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

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

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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.
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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 players
training burden. I describe the critical variableacute:
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 tness,
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.
TRAININGPERFORMANCE 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, tness and performance. He
speculated that both inadequate and excessive train-
ing loads would result in increased injuries,
reduced tness and poor team performance (see
gure 1). The relationship between training load,
injury, tness 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-
ed personal trainers, etc). Our eldsports per-
formance and sports injury prevention is a
multidisciplinary one and this paper is relevant to
the eld 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 (tness). 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 inuence of
training volume, intensity and frequency on athletic
performance, with performance generally improved
with increases in training load.
410
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,
signicant associations were found between greater
training volume (r=0.500.80) and higher training
intensity (r=0.600.70) 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.
1015
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, unt 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-specic 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 LOADTHE ATHLETES 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 110 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
7001000 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.
1921
A wide range of subjective questionnaires
are used with many of them employing a simple 5, 7 or
10-point Likert scale.
1923
Longer, more time consuming
surveys are also employed.
24 25
Figure 1 Hypothetical relationship between training loads, tness,
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 athletes 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 reects 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 72186 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,
2830
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 ndings 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 33.99 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 ndings on external loads are consistent with results from
studies on internal loads; higher training loads were associated
with greater injury rates.
11 15 3336
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 (gure 2).
Furthermore, over a 3-year period, reduced training loads mark-
edly reduced injury rates in the same cohort of players (gure
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 eld injury (r=0.82), and
contact eld injury (r=0.80) rates.
35
Signicant relationships
were also observed between the eld training load and overall
eld injury (r=0.68), non-contact eld injury (r=0.65), and
contact eld injury (r=0.63) rates. Strength and power training
loads were signicantly related to the incidence of strength and
power injuries (r=0.63). There was no signicant relationship
between eld training loads and the incidence of strength and
power injuries. However, strength and power training loads
were signicantly associated with the incidence of contact
(r=0.75) and non-contact (r=0.87) eld training injuries.
Collectively, these ndings 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 eld injuries. Monitoring of training loads and careful sched-
uling of eld 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 Inuence of reductions in
preseason training loads on injury
rates and changes in aerobic tness 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 inuences adaptations to training.
38 39
Gabbett
38
investigated training loads, injury rates and physical
performance changes associated with a 14-week eld 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 modied 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 (13 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 rst to third year players.
Clearly, further research investigating the doseresponse rela-
tionship between training and injury in athletes of different ages
and genders is warranted.
41
MODELLING THE TRAINING LOADINJURY 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 5080% 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 (17003000
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 (gure 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 fty-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 5080% likely to sustain a preseason injury within the
training load range of 30005000 arbitrary units. These training load
thresholds were considerably reduced (17003000 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
Load2814.85)/609.951)). Early Competition Model: Likelihood of
Injury=0.713272×(1exp(0.00038318×Training Load)). Late
Competition Model: Likelihood of Injury=0.943609/(1+exp((Training
Load1647.36)/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 Positivepredicted injury but player did not sustain injury; False Negative’—no injury predicted but player sustained
injury; True Negativeno injury predicted and player did not sustain injury. Sensitivityproportion of injured players who were predicted to be injured; Specificityproportion of
uninjured players who were predicted to remain injury-free. Likelihood ratio positivesensitivity/(1specificity); Likelihood ratio negative(1sensitivity)/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
identied 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 ndings provide information on the trai ning
doseresponse relationship in elite rugby league players , and a
scientic 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 inuence 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 (gure 5). However, when training load was increased by
15% above the previous weeks 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 TRAININGINJURY
RELATIONSHIP?
Is there a benet in modelling the traininginjury 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 36 weeks of training. In this respect, chronic train-
ing loads are analogous to a state of tness 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 tness), 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 tness), 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 rst 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 24 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
specic 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 specic preparatory phase. Each player participated in
up to ve organised eld 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.
Gabbett TJ. Br J Sports Med 2016;0:19. doi:10.1136/bjsports-2015-095788 5
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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 gure 6.
