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The training-injury prevention paradox: should
athletes be training smarter and harder?
Tim J Gabbett
1,2
1
School of Exercise Science,
Australian Catholic University,
Brisbane, Queensland,
Australia
2
School 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;
tim_gabbett@yahoo.com.au
Accepted 16 November 2015
To cite: Gabbett TJ. Br J
Sports Med Published Online
First: [please include Day
Month Year] doi:10.1136/
bjsports-2015-095788
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 determi nant 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 Orchard
1
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 al
2
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,
48
and higher training intensity
568
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).
RELATIO NSHIP BETWEEN TRA INING LOADS AND IN JURY
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 work
11
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 fi eld 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
Gabbett
38
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 al
39
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 RELAT IONSHIP
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, Gabbett
42
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-rel ated 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 p redicts
injury is best suited to the population from which it is
der ived. Caution should be applied when extrapolating these
results to other sports and popu lations. De spi te thi s potenti al
limitation, these findings provide information on the trai ning
dose–response relationship in elite rugby league players , and a
scientific method of monitoring and regulating training
load in these athlete s. 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 al
34
showed that
40% of injuries were associated with a rapid change (>10%) in
weekly training load in the preceding week. Rogalski et al
39
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:chronic workload ratio and injury risk was performed on
elite cricket fast bowlers.
43
Training loads were estimated from
both session-RPE and balls bowled. When acute workload was
similar to, or lower than the chronic workload (ie, acute:
chronic workload ratio ≤0.99) the likelihood of injury for fast
bowlers 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 the
bowler was prepared for), the risk of injury was 2–4 times
greater 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 over
three preseason preparation periods. Training loads were measured
using the session-rating of perceived exertion method. Training loads
were progressively increased in the general preparatory phase of the
preseason (ie, November through January) and then reduced during the
specific preparatory phase of the preseason (ie, February). The training
programme progressed from higher volume-lower intensity activities in
the general preparatory phase to lower volume-higher intensity
activities in the specific preparatory phase. Each player participated in
up to five organised field training sessions and four gymnasium-based
strength and power sessions per week. Over the three preseasons, 148
injuries were sustained. Data are reported as likelihoods ±95% CIs.
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Using total weekly distance as a predictor variable, almost
identical results have been found in elite rugby league
44
and
soccer
45
players; ‘spikes’ in acute load relative to chronic load
(ie, when the acute:chronic workload ratio exceeded 1.5) were
associated with an increased risk of injury.
Taken from three different sports (cricket, Australian football
and rugby league), a guide to interpreting and applying acute:
chronic workload ratio data is shown in figure 6.
46
In terms of
injury risk, acute:chronic workload ratios within the range of
0.8–1.3 could be considered the training ‘sweet spot’, while
acute:chronic workload ratios ≥1.5 represent the ‘danger zone’.
To minimise injury risk, practitioners should aim to maintain
the acute:chronic workload ratio within a range of
approximately 0.8–1.3. It is possible that different sports will
have different training load–injury relationships; until more data
is available, applying these recommendations to individual sport
athletes should be performed with caution.
THE BALANCE BETWEEN INJURY PRE VENTION AND HIGH
PERFORMANCE: TRAINING TOO MUCH OR NOT TRAINING
ENOUGH
Successful sporting teams report lower injury rates and greater
player availability than unsuccessful teams.
47–49
Although the
evidence linking greater training loads with high injury rates is
compelling, focusing on the negative aspects of training detracts
from the many positive adaptations that arise from the training
process. In addition, there are several reasons why the results
linking high training loads to injury should be taken in context
with 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 ideal
level (maximising performance while also maintaining a low risk
of non-contact soft-tissue injuries)?
DOES THIS MEAN AT HLETES SHOULD STOP TRAINING?!
Although studies have shown a positive relationship between
training load and injury, there is also evidence demonstrating
that training has a protective effect against injury. The results of
these 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 at
reduced risk of sustaining a subsequent injury.
50
These find-
ings are consistent with others
43 44
who have shown that
high chronic workloads may decrease the risk of injury.
Furthermore, greater training prior to entering an elite
junior soccer programme was associated with a decreased
risk of developing groin pain.
51
2. Second, across a wide range of sports, well-developed phys-
ical qualities are associated with a reduced risk of
injury.
50 52–54
Clearly, for athletes to develop the physical
capacities required to provide a protective effect against
injury, 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, cricket
fast bowlers who bowled fewer deliveries per week with
greater recovery between sessions were at an increased risk
of injury, while bowlers who bowled more deliveries per
week with less recovery between sessions were also at an
increased risk of injury. Similar findings have been reported
in 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 are
associated with injury.
Collectiv ely, these results emphasise that reductions in work-
loads may not always be the best approach to protect against
injury. How do practitioners find the ‘sweet spot’ of training load?
TRAINING SMARTER AND HARDER—THE MECHANISMS
THAT MAY U NDERPIN THESE FINDINGS
Although high training loads have been associated with higher
injury 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:chronic
workload ratios where injury risk is low. The red-shaded area (‘danger zone’) represents acute:chronic workload ratios where injury risk is high. To
minimise injury risk, practitioners should aim to maintain the acute:chronic workload ratio within a range of approximately 0.8–1.3. Redrawn from
Blanch and Gabbett.
46
6 Gabbett TJ. Br J Sports Med 2016;0:1–9. doi:10.1136/bjsports-2015-095788
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In this section I elaborate on the data shown in tables 1 and
2. Table 1 above shows that players who performed greater
amounts of very high-speed running were 2.7 times more likely
to sustain a non-contact soft-tissue injury than players with
lower running loads.
