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Training and game loads and injury risk in elite Australian footballers

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  • West Coast Eagles
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Abstract and Figures

Objectives: To examine the relationship between combined training and game loads and injury risk in elite Australian footballers. Design: Prospective cohort study. Methods: Forty-six elite Australian footballers (mean±SD age of 22.2±2.9 y) from one club were involved in a one-season study. Training and game loads (session-RPE multiplied by duration in min) and injuries were recorded each time an athlete exerted an exercise load. Rolling weekly sums and week-to-week changes in load were then modelled against injury data using a logistic regression model. Odds ratios (OR) were reported against a reference group of the lowest training load range. Results: Larger 1 weekly (>1750 AU, OR=2.44-3.38), 2 weekly (>4000 AU, OR=4.74) and previous to current week changes in load (>1250 AU, OR=2.58) significantly related (p<0.05) to a larger injury risk throughout the in-season phase. Players with 2-3 and 4-6 years of experience had a significantly lower injury risk compared to 7+ years players (OR=0.22, OR=0.28) when the previous to current week change in load was more than 1000 AU. No significant relationships were found between all derived load values and injury risk during the pre-season phase. Conclusions: In-season, as the amount of 1-2 weekly load or previous to current week increment in load increases, so does the risk of injury in elite Australian footballers. To reduce the risk of injury, derived training and game load values of weekly loads and previous week-to-week load changes should be individually monitored in elite Australian footballers.
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Journal of Science and Medicine in Sport 16 (2013) 499–503
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Original Research
Training and game loads and injury risk in elite Australian footballers
Brent Rogalskia,b,, Brian Dawsona,b, Jarryd Heasmanb, Tim J. Gabbettc
aSchool of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia
bWest Coast Eagles Football Club, Perth, Australia
cSchool of Exercise Science, Australian Catholic University, Brisbane, Australia
article info
Article history:
Received 25 May 2012
Received in revised form 7 December 2012
Accepted 13 December 2012
Injury prevention
Load monitoring
Team sport
Odds ratios
Objectives: To examine the relationship between combined training and game loads and injury risk in
elite Australian footballers.
Design: Prospective cohort study.
Methods: Forty-six elite Australian footballers (mean ±SD age of 22.2 ±2.9 y) from one club were involved
in a one-season study. Training and game loads (session-RPE multiplied by duration in min) and injuries
were recorded each time an athlete exerted an exercise load. Rolling weekly sums and week-to-week
changes in load were then modelled against injury data using a logistic regression model. Odds ratios
(OR) were reported against a reference group of the lowest training load range.
Results: Larger 1 weekly (>1750 AU, OR= 2.44–3.38), 2 weekly (>4000 AU, OR=4.74) and previous to cur-
rent week changes in load (>1250 AU, OR = 2.58) significantly related (p< 0.05) to a larger injury risk
throughout the in-season phase. Players with 2–3 and 4–6 years of experience had a significantly lower
injury risk compared to 7+ years players (OR = 0.22, OR= 0.28) when the previous to current week change
in load was more than 1000 AU. No significant relationships were found between all derived load values
and injury risk during the pre-season phase.
Conclusions: In-season, as the amount of 1–2 weekly load or previous to current week increment in
load increases, so does the risk of injury in elite Australian footballers. To reduce the risk of injury,
derived training and game load values of weekly loads and previous week-to-week load changes should
be individually monitored in elite Australian footballers.
© 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction
Playing Australian football requires repeated physical contact
and movements involving endurance, speed and acceleration1
over match durations of 100+ min.2Recently, Australian Foot-
ball League (AFL) interchange rates have dramatically increased,
allowing players additional breaks throughout games, possibly
contributing to higher mean game speeds.3With greater player
physical demands,1,4 injury incidence and prevalence rates have
also increased.3During 2010, each AFL club (on average) expe-
rienced 38.6 new injuries, causing a player to miss one or more
games. Overall, player injuries resulted in an average of 153.8
missed games per club.3
Understanding potential mechanisms of sporting injuries is
important to AFL medical and conditioning staff, as they manage
their players to be fit for matches. Training and game overload is
one possible cause of injury, therefore monitoring these loads in
Corresponding author.
