ArticlePDF Available

Training load-injury paradox: Is greater preseason participation associated with lower in-season injury risk in elite rugby league players?

April 2016
British Journal of Sports Medicine 51(8)

DOI:10.1136/bjsports-2016-095973

Authors:

Johann Windt

Vancouver Whitecaps FC

Tim Gabbett

Gabbett Performance Solutions

Daniel Ferris

University of New England

Karim M Khan

University of British Columbia

Aim: To determine whether players who completed a greater number of planned preseason training sessions were more or less likely to be injured during the competitive season. Methods: A cohort of 30 elite rugby league players was prospectively studied during their 17-week preseason and 26-round competitive season. Injuries were recorded using a match time loss definition. Preseason participation was quantified as the number of 'full' training sessions that players completed, excluding modified, rehabilitation or missed sessions. In-season training load variables, collected using global positioning system (GPS) data, included distance covered (m), high-speed distance covered (m) and the percentage of distance covered at high speeds (%). Multilevel logistic regression models were used to determine injury likelihood in the current and subsequent week, with random intercepts for each player. Odds ratios (OR) were used as effect size measures to determine the changes in injury likelihood with (1) a 10-session increase in preseason training participation or (2) standardised changes in training load variables. Results: Controlling for training load in a given week, completing 10 additional preseason sessions was associated with a 17% reduction in the odds of injury in the subsequent week (OR=0.83, 95% CI=0.70 to 0.99). Increased preseason participation was associated with a lower percentage of games missed due to injury (r=-0.40, p<0.05), with 10 preseason sessions predicting a 5% reduction in the percentage of games missed. Conclusions: Maximising participation in preseason training may protect elite rugby league players against in-season injury.

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

…

Figures - uploaded by Tim Gabbett

Content may be subject to copyright.

Content uploaded by Tim Gabbett

Content may be subject to copyright.

Training load–injury paradox: is greater preseason

participation associated with lower in-season injury

risk in elite rugby league players?

Johann Windt,

1,2,3

Tim J Gabbett,

4,5

Daniel Ferris,

Karim M Khan

1,2,3

Experimental Medicine

Program, University of British

Columbia, Vancouver, British

Columbia, Canada

Centre for Hip Health and

Mobility, University of British

Columbia, Vancouver, British

Columbia, Canada

Department of Family Practice,

University of British Columbia,

Vancouver, British Columbia,

Canada

School of Human Movement

Studies, The University of

Queensland, Brisbane,

Queensland, Australia

School of Exercise Science,

Australian Catholic University,

Brisbane, Queensland,

Australia

High Performance Unit, Manly

Sea Eagles, Sydney, New South

Wales, Australia

Correspondence to

Johann Windt, Experimental

Medicine Program University of

British Columbia, 2635 Laurel

Street, Vancouver, British

Columbia, Canada; V5Z 1M9;

johannwindt@gmail.com

Accepted 6 March 2016

To cite: Windt J,

Gabbett TJ, Ferris D, et al.

Br J Sports Med Published

Online First: [please include

Day Month Year]

doi:10.1136/bjsports-2016-

095973

ABSTRACT

Aim To determine whether players who completed a

greater number of planned preseason training sessions

were more or less likely to be injured during the

competitive season.

Methods A cohort of 30 elite rugby league players

was prospectively studied during their 17-week

preseason and 26-round competitive season. Injuries

were recorded using a match time loss deﬁnition.

Preseason participation was quantiﬁed as the number of

‘full’training sessions that players completed, excluding

modiﬁed, rehabilitation or missed sessions. In-season

training load variables, collected using global positioning

system (GPS) data, included distance covered (m), high-

speed distance covered (m) and the percentage of

distance covered at high speeds (%). Multilevel logistic

regression models were used to determine injury

likelihood in the current and subsequent week, with

random intercepts for each player. Odds ratios (OR) were

used as effect size measures to determine the changes

in injury likelihood with (1) a 10-session increase in

preseason training participation or (2) standardised

changes in training load variables.

Results Controlling for training load in a given week,

completing 10 additional preseason sessions was

associated with a 17% reduction in the odds of injury in

the subsequent week (OR=0.83, 95% CI=0.70 to 0.99).

Increased preseason participation was associated with a

lower percentage of games missed due to injury (r=

−0.40, p<0.05), with 10 preseason sessions predicting

a 5% reduction in the percentage of games missed.

Conclusions Maximising participation in preseason

training may protect elite rugby league players against

in-season injury.

INTRODUCTION

Athletic injuries are common in team sports,

compromising team success

3–5

and posing a signiﬁ-

cant ﬁnancial burden to organisations.

High train-

ing loads and substantial spikes (rapid increases) in

training volume have been associated with

increased injury rates.

378

External (work com-

pleted)

9–11

and internal (physiological response

such as perceived exertion or heart rate)

12–15

train-

ing load measures have been used to identify the

association between workloads and injury risk.

Traditionally, workload-injury investigations

focused on absolute workloads and injury,

14 16

and

higher workloads were associated with greater rates

of injuries.

However, high training loads are

necessary for beneﬁcial physiological adaptation

such as increased aerobic capacity, strength and

repeat sprint ability, along with optimal body

composition,

17 18

many of which are associated

with decreased injury risks.

11 19 20

Recently, load-injury investigations have highlighted

that the relationship between acute 1-week and

chronic (rolling 4-week total averaged to 1-week)

workloads, termed the acute:chronic workload ratio,

may better predict injury risk than total workloads.

Moreover, Hulin et al

demonstrated that as long as

players’acute:chronic workload ratios were kept

withinamoderatelevel(0.85–1.35), high chronic

workloads may reduce injury risk in rugby league

players—the training load–injury paradox.

Preseason training provides several physical bene-

ﬁts for sporting teams. It allows players to reach

high chronic workloads,

as well as to develop the

physical capacities associated with reduced injury

risks.

11 19 20

Indeed, preseason periods often

include higher training loads than in-season

periods.

315

Theoretically, players who have a more

‘successful’preseason may be more resilient to

injury when faced with the demands of the com-

petitive season.

To the best of our knowledge, no study has

investigated whether preseason training provides a

foundation which decreases in-season injury risk in

elite team sport athletes. Therefore, we investigated

whether elite rugby league players who participated

in a greater number of preseason sessions were

more or less likely to miss games due to injury

throughout the competitive season, while account-

ing for their external training loads during the

competitive season.

METHODS

Study design

We prospectively followed 30 rugby league players

(mean±SD age, 25±3 years) from one elite rugby

league club throughout their 17-week preseason

period and 26-round competitive season. All parti-

cipants provided informed written consent and

received a clear explanation of the study. All experi-

mental procedures were approved by the

Institutional Review Board for Human

Investigation at Australian Catholic University.

Measures

For the purpose of this study, we collected time-

varying and time-invariant variables. Time-varying

variables were summarised weekly and included

injury status and daily training load variables.