46
In terms of
injury risk, acute:chronic workload ratios within the range of
0.81.3 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 0.81.3. It is possible that different sports will
have different training loadinjury 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.
4749
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-eld 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 inu-
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 nd-
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 5254
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 ndings 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 nd the sweet spot of training load?
TRAINING SMARTER AND HARDERTHE 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 0.81.3. Redrawn from
Blanch and Gabbett.
46
6 Gabbett TJ. Br J Sports Med 2016;0:19. 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
(23 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-specic 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 specic 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 ndings 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 gure 4, on the steep portion of the
training loadinjury 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 loadinjury relationship is almost completely
at. 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 sufcient pre-
dictive accuracy to warrant systematic use in an elite team sport
programme, a ne 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 TRAININGA 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 tness 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 squada player who
Figure 7 Relationship between
physical qualities, training load, and
injury 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 to
match demands. The data presented suggest that prescribing
high training loads can lead to improved levels of tness, 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 (gure 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 ndings?
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, tness 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 tness (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 tness 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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... Training load is usually classified into measures of external and internal load. External load refers to all movements of the players, and can be assessed by some micro electromechanical devices such as global positioning systems (GPS), local positioning systems, and inertial measurement units (Gabbett, 2016); while internal load refers to the athletes' biological responses to an external load, such as heart rate and ratings of perceived exertion (Bourdon et al., 2017). GPS is the most used tracking tool to collect external workloads during training in team sports (Akenhead and Nassis, 2016), and it has been shown that GPS is a valid and reliable tool to monitor training (Nikolaidis et al., 2018). ...
... Impellizzeri et al. (2020a) concluded that no evidence suggests the use of ACWR in managing training loads to reduce injury risk. However, Gabbett (2016) stated that excessive and rapid increases in training loads are probably responsible for most non-contact injuries. In 2016, the International Olympic Committee published a consensus statement which suggests the use of the ACWR approach for injury prevention . ...
Article
Full-text available
Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
... [35][36][37]. In this way, a higher accumulated loading [38,39], more extensive injury history [40], and greater competitiveness with advancing age [41] may augment the risk of sustaining ankle sprains in adult basketball players compared to adolescents. Nevertheless, our findings support the potential utility of a multicomponent warm-up program in reducing the risk of sustaining ankle sprains. ...
Article
Objective: To assess the effects of a novel multicomponent neuromuscular warm-up program on lower-extremity injury incidence in basketball players competing at the regional level. Methods: A cluster randomized controlled experimental design was adopted to compare injury incidence between players exposed to the injury prevention warm-up program and those exposed to a typical warm-up program across an entire basketball season. Four teams consisting of 57 players (male: n = 42; female: n = 15) were allocated to the intervention group (age: 21.6 ± 2.5 years; height: 186.2 ± 8.8 cm; body mass: 80.0 ± 10.4 kg) and four teams consisting of 55 players (male: n = 43; female: n = 12) were allocated to the control group (age: 21.6 ± 2.6 years; height: 186.9 ± 9.1 cm; body mass: 81.5 ± 10.9 kg). The novel warm-up combined running exercises with active stretching, plyometrics, balance, strength, and agility drills. Coaching and medical staff provided details on injury incidence each week. Data analyses included the use of poisson regression analyses and the incidence rate ratio (IRR) with 95% confidence intervals (CI). Results: The intervention group experienced a significantly lower ankle sprain incidence rate (IRR = 0.26, 95% CI = 0.05, 0.98, p = 0.02) and a tendency toward a lower knee injury incidence rate (IRR = 0.32, 95% CI = 0.03, 1.78, p = 0.07) compared to the control group. Considering only non-contact lower-extremity injuries of any type, the intervention group experienced a significantly lower incidence rate compared to the control group (IRR = 0.26, 95% CI = 0.05, 0.98, p <0.001). Conclusion: This multi-team study demonstrated a novel multicomponent warm-up program resulted in less non-contact lower-extremity injuries, particularly ankle sprains and knee injuries, compared to a typical warm-up program in regional-level male and female basketball players.