E-mail addresses:,
(B. Rogalski).
players is important. Measuring training and game loads exerted
by athletes can be done by multiplying session rating of perceived
exertion5(Borg CR10 RPE) and duration (min). Previous studies
have analysed the relationship between load exerted and injury risk
in team sports including sub-elite6and professional rugby league,7
soccer,8basketball9and cricket.10
Gabbett and Domrow6analysed training loads and injuries
of 183 sub-elite rugby league players, finding increases in odds
of injury in pre-season (OR = 2.12, p= 0.01), early competition
(OR = 2.85, p= 0.01) and late competition (OR = 1.50, p= 0.04)
phases, for each increase in a log (150 arbitrary units) of training
load. Orchard et al.10 reported cricket bowlers completing more
than 50 overs in a match had a significantly increased risk (1.77
times) of injury in the next 14–21 days compared to bowlers com-
pleting less than 50 overs. The delayed effect of the load of previous
weeks is important to consider when analysing load and injury
Piggott et al.11 analysed the relationship of injury and illness
with weekly training load in 16 AFL players across a 15-week pre-
season training phase. No significant relationships were reported
between injuries or illness and training load across this period.
However, studies using a larger sample and conducted over a longer
1440-2440/$ – see front matter © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
500 B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503
time period may provide a more comprehensive understanding of
the relationship between training load and injury in AFL players.
Each year approximately 4–8 new rookie players are drafted into
AFL clubs, coming primarily from junior competitions and State
leagues. Senior AFL players have significantly more lean body mass
and bone mineral density than State junior players,12 which is likely
a result of physical maturation from previous training and game
loads within the elite system. Greater movement demands in the
AFL compared to State leagues13 further highlights the increased
physical demands required of junior or sub-elite players in making
the transition into the professional AFL game and training environ-
ment. Therefore, exploring the training and game load tolerance
of players with different years of experience at an elite level is
To date, studies of the training load-injury relationship of AFL
players are limited, with the only study performed restricted to
a small sample of AFL players over a pre-season period.11 There-
fore, the aim of the present study was to examine the relationship
between training and game loads and injury risk in AFL players
from a full club squad, across an entire season. Identifying a rela-
tionship between load and injury may allow club staff to make
more objective decisions on when players are at increased risk of
2. Methods
Elite (n= 46) Australian footballers were involved in this
prospective study. Their mean ±SD age, stature and body mass
were 22.2 ±2.9 years, 187.7 ±7.5 cm and 85.4 ±8.9 kg, respectively.
All were from one AFL club and competed in matches in the AFL
or Western Australian Football League (WAFL) during 2010. The
AFL team played 22 competition matches but did not qualify for
finals. All players provided informed written consent prior to par-
ticipation and all data were obtained anonymously. Ethics approval
was obtained from the Human Research Ethics Committee of The
University of Western Australia.
The 2010 season was split into two main phases to match
the training and game demands required of each period. Dur-
ing pre-season (November to mid-March), players performed 3
field sessions, 3 weights sessions, and several cross training and
running conditioning sessions each week. Late pre-season saw a
gradual reduction in field training loads with the introduction of
pre-season practice games. In-season (mid-March to late-August)
consisted of two weight sessions, one main field training session,
with two lighter field sessions planned around the main session,
followed by a game at the end of the week.
Intensity of training sessions and games were estimated by
each player using the modified Borg CR-10 RPE scale5approx-
imately 30 min following each session. Training and game load
arbitrary units (AU) for each player were then derived by multiply-
ing session-RPE by session/game duration (min). Measurements of
blood lactate concentration and heart rate have correlated strongly
with session-RPE in rugby league6and Australian football14,15
training, suggesting that session-RPE is a valid method for quan-
tifying training loads in team sports.
All injuries were categorised by the club’s physiotherapist and
defined as incidents resulting in a modified training program,
missed training sessions or games. Injuries were classified as being
low severity, resulting in training modification or 1–2 missed train-
ing sessions; moderate severity, where a player was unavailable
for 1–2 games; or high severity, where a player missed 3+ games.
Injuries were also categorised for injury type (description), body
site (injury location) and intrinsic (internal; overuse, overexer-
tion) or extrinsic (external; collision, contact) factors. The club’s
injury definition differs slightly from that of Orchard and Seward’s,3
in that all injuries, including those that limited a player’s capac-
ity to complete training, were taken into account in assessing a
load/injury relationship.