Time-invariant variables included the number of

preseason sessions completed, player position and

age at the start of the preseason. The competitive

Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973 1

Original article

BJSM Online First, published on April 13, 2016 as 10.1136/bjsports-2016-095973

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

season was divided into three time periods for descriptive pur-

poses, around the representative (ie, ‘State of Origin’interstate

series) period: ‘pre-origin’(weeks 1–8), ‘origin’(weeks 9–17)

and ‘post-origin’(weeks 18–26).

Injury status

The team’s medical staff (including physician and physiotherap-

ist) diagnosed injuries, while the team physiotherapist updated

and maintained the injury reports. For the purpose of this inves-

tigation, an injury was deﬁned as any injury that resulted in a

loss of match time—‘match time loss only’.

Injury incidence

was calculated as the number of injuries per 1000 participation

hours.

Preseason attendance

For each training day throughout the preseason and competitive

season, players’participation in training was recorded as ‘full’,

‘modiﬁed’,‘rehab’or ‘away’. Players’individual preseason par-

ticipation levels were quantiﬁed as the number of ‘full’pre-

season sessions they completed.

Quantifying in-season training loads

External workloads were obtained using global positioning

system (GPS) devices (GPSports, SPI-HPU 5 Hz (interpolated

15 Hz), Canberra, Australia). Load variables collected included

total distance, high-speed (>5 m/s) distance covered and the

percentage of total distance completed at high speeds. Our ana-

lysis included all ﬁeld training sessions and National Rugby

League matches throughout the 2015 season.

Data collection and analysis procedures

Data were categorised into weekly blocks from Monday to

Sunday throughout the 26-week season. If GPS data were

missing for players who were recorded as attending the ‘full’

training session, load data were estimated by calculating the

average workload for players of the same position who partici-

pated in the full session. Since models were ﬁtted to determine

the likelihood of sustaining a time-loss injury in a given week or

subsequent week, players’data for a given week were excluded if

they were already injured, suspended or released from the team.

Statistical analysis

All data were analysed in the open-source statistical software,

R(V.3.2.2). Independent random effect (multilevel) logistic

regression models were ﬁtted for each independent variable

using the R’s lme4 package, with the likelihood of sustaining a

time-loss injury as the outcome variable, and random intercepts

for each player. These models were used to determine which

variables were associated with an increased or decreased risk for

injury throughout the season, not controlling for other covari-

ates. Random-effect models were chosen for their ability to

handle unbalanced data with varying number of follow-up

observations, their capacity to generate individual-speciﬁcpre-

dictions and for their recommended use in analysing repeated-

measures designs with correlated data.

In ﬁtting the regression models, all training load variables

were standardised owing to the different scales of the measures

and subsequent failure of the models to converge in the statis-

tical software with unadjusted predictor variables. Odds ratios

(OR) were calculated to determine the effect size associated

with a 1 SD increase in training load variables. For preseason

participation, ORs were calculated to examine the effect sizes

associated with an increase of 10 ‘full’preseason sessions.

Statistical signiﬁcance was set at p<0.05 for all analyses, and

ORs were calculated as an effect size for all models.

Two separate multilevel logistic regression models were ﬁtto

determine the effect of preseason participation on injury likeli-

hood, controlling for training loads. One model was ﬁt to deter-

mine the likelihood of injury in the current week. A second

model was ﬁt to determine the likelihood of injury in the subse-

quent week. The ﬁnal models were ﬁrst ﬁtted by including vari-

ables shown to be signiﬁcant predictors from univariate models.

From here, all other training load variables, as well as time-

invariant covariates (age, position, season period), and inter-

action terms were added to the model to optimise model ﬁt.

Model ﬁt was assessed by minimising the model deviance, the

values of model diagnostics criteria (Akaike information criter-

ion (AIC)/Bayesian information criterion (BIC)) and the SD of

the random intercepts. Variables that did not improve the model

ﬁt were excluded from the ﬁnal models.

RESULTS

Injuries

A total of 40 injuries were sustained during the competitive

season (29.0/1000 h). These led to 241 total matches missed.

There were no signiﬁcant differences in injury likelihood when

comparing positions (p=0.73) or season period ( p=0.46).

Preseason participation

During the preseason period (3 November 2014–27 February

2015), the team had 87 preseason training sessions. Players

completed an average of 64±19 ‘full’preseason sessions (range

12–86).

Preseason loads and injury risk

There was a signiﬁcant correlation between the number of full

preseason training sessions that players completed and the

number of full in-season sessions completed (r=0.59,

p<0.001). Further, there was a signiﬁcant association (r=−0.40,

p<0.05) between the number of preseason sessions players com-

pleted and the percentage of games they missed due to injury

(ﬁgure 1). Without adjusting for training loads, greater pre-

season participation was associated with a decreased likelihood

of injury throughout the competitive season during the current

(OR=0.82, 95% CI 0.69 to 0.97) and subsequent week

(OR=0.80, 95% CI 0.68 to 0.94).

In-season training loads and injury risk

Training load measures collected during the competitive season

are summarised in table 1. The average distance and injury inci-

dence for each week of the competitive season are displayed in

ﬁgure 2.

Higher acute 1-week distances were associated with lower

injury likelihoods in the current week (OR=0.64, 95% CI 0.46

to 0.90) but not in the subsequent week. A greater percentage

of total distance completed at high speeds was associated with

an increased likelihood of injury both in the current (OR=1.34,

95% CI 1.03 to 1.73) and subsequent week (OR=1.07, 95% CI

1.06 to 1.08). Absolute high-speed running distance was not

associated with injury likelihood in either the current or subse-

quent week.

Chronic workloads were not signiﬁcantly associated with

injury risk in either the current or subsequent week. Similarly,

acute:chronic workload ratios for all training load variables

were not associated with signiﬁcant changes in injury likelihood.

Table 2 summarises all models of single training load variables

and their associated effects on injury risk.

2 Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

Full injury prediction models

Two multivariate injury prediction models quantiﬁed the effect

of preseason participation on injury risk, controlling for training

loads (table 3). Training load variables included in the ﬁnal

models were those that had a signiﬁcant association with injury

risk in independent univariate models, speciﬁcally 1-week total

distance and the 1-week proportion of distance performed at

high speeds. The ﬁt of these ﬁnal models was not improved

with the addition of any other variable, nor with the addition of

a random slope to the model, so none were included.

Model 1 predicts the likelihood of injury in the current week.

Controlling for training load, increased preseason participation

was still associated with a reduced odds of injury, though this

was no longer statistically signiﬁcant (OR=0.85, 95% CI 0.70

to 1.02). Similarly, a greater percentage of distance run at high

speeds appeared to be associated with an increased injury risk,

but the effect was no longer signiﬁcant when controlling for

preseason participation and acute distance (OR=1.27, 95% CI

0.99 to 1.63). Finally, as with univariate models, greater acute

distance was associated with a signiﬁcantly reduced likelihood

of injury (OR=0.56, 95% CI 0.36 to 0.87).