... Yüksek şiddette ve hızda gerçekleştirilen bu hareketler kat edilen toplam mesafenin küçük bir kısmını oluştursa da oyunun kritik anlarında daha büyük bir etkiye sahiptir ve top kazanma, rakip atağı durdurma veya gol atma gibi sonuca doğrudan etki eden durumlarda belirleyici bir rol oynarlar (Little ve Williams, 2003;Stolen ve ark., 2005). Oyunun sonucuna bu denli etkisi nedeniyle maç boyunca sergilenen ivmelenme, çeviklik, sprint ve sıçrama gibi benzer morfolojik ve biyokimyasal özellikteki beceriler iş yükü ile birlikte sakatlanma riskini de artırmaktadır (Gabbett ve Ullah, 2012;Gabbett, 2016;Little ve Williams, 2003). Bu nedenle, birçok araştırmacı bu tür aktiviteler öncesinde vücudu mental/fiziksel olarak hazırlamak ve performansı artırmak, bunun yanı sıra sakatlık riskini azaltmak için ısınma egzersizleri yapılmasının gerekli olduğunu belirtmektedir (Blazevich ve ark., 2018;Thacker ve ark., 2004; Van den Tillaar ve ark., 2019). ...
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Öz: Bu çalışmanın amacı genç futbolcularda direnç bandı egzersizlerinin bazı performans parametrelerine akut etkisini araştırmaktır. Bu çalışmaya, 15-16 yaş grubu, aktif futbol oynayan antrenmanlı 16 gönüllü erkek futbolcu (yaş: 15,18 ± ,40 yıl; boy uzunluğu: 170,81 ± 7,21 cm; vücut ağırlığı: 59,43 ± 8,61 kg; beden kitle indeksi (BKİ): 20,26 ± 1,60 kg/m 2) katılmıştır. Araştırma grubuna art arda olmayan günlerde antrenman öncesi jogging+dinamik germe egzersizleri (DGE) ve jogging+dinamik germe+direnç bandı egzersizlerini (DBE) içeren farklı iki ısınma ve egzersiz protokolü uygulanmıştır. Isınma protokolleri sonrası 3 dakikalık pasif dinlenme periyodunu takiben futbolculara denge testi, reaksiyon zamanı testi, dikey sıçrama ve anaerobik güç testi, Illinois çeviklik testi, 30 m sürat testi ve top hızı ölçümleri gerçekleştirilmiştir. Hata terimlerinin normal dağılım gösterip göstermediği Shapiro-Wilk normallik testi kullanılarak kontrol edilmiştir. Gruplar arası karşılaştırma bağımlı örneklem t-test ile analiz edilmiştir. Ayrıca etki büyüklüğünün hesaplanması için Cohen's d formülü uygulanmıştır. Verilerin istatistiksel analizi ve yorumları p<0,05 önem seviyesinde anlamlı kabul edilmiştir. Denge, dikey sıçrama, Illinois çeviklik ve 30 m sürat testleri sonuçları iki grup arasında karşılaştırıldığında, tüm test sonuçlarında direnç bandı egzersizlerinin performansa olumlu etki ettiği saptanmıştır, bununla birlikte istatistiksel olarak anlamlı farklılıklar denge, çeviklik ve sürat testleri değerlerinde bulunmuştur (p<0,05). Sonuç olarak, direnç bandı egzersizleri sonrası futbolcularda denge, dikey sıçrama, Illinois çeviklik ve 30 m sürat parametrelerinde performans artışı sağlandığı tespit edilmiştir. Bu doğrultuda, antrenör ve sporculara direnç bandı egzersizlerine branşa özgü ısınma protokollerinde yer vermeleri ve futbolcularda yüksek performans sağlamak için antrenman öncesi direnç bandı egzersizlerinin uygulanması önerilmektedir. Abstract: The aim of this study was to investigate the acute effect of resistance band exercises on some performance parameters in young football players. Active and trained 16 male football players (age: 15.18 ± .40 years; height: 170.81 ± 7.21 cm; weight: 59.43 ± 8.61 kg; body mass index (BMI): 20.26 ± 1.60 kg/m 2) voluntarily participated in this study. Subjects performed two different warm-up protocols, including jogging+dynamic stretching exercises and jogging+dynamic stretching+resistance band exercises on non-consecutive days. Following the warm-up protocols and then three minutes of passive recovery, subjects were tested on the balance test, reaction time test, vertical jump and anaerobic power test, Illinois agility test, 30-m sprint, and ball kicking speed. Data were