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Calculating acute:chronic workload ratios using
exponentially weighted moving averages provides
a more sensitive indicator of injury likelihood
than rolling averages
Nicholas B Murray,
1
Tim J Gabbett,
2
Andrew D Townshend,
1
Peter Blanch
3,4
1
School of Exercise Science,
Australian Catholic University,
Brisbane, Queensland,
Australia
2
Gabbett Performance
Solutions, Brisbane,
Queensland, Australia
3
Brisbane Lions Australian
Football Club, Brisbane,
Queensland, Australia
4
School of Allied Health
Sciences, Griffith University,
Gold Coast, Queensland,
Australia
Correspondence to
Nick B Murray, School of
Exercise Science, Australian
Catholic University, Brisbane,
QLD 4014, Australia;
nbmurr001@myacu.edu.au
Accepted 1 December 2016
To cite: Murray NB,
Gabbett TJ, Townshend AD,
et al.Br J Sports Med
Published Online First:
[please include Day Month
Year] doi:10.1136/bjsports-
2016-097152
ABSTRACT
Objective To determine if any differences exist
between the rolling averages and exponentially weighted
moving averages (EWMA) models of acute:chronic
workload ratio (ACWR) calculation and subsequent injury
risk.
Methods A cohort of 59 elite Australian football
players from 1 club participated in this 2-year study.
Global positioning system (GPS) technology was used to
quantify external workloads of players, and non-contact
‘time-loss’injuries were recorded. The ACWR were
calculated for a range of variables using 2 models:
(1) rolling averages, and (2) EWMA. Logistic regression
models were used to assess both the likelihood of
sustaining an injury and the difference in injury
likelihood between models.
Results There were significant differences in the ACWR
values between models for moderate (ACWR 1.0–1.49;
p=0.021), high (ACWR 1.50–1.99; p=0.012) and very
high (ACWR >2.0; p=0.001) ACWR ranges. Although
both models demonstrated significant (p<0.05)
associations between a very high ACWR (ie, >2.0) and
an increase in injury risk for total distance ((relative risk,
RR)=6.52–21.28) and high-speed distance (RR=5.87–
13.43), the EWMA model was more sensitive for
detecting this increased risk. The variance (R
2
) in injury
explained by each ACWR model was significantly
(p<0.05) greater using the EWMA model.
Conclusions These findings demonstrate that large
spikes in workload are associated with an increased
injury risk using both models, although the EWMA
model is more sensitive to detect increases in injury risk
with higher ACWR.
INTRODUCTION
The acute:chronic workload ratio (ACWR) is a
model that provides an index of athlete prepared-
ness. It takes into account the current workload (ie,
acute; rolling 7-day workload) and the workload
that an athlete has been prepared for (ie, chronic,
rolling 28-day workload).
1–3
Based on early
research by Banister et al
45
the ACWR is likened
to the fitness-fatigue model, where the chronic load
is analogous to a state of ‘fitness’and the acute
load is analogous to a state of ‘fatigue’.
12
If per-
formance represents the difference between fitness
and fatigue, the ACWR aims to predict perform-
ance by comparing acute and chronic loads as a
ratio.
145
Further, the ACWR has been used to
quantify injury likelihood, where very high ACWR
ranges were associated with a significantly increased
risk of injury.
1–3
The original work by Hulin et al
1
aimed to
determine whether acute and chronic workload
and the ACWR were associated with injury risk in
elite cricket fast bowlers. They reported that large
increases in acute bowling workload (ie, balls
bowled), represented by a high ACWR, were asso-
ciated with an increased risk of injury in the week
following exposure (relative risk (RR)=2.1 (CI
1.81 to 2.44), p=0.01). In addition, a high ACWR
for internal workload (measured via session rating
of perceived exhaustion (session-RPE)) was asso-
ciated with an increased risk of injury in the subse-
quent week (RR=2.2 (CI 1.91 to 2.53), p=0.009).