Model 2 predicts the likelihood of injury in the subsequent

week with a given preseason participation, acute distance and

acute percentage of distance run at high speeds. In this model,

when controlling for distance and percentage of distance at high

speed, increased preseason participation was associated with a

reduced likelihood of injury (OR=0.83, 95% CI 0.70 to 0.99).

Within this model, neither acute distance nor percentage of dis-

tance run at high speeds was signiﬁcantly associated with injury

risk in the subsequent week (ﬁgure 3).

DISCUSSION

In this sample of rugby league players, greater preseason partici-

pation was associated with a decreased injury risk during the

competitive season. Players who participated in a greater

number of full preseason sessions had a reduced likelihood of

injury throughout the competitive season, completed more

in-season training sessions and missed fewer games due to

injury. This reduced injury likelihood in the subsequent week

was maintained even when controlling for training load vari-

ables (acute distance and acute percentage of total distance com-

pleted at high speeds).

Figure 1 The association of

preseason participation with games

missed due to injury (r=−0.40,

p<0.05). The linear regression slope

shows that for every 10 additional

preseason sessions, the predicted

percentage of games missed decreases

by 5%. Percentage of games missed

due to injury was calculated by

dividing the number of games missed

due to injury by the number of games

that players were eligible to play.

Games were excluded from calculation

if players were ineligible due to

suspension or being traded during the

season and therefore not eligible to

play.

Table 1 Descriptive statistics for players’average workload and injuries over the duration of the study

Variable

Phase of season Total

Pre-origin Origin Post-origin

Injuries (n) 14 15 11 40

Distance (m) 9574 (4099) 10 815 (5311) 8960 (5871) 9780 (5205)

High-speed distance (m) 677 (462) 668 (467) 525 (442) 621 (461)

High-speed distance/total distance (%) 7.2 (4.8) 6.1 (2.4) 6.0 (3.1) 6.4 (3.7)

Data are mean (SD) for load measures and count for injuries. Phase of season divides the season into three periods around the ‘State of Origin’interstate series: ‘pre-origin’(weeks

1–8), ‘origin’(weeks 9–17) and ‘post-origin’(weeks 18–26).

Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973 3

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

Training loads and injury likelihood

In contrast to recent studies,

neither acute:chronic workload

ratios nor chronic workloads in isolation were signiﬁcantly asso-

ciated with injury. It may be that the current sample size was too

small to detect these effects. However, two separate measures of

acute (1-week) training loads, (1) distance covered and (2) per-

centage of total distance covered at high speeds, were signiﬁ-

cantly associated with changes in injury likelihood.

Higher 1-week total distances were associated with a reduc-

tion in injury likelihood in the current week but not in the sub-

sequent week. Similarly, previous data in rugby league players

showed that greater distances completed at lower intensities

were associated with a reduction in injury risk.

However, it

should be noted that the reduced likelihood of injury associated

with higher distances in the current week may be partly attribut-

able to players sustaining an injury earlier in the week. In this

case, it may be that increased distances are not preventing injury

but are accumulated by players who are healthy.

In contrast to total distance, the percentage of total weekly dis-

tance performed at high speeds was associated with an increased

risk of injury in the current and subsequent week. This increased

injury risk with greater high-speed running loads has been previ-

ously seen in team sport athletes.

10 11

Notably, when controlling

for players’preseason attendance, the percentage of running per-

formed at high speeds was no longer signiﬁcantly associated with

injury. This may indicate that players who had accumulated more

of the beneﬁts of a successful preseason were better able to toler-

ate the stress of the competitive season. Similarly, it has previ-

ously been shown that team sport athletes who performed

>18 weeks of training before sustaining initial injuries were at a

reduced risk of sustaining a subsequent injury.

Collectively, the

present and previous

results demonstrate the protective effect

of preseason training in team sport athletes.

Preseason participation and in-season injury: providing

protection or revealing underlying differences

(ie, identifying the robust players)?

We speculate that there may be two potential mechanisms

responsible for the associated reduction in injury likelihood

with increased preseason participation. From a physical stand-

point, preseason participation may be protective by allowing

players to accumulate high chronic workloads

and develop

greater strength and aerobic capacity.

Further, players who

participate in a greater proportion of preseason training sessions

may also be better prepared mentally and tactically within the

team environment.

On the other hand, increased preseason participation may

merely identify players who are inherently more robust to injury

and therefore more likely to handle the preseason training loads

and the rigours of the competitive season. Thus, the association

between preseason training and in-season injury risk may stem

from protective and revelatory effects of the preseason.

Figure 2 Average weekly distance per player and total team injury incidence during each week of the competitive season.

Table 2 Association of training load variables with injury

likelihood in the current and subsequent week

Load calculation 1 SD

Effect of 1 SD

increase on

current week

injury likelihood

OR (95% CI)

Effect of 1 SD

increase on

subsequent week

injury likelihood

OR (95% CI)

Acute (1-week loads)

Distance 5205 m 0.64* (0.46 to 0.90) 0.86 (0.61 to 1.22)

High-speed distance 461 m 0.83 (0.58 to 1.20) 0.83 (0.57 to 1.19)

High-speed distance/

total distance (%)

3.7% 1.34* (1.03 to 1.73) 1.07* (1.06 to 1.08)

Chronic (rolling 4-week average loads)

Distance 3389 m 0.80 (0.58 to 1.11) 0.82 (0.57 to 1.18)

High-speed distance 346 m 0.86 (0.60 to 1.22) 0.99 (0.70 to 1.40)

High-speed distance/

total distance (%)

3.1% 1.13 (0.83 to 1.54) 1.22 (0.90 to 1.66)

Acute:chronic workload ratios

Distance 0.72 0.72 (0.48 to 1.07) 0.89 (0.63 to 1.27)

High-speed distance 0.90 0.91 (0.64 to 1.31) 0.88 (0.61 to 1.27)

High-speed distance/

total distance (%)

0.42 1.64 (0.83 to 3.24) 1.13 (0.53 to 2.42)

*p<0.05.

4 Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

Preparation through preseason participation: practical

implications

Our ﬁndings do not necessarily indicate a need for high train-

ing loads during the preseason. Rather, preseason training

should be conducted so that players are able to participate in

the highest proportion of team sessions possible. This is espe-

cially pertinent given that the majority of training-related rugby

league injuries occur during the preseason periods.

Even

though 24% of a rugby union team’s annual training occurred

during the preseason period, 34% of training-related injuries

occur during this time.

High injury incidence during the pre-

season period may be partly attributable to training loads,

which are generally higher than in-season periods.

323

Moreover, a reduction in preseason training loads signiﬁcantly

reduced injury incidence in rugby league players.