checked for normality by using Shapiro-Wilk test. Comparison between groups was analyzed with paired sample t-test. Besides, Cohen's d was utilized in the calculation of effect size. Statistical analyses and interpretations of the data were accepted as p<0.05. In the comparison of the balance, vertical jump, 30-m sprint, and Illinois agility test results between two groups, resistance band exercises were found to have positive effects on performance in all tests. However, statistically significant differences were detected in balance, agility, and sprint tests (p<0.05). In conclusion, the balance, vertical jump, 30-m sprint, and Illinois agility test performance parameters of football players improved following the resistance band exercises. Accordingly, it is recommended that coaches and athletes incorporate resistance band exercises into sport-specific warm-up protocols, and resistance band exercises should be performed in pre-training warm-up sessions to achieve high performance in football players.
... Yüksek şiddette ve hızda gerçekleştirilen bu hareketler kat edilen toplam mesafenin küçük bir kısmını oluştursa da oyunun kritik anlarında daha büyük bir etkiye sahiptir ve top kazanma, rakip atağı durdurma veya gol atma gibi sonuca doğrudan etki eden durumlarda belirleyici bir rol oynarlar (Little ve Williams, 2003;Stolen ve ark., 2005). Oyunun sonucuna bu denli etkisi nedeniyle maç boyunca sergilenen ivmelenme, çeviklik, sprint ve sıçrama gibi benzer morfolojik ve biyokimyasal özellikteki beceriler iş yükü ile birlikte sakatlanma riskini de artırmaktadır (Gabbett ve Ullah, 2012;Gabbett, 2016;Little ve Williams, 2003). Bu nedenle, birçok araştırmacı bu tür aktiviteler öncesinde vücudu mental/fiziksel olarak hazırlamak ve performansı artırmak, bunun yanı sıra sakatlık riskini azaltmak için ısınma egzersizleri yapılmasının gerekli olduğunu belirtmektedir (Blazevich ve ark., 2018;Thacker ve ark., 2004; Van den Tillaar ve ark., 2019). ...
Article
The aim of this study was to investigate the acute effect of resistance band exercises on some performance parameters in young football players. Active and trained 16 male football players (age: 15.18 ± .40 years; height: 170.81 ± 7.21 cm; weight: 59.43 ± 8.61 kg; body mass index (BMI): 20.26 ± 1.60 kg/m2) voluntarily participated in this study. Subjects performed two different warmup protocols including jogging+dynamic stretching exercises and jogging+dynamic stretching+resistance band exercises on non-consecutive days. Following the warm-up protocols and then three minutes of passive recovery, subjects were tested on the balance test, reaction time test, vertical jump and anaerobic power test, Illinois agility test, 30-m sprint, and ball kicking speed. Data were checked for normality by using Shapiro-Wilk test. Comparison between groups was analyzed with paired sample t-test. Besides, Cohen’s d was utilized in calculation of effect size. Statistical analyses and interpretations of the data were accepted as p<0.05. In comparison of the balance, vertical jump, 30-m sprint, and Illinois agility tests results between two groups, resistance band exercises were found to have positive effects on performance in all tests. However, statistically significant differences were detected in balance, agility, and sprint tests (p<0.05). In conclusion, balance, vertical jump, 30-m sprint, and Illinois agility test performance parameters of football players improved following the resistance band exercises. Accordingly, it is recommended that coaches and athletes incorporate resistance band exercises into sport-specific warmup protocols, and resistance band exercises should be performed in pre-training warm up session for achieving high performance in football players.
