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Despite association, the acute:chronic work load ratio does not predict non-contact injury in elite footballers

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Purpose: To examine association and prediction of load-based markers (rate of perceived exertion) with non-contact injuries. Materials and methods: Thirty-four elite Italian football players (age 26 ± 5 y, height 182 ± 5 cm, body mass 78 ± 4 kg) participated in a 3-seasons prospective study. Markers examined were: RPE, exposure, weekly load, week-to-week load change, cumulative 2, 3, 4 WL, acute:chronic 1:2 (acute:chronic2), 1:3 (acute:chronic3) and 1:4 (acute:chronic4) WL ratios. After checking multicollinearity between markers, a Generalized Estimating Equation analysis was used to examine association with a non-contact injury in the subsequent week. The associated markers were split into four groups based on 15th, 50th, 85th percentile to compare injury risk (IR) in different zones. Prediction was examined with receiver operating characteristic curve, area under the curve (AUC) and Youden index. Results: IR increased when acute:chronic2 of 1.00–1.20, >1.20 were compared to <0.81 (odds ratio (OR), 90% confidence interval (CI): 1.6, 0.79–3.29; 2.2, 1.03–4.74). IR increased when comparing acute:chronic3 of 1.01–1.23, >1.23 vs. <0.80 (OR, 90% CI: 1.9, 0.9–3.8; 2.5, 1.2–5.4). IR increased when comparing acute:chronic4 of 0.78–1.02, 1.02–1.26, >1.26 vs. <0.78 (OR, 90% CI: 2.4, 1.4–3.9; 3.3, 1.6–6.6; 3.5, 1.7–7.4). The AUC ≤0.60 for all markers and Youden Index (close to 0) showed poor prediction. Conclusion: Acute:chronic markers showed association however with poor prediction ability.
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Science and Medicine in Football
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Despite association, the acute:chronic work load
ratio does not predict non-contact injury in elite
Maurizio Fanchini, Ermanno Rampinini, Marco Riggio, Aaron J. Coutts,
Claudio Pecci & Alan McCall
To cite this article: Maurizio Fanchini, Ermanno Rampinini, Marco Riggio, Aaron J. Coutts,
Claudio Pecci & Alan McCall (2018): Despite association, the acute:chronic work load ratio
does not predict non-contact injury in elite footballers, Science and Medicine in Football, DOI:
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Published online: 24 Jan 2018.
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Despite association, the acute:chronic work load ratio does not predict non-contact
injury in elite footballers
Maurizio Fanchini
, Ermanno Rampinini
, Marco Riggio
, Aaron J. Coutts
, Claudio Pecci
and Alan McCall
US Sassuolo Football Club, Sassuolo, Italy;
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona,
MAPEI Sport Research Centre, Olgiate Olona, Italy;
Sport and Exercise Discipline Group, University Technology Sydney (UTS), Moore Park,
NSW, Australia;
Research & Development Department, Arsenal Football Club, London, UK;
Research & Development Department, Edinburgh
Napier University, Edinburgh, UK
Purpose: To examine association and prediction of load-based markers (rate of perceived exertion) with
non-contact injuries.
Materials and methods: Thirty-four elite Italian football players (age 26 ± 5 y, height 182 ± 5 cm, body
mass 78 ± 4 kg) participated in a 3-seasons prospective study. Markers examined were: RPE, exposure,
weekly load, week-to-week load change, cumulative 2, 3, 4 WL, acute:chronic 1:2 (acute:chronic2), 1:3
(acute:chronic3) and 1:4 (acute:chronic4) WL ratios. After checking multicollinearity between markers, a
Generalized Estimating Equation analysis was used to examine association with a non-contact injury in
the subsequent week. The associated markers were split into four groups based on 15th, 50th, 85th
percentile to compare injury risk (IR) in different zones. Prediction was examined with receiver operat-
ing characteristic curve, area under the curve (AUC) and Youden index.
Results: IR increased when acute:chronic2 of 1.001.20, >1.20 were compared to <0.81 (odds ratio (OR),
90% confidence interval (CI): 1.6, 0.793.29; 2.2, 1.034.74). IR increased when comparing acute:chronic3
of 1.011.23, >1.23 vs. <0.80 (OR, 90% CI: 1.9, 0.93.8; 2.5, 1.25.4). IR increased when comparing acute:
chronic4 of 0.781.02, 1.021.26, >1.26 vs. <0.78 (OR, 90% CI: 2.4, 1.43.9; 3.3, 1.66.6; 3.5, 1.77.4). The
AUC 0.60 for all markers and Youden Index (close to 0) showed poor prediction.
Conclusion: Acute:chronic markers showed association however with poor prediction ability.
Accepted 10 January 2018
Team sport; soccer; injury
prevention; training load
The relationship between training load and injury in ath-
letes has recently received increased attention in research
and practical settings. Indeed, the International Olympic
Committee (IOC) recently released a consensus statement
(Soligard et al. 2016) highlighting that the relative load (i.e.,
the rate of load application relative to what the athlete has
been prepared for) is the most pertinent measure of load
associated with injury in athletes. Referred to as the acute:
chronic ratio(acute:chronic) (typically, acute = current
week and chronic = rolling 4 week average), this measure
has been suggested to reflect the ratio between fatigue (i.e.,
acute) and fitness (i.e., chronic) of the player and has been
associated with injury in many sports (Malone, Owen, et al.
Murray et al. 2016; Weiss et al. 2017). In general, it is
suggested that injury likelihood is low when the acute:
chronic ratio is within a range of 0.81.3, and more than
double when the acute:chronic ratio exceeds 1.5 (Soligard
et al. 2016). Additional load measures such as high acute
weekly (Malone, Owen, et al. 2016), cumulated weekly
(Malone, Owen, et al. 2016;Rogalskietal.2013;Cross
et al. 2016) and week-to-week changes (Cross et al. 2016)
have also been associated with increased injury risk (IR),
whilst high chronic loads have been reported to offer pro-
tective effects (Hulin et al. 2016a). However, a concomitant
high acute:chonic ratio with high chronic load has been
shown to increase the risk (Hulin et al. 2016b).
Whilst the understanding of the relationships between
training load and IR in sports is increasing, some issues and
limitations of other studies should be addressed. For example,
studies on elite football players are lacking, with only one
study in male senior (Malone, Owen, et al. 2016) and another
in elite youth players (Bowen et al. 2017). It has also been
suggested that different acute:chronic durations should be
used to better fit the specificity of training/competitive pat-
terns of football (e.g., 2 and 3 weekly chronic loads) (Buchheit
2017). In addition, there is a lack of a standard injury definition
(Hulin 2017) with some studies investigating all injuries includ-
ing medical attention injuries(i.e., injuries without any time
loss) (Gabbett 2004), while others consider only time-loss
injuries (Malone, Owen, et al. 2016; Malone, Roe et al. 2016;
Hulin et al. 2016a,2016b). Moreover, despite earlier recom-
mendation (Gabbett 2010), only two studies have further clas-
sified time-loss injuries into contact and non-contact injury
mechanisms (Rogalski et al. 2013; Bowen et al. 2017). While
research has shown that non-contact injuries can be pre-
vented (to a certain extent) with exercise-based programmes
(Thorborg et al. 2017) in amateur football, there is still a lack of
CONTACT Maurizio Fanchini
© 2018 Informa UK Limited, trading as Taylor & Francis Group
understanding on the relationship between load characteris-
tics and non-contact injuries. Therefore, there is a need to
further examine the relationships between load characteristics
and non-contact injuries specifically in elite football players.
One controversial aspect of previous research that requires
clarification is the supposition that significant associations
between load and injury have prompted load and its derivatives
to be deemed as predictiveof injury (Hulin et al. 2016a,2016b).