Further work across a range of sports,
6
specifically
elite rugby league,
78
Australian football (AF),
3
Gaelic football
910
and soccer,
11
has continued to
examine the relationship between the ACWR and
injury likelihood. The common theme of findings
from these studies is that (1) sharp increases or
‘spikes’in acute workload, resulting in a high
ACWR, are significantly related to injury both in
the week the workload is performed and the subse-
quent week,
12
and (2) higher chronic workloads
may offer a protective effect against injury.
12 13
A recent British Journal of Sports Medicine
(BJSM) editorial
14
has raised concerns surrounding
the use of rolling averages to assess workload,
citing that they do not consider the time frame in
which a given stimulus occurred, nor the decaying
nature of fitness and fatigue effects over time.
14 15
While this may be the case, the ACWR model is
evidence-based
216
and is considered a best-practice
approach for modelling the relationship between
load and injury across a range of sports.
17
It is
hypothesised that a non-linear training load model
may be better suited to quantify injury risk;
14
however, there is currently no evidence that this
type of model is superior to the current ACWR
model.
17
Recently, Williams et al
18
proposed the use of
‘exponentially weighted moving averages (EWMA)’
19
as a new method to calculate acute and chronic
loads to address the decaying nature of fitness and
fatigue. This method assigns a decreasing weighting
to each older load value, thereby giving more
weighting to the recent load undertaken by the
athlete. This method differs from the current
model of acute and chronic load calculation, where
a rolling average considers a training session carried
out the day before the analysis and a session
Murray NB, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-097152 1
Original article
BJSM Online First, published on December 21, 2016 as 10.1136/bjsports-2016-097152
Copyright Article author (or their employer) 2016. Produced by BMJ Publishing Group Ltd under licence.
group.bmj.com on December 21, 2016 - Published by http://bjsm.bmj.com/Downloaded from
occurring 4 weeks before as equal.
14
It is suggested that the
EWMA approach may be better suited to calculate the ACWR
and model load and injury relationships than the current rolling
averages method.
18
Until now, no research has investigated the difference
between the previously established rolling average ACWR model
and the newly proposed EWMA model. Therefore, the aim of
the present study was to investigate if any differences existed
between the rolling average and EWMA methods of ACWR cal-
culation and subsequent injury risk in elite Australian
footballers.
METHODS
Participants
Fifty-nine elite players from one club competing in the
Australian Football League (AFL) (age, 23.5±4.4 years; height,
189.7±7.3 cm; mass, 88.9±8.6 kg) participated in this 2-year
study. A total of 92 individual seasons were recorded, where 33
(56%) participants competed in both seasons and 26 (44%) par-
ticipants competed in one season. Each season consisted of a
16-week preseason phase comprising running and football-based
sessions, followed by a subsequent 23-week in-season competi-
tive phase. All experimental procedures were approved by the
Australian Catholic University Human Research Ethics
Committee.
Quantifying workloads
Global positioning system (GPS) technology, sampling at 10 Hz
(Optimeye S5; Catapult Innovations, Melbourne, Australia), was
used to quantify training and match workloads of players. The
GPS units also housed a triaxial accelerometer, gyroscope and
magnetometer, each sampling at 100 Hz. This technology has
demonstrated acceptable reliability and validity when measuring
distance, velocity, acceleration and player load.
20 21
Workload
variables consisted of: (1) total distance (m), (2) low-speed dis-
tance (<6.00 km/h), (3) moderate-speed distance (6.00–
18.00 km/h), (4) high-speed distance (18.01–24.00 km/h), (5)
very high-speed distance (>24.00 km/h), and (6) player load
(au). Player load was measured as a modified vector magnitude
using accelerometer data from each vector (X, Y, and Z axis),
and was expressed as the instantaneous rate of change in each
vector.