26
Given the high risk of injury with
greater running loads, it is tempting to suggest that athletes
should avoid very high-speed running in training to minimise
the risk of injury. However, by restricting running loads in an
attempt to reduce injury risk, it is possible that during critical
passages of play when players are required to exert maximally
they are inadvertently put at greater risk of injury due to being
under 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 of
lower intensity activity.
56
In competition, longer high-speed
efforts are uncommon.
57
Given that high training loads can be achieved in different
ways (ie, volume, intensity and frequency of training, as well as
the balance of training activities performed) it is inappropriate
to consider all ‘high training loads’ as carrying identical injury
risk. To be explicit, ‘high training loads’ per se may not be the
largest contributing factor to increased injury risk, but rather
the type of ‘high training load’ that is prescribed may be an
important predictor of injury. Greater amounts of short, high-
intensity acceleration effort training and game-specifi c aerobic
activity may provide team sport athletes with the appropriate
physical qualities to not only perform at a high level, but also
protect against injury.
Table 2 illustrates the accuracy of an injury prediction model.
It demonstrates that the training load model was both sensitive
and 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) than
it was at predicting injuries. These findings are intuitive; if per-
formance staff focus on injury prevention, and prevent injuries
through ‘managing athletes away from training’, then the low
numbers of training-load related injuries may be expected, as
athletes are unlikely to ever train with adequate volumes or
intensities to sustain an injury.
Equally, note that in figure 4, on the steep portion of the
training load–injury curve small changes in training load (either
increases or decreases) result in large changes in injury risk (in
the respective direction). Under-emphasised in this study, was
that due to the sigmoidal nature of the curve, at large training
loads the training load–injury relationship is almost completely
‘flat’. On this portion of the curve, large changes in training
load result in very small changes in injury risk. Thus, if athletes
can safely train through the ‘high risk’ portions of the curve
(using the acute:chronic workload ratio model), then they may
develop greater resilience and training tolerance.
Although injury prediction models may have sufficient pre-
dictive accuracy to warrant systematic use in an elite team sport
programme, a fine balance exists between training, detraining
and overtraining. Training programmes must be physiologically
and psychologically appropriate
58
to allow players to cope with
the demands of competition. With this in mind, it may be
argued 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 under
physically stressful situations (these players most likely will not
tolerate the intensity and fatigue of competition), and which
players are not susceptible to injury under physically stressful
situations (these players are more likely to tolerate the intensity
and fatigue of competition).
A NEW VIEW OF TRAINING—A ‘VACCINE’ AGAINST
INJURIES!
This paper proposes the training-injury prevention paradox.
Physically hard (and appropriate) training may protect against
injuries. 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 to
be soft tissue injury risk. To address this risk, training loads
could be reduced to decrease the incidence of injury, however
low training loads (in the form of reduced training volumes)
have also been associated with increased injury risk; exposing
players to low training loads may place them at risk of further
injury. Once players enter the rehabilitation process, it is a chal-
lenge for practitioners to expose them to appropriate loads to
enhance physical qualities which provide a protective effect
against injury, and prevent the ‘spike’ in loads when players
return to full training. As a result, it is not uncommon for teams
to have a constant ‘rehab-er’ in their squad—a player who
Figure 7 Relationship between
physical qualities, training load, and
injury risk in team sport athletes.
Gabbett TJ. Br J Sports Med 2016;0:1–9. doi:10.1136/bjsports-2015-095788 7
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breaks down repeatedly (potentially with different injuries)
because his or her training load is not high enough to adapt to
match demands. The data presented suggest that prescribing
high training loads can lead to improved levels of fitness, which
in turn offers a protective effect against injury, ultimately
leading to (1) greater physical outputs and resilience in competi-
tion, and (2) a greater proportion of the squad available for
selection each week (figure 7).
CONCLUSIONS
In conclusion, while there is a relationship between high train-
ing loads and injury, this paper demonstrates that the problem is
not with training per se, but more likely the inappropriate train-
ing that is being prescribed. Excessive and rapid increases in
training loads are likely responsible for a large proportion of
non-contact, soft-tissue injuries. However, physically hard (and
appropriate) training develops physical qualities, which in turn
protects against injuries. This paper highlights the importance of
monitoring training load, including the load that athletes are
prepared for (by calculating the acute:chronic workload ratio),
as a best practice approach to the long-term reduction of
training-related injuries.
What are the findings?
▸ Dogma exists around the effects of high (and low) training
loads on injury.
▸ This review highlights the positive and negative effects of
high training loads on injury risk, fitness and thus,
performance.
▸ There is a relationship between high training loads and
injuries but well-developed physical qualities protect against
injury.
▸ The ratio of acute to chronic training load is a better
predictor 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 are
reported on a week-to-week basis. Recording acute and
chronic training loads, and modelling the acute:chronic
workload ratio allows practitioners to determine if athletes
are in a state of ‘fitness’ (ie, net training recovery, lower
than average risk of injury) or ‘fatigue’ (ie, net training
stress, higher than average risk of injury).
▸ The Training-Injury Prevention Paradox Model allows
practitioners to monitor and prescribe training to team sport
athletes on an individual basis.
▸ Providing evidence around the effects of acute and chronic
training load on injury risk, physical fitness and performance
will allow practitioners to systematically prescribe high
training loads while minimising the risk of athletes
sustaining a ‘load-related’ injury.
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 the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly 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
The training-injury prevention paradox:
Tim J Gabbett
published online January 12, 2016Br J Sports Med
http://bjsm.bmj.com/content/early/2016/01/12/bjsports-2015-095788
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