However, as explained by Professor Roald Bahr (Bahr 2016),
associations with injury and predicting injury are different terms
and are analysis that should be used for different purposes (i.e.,
explanation and classification). Indeed, it has been shown that
even strong associations may not predict injury (Pepe 2004).
While associations such as odds ratio (OR) or relative risk can
be useful to characterize the risk of a population, these statistical
outcomes do not infer prediction(Pepe 2004). In order to
determine the true use of a marker, it is important to assess not
only an association but also the predictive validity and optimal
cut-off value (Fawcett 2006). Predictive validity of a marker is
typically assessed with receiver operating characteristic (ROC)
curve (De Vet et al. 2011).
Utilising a simple, practical and valid measure of internal
load derived from the rate of perceived exertion (RPE), the aim
of the present study was to investigate (1) the association of
internal perceived load with non-contact injury and (2) the
ability of these measures to predict injury in elite football
players. We hypothesised that there would be a significant
association between various load measures and non-contact
injury, though load measures would show inadequate level of
predictive validity.
During three consecutive, competitive seasons (2013/14, 2014/
15, 2015/16), 34 professional football players from the same
SerieA team were prospectively followed (age: 26 ± 5 y; height:
182 ± 5 cm; body mass: 78 ± 4 kg). Only players competing at
least 1 full season were included in the analysis, specifically, 12
players during three seasons, 10 during two seasons and 12 for
one season. Each season was split into three distinct periods
depending on the year and league start date: pre-season (early
July to mid-end August), early and late in-season (mid-end
August to end December and early January to mid-May, respec-
tively, early and late in-season). Pre-season period lasted
38 ± 3 days, the in-season (i.e., competitive) periods were
128 ± 4 and 147 ± 7 days in early and late in-season, respectively.
The study is in accordance with the spirit of the Helsinki
Declaration. Approval for the study was obtained from the Club
as players data were routinely collected over the course of the
season (Winter and Maughan 2009).
Quantification of internal load
Internal load was assessed using session-RPE method (Foster
et al. 2001) and validated in football (Impellizzeri et al. 2004)
whereby the intensity of the session is multiplied by the
duration for each player for each training session or match.
The intensity was determined using the Borg CR-10 scale
modified by Foster et al. (2001) within 30 min after completion
of the session/match and was collected by the same fitness
coach throughout all seasons. The mean weekly RPE was
calculated as indicator of the week intensity as well as the
total exposure was calculated by summating the duration of
each session. The weekly load (WL) corresponded to the sum
of load for all training sessions and matches for each week.
The absolute week-to-week change in load (W-WL) was calcu-
lated as the difference between current WL and previous WL.
Cumulative loads were calculated for 2, 3 and 4 weeks.
Chronic loads were calculated as rolling averages for 2, 3
and 4 weeks. An acute (WL) to chronic (rolling average of
previous weeks) ratio was calculated for 2, 3 and 4 weeks
(acute:chronic2, acute:chronic3, acute:chronic4, respectively).
Injury data collection
A non-contact injury was considered when a player was
unable to take a full part in future football training or match
due to physical complaints (Fuller et al. 2006) (all injuries
diagnosed by the same club doctor). Injuries were recorded
according to the classifications proposed by Fuller et al. (2006).
Training exposure was considered as the duration (h) corre-
sponding to team-based and individual physical activities.
Injury incidence was calculated as all non-contact injuries per
1000 h of football activity (training + matches).
Statistical analysis
Data are presented as mean ± standard deviations (mean ± SD).
Generally, in elite football teams, there are several differences in
the training structure between pre- and in-season periods,
including the organization, quality and quantity of training
(e.g., general vs. specific, once vs. twice a day training session)
and this results in different periodization and overall loads
(Impellizzeri et al. 2005). Differences in load between pre- and
in-season (early and late in-season, before and after Christmas
break, respectively) have been examined in the present cohort
with a one-way ANOVA. If WLs were different between periods
(P< 0.05), only in-season data were considered in the analysis.
Effect size (partial eta squared, η
) was also calculated and
values of 0.01, 0.06 and >0.15 were interpreted as small, med-
ium and large, respectively. PearsonsmomentofCorrelation
and variance inflation factors (VIF) were determined to detect
multicollinearity between markers; if a VIF 10 was found, the
variable was excluded from the analysis.
Determining association
A generalized estimating equations (GEE) analysis was used to
determine the association between load measures and non-
contact injury in the subsequent week (Williamson et al. 1996).
To analyse longitudinal data with a binary outcome distribu-
tion (injury: yes/no), a logit link function was used and
exchangeable working correlations matrix was chosen, based
on lower quasi-likelihood under the independence model cri-
terion. If GEE analysis was significant (P< 0.05), the variable
was split in four groups based on 15th, 50th, 85th percentile
to compare IR between zones of different load: extremely low
(<15th percentile), moderate low (>1550th percentile), mod-
erate high (from >50 to 85th percentile) and extremely high
(>85th percentile). The percentage IR was calculated as a
proportion of weeks with a subsequent injury for each group
(Hopkins et al. 2007).
ORs and 90% confidence intervals (CIs) were calculated for
comparison between risks in different load groups (MedCalc
Software, Ostend, Belgium). Magnitude-based inferences were
used to interpret change in percentage risk between groups
(Hopkins et al. 2009). The smallest beneficial and harmful effect
for a risk ratio was considered as an OR of <0.90 and >1.11,
respectively. The effect was considered unclear if the chance of
the true values of beneficial was >25% with the OR <66. If the effect
was considered clear, thresholds for assigning qualitative terms
of beneficial, trivial, harmful were as follows: <0.5%, most likely;
0.55%, very unlikely; 525%, unlikely; 2575%, possible; 7595%,
likely; 9599.5%, very likely; >99.5%, most likely (Hopkins 2007).
Determining prediction
Load variables that exhibited a significant association follow-
ing GEE analysis were tested for predictive ability using ROC
curve. The ROC curve examines the discriminant ability of a
marker to classify players in two groups and plots the true
positive rate (sensitivity) against the true negative rate (speci-
ficity) producing an area under the curve (AUC). An AUC of
1.00 (100%) represents perfect discriminant power, where 0.50
(50%) would represent no discriminatory power (Fawcett
2006; Crowcroft et al. 2016). An AUC >0.70 and the lower CI
>0.50 was classified as a goodbenchmark (Menaspà et al.
2010). All ROC curve results were presented as AUC ± 90% CI.
To examine the predictive ability of the higher cut-off,
sensitivity [true positive/(true positive + false negative)*100]
and specificity [false positive/(false positive + true negative)
*100] were calculated for each load-based marker using the
85th percentile as cut-off (Kent and Hancock 2016). In addi-
tion, the Youden Index (J) was calculated (J= sensitivity+spe-
cificity1) from all ROC curve plots to determine the point
where the sensitivity and specificity were optimised (i.e., high
J) and considered the score at which a cut-offvalue from
each load marker might be acceptable to discriminate a player
at risk of injury. The maximum Jindex of 1 would suggest
perfect discriminatory value, whilst a score of 0 would reflect
no diagnostic value (Schisterman et al. 2005).
All analyses were performed using SPSS (Version 21. IBM
Company, New York, USA) and Excel (Microsoft Excel 2011 for
Mac, Microsoft Corporation, USA).