From our

experience, those who manage team training loads should aim

to design preseason training periods which induce positive

physiological adaptations while minimising injury risk and

maximising player availability. Accurate season-by-season

records of injury and training loads will help teams ﬁnd their

‘sweet spot’.

Potential limitations

The present study included a sample size (30 players), and total

injury occurrences (40), which limits the number of variables

that could be included in the injury prediction models and

reduces the sensitivity of the models. Owing to the limited avail-

ability of GPS devices, the number of players who could be

monitored was restricted to 30, as opposed to all members of

the rugby league club. Although more injuries would have been

captured with a broader deﬁnition of injury, ‘match time loss

only’is accepted as an accurate and reliable deﬁnition used in

team sport contexts.

Although we used GPS-derived total and high-speed running

distances, the inclusion of other external load variables (eg,

accelerations, decelerations, collisions) would likely add value to

any investigation of the relationship between preseason training

load and in-season injury risk in rugby league players.

26 27

However, as discussed in recent load injury investigations,

828

the ability of GPSports technology to accurately measure these

variables is limited. Further, internal load measures (eg, session

rating of perceived exertion (RPE) or heart rate) may also be

useful to investigate preseason and in-season training loads.

Table 3 Effect of preseason participation on injury likelihood in current and subsequent week, controlling for training load variables

Model Variable OR (95% CI)

Model 1: injury likelihood in current week 10 preseason sessions 0.85 (0.70 to 1.02)

High-speed distance/total distance 1.27 (0.99 to 1.63)

Acute distance (1 SD increase) 0.56* (0.36 to 0.87)

Model 2: injury likelihood in subsequent week 10 preseason sessions 0.83* (0.70 to 0.99)

High-speed distance/total distance 1.07 (0.79 to 1.45)

Acute distance 0.82 (0.55 to 1.21)

*p<0.05.

Figure 3 Predicted injury

probabilities from model 2 (Table 3),

based on high-speed running

percentage and preseason

participation. The model predicts the

probability that a player will sustain a

match time-loss injury in the

subsequent week, controlling for pre-

season participation, as well as the

total distance and the percentage of

total distance run at high speeds in

the current week. Preseason

participation has been divided into

three equal tertiles, such that ‘low’

equals <59 sessions (n=10),

‘moderate’equals 59–75 sessions

(n=10) and ‘high’equals >75 sessions

(n=10).

Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973 5

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

Finally, while we quantiﬁed preseason participation in sessions,

future investigations may quantify preseason training loads to

further distinguish characteristics of preseason training that

facilitate the development of athlete resilience.

Summary and conclusions

In this ﬁrst study to investigate the association of preseason train-

ing participation and injury likelihood during the competitive

season, players who completed a greater number of preseason

sessions were less likely to be injured during the competitive

season, even when controlling for their external training loads.

Total distance covered was associated with a decreased likelihood

of injury in the current week, while players who completed a

greater percentage of their total distance at high speeds were at

increased risk of injury in the current and subsequent week.

What are the ﬁndings?

▸Players who participated in a greater number of preseason

sessions had a lower likelihood of injury throughout the

competitive season. Ten additional preseason sessions

reduced the odds of injury by at least 17% in the current

and subsequent week. The association between preseason

training participation and risk of injury in the subsequent

week remained statistically signiﬁcant even when controlling

for in-season training load variables.

▸Running a higher percentage of total distance at high

speeds was associated with an increased injury risk, both in

the current and subsequent week. For example, a 3.7%

increase in the percentage of distance run at high speeds in

a given week increased the odds of injury by 34%. However,

when controlling for preseason participation, this association

was no longer signiﬁcant.

How might it impact on clinical practice in the future?

▸In addition to its role in preparing players for the

performance demands of competition, preseason training

may prevent injuries during the competitive season.

▸Future investigations may examine strategies to minimise

injury risk during the preseason period so that player

availability is maximised during this period.

▸These ﬁndings might contribute to a paradigm shift where

clinicians may appreciate that total external training load

(distance covered) is not necessarily associated with

increased injury risk and may in fact decrease risk. However,

greater percentages of time spent at high speeds in a given

training week may increase injury risk in the current or

subsequent week, especially when preseason participation is

low.

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

Acknowledgements The authors would like to acknowledge the players who

participated in this study, as well as Angela Yao for her statistical expertise.

Contributors JW was primarily responsible for the analysis of the study data. DF

and TJG were responsible for the data collection. All authors were responsible for

study concept and design, and contributed to writing and critical revision of the

manuscript.

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

Research.

Competing interests Karim Khan is editor-in-chief of BJSM and was not involved

in the peer-review. He is blinded to this paper in the ScholarOne manuscript system.

His collaboration with Tim J Gabbett has been documented with the BJSM

Publisher.

Patient consent Obtained.

Ethics approval Approval was sought and subsequently granted by the Australian

Catholic University.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement All data relevant to the study have been included in the

manuscript. In accordance with the original ethics approval, data may not be shared

outside this study and team’s staff.

REFERENCES

1 Gabbett TJ. Incidence of injury in junior and senior rugby league players. Sports

Med Auckl NZ 2004;34:849–59.

2 Hrysomallis C. Injury incidence, risk factors and prevention in Australian Rules

Football. Sports Med 2013;43:339–54.

3 Cross MJ, Williams S, Trewartha G, et al. The inﬂuence of in-season training loads

on injury risk in professional rugby union. Int J Sports Physiol Perform 2015.

Published Online First: 26 August 2015. doi:10.1123/ijspp.2015-0187.

4 Eirale C, Tol JL, Farooq A, et al. Low injury rate strongly correlates with team

success in Qatari professional football. Br J Sports Med 2013;47:807–8.

5 Hägglund M, Waldén M, Magnusson H, et al. Injuries affect team performance

negatively in professional football: an 11-year follow-up of the UEFA Champions

League injury study. Br J Sports Med 2013;47:738–42.

6 Hickey J, Shield AJ, Williams MD, et al. The ﬁnancial cost of hamstring strain

injuries in The Australian Football League. Br J Sports Med 2014;48:729–30.

7 Gabbett TJ. The development and application of an injury prediction model for

noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond Res

2010;24:2593–603.

8 Hulin BT, Gabbett TJ, Lawson DW, et al. The acute:chronic workload ratio predicts

injury: high chronic workload may decrease injury risk in elite rugby league players.

Br J Sports Med 2016;50:231–6.

9 Hulin BT, Gabbett TJ, Blanch P, et al. Spikes in acute workload are associated with

increased injury risk in elite cricket fast bowlers. Br J Sports Med 2014;48:708–12.

10 Colby MJ, Dawson B, Heasman J, et al. Accelerometer and GPS-derived running

loads and injury risk in elite Australian footballers. J Strength Cond Res

2014;28:2244–52.

11 Gabbett TJ, Ullah S. Relationship between running loads and soft-tissue injury in

elite team sport athletes. J Strength Cond Res 2012;26:953–60.