Each day a player was involved in a training session or game,
their previous 1, 2, 3 and 4 weekly individual loads were calcu-
lated. Relationships between training and game loads and injury
were investigated in two ways. Firstly, the likelihood that an accu-
mulation of load could contribute to an injury at a later date was
considered by examining the link between 1, 2, 3 and 4 weekly
cumulative loads and subsequent injury. Secondly, whether a large
increment in load between weeks contributed to an injury was also
explored. This involved analysing week-to-week change between
the current and previous week’s totals. Cumulative and absolute
changes in load are further explained in Supplementary figs. A, B,
C and D (online supplementary data). Load exposure values and
injury data (injury vs. no injury) were then modelled in a logistic
regression analysis. Data were divided into four groups, with the
lowest training and game load range being the reference group.
When an odds ratio (OR) was greater than 1, an increased odds of
injury was reported. Conversely, when an OR was less than 1, a
decreased odds of injury was reported. For an OR to be significant,
95% confidence intervals (CI) would not contain the null OR of 1.00.
Injury incidence was calculated by dividing total number of
injuries by exposure time and reported as rates per 1000 train-
ing and game hours. Chi square analysis compared the frequency
of injuries between pre-season and in-season periods. Differences
in training and game loads between players of different AFL expe-
rience (1, 2–3, 4–6 and 7+ years) were analysed using a one-way
ANOVA and group means compared using a Scheffé post hoc test.
Data were analysed using IBM SPSS Statistics 20.0 and reported as
means and 95% CI. Significance was accepted at p< 0.05.
3. Results
Additional data pertaining to classifications of pre-season and
in-season injuries are provided in (online) Supplementary Table A.
Injury incidence increased (2= 9.37, df = 1, p= 0.002) from
pre-season (21.9 per 1000 h) to in-season (32.8 per 1000 h)
(Supplementary Table B). The thigh (7.3 per 1000 h, 22.2%)
and hip/groin (5.9 per 1000 h, 18.1%) were the most com-
mon sites of injuries in-season, with the most common types
of injuries being muscle strains (10.7 per 1000 h, 32.6%) and
haematomas/contusions (9.3 per 1000 h, 28.5%). Extrinsic injuries
(18.9 per 1000 h, 57.6%) and intrinsic injuries (13.9 per 1000 h,
42.4%) were not different in rate of occurrence in-season.
Average field session loads decreased (p< 0.001) from pre-
season (151 AU) to in-season (97 AU) (Table 1). Game loads
from late pre-season practice matches (642 AU) were signif-
icantly lower (p< 0.001) than in-season competition matches
(912 AU). Similarly, session-RPE intensity measures were lower
in pre-season practice matches (8.5) than in-season competi-
tion matches (8.9), whereas field training intensities were lower
in-season (4.7) compared to pre-season (5.3) sessions. Aver-
age individual weekly loads were greater (p< 0.001) in pre-season
(2027 AU) than in-season (1651AU). Injury incidence in-season
was lowest for 1 year players (28.2 per 1000 h) and highest for
7+ year players (45.4 per 1000 h), however, no significant dif-
ferences between groups were found (Table 1). Players with 7+
years of AFL experience completed significantly (p< 0.01) less load
(29,371 AU) in-season, compared to 2–3 (40,788 AU) and 4–6
year (40,238 AU) players.
Players who exerted 1 weekly loads in-season of >1750 AU were
at significantly higher risk of injury compared to the reference
group of <1250 AU (OR =2.44, 95% CI 1.28–4.66, p= 0.007) (Table 2).
Similarly, players who had completed a 2 weekly load in-season
B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503 501
Table 1
Load type per session for season phases, total training and game loads (arbitrary units) and injury incidence for different years of AFL experience. Data are mean (95%
Confidence Intervals).