A total of 90 (of which 72 occurred in-season and 18 classified as
re-injuries) time-loss non-contact injuries were incurred during
the three seasons. Non-contact injury incidences were 5.2/
1000 h, 4.6/1000 h and 5.5/1000 h for pre-season, early in-season
and late in-season (5.0/1000 h for all in-season periods). Injury
occurrence, exposure and injury incidence per match and train-
ing are presented in Table 1. Details about type, location and re-
injuries are presented in Appendix 1. WL was significantly differ-
ent at different time points during the season (P<0.0001,
late in-season periods (2082 ± 700 vs. 1528 ± 466 AU, P< 0.0001
and vs. 1526 ± 427 AU, P< 0.0001, respectively). Pre-season data
were excluded from the analysis; therefore, 72 non-contact inju-
ries were considered. Cumulative load of 2, 3 and 4 weeks
showed substantial multi-collinearity (i.e., VIF >10) and therefore
were excluded from the analysis.
The results of the GEE analysis showed (Table 2)thatweekload
and week-to-week load absolute change were significantly but
Table 1. Injury occurrence, exposure and injury incidence during all seasons (pre- and in-season) per training and match.
2013/14 2014/15 2015/16
Pre-season In-season Pre-season In-season Pre-season In-season
Non-contact injuries, training (n°) 7 17 6 7 5 15
Non-contact injuries, match (n°) 9 14 10
Exposure, training (h) 840 4146 1309 4309 1295 4134
Exposure, match (h) 494 592 593
Injury incidence, training (n°/1000 h) 8.3 4.1 4.6 1.6 3.9 3.6
Injury incidence, match (n°/1000 h) 18.2 23.7 16.9
Table 2. Association and prediction of different load markers. Odds ratios, 90% confidence intervals (90% CI) and P-level from the Generalized Estimating Equation
analysis for load-based markers and binary outcome (injury: yes/no). Area Under the Curve (AUC) and 90% CI from the Receiving Operator Characteristic curve (ROC)
analysis and the Youden Index (J).
Association Prediction
Marker OR (90% CI) P-level Clinical inference AUC (90% CI) J
Duration (min) 1.00 (1.001.00) 0.08 Unclear
RPE (AU) 1.37 (0.971.93) 0.13 Likely harmful 0.54 (0.480.59) 0.11
WL (AU) 1.00 (1.001.00) 0.02 Unclear 0.55 (0.500.59) 0.15
W-WL (AU) 1.00 (1.001.00) 0.04 Unclear 0.56 (0.510.62) 0.14
Acute:chronic2 (AU) 2.98 (1.874.75) 0.00 Most likely harmful 0.57 (0.520.63) 0.15
Acute:chronic3 (AU) 2.46 (1.434.24) 0.01 Very likely harmful 0.60 (0.540.65) 0.19
Acute:chronic4 (AU) 2.91 (1.585.36) 0.00 Most likely harmful 0.57 (0.520.63) 0.15
RPE: rate of perceived exertion; WL: week load; W-WL: week-to-week absolute difference in load; Acute:chronic2, Acute:chronic3, Acute:chronic4 WL ratios; OR: odds
ratio; AUC: Area Under the Curve; J: Youden Index.
unclearly associated with injuries whereas acute:chronic2, acute:
chronic3, acute:chronic4 showed clear association with injuries
(P< 0.05). Weekly duration and RPE showed no significant
(unclear and clear, respectively) associations with injuries
(Table 2). The IR, OR and 90% CI calculated for comparison
between different load groups in each load marker (<15th,
1550th, 5085th, >85th percentile) and corresponding magni-
tude-based inferences are presented in Table 3.
The ROC curve (Figure 1), the values AUC (90% CI) and the J
for each load marker (Table 2) showed poor predictive ability
of injury (AUC: 0.550.60). When using the 85th percentile as
cut-off, each marker showed poor sensitivity (Table 4). Given
the decrease of IR in the higher group of the WL compared to
intermediate groups (i.e., 3.1% vs. 4.5%, 4.1%, respectively in
4th, 2th, 3th group), a further analysis was performed to
examine the high frequency of non-contact injuries group
Table 3. Injury risk comparisons between different zones of load (<15th, 1550th, 5085th, >85th percentile).
Marker (AU)
Injury risk
(%) Load zones
Odds ratio
(90% CI)
Qualitative term for clinical
Chances (%) the effect is
WL 1.4 <1086 (reference)
4.5 1086 to 1542 3.4 (1.428.28) Very likely Harmful 1/1/98
4.1 >1542 to 1985 3.1 (1.277.50) Very likely Harmful 1/2/97
3.1 >1985 2.3 (0.846.19) Likely harmful 6/6/88
1086 to 1542 (reference)
>1542 to 1985 0.9 (0.581.40) Unclear 50/29/22
>1985 0.7 (0.351.25) Unclear 78/12/9
>1542 to 1985 (reference)
>1985 0.7 (0.391.40) Unclear 69/16/15
W-WL 2.7 <572 (reference)
3.4 572 to 1 1.2 (0.632.47) Possibly harmful 22/17/61
4.1 >1 to 614 1.5 (0.782.97) Likely harmful 10/12/78
4.5 >614 1.7 (0.783.51) Likely harmful 9/10/81
572 to 1 (reference)
>1 to 614 1.2 (0.761.96) Possibly harmful 14/23/63
>614 1.3 (0.742.38) Possibly harmful 14/17/69
>1 to 614 (reference)
>614 1.1 (0.621.91) Unclear 29/23/47
Acute:chronic2 2.4 <0.81 (reference)
3.5 0.81 to 1.00 1.5 (0.733.05) Possibly harmful 12/13/75
3.8 >1.00 to 1.20 1.6 (0.793.29 Likely harmful 9/11/81
5.1 >1.20 2.2 (1.034.74) Likely harmful 3/4/93
0.81 to 1.00 (reference)
>1.00 to 1.20 1.1 (0.681.74) Unclear 26/27/47
>1.20 1.5 (0.852.58) Likely harmful 7/13/80
>1.00 to 1.20 (reference)
>1.20 1.4 (0.792.36) Possibly harmful 11/16/73
Acute:chronic3 2.4 <0.80 (reference)
2.7 0.80 to1.01 1.1 (0.532.34) Unclear 32/18/50
4.4 >1.01 to 1.23 1.9 (0.943.81) Likely harmful 4/7/89
5.9 >1.23 2.5 (1.205.38) Very likely harmful 1/2/96
0.80 to1.01 (reference)
>1.01 to 1.23 1.7 (1.032.79) Likely harmful 2/6/92
>1.23 2.3 (1.294.01) Very likely harmful 0/2/98
>1.01 to 1.23 (reference)
>1.23 1.3 (0.802.24) Possibly harmful 10/17/73
Acute:chronic4 1.4 <0.78 (reference)
3.3 0.781.02 2.4 (0.985.92) Likely harmful 4/4/92
4.5 >1.021.26 3.3 (1.378.02) Very likely harmful 1/1/98
4.9 >1.26 3.6 (1.419.31) Very likely harmful 1/1/98
0.781.02 (reference)
>1.021.26 1.4 (0.862.21) Likely harmful 7/16/78
>1.26 1.5 (0.852.68) Likely harmful 7/12/81
>1.021.26 (reference)
>1.26 1.1 (0.631.89) Unclear 28/24/48
WL: week load; W-WL: week-to-week absolute change in week load; Acute:chronic2, Acute:chronic3, Acute:chronic4 WLs ratio.
1 - Specificity
Reference Line
Source of the
Figure 1. Receiving operating characteristic (ROC) curves for the acute:chronic2,
acute:chronic3, acute:chronic4 WL ratios markers.
compared to the other (i.e., WL between >1086 and 1542 vs.
other groups), and sensitivity and specificity were 43.1% and
65.3%, respectively.
In accordance with our hypotheses, the main and novel find-
ings of this study were that various load markers were clearly
associated with the occurrence of non-contact injury in elite
footballers; however, they showed poor predictive ability to
detect individuals that will go on to incur a non-contact injury.