20
Definition of injury
For the purpose of this study, and as previously used,
322
an
injury was defined as any non-contact ‘time-loss’injury sus-
tained during training or competition that resulted in a subse-
quent missed training session or game. Medical staff at the
football club classified and maintained injury records through-
out the study. Injury likelihoods were calculated based on the
total number of injuries relative to the total exposure to a given
workload range. Injury likelihoods and RR were subsequently
calculated.
23
ACWR calculation
To calculate a daily rolling averages ACWR, 1-week rolling
workload data represented the acute workload, and the rolling
4-week average workload data represented the chronic work-
load. If a player completed zero external workload (ie, 0 m run)
in a week, these workload data were excluded in the week
where no workload was performed, but these data were still
included in the analysis of chronic workload. The ACWR was
divided into the following ranges: (1) very low, ≤0.49, (2) low,
0.50–0.99, (3) moderate, 1.0–1.49, (4) high, 1.50–1.99, and (5)
very high, ≥2.0.
1–3
Each ACWR contained a unique amount of
observations based on the data, ranging from 468 to 5722
observations.
Rolling averages ACWR
The rolling averages ACWR was calculated by dividing the acute
workload by the chronic workload.
1–3
Where the chronic work-
load was greater than the acute workload, a lower ACWR was
recorded. Similarly, where the acute workload was greater than
the chronic workload, a higher ACWR was recorded.
EWMA ACWR
The EWMA was calculated as described by Williams et al.
18
The
EWMA for a given day was calculated as:
EWMAtoday ¼Loadtoday
l
aþðð1
l
aÞEWMAyesterdayÞ
Where λ
a
is a value between 0 and 1 that represents the degree
of decay, with higher values discounting older observations in
the model at a faster rate. The λ
a
is calculated as:
l
a¼2=(Nþ1)
Where N is the chosen time decay constant, with a 1- week
workload (ie, 7 days) and 4-week workload (ie, 28 days) used to
represent acute and chronic workloads, respectively. To calculate
an EWMA ACWR value, an EWMA for acute workload (ie,
7-day workload) and chronic workload (ie, 28-day workload)
was calculated using the above formula. The EWMA ACWR
value was then calculated by dividing the EWMA acute work-
load by the EWMA chronic workload. To begin the EWMA cal-
culation, the first observation in the series is arbitrarily recorded
as the first workload value in the series. From this value, the
aforementioned EWMA calculation can be used for acute and
chronic workload calculation.
Statistical analysis
Data were analysed using SPSS V.24.0 (SPSS, Chicago, Illinois,
USA). The likelihood of sustaining an injury was analysed using
two binary logistic regression models with significance set at
p<0.05. The ACWR was independently modelled as the pre-
dictor variable, and injury/no injury as the dependent variable.
The very high ACWR (ie, ≥2.0) was used as the reference group
to which each other group was compared. Differences in
ACWR calculation between the rolling averages ACWR model
and the EWMA model for each ACWR ratio range were deter-
mined using a 1-way analysis of variance (ANOVA). The R
2
value for each model was determined, and logistic regression
models were used to determine the differences between the
models. Given the real-world nature of the study, magnitude-
based inferences were used to determine any practically signifi-
cant differences between groups, along with 95% CIs.
24 25
Likelihoods were subsequently generated and thresholds for
assigning qualitative terms to chances were assigned as follows:
<1%, almost certainly not; <5%, very unlikely; <25%,
unlikely; <50%, possibly not; ≥50%, possibly; ≥75%, likely;
≥95%, very likely; ≥99%, almost certainly. The magnitudes of
differences between groups were considered practically mean-
ingful when the likelihood was ≥75%.
24 25
2 Murray NB, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-097152
Original article
group.bmj.com on December 21, 2016 - Published by http://bjsm.bmj.com/Downloaded from
RESULTS
Injuries
A total of 40 injuries were sustained during the 2-year period.
Of these, 18 were sustained during the preseason period, and
22 were sustained during the in-season period. The hamstring
(53%) was the most commonly injured site, followed by other
thigh injuries (ie, quadriceps, adductors) (18%) and calf (18%).