Pre-season In-season Whole-season
Playing experience (y) Injury incidence (per 1000 h)
1 year (n= 7) 12.4 (0.0–27.8) 28.2 (17.3–39.1) 22.0 (15.1–28.8)
2–3 years (n= 15) 23.3 (13.9–32.7) 34.6 (24.2–44.9) 28.7 (21.8–35.6)
4–6 years (n= 13) 24.8 (13.9–35.8) 33.9 (22.9–45.0) 29.2 (19.9–38.5)
7+ years (n= 11) 25.4 (8.3–42.6) 45.4 (7.7–83.1) 31.8 (11.2–52.3)
Training and game load (sum)
1 year 23,475 (20,811–26,140)*35,212 (32,201–38,223) 58,688 (53,399–63,976)
2–3 years 40,986 (38,541–43,430) 40,788 (37,705–43,872) 81,774 (76,693–86,855)
4–6 years 38,303 (34,714–41,891) 40,238 (37,603–42,873) 78,540 (73,522–83,559)
7+ years 33,611 (30,309–36,914)** 29,371 (23,764–34,978)62,982 (54,866–71,098)
Load type Mean load per session
Cross training 294 (286–301)a241 (231–250) 276 (270–282)
Field 151 (147–155)a97 (94–100) 125 (122–128)
Game 642 (612–617) 912 (898–925)b856 (842–871)
Running conditioning 113 (110–117) 111 (104–117) 113 (110–116)
Weights 268 (266–271)a231 (229–233) 250 (248–252)
Note: No significant differences were found between injury incidence in AFL years experience groups.
*1 year (p< 0.001) significantly lower than 2–3, 4–6 and 7+ years.
** 7+ years (p< 0.01) significantly lower than 2–3 years.
7+ years (p< 0.01) significantly lower than 2–3 and 4–6 years.
1 year and 7+ years (p< 0.01) significantly lower than 2–3 and 4–6 years.
aPre-season (p< 0.001) significantly greater load than in-season.
bIn-season (p< 0.001) significantly greater load than pre-season.
of >4000 AU were at significantly higher risk of injury compared
to the reference group of <2000 AU (OR = 4.74, 95% CI 1.14–19.76,
p= 0.033). Injury occurrence in-season was also higher for play-
ers who experienced a previous to current week change in load of
>1250 AU (OR = 2.58, 95% CI 1.43–4.66, p= 0.002) compared to the
reference group of <250 AU.
Players with 2–3 (OR = 0.22, 95% CI 0.07–0.68, p= 0.009) and 4–6
(OR = 0.28, 95% CI 0.10–0.82, p= 0.020) years of AFL experience were
found to have a significantly lower risk of injury compared to 7+
year players when a previous to current week change in load was
>1000 AU (Table 3). Interestingly, 1 year players had a significantly
lower injury risk (OR = 0.39, 95% CI 0.16–0.93, p= 0.035) when com-
pared to the 7+ year reference group when experiencing a 1 week
load of >1650 AU.
Table 2
In-season training and game load risk factors for injury in elite Australian footballers.
Load calculation In-season
OR 95% CI p-Value
Exp(B) Lower Upper Sign.
Cumulative load (sum)
1 week
<1250 AU (reference) 1.00
1250 AU to <1750 AU 1.95 0.98 3.85 0.056
1750 AU to <2250 AU 2.44 1.28 4.66 0.007
>2250 AU 3.38 1.69 6.75 0.001
2 weeks
<2000 AU (reference) 1.00
2000 AU to <3000 AU 2.98 0.70 12.66 0.138
3000 AU to <4000 AU 4.03 0.98 16.53 0.053
>4000 AU 4.74 1.14 19.76 0.033
Absolute change (±)
Previous to current week
<250 AU (reference) 1.00
250 AU to <750 AU 1.34 0.90 2.01 0.148
750 AU to <1250 AU 0.89 0.50 1.58 0.680
>1250 AU 2.58 1.43 4.66 0.002
Note: No significant odds ratios were calculated in the pre-season phase.
OR, odds ratio; CI, confidence intervals.
4. Discussion
The purpose of this study was to examine whether a relationship
existed between training and game loads and injury in AFL players.
These results indicate injury risk is significantly higher for players
who exert larger 1 (>1750 AU) and 2 weekly loads (>4000 AU) or a
large previous to current week increment (>1250 AU) in compari-
son to lower training and game load ranges (<1250 AU, <2000 AU,
<250 AU), respectively. These findings suggest that the training and
game loads of elite Australian football players should be individu-
ally monitored on a weekly basis.
Non-contact and soft tissue intrinsic injuries are considered
largely preventable, whereas contact and collision extrinsic injuries
are considered generally unavoidable.7A range of intrinsic (42.4%)
and extrinsic injuries (57.6%) were found during in-season. The
inclusion of extrinsic injuries within this study is consistent with
previous research6,8 as Gabbett and Jenkins16 reported training and
game loads in professional rugby league to be strongly correlated
Table 3
AFL years experience risk factors for injury above certain training and game load
Load calculation In-season
OR 95% CI p-Value
Exp(B) Lower Upper Sign.
