 These find-ings are consistent with others43 44 who have shown thathigh chronic workloads may decrease the risk of injury.Furthermore, greater training prior to entering an elitejunior soccer programme was associated with a decreasedrisk of developing groin pain.51

2. Second, across a wide range of sports, well-developed phys-ical qualities are associated with a reduced risk ofinjury.50 52–54 Clearly, for athletes to develop the physicalcapacities required to provide a protective effect againstinjury, they must be prepared to train hard.

3. Importantly, there is evidence that over-training and under-training may increase injury risk.14 28 32 For example, cricketfast bowlers who bowled fewer deliveries per week withgreater recovery between sessions were at an increased riskof injury, while bowlers who bowled more deliveries perweek with less recovery between sessions were also at anincreased risk of injury. Similar findings have been reportedin baseball and rugby union.14 28 The ‘U’-shaped relation-ship between workload and injury from these data demon-strate that both inadequate and excessive workloads areassociated with injury.Collectively, these results emphasise that reductions in work-

loads may not always be the best approach to protect againstinjury. How do practitioners find the ‘sweet spot’ of training load?

TRAINING SMARTER AND HARDER—THE MECHANISMSTHAT MAY UNDERPIN THESE FINDINGSAlthough high training loads have been associated with higherinjury rates, results are equivocal with recent evidence also dem-onstrating a protective effect of high chronic training loads.43 44

Figure 6 Guide to interpreting and applying acute:chronic workload ratio data. The green-shaded area (‘sweet spot’) represents acute:chronicworkload ratios where injury risk is low. The red-shaded area (‘danger zone’) represents acute:chronic workload ratios where injury risk is high. Tominimise injury risk, practitioners should aim to maintain the acute:chronic workload ratio within a range of approximately 0.8–1.3. Redrawn fromBlanch and Gabbett.46

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In this section I elaborate on the data shown in tables 1 and2. Table 1 above shows that players who performed greateramounts of very high-speed running were 2.7 times more likelyto sustain a non-contact soft-tissue injury than players withlower running loads.26 Given the high risk of injury withgreater running loads, it is tempting to suggest that athletesshould avoid very high-speed running in training to minimisethe risk of injury. However, by restricting running loads in anattempt to reduce injury risk, it is possible that during criticalpassages of play when players are required to exert maximallythey are inadvertently put at greater risk of injury due to beingunder prepared.

Greater amounts of very high-speed running may be asso-ciated with increased injury risk, however there is evidence(from the same data set) of lower injury risk when players per-formed greater amounts of low-intensity activity and short accel-eration efforts.26 High-intensity team sports such as soccer,basketball and the rugby codes require players to perform short(2–3 s) acceleration efforts,55 followed by longer durations oflower intensity activity.56 In competition, longer high-speedefforts are uncommon.57

Given that high training loads can be achieved in differentways (ie, volume, intensity and frequency of training, as well asthe balance of training activities performed) it is inappropriateto consider all ‘high training loads’ as carrying identical injuryrisk. To be explicit, ‘high training loads’ per se may not be thelargest contributing factor to increased injury risk, but ratherthe type of ‘high training load’ that is prescribed may be animportant predictor of injury. Greater amounts of short, high-intensity acceleration effort training and game-specific aerobicactivity may provide team sport athletes with the appropriatephysical qualities to not only perform at a high level, but alsoprotect against injury.

Table 2 illustrates the accuracy of an injury prediction model.It demonstrates that the training load model was both sensitiveand specific for predicting non-contact, soft tissue injuries.However, the injury prediction model was far better at identify-ing when injuries were unlikely to occur (ie, true negatives) thanit was at predicting injuries. These findings are intuitive; if per-formance staff focus on injury prevention, and prevent injuriesthrough ‘managing athletes away from training’, then the lownumbers of training-load related injuries may be expected, asathletes are unlikely to ever train with adequate volumes orintensities to sustain an injury.

Equally, note that in figure 4, on the steep portion of thetraining load–injury curve small changes in training load (eitherincreases or decreases) result in large changes in injury risk (inthe respective direction). Under-emphasised in this study, wasthat due to the sigmoidal nature of the curve, at large trainingloads the training load–injury relationship is almost completely‘flat’. On this portion of the curve, large changes in trainingload result in very small changes in injury risk. Thus, if athletescan safely train through the ‘high risk’ portions of the curve(using the acute:chronic workload ratio model), then they maydevelop greater resilience and training tolerance.