Evidence for implementing a global load monitoring
The results of the present study support the IOC consensus
statement (Soligard et al. 2016), which highlights the asso-
ciation of load and IR. However, despite being significantly
associated (P< 0.05) with non-contact injury, the absolute
loads in the current study (i.e., weekly acute load and week-
to-week absolute change) did not exhibit an increased risk
of injury (OR 1, unclear clinical inference). Despite RPE (i.e.,
intensity) showing no significant association (P> 0.05) with
non-contact injury using traditional statistical analyses, mag-
nitude-based inference statistics highlighted a likely harmful
effect (Table 2). As recommended by the IOC, it may be the
rate of load application that is a more pertinent measure
associated with injury. The acute:chronic ratios of 2, 3 and
4 weeks investigated in the current study all showed a
significant association with non-contact injury coupled with
an increased risk (OR 3.0, 2.5 and 2.9, respectively). These
results support the findings in other sports that the acute:
chronic ratio offers some explanation into non-contact injury
occurrence with rapid increases in relative load (Soligard
et al. 2016). Unfortunately, our results showed similar asso-
ciation with IR in acute:chronic 2, 3, 4 ratios; therefore,
further work is needed not only to validate our findings
but also to identify the optimal combination of acute and
chronic durations. Recently, a protective effect of a tradi-
tional acute:chronic4 between 1.00 and 1.25 has been found
in elite football players (Malone, Owen, et al. 2016).
However, the present study investigated OR between differ-
ent load groups (i.e., based on percentiles) for all acute:
chronic ratios and it was found that IR increased (OR >1)
as the acute:chronic values increased (Table 3). Collectively,
these results show that there was no protective effect of
load in the present cohort.
But, can we predict when a player will get injured?
While the findings of significant association with non-contact
injury provides the practitioner with evidence to implement a
load monitoring preventative strategy, such information does
not imply an ability to predict individual players who will incur
the injury (i.e., diagnostic accuracy). The poor diagnostic char-
acteristics of load variables in the present study (Table 2) show
that load monitoring cannot be confidently used as a tool to
predict injury in individual players. In the present sample, ROC
curve analysis revealed AUC 0.60 for WL, week-to-week
change as well as acute:chronic2, 3 and 4 (Table 2), which is
lower than the AUC >0.70 which has to be reported to estab-
lish some predictive ability (Crowcroft et al. 2016). In addition,
the Youden Index, which assesses the balance between sensi-
tivity and specificity to quantify cut-off values (Crowcroft et al.
2016), also showed poor discriminatory value (J= 0.140.19,
Table 2). A Jof 1 suggests a perfect discriminatory value and J
of 0 reflects no diagnostic value (Schisterman et al. 2005);
unfortunately, our results were close to 0. While, to our knowl-
edge (Schisterman et al. 2005), this is the first study to use a
predictive analysis for load variables as predictors of injury
occurrence in elite athletes, it is in line with other studies
showing poor diagnostic ability of subjective monitoring vari-
ables used to identify performance change (Crowcroft et al.
2016; Saw et al. 2016). In addition, mathematical coupling has
been found between numerator and denominator in the
acute:chronic ratio (i.e., acute is a part of the chronic load)
providing spurious correlations and therefore limiting infer-
ences (Lolli et al. 2017).
What if the acute:chronic ratio is really high?
In the present study, we investigated the sensitivity (i.e., the
ability of this load measure to correctly identify a player
incurring non-contact injury) of very high acute:chronic ratios
(>85th percentile versus 85th percentile). With the acute:
chronic2, 3 and 4, we found 21%, 24% and 20% sensitivity,
respectively. To put this into perspective, in absolute number
of non-contact injuries, acute:chronic2 identified 15 true posi-
tives and 277 false positives, acute:chronic3: 17 true positives
and 273 false positives and acute:chronic4: 14 true positives
and 274 false positives (Table 4). Given these findings, even
with very high acute:chronic ratios (considering the present
sample), it seems unlikely to predict injury. It is important to
highlight that the value of acute:chronic4 > 85th percentile in
the present study is lower compared to that reported in the
Table 4. Sensitivity and specificity for each marker when the load is extremely high (i.e., >85th percentile).
(AU) True positive False positive False negative True negative Sensitivity (%) Specificity (%)
WL 9 284 63 1597 12.5 84.9
WL 2nd vs. other 31 653 41 1229 43.1 65.3
W-WTL 13 279 59 1593 18.1 85.1
Acute:chronic2 15 277 57 1596 20.8 85.2
Acute:chronic3 17 273 55 1588 23.6 85.3
Acute:chronic4 14 274 56 1576 20.0 85.2
True positive: the players with injury with marker >85th percentile; False positive: the players without injury with marker >85th percentile; False negative: the
players with injury with marker <85th percentile; True negative: the players without injury with marker <85th percentile. Sensitivity and Specificity.
IOC consensus (1.26 vs. 1.50 AU, respectively) and this may
explain the difference in results (Soligard et al. 2016).
Limitations and future directions
There are some limitations to the current study. First, the
cohort used in this study belong to one team and limits
generalization of the findings. Future studies should include
more teams over multiple seasons. Second, we did not
account for potential confounding variables such as previous
injury, multiple injuries in the same player, fitness or age.
Future studies would benefit from methods and analyses
that account for these confounders. In addition, the present
study examined only internal load while other studies exam-
ined external loads (i.e., running loads); future studies should
examine both internal and external measures together. The
strengths of this study include a longitudinal period studied
over three seasons and statistical analyses accounting for
repeated measures in addition to investigating predictive
The findings of the present study support the association
between session-RPE derived training and match load mea-
sures, especially relative measures of acute:chronic2, 3 and 4
with non-contact injury. However, while significantly asso-
ciated, this should not be confused with ability to predict
injury at an individual player level. Overall, our findings pro-
vide justification for the implementation of a team-wide mon-
itoring strategy of internal load in elite footballers; however,
caution should be taken when making decisions at the indivi-
dual player level.
Practical implications
The load markers based on internal perceived load (session-
RPE) as acute:chronic ratios (e.g., 1:4 weeks, 1:3 weeks and 1:2
weeks) are significantly associated with non-contact injury in
elite football players. While internal perceived load markers
are associated with non-contact injury occurrence, such mar-
kers have poor predictive validity to identify individual players
who will go on to incur such an injury.
Disclosure statement
No potential conflict of interest was reported by the authors.
Maurizio Fanchini
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Appendix 1. Details of re-injuries during pre- and in-season periods*
Season Location Type of injury Event (Training/match)
Pre-season Thigh (H) Muscle strain Training
Thigh (Q) Muscle strain Training
Knee Sprain Training (Friendly match)
Thigh (Ad) Muscle strain Training (Friendly match)
Thigh (H) Muscle strain Training (Friendly match)
Thigh (H) Muscle strain Training (Friendly match)
In-season Knee Lesion of meniscus Training
Hip Muscle tear Training
Pre-season Lower back Muscle strain Training
Lower leg Muscle strain Training
Lower leg Muscle strain Training
Thigh (H) Muscle strain Training (Friendly match)
Thigh (Ad) Muscle strain Training
Lower leg Muscle strain Training
In-season Knee Ligament injury Match
Thigh (H) Muscle tear Match
Thigh Muscle strain Match
Thigh (Q) Muscle strain Match
Pre-season Thigh (Q) Muscle tear Training (Friendly match)
Knee (PT) Tendinosis Training
Lower leg Muscle strain Training
Foot Other injuries Training
Thigh (H) Muscle strain Training (Friendly match)
In-season Knee (CL) Ligament injury Training
Thigh (H) Muscle strain Match
Lower leg/Achilles tendon Tendinosis Training
Thigh (H) Muscle strain Match
Thigh (H) Muscle tear Match
Knee (PT) Tendinosis Training
Knee (PT) Tendinosis Training
Groin Muscle strain Training
Thigh Muscle strain Training
Thigh (H) Muscle tear Match
Thigh (H) Muscle tear Match
Knee Other bone injuries Match
H: hamstring; Q: quadriceps; Ad: adductor; PT: patellar tendon; CL: collateral ligament.
*Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, Hagglund M, McCrory P, Meeuwisse WH. 2006. Consensus statement
on injury definitions and data collection procedures in studies of football (soccer) injuries. Br J Sports Med. 40(3):193201.
... Workload can have both a protective or harmful effect on injury risk (Hulin et al., 2014;Schwellnus et al., 2016;Soligard et al., 2016), depending on how athletes musculoskeletal characteristics respond to an external load stimulus (Clarke et al., 2013;Schwellnus et al., 2016;Soligard et al., 2016). Workload derivate ACWR has been the most common external risk factor studied in recent research (Bowen et al., 2019(Bowen et al., , 2017Jones et al., 2019;Wind et al., 2017), but as it still lacks consensus on specifications and methodology, its association with injury is conflicting and questionable (Burgess, 2017;Bowen et al., 2020;Fanchini et al., 2018;Impellizzeri et al., 2020aImpellizzeri et al., , 2020bLolli et al., 2020;Sedeaud et al., 2020;. Recent research (Dalen-Lorentsen et al., 2020;Impellizzeri et al., 2020aImpellizzeri et al., , 2020bLolli et al., 2017;Williams et al., 2017) highlighted the mathematical flaws of ACWR as a predictive tool to injury occurrence. ...
... Multiple other authors have studied the association of workload with injury in team sports, using several metrics to quantify load with injury (Bowen et al., 2019;Delecroix, Delaval et al., 2019;Delecroix et al., 2018;Delecroix, Mccall et al., 2019;Fanchini et al., 2018;Jaspers et al., 2018;. The ACWR has been prospectively associated with injury incidence in several team sports, making it one of the most used tools for workload monitoring and management (Gabbet et al., 2007;Gabbet et al., 2011;Hulin et al., 2014). ...
... The timeframes used by the authors ranged from acute 3 to 7 days, and chronic 7, 14, 21, and 28 days. In respect of the coupling for acute and chronic weeks, coupled and uncoupled ratios were used, where the acute week is left out the denominator (Bowen et al., 2019(Bowen et al., , 2017Delecroix, Delaval, et al., 2019;Delecroix et al., 2018;Fanchini et al., 2018). ...
Full-text available
Purpose: (A) To characterize the epidemiology of injury at an elite youth football academy. (B) To investigate the differences between injured and non-injured elite youth footballers in musculoskeletal screening and workload variables, for lower extremity non-contact soft tissue injuries; and for groin located and muscular type injuries. Methods: (A) Prospective analysis of time-loss injuries from one hundred eighty-four elite youth male football players (age: 16.2±2.2 yrs) in a Portuguese academy (U14-U23) during the 2019-2020 season. Injury frequency, burden, incidence, and patterns were calculated. (B) A match-paired case approach was used to investigate differences between injured (n= 56) and non-injured (n= 56) groups for preseason musculoskeletal screening variables (passive knee fall out (PKFO), adductor squeeze (ASQZ), adductor squeeze bodyweight ratio (ASQZ/BWratio), dorsiflexion lunge test (DLT); single-leg countermovement jump (SL-CMJ)) and workload variables before injury (Cumulative sum; monotony; strain; acute: chronic workload ratio (ACWR); week to week change) using internal load (sRPE). Groin located injuries (n=14 vs n=14) and muscular injuries (n= 27 vs n=27) were also investigated. Results: (A) A total of 129 time-loss injuries were observed. Injuries were more frequent in training but had a higher incidence and burden rate in match context. Overall incidence was 2.7 per 1000 hours, and burden rate 59.3 days lost per 1000 hours. The thigh was the most frequent location. Quadriceps was the most injured muscle group, mainly by sprinting and shooting mechanisms. Moderate injuries were more frequent, with a mean of 21.9±28 days lost to injury. Under 17 was the most affected team, with the highest-burden cross-product. (B) ASQZ/BWratio was higher in non-injured players compared with injured players for lower body non-contact (0.64±0.11 vs 0.59±0.11; p=0.025) and groin injuries (0.64±0.08 vs 0.54±0.11; p=0.007). No other workload and musculoskeletal variable had significant differences between groups. Conclusions: Characteristics of injury incidence, burden, and patterns differ among squads in elite youth football. Non-contact injuries in pre-adolescent players remain frequent, representing a threat to the young football player's safe development. ASQZ/BWratio could be used to identify risk of injury for lower body non-contact and groin injuries. More data is necessary to clarify which musculoskeletal and workload factors are relevant to youth football injury occurrence.
... 26 In the case of ACWR, some recent criticisms have been made on the abusive use of the workload measure to ''predict'' injury risk. [27][28][29] Recently, the suggestion is reframing the conceptual model of ACWR, 29 a similar approach is needed for training monotony because variability should be understood beyond load, but also considering load orientation. 30 Thus, more than looking for workload measures as ''predictors'' of injury, they should be seen as indicators of how the training load has been administered following the training principles of progression (inter-week variation) or variability (intra-week variation). ...
... The characteristics of the studies that reported acute: chronic workload ratio (ACWR) are detailed in Table 2. From the 27 studies selected, a total of 17 studies analyzed ACWR, [27][28][29]37,39,[42][43][44][45][46][47][48][50][51][52]54,59 in which nine studies used internal load variables, 28,29,39,[42][43][44][45]50,52 six studies used external load variables 27,37,46,48,51,58 and only two used both internal and external variables 47,54 to calculate ACWR. ...
... The characteristics of the studies that reported acute: chronic workload ratio (ACWR) are detailed in Table 2. From the 27 studies selected, a total of 17 studies analyzed ACWR, [27][28][29]37,39,[42][43][44][45][46][47][48][50][51][52]54,59 in which nine studies used internal load variables, 28,29,39,[42][43][44][45]50,52 six studies used external load variables 27,37,46,48,51,58 and only two used both internal and external variables 47,54 to calculate ACWR. ...
Acute: chronic workload ratio (ACWR) and training monotony have been criticized as injury risk predictors. Therefore, the use of intensity measures should be oriented to understand the variations of intensity across the season. The aim of this systematic review is to summarize the main evidence about the ACWR and training monotony variations over the season in professional soccer players. The search was made in PubMed, SPORTDiscus, and FECYT according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From the 225 studies initially identified, 27 were fully reviewed, and their outcome measures were extracted and analyzed. Existing literature revealed a variety of designs, ACWR and training monotony ranges, variables assessed and durations of the studies. Overall, the range values for ACWR were 0.4–3.39 AU, while those focused on monotony were 0.49–5.7 AU. Regarding ACWR, the ratios located around 0.85-1.25 could predict lower risk values and ratios around ≥ 1.50 could predict higher risk values. On the contrary, with respect to training monotony, the ratios are approximately between 0.5 to 2.00 (low values in the preseason and low competition weeks and high values when soccer players are in highly scheduled competition weeks). Nevertheless, ACWR and training monotony methods should be addressed and considered based on their real value before using this indicator to reduce injury risk. In fact, the data did not conclusively define injured and non-injured players. For this reason, utilizing standardized approaches will allow for more precise conclusions about professional soccer players.