Cumulative load (sum)
1 week
>1650 AU
7+ years (reference) 1.00
1 year 0.39 0.16 0.93 0.035
2–3 years 0.74 0.43 1.25 0.258
4–6 years 0.67 0.38 1.17 0.160
Absolute change (±)
Previous to current week
>1000 AU
7+ years (reference) 1.00
1 year 0.14 0.02 1.13 0.065
2–3 years 0.22 0.07 0.68 0.009
4–6 years 0.28 0.10 0.82 0.020
Note: No significant odds ratios were calculated in the pre-season phase.
OR, odds ratio; CI, confidence intervals.
502 B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503
with contact injuries (r= 0.80, p< 0.01). However, intrinsic injuries
are thought to be more directly linked with training and game
loads.7A limitation of the injury classification within the present
study was that recurrent or new injuries were not documented.
The risk of injury in-season for elite Australian footballers
increased as the amount of 1 weekly load increased from the
range of 1750 AU to <2250 AU (OR = 2.44) and >2250 AU (OR = 3.38)
when compared to the reference group of <1250 AU. Gabbett and
Domrow6also found significant relationships between 1-weekly
training loads and injury risk in sub-elite rugby league players in
early (OR =2.85) and late (OR = 1.50) in-season periods.
As this study completed a rolling day-by-day analysis of train-
ing and game loads and injuries, it accounted for instances where
two games were played within a 6-day period (e.g. Sunday game
followed by a Saturday game). Injury rates of elite soccer players
who played 2 matches within a week were significantly higher
when compared to players involved in only 1 match.17 Although
this study analysed elite soccer players playing 2 matches within 4-
days, our results suggest that AFL players participating in 2 matches
within 6-days may be at an elevated risk of injury. However, more
specific research on the turnaround time between matches and
injury risk using a larger sample of teams is required.
After recovering from an injury, a player does not always have
sufficient time to gradually increase their week-to-week training
load prior to returning to large game loads. To our knowledge, this is
the first study to highlight the importance of monitoring the week-
to-week change in training and game loads. Players who exerted a
previous to current week change in load of >1250 AU were found to
be 2.58 times more likely to be injured in comparison to the refer-
ence group of <250 AU. Players returning from previous hamstring
injuries have been shown to have a 9% chance of re-injury within
the week of returning to matches.18 A more conservative approach
by gradually increasing the week-to-week training loads of previ-
ously injured AFL players, before large game loads occur, may result
in a reduced chance of re-injury. Potentially, players returning from
injury may benefit by being used as a substitute as a method to limit
their initial game load and reduce injury risk.
During pre-season, no significant relationships between weekly
or week-to-week changes of load and odds of injury were found.
Similarly, no relationships were reported between average weekly
pre-season training load and team injury incidence in elite rugby
league19 and AFL11 players. However, in a study analysing the pre-
season training loads and odds of injury of sub-elite rugby league
players, an increase in log of training load (150 AU) per week was
found to significantly increase the odds of injury (OR = 2.12).6There
is greater perceived control over the load exerted by elite play-
ers during pre-season, as session durations are usually planned
by experienced conditioning staff and intensities are predicted by
selecting activity/drill types (based on session-RPE averages). Play-
ers involved in this study would have had their pre-season training
loads closely monitored and modified, which may have influenced
the insignificant pre-season results found. Furthermore, the spe-
cific details of the training program of the AFL club involved within
the study are currently unavailable for publication, as they are con-
sidered highly confidential.
Musculoskeletal immaturity of 1 year AFL players12 was hypo-
thesised here to cause an increased injury risk per training and
game load, in comparison to 2–3 and 4–6 year players. However, no
significant relationships were found between load derived values
and injury risk. These results differ from a study reporting 1-weekly
training loads to significantly relate to traumatic injuries in elite
youth soccer players.8The in-season loads of the 1 year players
within our study were highly monitored and modified, resulting in
5000 AU lower loads compared to 2–3 and 4–6 year players. Due
to the strict load modification strategy of 1 year players, they were
not exposed to high weekly or week-to-week load changes and
recorded the lowest in-season injury incidence (28.2/1000 h). Con-
sequently, training and game load modification of immature first
year players entering the elite system may be useful for preventing
injuries. Reductions in pre-season training loads in sub-elite rugby
league players have also been found to reduce injury rates.20 The
influence of pre-season training load on in-season injury risk in
elite Australian footballers is an interesting concept which is yet to
be examined.