Although injury prediction models may have sufficient pre-dictive accuracy to warrant systematic use in an elite team sportprogramme, a fine balance exists between training, detrainingand overtraining. Training programmes must be physiologicallyand psychologically appropriate58 to allow players to cope withthe demands of competition. With this in mind, it may beargued that it is worthwhile using preseason training and train-ing camps to prescribe high training loads (note, not excessive)to determine which players are most susceptible to injury underphysically stressful situations (these players most likely will nottolerate the intensity and fatigue of competition), and whichplayers are not susceptible to injury under physically stressfulsituations (these players are more likely to tolerate the intensityand fatigue of competition).

A NEW VIEW OF TRAINING—A ‘VACCINE’ AGAINSTINJURIES!This paper proposes the training-injury prevention paradox.Physically hard (and appropriate) training may protect againstinjuries. There is no disputing that high training loads are gener-ally associated with better developed fitness and thus, good per-formance. One cost of high training load is often considered tobe soft tissue injury risk. To address this risk, training loadscould be reduced to decrease the incidence of injury, howeverlow training loads (in the form of reduced training volumes)have also been associated with increased injury risk; exposingplayers to low training loads may place them at risk of furtherinjury. Once players enter the rehabilitation process, it is a chal-lenge for practitioners to expose them to appropriate loads toenhance physical qualities which provide a protective effectagainst injury, and prevent the ‘spike’ in loads when playersreturn to full training. As a result, it is not uncommon for teamsto have a constant ‘rehab-er’ in their squad—a player who

Figure 7 Relationship betweenphysical qualities, training load, andinjury risk in team sport athletes.

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breaks down repeatedly (potentially with different injuries)because his or her training load is not high enough to adapt tomatch demands. The data presented suggest that prescribinghigh training loads can lead to improved levels of fitness, whichin turn offers a protective effect against injury, ultimatelyleading to (1) greater physical outputs and resilience in competi-tion, and (2) a greater proportion of the squad available forselection each week (figure 7).

CONCLUSIONSIn conclusion, while there is a relationship between high train-ing loads and injury, this paper demonstrates that the problem isnot with training per se, but more likely the inappropriate train-ing that is being prescribed. Excessive and rapid increases intraining loads are likely responsible for a large proportion ofnon-contact, soft-tissue injuries. However, physically hard (andappropriate) training develops physical qualities, which in turnprotects against injuries. This paper highlights the importance ofmonitoring training load, including the load that athletes areprepared for (by calculating the acute:chronic workload ratio),as a best practice approach to the long-term reduction oftraining-related injuries.

What are the findings?

▸ Dogma exists around the effects of high (and low) trainingloads on injury.

▸ This review highlights the positive and negative effects ofhigh training loads on injury risk, fitness and thus,performance.

▸ There is a relationship between high training loads andinjuries but well-developed physical qualities protect againstinjury.

▸ The ratio of acute to chronic training load is a betterpredictor of injury than acute or chronic loads in isolation.

How might it impact on clinical practice in the future?

▸ In many high performance settings, training loads arereported on a week-to-week basis. Recording acute andchronic training loads, and modelling the acute:chronicworkload ratio allows practitioners to determine if athletesare in a state of ‘fitness’ (ie, net training recovery, lowerthan average risk of injury) or ‘fatigue’ (ie, net trainingstress, higher than average risk of injury).

▸ The Training-Injury Prevention Paradox Model allowspractitioners to monitor and prescribe training to team sportathletes on an individual basis.

▸ Providing evidence around the effects of acute and chronictraining load on injury risk, physical fitness and performancewill allow practitioners to systematically prescribe hightraining loads while minimising the risk of athletessustaining a ‘load-related’ injury.

Correction notice The paper has been amended since it was published OnlineFirst. The title of the paper has been changed slightly.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Open Access This is an Open Access article distributed in accordance with theCreative Commons Attribution Non Commercial (CC BY-NC 4.0) license, whichpermits others to distribute, remix, adapt, build upon this work non-commercially,and license their derivative works on different terms, provided the original work isproperly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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harder?andshould athletes be training smarter

injury prevention paradox:−−The training

Tim J Gabbett

doi: 10.1136/bjsports-2015-0957882016

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