... Of the current systematic review seems that there is an association (n=9) between ITL and injury incidences, and only two studies (57,65) recruited academy soccer players that aging of 13.5 ± 0.26 and 17.1 ± 0.7 (59,67). Those nine studies (56,57,60,62,64,65,67,69,70) correlated the noncontact injury incidence with cumulative weekly load, week-to-week change, ACWR, training monotony and training strain. In agreement with those results, many other team-sport investigations indicated significant correlations between sRPE load and injury occurrences (49,50,78,81,82 HSR (58,64,72) were the mostly significantly correlated variables with noncontact injury incidences. ...
... In addition to the time-window for ACWR, finding out an optimal load ratio is also highly essential in terms of aiming to prevent players against injury risks along with sustaining to improve their physical fitness level. Regarding, Malone et al. (78) found a protective effect of in-season ACWR of 1.00-1.25 for sRPE against injury risk in soccer players which supports previous included studies (56,62) that practitioners should control ACWR not to exceed the ratio of 1.20-1.30 for sRPE. ...
... Of the six studies did not indicate any significant correlation, four (55,63,65,66) recruited only academy players which could be a reason of not finding any significant correlation.Regarding the other two studies, one (61) had mainly focused on the injury type rather than the number of occurrences, and another one (64) did not find out excessively inflated ACWR results which may affect the results of the studies with decreasing the possibility to reach significant correlations. 13 studies(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67) utilized ranges of ACWR models including coupled, uncoupled, RA, EWMA, timewindow of 7:28 (4-week chronic load), 7:21 (3-week chronic load) and 7:14(2- week chronic load). ACWRRA with 7:28 time-window was mostly used method to calculate ACWR. ...
Full-text available
A systematic review investigating the relationship between training load variables and non-contact injury incidence in soccer players
... While ACWR is a powerful monitoring tool, ACWR alone cannot be used as a predictive value for becoming injured. 7,8 Recent literature has proposed that ACWR be dismissed as a model because the ratio creates an increased risk of artefacts that have no association with injury. 7 While these arguments are noted, the present study did not use the ACWR in any relation to injuries. ...
... 7,8 Recent literature has proposed that ACWR be dismissed as a model because the ratio creates an increased risk of artefacts that have no association with injury. 7 While these arguments are noted, the present study did not use the ACWR in any relation to injuries. Rather, the value of using ACWR in athlete monitoring is that it addresses general principles of training such as individualization, variation, progression, and overload, and it can be used in combination with other measures when evaluating an athlete's performance and injury risk. ...
Full-text available
The purpose of this study was to describe the in-season variations of acute:chronic workload ratio (ACWR) of distance, high intensity distance (HID), sprints, accelerations, and decelerations between player positions of a Division I collegiate women’s lacrosse team. Data were collected via wearable microtechnology across a total of 17 games and 64 training sessions on a total of 15 participants (attackers n=5, midfielders n=5, defenders n=5). ACWRs were calculated weekly by dividing the workload from the past seven days by the workload from the past 28 days for each metric. Two repeated measures analyses of variance (RM-ANOVA) were used to compare positional differences and weekly changes in all five metrics for 1) ACWR and 2) weekly training totals. There were several differences in weekly totals and ACWRs across all five metrics evaluated (p<.05), but no positional differences were noted. Apart from the early training weeks, ACWR primarily stayed within the optimal window of 0.8-1.5 to maximize performance and reduce injury risk. These data indicate that there is variation in weekly totals for the main five metrics studied that cause “spikes” and “valleys” in workload. However, the athletes had built enough of a base in their chronic workload that it did not affect their ACWR to move outside of the optimal window. Using this information, coaches and team physicians can more effectively program training not only to optimize performance, but also to limit injuries, fatigue, and lack of fitness.
... On the other hand, further research does not support the relationship of ACWR with non-contact injuries. More specifically, no differences were found in accumulated weekly loads and ACWR calculations regarding injured athletes [21], a conclusion which was supported by related research [22][23][24], while Impellizzeri et al. [13] mentions that ACWR is a rescaling of the explanatory variable (AL, numerator), in turn magnifying its effect estimates and decreasing its variance despite conferring no predictive advantage. ...
... Delecroix et al. [20], reporting on acute: chronic workloads using combinations of two, three, and four weeks, provided some evidence to predict an injury, but not to an absolute degree. Fanchini et al. [22] provided evidence for internal load as a risk factor for non-contact injury. ...
Full-text available
This study was conducted to determine if the acute: chronic workload ratio (ACWR) is related to the incidence of non-contact injuries. The purpose is to compare the external load of injured and non-injured soccer players with the same characteristics, such as position and age. The present analysis considers both the four and the two weeks preceding an injury. Physical characteristics were recorded and analyzed through global positioning systems (GPS) evaluation over one season of 24 competitive microcycles, 144 training sessions, and 32 matches in a total of 35 professional soccer players from the Greek Super League 1 and Super League 2. The loads calculated were total distance (TD), 15–20 km/h, 20–25 km/h, 25–30 km/h, accelerations (ACC) > 2.5 m/s2, and decelerations (DEC) > 2.5 m/s2). Nine injured athletes exceeded the critical threshold of an ACWR > 1.3 several times compared with non-injured athletes that did not reach this level. The present study showed that ACWR is related to a subsequent occurrence of injury but that the threshold of an ACWR can vary. This seems to be mainly influenced by assessing the load of the last two weeks compared with that of the four weeks before the injury.
... Since clubs and national teams may present different (sometimes antagonistic) goals at any given moment, it is fundamental to foster communication between the different multidisciplinary teams for a long-term non-zero sum for all intervenient (club-player, national team) [21]. Communication (e.g., information exchange) between these two entities is considered vital for the mitigation of injury risk and training program development [47], given the documented association between training load (internal and external) and performance [48][49][50][51][52] and injury in soccer [53][54][55][56][57][58][59][60]. Otherwise, reduced personal responsibility regarding performance may ensue, resulting in decreased productivity [36,37]. ...
Full-text available
The increase in the economic value of soccer occurred in parallel with an increase in competing demands. Therefore, clubs and federations evolved to greater specialization (e.g., state-of-the-art facilities and high-profile expertise staff) to support players’ performance and health. Currently, player preparation is far from exclusively club or national team centered, and the lack of control in each player’s environment can be more prevalent than expected. For example, an elite group of professional players faces disruptions in the season club-oriented planification due to involvement in national teams. Moreover, as elite players’ financial resources grow, it is common for them to employ specialized personal staff (e.g., strength and conditioning, nutritionist, and sports psychologist) to assist in their preparation, resulting in complex three-fold relationships (i.e., club, player’s staff, national team). Although efforts have been made to improve communication with and transition from the club to the national team supervision, this new reality (club-players’ staff) may generate serious compound role-related problems and difficulties in monitoring load and training adaptation and having a unified message. Therefore, efforts must be implemented to ensure a more informed management of the players’ performance environment, where the existence and impact of these various personal staff are considered to avoid a long-term non-zero sum for all intervening parties. If left unchecked, current professional thinking may collide or overlap, potentially triggering conflict escalation and impairing athletic performance or health, especially if effective communication routes are not adequately established. Moreover, diluted personal responsibility regarding performance may ensue, resulting in decreased productivity from all involved, which may cause more harm than benefits for the player’s overall health and performance. This emerging reality calls for developing a joint working framework (i.e., between the player’s personalized support team and the clubs’ team) and better managing of a player-centered process.
... Claims have been made that spikes in ACWR (e.g., >2.0 for decelerations, accelerations, and low-intensity distances) increased injury risk 5-7 times in men soccer players [100]. Despite the claims that the ACWR could be used to predict injury risk and that it therefore should be used for training load management [98][99][100][101][102], prospective [103][104][105] and even retrospective studies [106] have failed to demonstrate such relationship, and the concept and methods themselves have been highly criticized in the literature [107][108][109][110]. There are hints that accumulated loads may be more closely associated with lower limb injuries than the ACWR [111]. ...