Different methods of ACWR calculation
The average ACWR for each day over the duration of the study
was calculated using the rolling averages and EWMA models
and is displayed in figure 1. The two methods of ACWR calcula-
tion were significantly different (p=0.001) and poorly related
(R
2
=0.43). Using the EWMA model for ACWR calculation
resulted in a significantly lower value than that calculated by the
rolling averages ACWR model for the same daily observations
for moderate (mean±SD, 1.07±0.22 vs 1.19±0.12; p=0.021),
high (1.27±0.21 vs 1.64±0.12; p=0.012) and very high (1.51
±0.22 vs 2.29±0.20; p=0.001) ACWR ranges. There were no
significant differences (p>0.05) between the model calculations
at a very low and low ACWR range.
Injury likelihoods for each ACWR model
Preseason
The likelihood of injury during the preseason phase is shown in
figure 2. A rolling averages ACWR of >2.0 for total distance
was significantly associated with an increased risk of injury com-
pared with those with an ACWR of 1.0–1.49 (RR=8.41, 95%
CI 1.09 to 64.93, p=0.048, 97.4% very likely). No other sig-
nificant relationships were observed between the rolling averages
ACWR and injury likelihood during the preseason period. Using
the EWMA model, there were multiple significant relationships
shown between an ACWR of >2.0 and an increased injury like-
lihood when compared with lower ACWR ranges. Specifically,
compared with an ACWR of 1.0–1.49, the likelihood of injury
was increased sixfold to ninefold for: total distance (RR=8.74,
95% CI 7.35 to 10.39, p=0.002, 99.9% almost certainly),
moderate-speed distance (RR=6.03, 95% CI 2.21 to 16.47,
p=0.028, 98.4% very likely), and player load (RR=9.53, 95%
CI 5.31 to 17.11, p=0.013, 99.3% almost certainly).
In-season
During the in-season period, a rolling average ACWR of >2.0
had an increased likelihood of injury compared with a lower
ACWR for a range of variables. When compared with an
ACWR of 1.0–1.49, an ACWR of >2.0 was associated with an
increase in injury risk for total distance (RR=6.52, 95% CI
4.83 to 8.80, p=0.008, 99.6% almost certainly), high-speed dis-
tance (RR=4.66, 95% CI 4.12 to 5.27, p=0.004, 99.8% almost
certainly), and player load (RR=5.87, 95% CI 4.12 to 8.36,
p=0.010, 99.4% almost certainly). Using the EWMA model,
players who exceeded an ACWR of >2.0 experienced an injury
risk 5–21 times greater than players who maintained an ACWR
of 1.0–1.49 for total distance (RR=21.28, 95% CI 20.02 to
22.62, p=0.001, 99.9% almost certainly), moderate-speed dis-
tance (RR=18.19, 95% CI 17.17 to 19.27, p=0.001, 99.9%
almost certainly), and player load (RR=13.43, 95% CI 12.75 to
14.14, p=0.001, 99.9% almost certainly) (figure 3).
Between-model comparisons
The variance (R
2
) in injury for each variable for each model of
ACWR calculation are shown in table 1. While each model
demonstrated significant relationships between very high ACWR
results and injury likelihood during both the preseason and
in-season periods, there were notable differences between the
models. Using the rolling averages ACWR model, for total dis-
tance during the preseason phase the regression equation
demonstrates that 21% (R
2
=0.21) of the variance was explained
using the ACWR. In comparison, 87% of the variance
(R
2
=0.87, p=0.042) in injury likelihood was explained by the
EWMA. During the preseason period, the EWMA for high-
speed distance and player load explained 77% (R
2
=0.77,
p=0.041) and 76% (R
2
=0.76, p=0.044), respectively, while the
variance explained by the rolling averages ACWR was much
lower (R
2
=0.13 and R
2
=0.46). Similarly, during the in-season
period, the R
2
value for each modelled variable was improved
when using the EWMA model.