... Si bien el estudio demostró una baja en la incidencia de lesión en los deportes de combate, de tiempo y marca posterior al programa, el bádminton reflejó lo contrario, situación que se pudo presentar debido a la falta de control en el estudio de las cargas de entrenamiento, las cuales tienen relación con la presencia de lesiones. Si bien es cierto que los aumentos excesivos de las cargas de entrenamiento benefician el desarrollo de cualidades físicas que se convierten en factor protector de la salud del atleta, el manejo inadecuado del proceso, sin darle la importancia a monitorizar la carga de trabajo y la posibilidad de aceptación de esta por parte de los deportistas en el momento adecuado, se convierten en componentes de reducción a largo plazo de lesiones relacionadas con el proceso de entrenamiento (30). ...
... The primary outcome of training is to improve individual and team performance via delivery of an optimal physiological and psychological stimuli (Tucker & Collins, 2012). Careful administration of training frequency, intensity, time and type is required to elicit desired physiological and psychological adaptations, while reducing the risk of injury or periods spent outside of training (Gabbett, 2016). ...
Thesis
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The primary aim of this thesis was to evaluate the dietary intake, energy expenditure and energy balance of young professional male rugby league players across the season.
... It is thought that at younger ages, high-volume, tournamentoriented play may not be necessary for elite success later on and may actually increase injury (Jayanthi & Esser, 2013). A high training load may increase the injury rate, but it is also a fact that training has protective effects against injuries (Gabbett, 2016). In a study, it was determined that athletes between the ages of 7-18 who have experienced injuries were specialized in a particular sport and experienced overuse injuries (Jayanthi & Esser, 2013). ...
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This research aims to investigate how tennis players' personality structures affect some variables related to the injuries they experience in sports. The research population consists of tennis players between the ages of 10-18 in Turkey. A total of 158 (female: 87, male: 71) tennis players between the ages of 10-18 who had at least one tennis-specific injury participated in the study. Demographic questions (gender, age, height, weight, time of sports injury, repetition of the same injury, and injury measure) used in the study were created by the researchers. The personality of the athlete was determined by the athlete himself, and the survey questions were determined by using the survey questions used in Kirişci's (2011) study. Data from tennis players were collected online via 'Google Form'. There is a low level of statistically positive correlation between re-experiencing the sports injury and the time of the sports injury (r=0.18, p=0.03). There is a low level of statistically positive correlation between the gender of the participants and taking precautions for sports injury (r=0.20, p=0.01). There is a low negative correlation between the gender of the participants and their athlete personality (r=26, p=0.001). There is no statistically significant difference between the participants' re-experiencing the same injury, taking precautions in sports injury, and athlete's personality (r=-0.013, p=0.87, r=0.010, p=0.90). It can be said that the sports injuries experienced by tennis players are related to their personality types and their gender. It can be said that injuries seen in tennis sports are more common during matches and women take more precautions for sports injuries than male athletes. In addition, it can be said that female athletes have both courageous-attentive and emotional-calm personality types, while males have the most courageous-active personality type.
... Incomplete and potentially inaccurate athlete load data can result in several deleterious outcomes and places athletes at risk of inappropriate training recommendations, potentially leading to physical unpreparedness, injury, or burnout [7]. An important example is the female rugby sevens competition environment in which teams play five or six games in a two-or three-day tournament, often with multiple tournaments happening in a few weeks. ...