Lower limbs muscle injuries (LLMI) are the most common sports-related injuries during practice and/or competition. The most affected muscle groups are the adductors, hamstrings, quadriceps, and calf muscles. These injuries generate a considerable competitive and economic burden, justifying a comprehensive investment in strategies focused on reducing injury risk. This chapter delivers an overview of potential risk reduction strategies of LLMI. Although the focus will be on exercise-based strategies, it should be recognized that strategies may be equally relevant (e.g., rules changes, proper equipment). Exercise-based strategies for reducing LLMI risk should consider two interacting features: modality and dose. The evidence surrounding different exercise modalities (e.g., strength training, balance training), dose-response relationships, timing of implementation (e.g., warm-up, postexercise), and mediator factors (e.g., adherence to interventions, interindividual variability in response) is explored. Potential trade-offs (e.g., reduction of injury risk versus performance impairment), the often-misunderstood role of asymmetry, and the value of screening tools are also debated. Currently, most of what is known derives from associative studies and causal relationships are largely unknown, while the focus on average data may be detracting from more personalized approaches to injury risk reduction. Therefore, although a conceptual model for reducing the risk of LLMI is provided, it should be considered tentative.
... In this regard, for some specific scenarios and variables, ROC curve analyses revealed AUC >0.70 (Table 1), which is the arbitrary cut-off value reported to establish an acceptable predictive ability. 13 Similarly, for the same scenarios, the Youden index, which has been reported to assess the balance between sensitivity and specificity to quantify cut-off values, 13 also showed moderate discriminative values greater than 0.30. 29 However, in practical "real-life" scenarios, the rather high number of false negatives (ie, low specificity, accounting for players with low match exposure that did not suffer an injury) present in most of the metrics and combinations of 1 to 2 matches before injury (Appendix Figure A2, available online) indicates a poor predictive ability to detect players that will go on to incur a hamstring injury. ...
Background: Hamstring strain injuries are one of the most prevalent injuries in football (soccer). We examined the influence of accumulated match-play exposure on the occurrence of hamstring strain injury in professional football from 2 teams (Spanish 1st Division, LaLiga) over 3 seasons, and determined specific cut-off points as indicators of injury risk. Hypothesis: Overloaded players would be more likely to sustain a hamstring injury. Study design: Prospective, controlled, observational study. Level of evidence: Level 2b. Methods: Playing time, total running distance, and high-speed running (>24 km/h) distance during official matches of players that sustained a hamstring injury were compared with uninjured, paired controls. Cumulative playing time and running performance of 4 matches before the injury was computed. Relative risk (RR) of injury occurrence was estimated by generalized estimating equations. Diagnostic accuracy was determined by receiver operating characteristics and the area under the curve. Results: Thirty-seven hamstring strain injuries occurred, representing 23 ± 18 absence days per injury. Thirty-seven controls (uninjured players) were used as comparators. Low match-play exposures during 1 and 2 matches before injury were likely to explain injury occurrence (RR: 14-53%; P < 0.01). Metrics from the match before the hamstring muscle strain demonstrated the best accuracy to predict injury occurrence: high-speed running distance ≤328 m (sensitivity, 64%; specificity, 84%), playing time ≤64 min (sensitivity, 36%; specificity, 97%), and running distance ≤5.8 km (sensitivity, 39%; specificity, 97%). Conclusion: Relatively reduced competitive exposure in the previous 2 matches was associated with higher hamstring injury risk in professional football players. Clinical relevance: Screening simple metrics such as the accumulated match exposure during official matches and considering specific cut-off points for some running variables may be good indicators of injury risk and may assist in better individual injury management in professional soccer players.
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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.
Fields, JB, Kuhlman, NM, Jagim, AR, Dulak-sigler, C, and Jones, MT. Analysis of accumulated workloads and performance testing across a collegiate women's lacrosse season. J Strength Cond Res XX(X): 000-000, 2023-Monitoring accumulated workloads, acute:chronic workload ratios (ACWR), and training monotony (TM) are practical methods for monitoring athlete physical stress. Performance testing provides useful information about the changing nature of physical abilities. Therefore, the purpose was to examine differences in accumulated workloads based on session type, explore seasonal trends in ACWR and TM, and assess changes in performance assessments in collegiate women's lacrosse athletes. Athletes, who were identified as starters (n = 12), wore positional monitoring technology during training sessions (n = 61) and games (n = 17) and completed preseason and postseason assessments of speed, agility, power (jump tests), strength, aerobic capacity, and body composition. Separate 1-way analyses of variance were used to determine differences in accumulated workloads for session type and differences in performance assessments from preseason to postseason (p < 0.05). When compared with games, practice sessions elicited greater (p < 0.001) accumulated total distance, player load, repeated high-intensity efforts, accelerations, change of direction, explosive efforts, high-speed efforts (p = 0.002), and high-speed distance (p = 0.002). Throughout the season, ACWR and TM ranged from 0.16 to 1.40 AU and 0.68-1.69 AU, respectively. The 40-yd sprint (p < 0.001) and pro-agility (p < 0.001) improved from preseason to postseason, whereas no changes in aerobic capacity, lower-body power, or strength were observed (p > 0.05). The monitoring of accumulated loads, ACWR and TM, and performance tests revealed novel information about the seasonal demands of collegiate women's lacrosse. Women lacrosse players are able to improve speed and agility throughout the season, while maintaining strength, power, and endurance, with minimal reductions in fat-free mass.
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The acute:chronic workload ratio is associated with injury risk in rugby league players.1 Researchers and practitioners have discussed the most appropriate way in which acute and chronic workloads should be modelled and compared with injury. Menaspa2 suggested that when workloads do not follow weekly patterns in team sport, rolling weekly averages may disregard variations in workload within the week and as such, are not ideal. However, Drew et al 3 highlighted that evidence was required before an alternative method could be considered. Williams et al ,4 suggested an exponentially weighted moving average calculation of acute and chronic workloads. To my knowledge, all this discussion has ignored definition of injury—this could influence the findings of any workload-injury analysis. Relating the findings from multiple injury investigations in team sport can be difficult due to the use of inconsistent definitions of injury.5 Both Hulin …
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Objectives: To examine the association between combined sRPE measures and injury risk in elite professional soccer. Design: Observational Cohort Study Methods: Forty-eight professional soccer players (mean ± SD age of 25.3 ± 3.1 yr) from two elite European teams were involved within a one season study. Players completed a test of intermittent-aerobic capacity (Yo-YoIR1) to assess player’s injury risk in relation to intermittent aerobic capacity. Weekly workload measures and time loss injuries were recorded during the entire period. Rolling weekly sums and week-to-week changes in workload were measured, allowing for the calculation of the acute:chronic workload ratio, which was calculated by dividing the acute (1-weekly) and chronic (4-weekly) workloads. All derived workload measures were modelled against injury data using logistic regression. Odds ratios(OR) were reported against a reference group Results: Players who exerted pre-season 1-weekly loads of ≥1500 to ≤ 2120 AU were at significantly higher risk of injury compared to the reference group of ≤1500 AU (OR = 1.95, p = 0.006). Players with increased intermittent-aerobic capacity were better able to tolerate increased 1-weekly absolute changes in training load than players with lower fitness levels (OR = 4.52, p = 0.011). Players who exerted in-season acute:chronic workload ratios of >1.00 to <1.25 (OR = 0.68, p = 0.006) were at significantly lower risk of injury compared to the reference group (≤ 0.85). Conclusions: These findings demonstrate that an acute:chronic workload of between 1.00 and 1.25 is protective for professional soccer players. A higher intermittent-aerobic capacityappears to offer greater injury protection when players are exposed to rapid changes inworkload in elite soccer players. Moderate workloads, coupled with moderate-low to moderate-high acute:chronic workload ratios, appear to be protective for professional soccer players
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Purpose: To assess measurement sensitivity and diagnostic characteristics of athlete monitoring tools to identify performance change. Methods: Fourteen nationally competitive swimmers (11 males, 3 females, age: 21.2 ± 3.2 y) recorded daily monitoring over 15 months. The "Self-report" group (n=7) reported general health, energy levels, motivation, stress, recovery, soreness and wellness. The "Combined" group (n=7) recorded sleep quality, perceived fatigue, total quality recovery (TQR) and heart rate variability measures. The week-to-week change in mean weekly values were presented as the co-efficient of variance (CV%). Reliability was assessed on three occasions and expressed as the typical error CV%. Week-to-week change was divided by the reliability of each measure to calculate the signal-to-noise ratio. The diagnostic characteristics for both groups were assessed with receiver operating curve analysis, where area under the curve (AUC), Youden index, sensitivity and specificity of measures were reported. A minimum AUC of 0.70 and lower confidence interval (CI) >0.50 classified a "good" diagnostic tool to assess performance change. Results: Week-to-week variability was greater than reliability for soreness (3.1), general health (3.0), wellness% (2.0), motivation (1.6), sleep (2.6), TQR (1.8), fatigue (1.4), R-R interval (2.5) and LnRMSSD:RR (1.3).Only general health was a "good" diagnostic tool to assess decreased performance (AUC-0.70, 95% CI, 0.61-0.80). Conclusions: Many monitoring variables are sensitive to changes in fitness and fatigue. However, no single monitoring variable could discriminate performance change. As such the use of a multi-dimensional system that may be able to better account for variations in fitness and fatigue should be considered.