12 Ahtiainen JP, Pakarinen A, Alen M, et al. Muscle hypertrophy, hormonal

adaptations and strength development during strength training in strength-trained

and untrained men. Eur J Appl Physiol 2003;89:555–63.

13 Owen AL, Forsyth JJ, Wong DP, et al. Heart rate-based training intensity and its

impact on injury incidence among elite-level professional soccer players. J Strength

Cond Res 2015;29:1705–12.

14 Killen NM, Gabbett TJ, Jenkins DG. Training loads and incidence of injury during

the preseason in professional rugby league players. J Strength Cond Res

2010;24:2079–84. http://dx.doi.org/10.1519/JSC.0b013e3181ddafff.

15 Gabbett TJ, Domrow N. Relationships between training load, injury, and ﬁtness in

sub-elite collision sport athletes. J Sports Sci 2007;25:1507–19.

16 Gabbett TJ. Inﬂuence of training and match intensity on injuries in rugby league.

J Sports Sci 2004;22:409–17.

17 Bartolomei S, Hoffman JR, Merni F, et al. A comparison of traditional and block

periodized strength training programs in trained athletes. J Strength Cond Res

2014;28:990–7.

18 Tønnessen E, Shalfawi SAI, Haugen T, et al. The effect of 40-m repeated sprint

training on maximum sprinting speed, repeated sprint speed endurance, vertical

jump, and aerobic capacity in young elite Male soccer players. J Strength Cond Res

2011;25:2364–70.

19 Grant JA, Bedi A, Kurz J, et al. Ability of preseason body composition and physical

ﬁtness to predict the risk of injury in male collegiate hockey players. Sports Health

2015;7:45–51.

20 Gabbett TJ, Ullah S, Finch CF. Identifying risk factors for contact injury in

professional rugby league players--application of a frailty model for recurrent injury.

J Sci Med Sport 2012;15:496–504.

21 Gabbett TJ. The training–injury prevention paradox: should athletes be training

smarter and harder? Br J Sports Med 2016;50:273–80.

22 Orchard J, Hoskins W. For debate: consensus injury deﬁnitions in team sports

should focus on missed playing time. Clin J Sport Med 2007;17:192–6.

23 Gabbett TJ, Domrow N. Risk factors for injury in subelite rugby league players. Am J

Sports Med 2005;33:428–34.

6 Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

24 Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in

rugby league players. Br J Sports Med 2004;38:743–9.

25 Brooks JHM, Fuller CW, Kemp SPT, et al. Epidemiology of injuries in English

professional rugby union: part 2 training Injuries. Br J Sports Med 2005;39:767–75.

26 Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med

2014;44:139–47.

27 Borresen J, Lambert PMI. The quantiﬁcation of training load, the training response

and the effect on performance. Sports Med 2012;39:779–95.

28 Hulin BT, Gabbett TJ, Caputi P, et al. Low chronic workload and the acute:chronic

workload ratio are more predictive of injury than between-match recovery time: a

two-season prospective cohort study in elite rugby league players. Br J Sports Med

2016 Published Online First: 5 February 2016. doi:10.1136/bjsports-2015-095364

Windt J, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-095973 7

Original article

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

league players?

lower in-season injury risk in elite rugby

preseason participation associated with

Training load--injury paradox: is greater

Johann Windt, Tim J Gabbett, Daniel Ferris and Karim M Khan

published online April 13, 2016Br J Sports Med

http://bjsm.bmj.com/content/early/2016/04/13/bjsports-2016-095973

Updated information and services can be found at:

These include:

References

#BIBL

http://bjsm.bmj.com/content/early/2016/04/13/bjsports-2016-095973

This article cites 26 articles, 10 of which you can access for free at:

service

Email alerting box at the top right corner of the online article.

Receive free email alerts when new articles cite this article. Sign up in the

Collections

Topic Articles on similar topics can be found in the following collections

(162)Rugby

Notes

http://group.bmj.com/group/rights-licensing/permissions

To request permissions go to:

http://journals.bmj.com/cgi/reprintform

To order reprints go to:

http://group.bmj.com/subscribe/

To subscribe to BMJ go to:

group.bmj.com on April 14, 2016 - Published by http://bjsm.bmj.com/Downloaded from

Monitoring training load and perceived recovery indicators during the preseason and in-season phases in professional female basketball players

Article

May 2024
J SPORT MED PHYS FIT

BACKGROUNDː During the basketball season, it is essential to carefully plan training and recovery activities to optimize players’ performance. This study monitored training load and perceived recovery indicators in professional female basketball players across the preseason and in-season phases. METHODSː Seventeen professional female basketball players (age: 19.6±3.1 years; height: 180.2±5.9 cm; playing position: 8 backcourt, 9 frontcourt) were monitored for eleven consecutive weeks. Each morning, players reported their perceived recovery using a 10-point Total Quality of Recovery (TQR) scale. After each training session or game, players reported their perceived exertion using the CR-10 scale, which was multiplied by the duration of the training or game to obtain the session load. Weekly load, monotony, strain, TQRAM (morning recovery) and TQRfw (recovery at the start of the following week) were calculated. Linear mixed models were performed to assess the effects of season phase (preseason; in-season), playing position (backcourt; frontcourt) and group (senior; under) on load and recovery variables. RESULTSː Weekly load, monotony and strain were higher in the preseason than the in-season phase (all p<0.001, ES: moderate-large). Strain was higher in senior players compared to under (p=0.045, ES: small). Regarding recovery variables, no effects were found for TQRAM, while TQRfw was higher in the preseason than in-season (p< 0.001, ES: moderate) phase. CONCLUSIONSː Professional female basketball players experience lower internal loads but poorer perceived recovery during the in-season phase. Practitioners should carefully consider the stress of competition and the cumulative fatigue from high preseason loads during the transition from the preseason to the regular season.

Workload Monitoring Tools in Field-Based Team Sports, the Emerging Technology and Analytics used for Performance and Injury Prediction: A Systematic Review

Article

Full-text available

Sep 2023

Training load (TL) is frequently documented among team sports and the development of emerging technology (ET) is displaying promising results towards player performance and injury risk identification. The aim of this systematic review was to identify ETs used in field-based sport to monitor TL for injury/performance prediction and provide sport specific recommendations by identifying new data generation in which coaches may consider when tracking players for an increased accuracy in training prescription and evaluation among field-based sports. Data was extracted from 60 articles following a systematic search of CINAHL, SPORTDiscus, Web of Science and IEEE XPLORE databases. Global positioning system (GPS) and accelerometers were common external TL tools and Rated Perceived Exertion (RPE) for internal TL. A collection of analytics tools were identified when investigating injury/performance prediction. Machine Learning showed promising results in many studies, identifying the strongest predictive variables and injury risk identification. Overall, a variety of TL monitoring tools and predictive analytics were utilized by researchers and were successful in predicting injury/performance, but no common method taken by researchers could be identified. This review highlights the positive effect of ETs, but further investigation is desired towards a ‘gold standard” predictive analytics tool for injury/performance prediction in field-based team sports.