The best starting 22 AFL players are generally older and
more experienced21 and frequently have accelerated returns from
injury to full training and game loads, in an attempt to enhance
team performance. Consequently, they are generally exposed to
high week-to-week load increments. The 7+ year group had the
largest in-season injury incidence (45.4 per 1000 h) and therefore
completed significantly lower in-season loads (6000–11,000 AU)
compared to the less experienced groups. The injury risk of 2–3
(OR = 0.22) and 4–6 year players (OR= 0.28) was significantly lower
than 7+ year players when experiencing a previous to current week
change in load of >1000 AU. The body’s ability to respond to rapid
force changes or recover from fatigue has been speculated to slowly
diminish as age and experience increases.22 Therefore, care should
be taken when exposing 7+ year players to a large previous to cur-
rent week change in load. A confounding variable in the elevated
odds of injury in more experienced AFL players is possibly previous
injury history, as it has been reported as an independent predictor
of subsequent hamstring injuries.23 Future studies should analyse
multiple seasons of data to more thoroughly investigate the effect
of increasing experience on training and game loads and injury risk
in AFL players.
Minimising injury risk is vitally important in elite team sports,
as low injury rates can be critical to team performance.24 How-
ever, training programs must elicit fitness improvements so that
players are adequately prepared to endure the demands of compet-
itive games. No fitness observations were made to analyse whether
reductions in training load were detrimental to performance. Mon-
itoring of global positioning system information (distance, sprint
and accelerometer loads) and psychological data such as perceived
muscle soreness, fatigue, mood, and sleep ratings,18 may provide
extra insight into injury risk relationships in elite Australian foot-
5. Conclusion
During an elite Australian football in-season, larger 1 (>1750 AU)
and 2 weekly loads (>4000 AU) and substantial previous to cur-
rent week change in load (>1250 AU) were found to significantly
increase injury risk when compared to lower training and game
load ranges (<1250 AU, <2000 AU, <250 AU), respectively. As a
method to reduce the risk of injury, derived training and game load
values of weekly loads and previous week-to-week load changes
should be monitored individually in elite Australian footballers.
Practical applications:
The non-invasive and simple session-RPE method is useful for
tracking training and game loads in respect to injury risk in elite
Australian footballers.
Weekly load sums and previous week-to-week changes in load
should be monitored in-season for individual elite Australian
footballers, as they are significantly related to injury risk.
Training and game load modification strategies for first year AFL
players may be important in achieving low injury incidence in
their first season.
Future in-season load management modifications could include
planned reduction in training or game loads (especially by being
the designated substitute, or player subbed out of game).
B. Rogalski et al. / Journal of Science and Medicine in Sport 16 (2013) 499–503 503
No external financial support was received for this study.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at
[1]. Wisbey B, Montgomery PG, Pyne DB et al. Quantifying movement demands of
AFL football using GPS tracking. J Sci Med Sport 2010; 13(5):531–536.
[2]. Gray AJ, Jenkins DG. Match analysis and the physiological demands of Australian
football. Sports Med 2010; 40(4):347–360.
[3]. Orchard JW, Seward H. Injury report 2010: Australian Football League. Sport
Health 2011; 29(2):15–29.
[4]. Norton KI, Craig NP, Olds TS. The evolution of Australian football. J Sci Med Sport
1999; 2(4):389–404.
[5]. Foster C, Florhaug JA, Franklin J et al. A new approach to monitoring exercise
training. J Strength Cond Res 2001; 15(1):109–115.
[6]. Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness
in sub-elite collision sport athletes. J Sports Sci 2007; 25(13):1507–1519.
[7]. Gabbett TJ. The development and application of an injury prediction model for
noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond
Res 2010; 24(10):2593–2603.
[8]. Brink MS, Visscher C, Arends S et al. Monitoring stress and recovery: new insights
for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports
Med 2010; 44(11):809–815.
[9]. Anderson L, Triplett-McBride T, Foster C et al. Impact of training patterns on
incidence of illness and injury during a women’s Collegiate basketball season. J
Strength Cond Res 2003; 17(4):734–738.