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Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.
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Objectives: To examine the relationship between chronic training loads, number of exposures to maximal velocity, the distance covered at maximal velocity, percentage of maximal velocity in training and match-play and subsequent injury risk in elite Gaelic footballers. Design: Prospective cohort design. Methods: Thirty-seven elite Gaelic footballers from one elite squad were involved in a one-season study. Training and game loads (session-RPE multiplied by duration in min) were recorded in conjunction with external match and training loads (using global positioning system technology) to measure the distance covered at maximal velocity, relative maximal velocity and the number of player exposures to maximal velocity across weekly periods during the season. Lower limb injuries were also recorded. Training load and GPS data were modelled against injury data using logistic regression. Odds ratios (OR) were calculated based on chronic training load status, relative maximal velocity and number of exposures to maximal velocity with these reported against the lowest reference group for these variables. Results: Players who produced over 95% maximal velocity on at least one occasion within training environments had lower risk of injury compared to the reference group of 85% maximal velocity on at least one occasion (OR: 0.12, p=0.001). Higher chronic training loads (≥4750AU) allowed players to tolerate increased distances (between 90 to 120m) and exposures to maximal velocity (between 10 to 15 exposures), with these exposures having a protective effect compared to lower exposures (OR: 0.22 p=0.026) and distance (OR=0.23, p=0.055). Conclusions: Players who had higher chronic training loads (≥4750AU) tolerated increased distances and exposures to maximal velocity when compared to players exposed to low chronic training loads (≤4750AU). Under- and over-exposure of players to maximal velocity events (represented by a U-shaped curve) increased the risk of injury.
Purpose: To establish the relationship between the acute:chronic workload ratio and lower extremity overuse injuries in professional basketball players over the course of a competitive season. Methods: The acute:chronic workload ratio was determined by calculating the sum of the current week's session rate of perceived exertion (sRPE) training load (acute load) and dividing it by the average weekly training load over the previous four weeks (chronic load). All injuries were recorded weekly using a self-reported injury questionnaire (Oslo Sports Trauma Research Centre Injury Questionnaire(20)) Workload ratios were modelled against injury data using a logistic regression model with unique intercepts for each player. Results: Substantially fewer team members were injured following workload ratios between 1-1.49 (36%) compared to very low (≤0.5; 54%), low (0.5-0.99; 51%) or high (≥1.5; 59%) workload ratios. The regression model provided unique workload-injury trends for each player, but all mean differences in likelihood of being injured between workload ratios were unclear. Conclusions: Maintaining workload ratios between 1-1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modelling and monitoring the training load-injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training load planning and prescription, and with developing athlete-specific recovery and rehabilitation strategies.
Objective To investigate the effect of FIFA injury prevention programmes in football (FIFA 11 and FIFA 11+). Design Systematic review and meta-analysis. Eligibility criteria for selecting studies Randomised controlled trials comparing the FIFA injury prevention programmes with a control (no or sham intervention) among football players. Data sources MEDLINE via PubMed, EMBASE via OVID, CINAHL via Ebsco, Web of Science, SportDiscus and Cochrane Central Register of Controlled Trials, from 2004 to 14 March 2016. Results 6 cluster-randomised controlled trials had assessed the effect of FIFA injury prevention programmes compared with controls on the overall football injury incidence in recreational/subelite football. These studies included 2 specific exercise-based injury prevention programmes: FIFA 11 (2 studies) and FIFA 11+ (4 studies). The primary analysis showed a reduction in the overall injury risk ratio of 0.75 (95% CI 0.57 to 0.98), p=0.04, in favour of the FIFA injury prevention programmes. Secondary analyses revealed that when pooling the 4 studies applying the FIFA 11+ prevention programme, a reduction in the overall injury risk ratio (incidence rate ratio (IRR) 0.61; 95% CI 0.48 to 0.77, p<0.001) was present in favour of the FIFA 11+ prevention programme. No reduction was present when pooling the 2 studies including the FIFA 11 prevention programme (IRR 0.99; 95% CI 0.80 to 1.23, p=0.940). Conclusions An injury-preventing effect of the FIFA injury prevention programmes compared with controls was shown in football. This effect was induced by the FIFA 11+ prevention programme which has a substantial injury-preventing effect by reducing football injuries by 39%, whereas a preventive effect of the FIFA 11 prevention programme could not be documented. Trial registration number PROSPERO CRD42015024120.
The use of the acute:chronic workload ratio (A/C) has received a growing interest in the past 2 years to monitor injury risk in a variety of team sports.1 ,2 This ratio is generally computed over 28 days (ie, load accumulated during the current week/load accumulated weekly over the past 28 days), using both internal (session-rate of perceive exertion (Session-RPE)×duration) and external (tracking variables, often Global Positioning System (GPS)-related, such as high-speed running and acceleration variables) measures of competitive and training load. While the potential benefit of such a metric is straight forward for practitioners, there remain several limitations to (1) the assessment of relative external load and in turn, injury risk in players differing in locomotor profiles and (2) the effective monitoring of overall load across all training and matches throughout the year. In turn, these limitations likely compromise the usefulness of the A/C ratio in elite football (soccer). Assessing player's locomotor profile and relative external load. 1. Speed : Considering that subtle differences in sprinting intensity such as high (85–95% of maximal sprinting speed) versus very high-speed running (>95%) may have important implications with regard to injury risk and prevention,3 the individualisation of high-speed running zones may be important. However, such a sprint-intensity classification requires the use of players' maximal sprinting speed as a reference, which is very rarely assessed in elite players. Therefore, considering the large variations in locomotor profiles between players within the same team, the use of absolute (fixed) speed thresholds to define high-speed running zones may limit the sensitivity of the A/C ratio with respect to high-speed running load3 and in turn, injury risk. 2. Fitness : Considering that fitness testing (eg, maximal aerobic speed) is also rare in professional football, and considering the clear impact of fitness on injury risk,4 it is difficult to …