"The Relationship Between Training Load Variables and Non- Contact Injury Incidence in Soccer Players: A Systematic Review"

Thesis

Full-text available

Jun 2023

Bartu Inal

A systematic review investigating the relationship between training load variables and non-contact injury incidence in soccer players

Wellness status and training load among Malaysian rugby players during Asia rugby men's champi-onship 2023: a comparative analysis

Article

Full-text available

Sep 2024

The Asia Rugby Men's Championship (ARMC) is a well-known platform for rugby teams in the area to exhibit their abilities and compete on an international level. The tournament's physical and psychological strains on competitors may have consequences for their overall health. The purpose of this study is to look at the training load and the acute-to-chronic workload ratio (ACWR) and to evaluate the wellness on the Malaysia Rugby squad during the Asia Rugby Men's Championship 2023. The study enlisted the participation of 30 Malaysia Rugby squad members. Throughout the competition, training load data such as session time, session rating of perceived exertion (sRPE), and numerous wellness indicators such as fatigue, sleep quality, mood, muscular soreness, and stress level were thoroughly evaluated. The ACWR was determined by dividing the chronic workload rolling average by the acute workload rolling average. The data demonstrated differences in training load patterns and ACWR values and self-report questionnaires were used to collect data for wellness status throughout the competition. The study emphasizes the significance of monitoring training load to optimize player performance and reduce the risk of injury during strenuous rugby tournaments. The study reveals wellness difficulties faced by Malaysia Rugby players at ARMC 2023, guiding personalized interventions to enhance well-being and performance during international rugby games. It also enhances understanding of training load management approaches and may guide future training methods for similar events. Keywords. training load monitoring, acute-to-chronic workload ratio (ACWR), Malaysia Rugby, athlete performance, injury risk, wellness status, player well-being.

“Health is Just the Basic Requirement for Optimal Performance and Winning”: Stakeholders’ Perceptions on Testing and Training in Competitive Alpine Skiing, Snowboarding and Freestyle Skiing

Article

Full-text available

Sep 2024
SPORTS MED

Background and Objective Competitive alpine skiing, snowboarding and freestyle skiing, all different in nature and risks, are known for their high injury and illness burden. Testing measures and training methods may be considered for athletes’ preparation to support performance enhancement while safeguarding their health. We explored the perspectives and perceptions of competitive alpine skiing, snowboarding and freestyle skiing stakeholders regarding testing and training practices in their competitive snow sports. Methods We conducted an exploratory qualitative study based on grounded theory principles through 13 semi-structured interviews about testing and training practices with athletes, on-snow and off-snow coaches, managers and healthcare providers from different national teams. The interviews were inductively analysed through a constant comparative data analysis. Results Participants described winning as the end goal of testing and training practices, which requires athletes to perform in their best condition. To do so, they mentioned two main targets: performance enhancement and health protection. Participants acknowledged health as a premise to perform optimally, considering testing and monitoring approaches, goal setting, and training to support and protect athlete performance. This continuous cyclic process is driven by communication and shared decision making among all stakeholders, using testing and monitoring outputs to inform goal setting, training (e.g. on-snow and off-snow) and injury prevention. Such an approach helps athletes achieve their goal of winning while being fit and healthy throughout their short-term and long-term athletic career development. Conclusions The ultimate goal of testing measures and training methods in such competitive snow sports is winning. Performance enhancement and health protection act as pillars in systematic, tailored and flexible processes to guarantee athletes’ best preparation to perform. Moreover, athletes’ assessments, goal setting, monitoring tools, open communication and shared decision making strongly guide this cyclic process.

Is it possible to propose a periodization strategy different from the inverted U in preseason? A comparison between two professional football teams

Article

Full-text available

Jun 2024

Purpose: the main aim of this study was to quantify and compare the weekly external loads of pre-season in two professional football teams. Methods: GPS devices monitored forty-five players in two teams daily in a five-week pre-season period. The external load measures were: number of sessions, total duration, acceleration load (aLoad), total distance (TD), distance at >21 km·h-1 (TD21), distance at >24 km·h-1 (TD24) and Player-Load® (PL). Results: there were differences in the weekly external load between both teams. Team1 trained 30% more time and training sessions than Team2, so the weekly load for all external load variables was higher except for aLoad and TD21 for W1 (Team2>Team1, p<0.05). These differences between teams were not similar for all weeks, with higher differences in weeks 2, 3, and 4. While Team2 proposed a distribution more stable and progressive in high-speed distances (TD21 and TD24) among weeks, Team1 used the inverted U model. In this line, variations between weeks were lower for Team2 (from -4% to 38%) than for Team1 (from -26% to 1,653%). Conclusions: The study's main conclusion was that in addition to a load management with an inverted U model, more widespread in professional football, a more stable and progressive distribution strategy can be proposed in pre-season in a professional setting. Keywords: GPS, training load, team sports, periodization, monitoring.

Acute Response to Training after Returning from the Off-Season in Elite Rugby League Athletes

Article

Full-text available

Apr 2024
J HUM KINET

The purposes of this study were to quantify the physiological response to the initial two-week preseason period in elite male rugby league (RL) athletes, and to determine if a repeated bout effect (RBE) occurs. Eighteen RL players were monitored for the initial two-week preseason period. Blood samples were collected on days (D)1, D2, D4, D5, D8, D9, D11 and D12 to measure creatine kinase (CK). Neuromuscular power was assessed on D1, D5, D8 and D12. During field-based sessions, the external training load was quantified using global positioning system technology, whilst the internal load was quantified using the training impulse and the session rating of perceived exertion. Resistance-based gym session volume was quantified by total repetitions x weight lifted. Perceived measures of fatigue and muscle soreness were assessed on all training days. Two-way (day x week) repeated measures analysis of variance and Bonferroni’s corrected post-hoc tests identified significant changes. There were no significant changes in CK activity (649.2 ± 255.0 vs. 673.8 ± 299.1 µL; p = 0.63) or internal training load measures from week 1 to week 2. External training load measures including total distance (4138.1 ± 198.4 vs. 4525.0 ± 169.2 m; p < 0.001) and repeated high-intensity efforts (12.6 ± 1.8 vs. 17.5 ± 1.8 au; p < 0.001) significantly increased in week 2 compared to week 1. Internal training loads and CK activity did not change in response to an increase in external training loads during the initial preseason. The current results provide support for a ‘real world’ perspective of the RBE phenomenon that may be more applicable for team sport practitioners.