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Rate of perceived exertion (RPE) is used to calculate athlete load. Incomplete load data, due to missing athlete-reported RPE, can increase injury risk. The current standard for missing RPE imputation is daily team mean substitution. However, RPE reflects an individual's effort; group mean substitution may be suboptimal. This investigation assessed an ideal method for imputing RPE. A total of 987 datasets were collected from women's rugby sevens competitions. Daily team mean substitution, k-nearest neighbours, random forest, support vector machine, neural network, linear, stepwise, lasso, ridge, and elastic net regression models were assessed at different missing-ness levels. Statistical equivalence of true and imputed scores by model were evaluated. An ANOVA of accuracy by model and missingness was completed. While all models were equivalent to the true RPE, differences by model existed. Daily team mean substitution was the poorest performing model, and random forest, the best. Accuracy was low in all models, affirming RPE as mul-tifaceted and requiring quantification of potentially overlapping factors. While group mean substitution is discouraged, practitioners are recommended to scrutinize any imputation method relating to athlete load.
Chapter
In professional sport, the mental health of elite athletes is a major concern. Given its high prevalence and the potential for multifaceted and severe consequences, major depressive disorder is one of the greatest concerns with respect to the mental health of elite athletes. The purpose of this chapter is to provide a review of major depressive disorder and depressive symptoms within the elite sport research literature. Along with information on the epidemiology of depressive symptoms, risk and protective factors will also be examined. Strategies to enhance diagnosis, treatment, management, and recovery, including culturally situated mental health literacy, will also be discussed. The chapter will conclude with suggestions for future research and clinical and organizational intervention. Collective action amongst mental health researchers and practitioners, as well as athletes, coaches, staff, and their families, can help drive continual improvements in the prevention and treatment of major depressive disorder and depressive symptoms.KeywordsDepressive symptomsMajor depressive disorderAthletesSportMental health
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Monitoring players’ fatigue is essential to maintaining the best performance of players during sports games. The level of fatigue can be measured by the external workload, the aggregated amount of physical activity or internal workload, which is an individual’s psycho-physiological response to that activity. There have been a growing number of studies focusing on the relationship between external and internal workloads for efficient fatigue monitoring. However, they utilize aggregated features to represent the external workload, losing raw data details such as sequential information. This study proposes a deep learning algorithm to predict Rate of Perceived Exertion (RPE) from players’ movement data instead of aggregated features. Electronic Performance and Tracking Systems (EPTS) powered by GPS sensors collected players’ movement data and the RPE from training and match sessions during a Korean professional soccer team season. We preprocessed the raw GPS data to obtain linear and angular components of velocity, acceleration, and jerk. Our proposed model, named FatigueNet, effectively predicted the RPE with mean absolute error (MAE) = 0.8494 ± 0.0557 and root mean square error (RMSE) = 1.2166 ± 0.0737 using the preprocessed movement features. To interpret the predictions of the FatigueNet, we also performed regression activation mapping to localize the discriminative time intervals that contributed more to the prediction results. Our experimental results imply the possibility of automated and objective fatigue monitoring systems based on deep learning instead of arduous manual data collection from players.
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The return to sport from injury is a difficult multifactorial decision, and risk of reinjury is an important component. Most protocols for ascertaining the return to play status involve assessment of the healing status of the original injury and functional tests which have little proven predictive ability. Little attention has been paid to ascertaining whether an athlete has completed sufficient training to be prepared for competition. Recently, we have completed a series of studies in cricket, rugby league and Australian rules football that have shown that when an athlete's training and playing load for a given week (acute load) spikes above what they have been doing on average over the past 4 weeks (chronic load), they are more likely to be injured. This spike in the acute:chronic workload ratio may be from an unusual week or an ebbing of the athlete's training load over a period of time as in recuperation from injury. Our findings demonstrate a strong predictive (R(2)=0.53) polynomial relationship between acute:chronic workload ratio and injury likelihood. In the elite team setting, it is possible to quantify the loads we are expecting athletes to endure when returning to sport, so assessment of the acute:chronic workload ratio should be included in the return to play decision-making process.