DISCUSSION
This study investigated if any differences existed between the
previously described rolling averages model of ACWR calcula-
tion
1–3
and a new EWMA ACWR calculation
18
in determining
injury likelihood. We found that spikes in workload, resulting in
an ACWR of >2.0, were significantly associated with an
increase in injury risk irrespective of the model used. We also
found significant differences in the values reported at
moderate-to-very high ACWR ranges (ie, 1.0–1.49, 1.50–1.99,
and >2.0) between the two models, although no significant dif-
ferences were reported at lower ACWR ranges (ie, <0.49, 0.50–
0.99). Further, our findings demonstrate that the EWMA model
offers greater sensitivity in identifying injury likelihood at
higher ACWR ranges (ie, 1.50–1.99 and >2.0) during both the
preseason and in-season periods.
Difference in ACWR calculation between the models
A key difference between the two proposed models of ACWR
calculation is that the EWMA model assigns a decreasing
weighting for each older workload value, whereas the rolling
averages model suggests that each workload in an acute and
chronic period (typically 7 days and 28 days, respectively) is
equal.
18
Similar to the rolling averages ACWR model, the
EWMA model requires the calculation of both an EWMA acute
Figure 1 The acute:chronic workload ratio (ACWR) modelled using
each method: rolling averages and exponentially weighted moving
averages.
Murray NB, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-097152 3
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and EWMA chronic workload value before the calculation of
the EWMA ACWR. Unlike the rolling averages ACWR model,
the values obtained for an EWMA acute and chronic workload,
provided using the aforementioned formula, are not able to be
considered in isolation due to weighting applied by the λ
a
value.
This is an important consideration given the protective effect of
moderate-to-high chronic workloads against injury.
238
We
modelled the daily average ACWR value for each day of the
study period using both models and found that significantly
lower ACWR values were obtained using the EWMA model at
Figure 2 Likelihood of injury at each ACWR range during the preseason period for the current day for (A) total distance, (B) moderate-speed
distance, (C) high-speed distance and (D) player load.*Denotes significantly different ( p<0.05) from the rolling averages ACWR model. ACWR, acute:
chronic workload ratio; EWMA, exponentially weighted moving averages; RA, rolling averages.
Figure 3 Likelihood of injury at each
ACWR range during the in-season
period for the current day for (A) total
distance, (B) moderate-speed distance,
(C) high-speed distance and (D) player
load.*Denotes significantly different
(p<0.05) from the rolling averages
ACWR model. ACWR, acute:chronic
workload ratio; EWMA, exponentially
weighted moving averages; RA, rolling
averages.
4 Murray NB, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-097152
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moderate, high and very high ACWR ranges while no differ-
ences were observed at very low and low ACWR ranges. Given
the current understanding of the relationship between very high
ACWR ranges and subsequent increases in injury risk,
126
the
importance of this finding is twofold. First, rolling averages con-
sider the relationship between load and injury as linear, and
therefore all workload in a given time frame is considered equal
—when it is not. The EWMA model places a greater emphasis
on the most recent workload a player has performed which will
alter the ACWR value for a given day. Second, since the EWMA
model alters the value for a given day, it influences where a
player sits on the ACWR spectrum. If this increases their ACWR
value, it may place them in a ‘danger zone’that would not be
recognised using the rolling averages ACWR model. Therefore,
if ACWR values differ at the higher end of the ACWR spectrum
using each model, it is important to consider this and its subse-
quent effect on injury risk.
What happens when workloads are spiked?
The findings of this study demonstrate that large spikes in acute
workload, relative to chronic workload, resulting in a very high
ACWR were significantly associated with an increased risk of
injury during both the preseason and in-season periods. This
finding was replicated across both models, suggesting that
regardless of which model of ACWR calculation was used, large
spikes in workload, coupled with a very high ACWR, resulted
in a significant increase in injury risk. The strength of the
ACWR is that it considers the workload a player has performed,
relative to the workload that the player has been prepared
for,
2626
while also acknowledging that the way the load is
achieved is as important as the ACWR itself.