Selection of Key Performance Indicators for Your Sport and Program: Proposing a Complementary Process- Driven Approach

Article

Oct 2023
Strength Condit

Key performance indicators (KPIs) are commonplace in business and sport. They offer an objective means to link data and processes with performance outcomes. Yet, their application in sports performance, particularly team sports, is not without issue. Here, we review 4 key issues relating to KPI application in team sports; lack of a universal definition, complexity of performance , drifting from on-field performance goals with off-field targets, and agency issues across different key stakeholders. With these issues relating to sports performance KPIs in mind, we propose a complementary approach to help practitioners focus on implementing the conditions that create performance environments and opportunities for success in a complex sporting environment. Ongoing process trackers (OPTs) are quantifiable measures of the execution of behaviors and processes that create the environments, cultures, and conditions for successful performance outcomes. This approach equips sports science practitioners with key questions they can ask themselves and their team when starting to select and use OPTs in their program.

Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions

Article

Sep 2024

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16–0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s⁻²), accelerations (>2.5 m s⁻²), acceleration distance (>2 m s⁻²), high-speed running (>15 km h⁻¹), very high-speed running (>21 km h⁻¹, VSHR), sprint running (>25 km h⁻¹, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67–0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0–0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches.

A kosárlabda-, kézilabda- és labdarúgás-sportteljesítményt meghatározó változók vizsgálata: Fókuszban GPS- és LPS-rendszerek

Article

Jan 2022

Low chronic workload and the acute: Chronic workload ratio are more predictive of injury than between-match recovery time: A two-season prospective cohort study in elite rugby league players

Article

Full-text available

Feb 2016

Background: Between-match recovery time, and acute and chronic workloads likely affect subsequent match-injury risk in elite rugby league players. Methods: Workloads of 28 players throughout two seasons were calculated during short (<7 days), and long (≥7 days) between-match recovery times. 'Acute' workloads (1 week) greater than 'chronic' workloads (4-week rolling average acute workload) resulted in acute:chronic workload ratios above 1. Results: No difference was found between the match-injury risk of short and long between-match recovery periods (7.5±2.5% vs 6.8±2.5%). When players had a short recovery between matches, high chronic workloads (18.9-22.0 km) were associated with a smaller risk of match injury than chronic workloads <18.9 km (relative risk (RR) range 0.27-0.32 (CI 0.08 to 0.92); likelihood range 90-95%, likely). Players who had shorter recovery and acute:chronic workload ratios ≥1.6, were 3.4-5.8 times likely to sustain a match injury than players with lower acute:chronic workload ratios (RR range 3.41-5.80 (CI 1.17 to 19.2); likelihood range 96-99%, very likely). Acute:chronic workload ratios between 1.2 and 1.6 during short between-match recovery times demonstrated a greater risk of match injury than ratios between 1.0 and 1.2 (RR=2.88 (CI 0.97 to 8.55); likelihood=92%, likely). Conclusions: Contrary to the philosophy that high workloads and shorter recovery equate to increased injury risk, our data suggest that high and very-high chronic workloads may protect against match injury following shorter between-match recovery periods. Acute:chronic workload ratios ∼1.5 are associated with a greater risk of match injury than lower acute:chonic workload ratios. Importantly, workloads can be manipulated to decrease the match-injury risk associated with shorter recovery time between matches.

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

Article

Full-text available

Jan 2016
BRIT J SPORT MED

Tim Gabbett

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.

The acute: Chronic workload ratio predicts injury: High chronic workload may decrease injury risk in elite rugby league players

Article

Full-text available

Oct 2015
BRIT J SPORT MED

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.

The Influence of In-Season Training Loads on Injury Risk in Professional Rugby Union

Article

Full-text available

Aug 2015

To explore the association between in-season training load measures and injury risk in professional Rugby Union players. Methods This was a one-season prospective cohort study of 173 Professional Rugby Union players from four English Premiership teams. Training load (duration x session-RPE) and time-loss injuries were recorded for all players for all pitch and gym based sessions. Generalised estimating equations were used to model the association between in-season training load measures and injury risk in the subsequent week. Injury risk increased linearly with one-week loads and week-to-week changes in loads, with a 2 standard deviation (SD) increase in these variables (1245 AU and 1069 AU, respectively) associated with odds ratios of 1.68 (95% CI 1.05-2.68) and 1.58 (95% CI: 0.98-2.54). When compared with the reference group (<3684 AU), a significant non-linear effect was evident for four-week cumulative loads, with a likely beneficial reduction in injury risk associated with intermediate loads of 5932 to 8651 AU (OR: 0.55, 95% CI: 0.22-1.38) (this range equates to around four weeks of average in-season training load), and a likely harmful effect evident for higher loads of >8651 AU (OR: 1.39, 95% CI: 0.98-1.98). Players had an increased risk of injury if they had high one-week cumulative loads (1245 AU), or large week-to-week changes in load (1069 AU). In addition, a 'U-shaped' relationship was observed for four-week cumulative loads, with an apparent increase in risk associated with higher loads (>8651 AU). These measures should therefore be monitored to inform injury risk reduction strategies.

Ability of Preseason Body Composition and Physical Fitness to Predict the Risk of Injury in Male Collegiate Hockey Players

Article

Full-text available

Jan 2015

Injuries in collegiate ice hockey can result in significant time lost from play. The identification of modifiable risk factors relating to a player's physical fitness allows the development of focused training and injury prevention programs targeted at reducing these risks. To determine the ability of preseason fitness outcomes to predict in-season on-ice injury in male collegiate ice hockey players. Prognostic cohort study. Level 3. Athlete demographics, percentage body fat, aerobic capacity (300-m shuttle run; 1-, 1.5-, 5-mile run), and strength assessment (sit-ups, push-ups, grip strength, bench press, Olympic cleans, squats) data were collected at the beginning of 8 successive seasons for 1 male collegiate ice hockey team. Hockey-related injury data and player-level practice/game athlete exposure (AE) data were also prospectively collected. Seventy-nine players participated (203 player-years). Injury was defined as any event that resulted in the athlete being unable to participate in 1 or more practices or games following the event. Multivariable logistic regression was performed to determine the ability of the independent variables to predict the occurrence of on-ice injury. There were 132 injuries (mean, 16.5 per year) in 55 athletes. The overall injury rate was 4.4 injuries per 1000 AEs. Forwards suffered 68% of the injuries. Seventy percent of injuries occurred during games with equal distribution between the 3 periods. The mean number of days lost due to injury was 7.8 ± 13.8 (range, 1-127 days). The most common mechanism of injury was contact with another player (54%). The odds of injury in a forward was 1.9 times (95% CI, 1.1-3.4) that of a defenseman and 3 times (95% CI, 1.2-7.7) that of a goalie. The odds of injury if the player's body mass index (BMI) was ≥25 kg/m(2) was 2.1 times (95% CI, 1.1-3.8) that of a player with a BMI <25 kg/m(2). The odds ratios for bench press, maximum sit-ups, and Olympic cleans were statistically significant but close to 1.0, and therefore the clinical relevance is unknown. Forwards have higher odds of injury relative to other player positions. BMI was predictive of on-ice injury. Aerobic fitness and maximum strength outcomes were not strongly predictive of on-ice injury.