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Aim: Investigate whether acute workload (1 week total distance) and chronic workload (4-week average acute workload) predict injury in elite rugby league players. Methods: Data were collected from 53 elite players over two rugby league seasons. The ‘acute:chronic workload ratio’ was calculated by dividing acute workload by chronic workload. A value of greater than 1 represented an acute workload greater than chronicworkload. All workload data were classified into discrete ranges by z-scores. Results Compared with all other ratios, a very-high acute:chronic workload ratio (≥2.11) demonstrated the greatest risk of injury in the current week (16.7% injury risk) and subsequent week (11.8% injury risk). High chronic workload (>16 095 m) combined with a very high 2-week average acute:chronic workload ratio (≥1.54) was associated with the greatest risk of injury (28.6% injury risk). High chronic workload combined with a moderate workload ratio (1.02–1.18) had a smaller risk of injury than low chronic workload combined with several workload ratios (relative risk range from 0.3 to 0.7×/÷1.4 to 4.4; likelihood range=88–94%, likely). Considering acute and chronic workloads in isolation (ie, not as ratios) did not consistently predict injury risk. Conclusions: Higher workloads can have either positive or negative influences on injury risk in elite rugby league players. Specifically, compared with players who have a low chronic workload, players with a high chronic workload are more resistant to injury with moderate-low through moderate-high (0.85–1.35)acute:chronic workload ratios and less resistant to injury when subjected to ‘spikes’ in acute workload, that is, very-high acute:chronic workload ratios ∼1.5.
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The RESTQ was developed to measure the frequency of current stress symptoms along with the frequency of recovery-associated activities. Through the simultaneous assessment of stress and recovery, a differentiated picture of the current recovery-stress state can be provided. Five forms of the RESTQ are available. A general version (RESTQ-Basic) with seven stress scales and five recovery scales is the foundation for the specific versions for athletes (RESTQ-Sport), for coaches (RESTQ-Coach), for adolescents (RESTQ-CA) and for the work context (RESTQ-Work). All versions contain scales measuring specific aspects of stress and recovery in their field. The modular structure is the unique feature of the RESTQ. Each version has its specific time frame of three or seven days/nights. A Likert-type scale is used with values ranging from 0 (never) to 6 (always) indicating how often the respondent participated in various activities or experienced relevant states. The profile of the RESTQ scales provides valuable information immediately on areas where improvement is needed. The questionnaire is useful for research on stress and recovery and ideal for applied settings. While the manual is provided in English, English and German speaking samples have been used to provide data of psychometrics. The German questionnaires are available as print version; the English versions are available on enquiry.
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This study investigated the relationship between GPS variables measured in training and gameplay and injury occurrences in professional soccer. Nineteen professional soccer players competing in the Australian Hyundai A-League were monitored for one entire season using 5Hz Global Positioning System (GPS) units (SPI-Pro GPSports, Canberra, Australia) in training sessions and pre-season games. The measurements obtained were Total Distance, High Intensity Running Distance, Very High Intensity Running Distance, New Body Load and Metres per Minute. Non-contact soft tissue injuries were documented throughout the season. Players' seasons were averaged over one and four week blocks according to when injuries occurred. These blocks were compared to each other and to players' seasonal averages. Players performed significantly higher Metres per Minute in the weeks preceding an injury compared to their seasonal averages (+9.6 % and +7.4 % for one and four week blocks respectively) (p<0.01), indicating an increase in training and gameplay intensity leading up to injuries. Furthermore, injury blocks showed significantly lower average New Body Load compared to seasonal averages (-15.4 % and -9.0 % for one and four week blocks respectively) (p<0.01 and p=0.01). Periods of relative under-preparedness could potentially leave players unable to cope with intense bouts of high intensity efforts during competitive matches. Although limited by FIFA regulations, the results of this study isolated two variables predicting soft tissue injuries for coaches and sports scientist to consider when planning and monitoring training.
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A methodology to assess work rate in competitive professional football was designed and validated. The technique required monitoring by observation the intensity and extent of discrete activities during match play and was found to have a measurement error of less than one percent. Performance was observed over 51 games. A complete match typically involved approximately nine hundred separate movement activities per player. The overall distance covered per game was observed to be a function of positional role, the greatest distance covered in outfield players being in mid fielders, the least in centre backs.