[10]. Orchard JW, James T, Portus M et al. Fast bowlers in cricket demonstrate up to
3- to 4-week delay between high workloads and increased risk of injury. Am J
Sports Med 2009; 37(6):1186–1192.
[11]. Piggott B, Newton MJ, McGuigan MR. The relationship between training load
and incidence of injury and illness over a pre-season at an Australian Football
League club. J Aust Strength Cond 2009; 17(3):4–17.
[12]. Veale JP, Pearce AJ, Buttifant D et al. Anthropometric profiling of elite junior
and senior Australian football players. Int J Sports Physiol Perform 2010;
[13]. Brewer C, Dawson B, Heasman J et al. Movement pattern comparisons in elite
(AFL) and sub-elite (WAFL) Australian football games using GPS. J Sci Med Sport
2010; 13(6):618–623.
[14]. Middleton P. Relationship of training intensity measures to player rating of
perceived exertion during Australian rules football training drills [Honours The-
sis (unpublished)]. Perth, The University of Western Australia, 2007.
[15]. Scott TJ, Black C, Quinn J et al. Validity and reliability of the session
RPE method for quantifying training in Australian Football: a com-
parison of the CR10 and CR100 scales. J Strength Cond Res 2012.
[16]. Gabbett TJ, Jenkins DG. Relationship between training load and injury in profes-
sional rugby league players. J Sci Med Sport 2011; 14(3):204–209.
[17]. Dupont G, Nedelec M, McCall A et al. Effects of 2 soccer matches in a
week on physical performance and injury rate. Am J Sports Med 2010; 38(9):
[18]. Orchard JW. Recurrent hamstring injury in Australian football. Med Sci Sports
Exerc 1998; 30(5):S52.
[19]. Killen NM, Gabbett TJ, Jenkins DG. Training loads and incidence of injury during
the preseason in professional rugby league players. J Strength Cond Res 2010;
[20]. Gabbett TJ. Reductions in pre-season training loads reduce training injury rates
in rugby league players. Br J Sports Med 2004; 38(6):743–749.
[21]. Young WB, Newton RU, Doyle TLA et al. Physiological and anthropometric char-
acteristics of starters and non-starters and playing positions in elite Australian
Rules football: a case study. J Sci Med Sport 2005; 8(3):333–345.
[22]. Maffey L, Emery C. What are the risk factors for groin strain injury in
sport? A systematic review of the literature. Sports Med 2007; 37(10):
[23]. Gabbe BJ, Bennell KL, Finch CF et al. Predictors of hamstring injury at the elite
level of Australian football. Scand J Med Sci Sports 2006; 16(1):7–13.
[24]. Eirale C, Tol JL, Farooq A et al. Low injury rate strongly correlates
with team success in Qatari professional football. Br J Sports Med 2012.
... Information on injuries was updated daily by the team's specialized medical staff. Based on a previous study, all injuries were recorded by type, location of the injury, and timing of the injury (Rogalski et al., 2013). The information used for the injuries is as follows: ...
... High AW (Malone et al., 2017), cumulated weekly (Rogalski et al., 2013;Cross et al., 2016), and week to week changes (Cross . /fpsyg. . ...
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Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team’s medical sta􀀀. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (1-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and 1-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and 1-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and 1-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and 1-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and 1-AW. ACWR and 1-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
... Accumulated total distance was significantly greater for starters compared to reserves for the total season (starters: 401.7 ± 31.9 vs. reserves: 272.9 ± 51.4 km; p < 0.001; d = 2.83 [1.62, 4.03]) and matches (starters: 222.0 ± 21.3 vs. reserves: 100.9 ± 38.6 km; p < 0.001; d = 3.61 [2.23, 4.98]) as presented in Figure 1A. Accumulated HSD was significantly greater for starters compared to reserves for all sessions (starters: 38.5 ± 11.9 vs. reserves: 24 ) did not differ between starters and reserves during practice sessions throughout the season. Significant and meaningful differences in accumulated time spent in the different heart rate zones and acceleration totals for matches throughout the entire season were observed as presented in Tables 1-3. ...
... Similarly, in a study examining weekly training and match loads of 46 elite Australian footballers, the authors reported that as the workload increased, so did the risk of injury. The authors, therefore, recommended that the practice of monitoring and adjusting workloads weekly, may help reduce the risk of injury [24]. These findings are supported by an extensive review conducted by Eckard et al. [25], in which the authors concluded that based on the current evidence there appears to be a strong relationship between training load and injury. ...