12 17
With that in
mind, it is clear that irrespective of the model used, linear
1217
or non-linear,
14 18
the use of the ACWR should be used to
maximise performance in players through developing high
chronic workloads to adequately prepare players for competi-
tion demands and minimising the risk of injury.
226
The EWMA model may be more sensitive
Anovelfinding of this study is the relationship between a very
high ACWR, calculated using the EWMA model, and an
increase in injury risk during the preseason period. While the
relationship between large spikes in workload and injury risk
during the in-season period is well defined,
3
the relationship
during the preseason period is not as clear. Unlike previous
work in elite AF,
3
our results demonstrate that using the EWMA
model, large spikes in workload during the preseason are asso-
ciated with a significant rise in injury risk. It has previously been
suggested that players are not as well equipped to handle spikes
in workload during the in-season period as they are during the
preseason period due to increased match and physical
demands,
27 28
coupled with an increased emphasis on perform-
ance and recovery. While the preseason period is typically
viewed as an opportunity to develop the required physical and
physiological qualities to successfully compete during the
in-season period,
29
it is crucial that high workloads are pre-
scribed systematically to apply adequate workloads to elicit a
positive physiological change, while also minimising the nega-
tive physiological response.
21229
It has been shown that greater
amounts of training during the preseason period may also offer
a protective effect against injury during the subsequent in-season
competitive period,
30 31
highlighting the further importance
placed on the preseason period. Using the EWMA model, it
appears that large workload spikes, during either the preseason
or in-season period, are associated with a clear threshold (ie,
ACWR>1.50) where injury risk increases rapidly. The use of
the EWMA model has increased the sensitivity of injury likeli-
hood, suggesting that the rolling averages ACWR model does
not: (1) accurately represent the variations in how workloads
are accumulated (ie, a workload performed 28 days ago is not
equal to a workload performed 3 days ago),
14 18
and (2)
account for the decaying nature of fitness and fatigue effects
over time.
14 18
Potential limitations
While the findings of this study hold important implications for
sports science and medicine staff, there are limitations that
warrant further discussion. First, the sample size was limited to
59 players from one club over a 2-year period. It is difficult to
draw competition-wide specific conclusions, as the findings may
be reflective of this particular cohort of players at this particular
point in time. Further, a small number of injuries (n=40) were
recorded due to the inclusion criteria of only non-contact soft-
tissue ‘time-loss’injuries as they are typically considered
‘workload-related’injuries. Further studies with players from
multiple clubs and a larger number of injuries would strengthen
these findings. Second, no internal measures of workload (eg,
session rating of perceived exertion or heart rate) were included
in this study. The inclusion of these may be useful to further
investigate the relationship between internal workload and
injury likelihood. While the majority of statistical information
provided in this study stems from logistic regression models, we
acknowledge that by running multiple models, and thus multiple
comparisons, the risk of a type I error may be inflated. Finally,
our results may be influenced by a smaller sample size at the
extremities of ACWR ranges (ie, >2.0). This may be due to
Table 1 Variance (R
2
) in injury explained by the rolling daily averages and exponentially weighted moving averages acute:chronic workload
ratio (ACWR) models
Rolling daily averages ACWR model Exponentially weighted moving averages ACWR model
Workload variable Preseason In-season Preseason In-season
Total distance (m) 0.21 (−0.24 to 0.66) 0.40 (−0.07 to 0.87) 0.87 (0.72 to 1.00)* 0.78 (0.54 to 1.00)*
Low-speed distance (m) 0.47 (0.02 to 0.92) 0.43 (−0.03 to 0.89) 0.79 (0.56 to 1.00) * 0.75 (0.48 to 1.00)*
Moderate-speed distance (m) 0.32 (−0.16 to 0.80) 0.47 (0.02 to 0.92) 0.82 (0.62 to 1.00)* 0.77 (0.52 to 1.00)*
High-speed distance (m) 0.13 (−0.26 to 0.52) 0.37 (−0.11 to 0.85) 0.77 (0.52 to 1.00)* 0.67 (0.33 to 1.00)
Very high-speed distance (m) 0.23 (−0.23 to 0.69) 0.21 (−0.24 to 0.66) 0.69 (0.37 to 1.00) 0.66 (0.31 to 1.00)
Player load (au) 0.46 (0.01 to 0.91) 0.38 (−0.09 to 0.85) 0.76 (0.50 to 1.00)* 0.72 (0.43 to 1.00)*
Data are variance (R
2
) with 95% CIs.