Heart Rate–Based Training Intensity and Its Impact on Injury Incidence Among Elite-Level Professional Soccer Players

Article

Full-text available

Dec 2014

Elite level professional soccer players are suggested to have increased physical, technical, tactical and psychological capabilities when compared to their sub-elite counterparts. Ensuring these players remain at the elite level generally involves training many different bodily systems to a high intensity or level within a short duration. This study aimed to examine whether an increase in training volume at high intensity levels were related to injury incidence, or increased the odds of sustaining an injury. Training intensity was monitored through time spent in high- (T-HI) and very high- (T-VHI) intensity zones of 85-<90% and ≥90% of maximal heart rate (HRmax), and all injuries were recorded over two consecutive seasons. Twenty-three elite professional male soccer players (mean±SD age 25.6±4.6 years, stature 181.8±6.8 cm, and body mass of 79.3±8.1 kg) were studied throughout the 2-yrs span of the investigation. The results showed a mean total injury incidence of 18.8 (95% CI 14.7 to 22.9) injuries per 1000 h of exposure. Significant correlations were found between training volume at T-HI and injury incidence (r=0.57, p=0.005). Further analysis revealed how players achieving more time in the T-VHI zone during training increased the odds of sustaining a match injury (odds ratio=1.87, 95% CI 1.12 to 3.12, p=0.02), but did not increase the odds of sustaining a training injury. Reducing the number of competitive match injuries amongst elite professional level players may be possible if greater focus is placed on the training intensity and volume over a period of time ensuring the potential reduction of fatigue or overuse injuries. In addition, it is important to understand the optimal training load at which adaptation occurs without raising the risk of injury.

Monitoring Training Load to Understand Fatigue in Athletes

Article

Full-text available

Sep 2014

Shona Halson

Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.

Accelerometer and GPS-Derived Running Loads and Injury Risk in Elite Australian Footballers

Article

Full-text available

Aug 2014

Colby, MJ, Dawson, B, Heasman, J, Rogalski, B, and Gabbett, TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res 28(8): 2244-2252, 2014-The purpose of this study was to investigate the relationship between overall physical workload (global positioning systems [GPS]/accelerometer) measures and injury risk in elite Australian football players (n = 46) during a season. Workload data and (intrinsic) injury incidence were monitored across preseason and in-season (18 matches) phases. Multiple regression was used to compare cumulative (1-, 2-, 3-, and 4-weekly loads) and absolute change (from previous-to-current week) in workloads between injured and uninjured players for all GPS/accelerometer-derived variables: total distance, V1 distance (total distance above individual's aerobic threshold speed), sprint distance, force load, velocity load, and relative velocity change. Odds ratios (ORs) were calculated to determine the relative injury risk. Cumulative loads showed the strongest relationship with greater intrinsic injury risk. During preseason, 3-weekly distance (OR = 5.489, p = 0.008) and 3-weekly sprint distance (OR = 3.667, p = 0.074) were most indicative of greater injury risk. During in-season, 3-weekly force load (OR = 2.530, p = 0.031) and 4-weekly relative velocity change (OR = 2.244, p = 0.035) were associated with greater injury risk. No differences in injury risk between years of Australian Football League system experience and GPS/accelerometer data were seen. From an injury risk (prevention) perspective, these findings support consideration of several GPS/accelerometer running load variables in Australian football players. In particular, cumulative weekly loads should be closely monitored, with 3-weekly loads most indicative of a greater injury risk across both seasonal phases.

A Comparison of Traditional and Block Periodized Strength Training Programs in Trained Athletes

Article

Jan 2014
J STRENGTH COND RES

The purpose of this study was to compare two different periodization models in strength and power athletes. Twenty-four experienced resistance trained man were randomly assigned to either a block periodization training program (BP; age = 24.2±3.1 years, body mass = 78.5±11.0 kg, height = 177.6±4.9 cm) or to a traditional periodization program (TP; age = 26.2±6.0 years; body mass = 80.5±13.3 kg, height = 179.2±4.6). Participants in both training programs performed four-training sessions per week. Each training program consisted of the same exercises and same volume of training (total resistance lifted per session). The difference between the groups was in the manipulation of training intensity within each training phase. Strength and power testing occurred before training (PRE) and following 15 weeks (POST) of training. Magnitude-based inferences were used to compare strength and power performance between the groups. Participants in BP were more likely (79.8%) to increase the area under the force-power curve than TP. Participants in BP also demonstrated a likely positive (92.76%) decrease in the load corresponding to maximal power at the bench press compared to TP group, and a possible improvement (∼ 60%) in maximal strength and power in the bench press. No significant changes were noted between groups in lower body strength or jump power performance following the 15 week training period. Results of this study indicate that BP may enhance upper-body power expression to a greater extent than TP with equal volume, however, no differences were detected for lower body performance and body composition measures.

The financial cost of hamstring strain injuries in the Australian Football League

Article

Oct 2013
BRIT J SPORT MED

Hamstring strain injuries (HSIs) have remained the most prevalent injury in the Australian Football League (AFL) over the past 21 regular seasons. The effect of HSIs in sports is often expressed as regular season games missed due to injury. However, the financial cost of athletes missing games due to injury has not been investigated. The aim of this report is to estimate the financial cost of games missed due to HSIs in the AFL. Data were collected using publicly available information from the AFL's injury report and the official AFL annual report for the past 10 competitive AFL seasons. Average athlete salary and injury epidemiology data were used to determine the average yearly financial cost of HSIs for AFL clubs and the average financial cost of a single HSI over this time period. Across the observed period, average yearly financial cost of HSIs per club increased by 71% compared with a 43% increase in average yearly athlete salary. Over the same time period the average financial cost of a single HSI increased by 56% from

A25 603 in 2003 to

A40 021 in 2012, despite little change in the HSI rates during the period. The observed increased financial cost of HSIs was ultimately explained by the failure of teams to decrease HSI rates, but coupled with increases in athlete salaries over the past 10 season. The information presented in this report highlights the financial cost of HSIs and other sporting injuries, raising greater awareness and the need for further funding for research into injury prevention strategies to maximise economical return for investment in athletes.

Training load-injury paradox: Is greater preseason participation associated with lower in-season injury risk in elite rugby league players?

Abstract and Figures

Recommended publications

Serum 25-hydroxyvitamin D and breast cancer in the military: A case-control study utilizing pre-diag...

Association Between Body Mass Index and Patient-Centered Outcomes After Hysterectomy

Chronic psychosocial stress at work and risk of depression: Evidence from prospective studies

Response to letter regarding article, "adiponectin and coronary heart disease: A prospective study a...