*Denotes significantly different (p<0.05) from the rolling daily averages ACWR model.
Murray NB, et al.Br J Sports Med 2016;0:1–7. doi:10.1136/bjsports-2016-097152 5
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established load monitoring systems to reduce the number of
exposures to very high ACWR ranges.
CONCLUSIONS
In this first study to investigate the difference between two pro-
posed models of ACWR calculation and injury likelihood in
elite AF players, a high ACWR was significantly associated with
an increase in injury risk for both models. Further, the EWMA
model had significantly greater sensitivity to detect increases in
injury likelihood at higher ACWR ranges during both the pre-
season and in-season periods. This finding supports the refine-
ment of the current ACWR model, although the concept that a
player performing a greater workload than what they are pre-
pared for is reinforced. While the ACWR model may be refined
to increase sensitivity, the basic concept of building chronic
workloads to prepare players to tolerate acute workloads will
remain the same. Similarly, the ACWR should not be considered
in isolation, but rather in context with acute and chronic work-
loads. Future work should attempt to quantify the direct (ie,
medical expenses, financial loss) and indirect (ie, missed training
and competition, etc) costs of workload-related (ie, spikes in
workload) injuries and/or the longitudinal effects of controlled
training loads on injury rates, as this may provide greater insight
than continued risk factor analysis.
What are the findings?
▸The exponentially weighted moving averages (EWMA) model
is more sensitive to detect increases in injury risk at higher
acute:chronic workload ratio (ACWR) ranges during the
preseason and in-season periods.
▸The EWMA model may be better suited to modelling
workloads and injury risk than the rolling averages ACWR
model.
▸Irrespective of the ACWR model used, large spikes in acute
workload are significantly associated with an increase in
injury risk.
How might it impact on clinical practice in the future?
▸Sharp spikes in workload for multiple variables should be
avoided, as they are associated with an increase in injury
risk.
▸The rolling averages model is evidence-based and supported
by the available literature to quantify injury risk; however,
the exponentially weighted moving averages model to
calculate acute:chronic workload ratio (ACWR) has greater
sensitivity for detecting increases in injury risk at higher
ACWR ranges and therefore should be used to model
workloads and injury risk.
▸Providing more evidence around different methods of ACWR
calculation and injury risk will enable practitioners involved
in the physical preparation of elite players to systematically
and ‘safely’prescribe high training loads to enhance the
physical qualities required to both compete and succeed at
the highest level of their chosen sport while minimising the
risk of workload-related injury.
Acknowledgements The authors would like to thank the players of the Brisbane
Lions Australian Football Club for their participation in this study.
Contributors NBM was primarily responsible for the collection and analysis of the
study data. All authors were responsible for the study concept and design, and
contributed to the writing and critical revision of the manuscript.
Competing interests None declared.
Ethics approval Approval was granted by the Australian Catholic University
Human Research Ethics Committee.
Provenance and peer review Not commissioned; externally peer reviewed.
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Original article
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of injury likelihood than rolling averages
averages provides a more sensitive indicator
using exponentially weighted moving
Calculating acute:chronic workload ratios
Blanch
Nicholas B Murray, Tim J Gabbett, Andrew D Townshend and Peter
published online December 21, 2016Br J Sports Med
http://bjsm.bmj.com/content/early/2016/12/21/bjsports-2016-097152
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