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Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.
... 4,28 Since 1996, the session rating of perceived exertion (sRPE) has been used extensively to quantify the internal TL of athletes in a wide variety of sports, such as basketball, Australian football, soccer, and rugby league. 13,25,29,30,40,47 The sRPE has previously been shown to offer a valid method of monitoring internal TL in highly competitive young and adult tennis players, as it correlates significantly with endocrine responses (cortisol concentration and testosterone-cortisol ratio), blood lactate concentration, mean heart rate, and mental exertion. 18,35,38 The TL performed over a more recent period (eg, the past 7 days) represents fatigue and is referred to as acute load. ...
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Background: During the COVID-19 pandemic, training routines of most athletes around the world were abruptly interrupted, potentially increasing the risk of injury. The purpose of this study was to compare three measures of training load (TL): tennis-specific TL, physical TL, and total TL management before, during and after lockdown in three professional male tennis players. Case Presentation: Three professional male tennis players were monitored throughout the 2020 season. Outcome and Follow-up: The median total weekly TL (CR 10 Borg RPE • session length in minutes) was 5783 AU. Before, during and after lockdown total weekly TL were 7195, 3753 and 5950, respectively. During lockdown tennis TL was reduced to 0 while physical TL (including preventive loads) increased by 73%. All players suffered an injury/illness during tennis training resumption following lockdown, two of them musculoskeletal related and the other due to COVID-19. There was no association between total ACWR and injury. However, one case had a spike (>1.5) in tennis-specific ACWR two weeks before injury, despite maintaining total ACWR between 0.8-1.5. Discussion: Tennis and physical differential TL monitoring should be carried out separately in order to ensure tennis-specific player readiness. If only total load is monitored during lockdown or rehabilitation from injury, subsequent increases in tennis load upon return to play could potentially increase the risk of injury. The three participants showed a similar pattern of total TL throughout the season with pre-lockdown loads being the highest.
... TL includes training intensity and duration, and research has shown that a higher TL increases the level of workload parameters, such as the acute:chronic workload ratio (ACWR), which leads to more non-contact injury occurrences and greater injury severity [12][13][14][15]. Some evidence has reported that many non-contact injuries occur when an athlete has a large amount of TL, in which case, any training or competition has the potential for athletic injury, indicating that an unfit workload can increase injury risk [16][17][18][19]. ...
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Background: The excessive and rapid increases in training load (TL) may be responsible for most non-contact injuries in soccer. This study's aims were to describe, week(w)-by-week, the acute (AW), chronic (CW), acute:chronic workload ratio (wACWR), total distance (wTD), duration training (wDT), sprint total distance (wSTD), repeat sprint (wRS), and maximum speed (wMS) between starter and non-starter professional soccer players based on different periods (i.e., pre-, early-, mid-, and end-season) of a full-season (Persian Gulf Pro League, 2019-2020). Methods: Nineteen players were divided according to their starting status: starters (n = 10) or non-starters (n = 9). External workload was monitored for 43 weeks: pre- from w1-w4; early- from w5-w17; mid- from w18-w30, and end-season from w31-w43. Results: In starters, AW, CW, and wACWR were greater than non-starters (p < 0.05) throughout the periods of early- (CW, p ≤ 0.0001), mid- (AW, p = 0.008; CW, p ≤ 0.0001; wACWR, p = 0.043), or end-season (AW, p = 0.035; CW, p = 0.017; wACWR, p = 0.010). Starters had a greater wTD (p ≤ 0.0001), wSTD (p ≤ 0.0001 to 0.003), wDT (p ≤ 0.0001 to 0.023), wRS (p ≤ 0.0001 to 0.018), and wMS (p ≤ 0.0001) than non-starters during early-, mid-, and end-season. Conclusion: Starters experienced more CW and AW during the season than non-starters, which underlines the need to design tailored training programs accounting for the differences between playing status.
... Les différentes méthodes de quantification de CE i nt servent à étudier l'effet "dose / réponse" de l'entrainement. Elles permettent de percevoir et de préciser l'influence du volume, de l'intensité et de la fréquence des entrainements sur (i) les risques de blessure (Brooks et al., 2008;Gabbett et Domrow, 2007;Gabbett et Jenkins, 2011;Rogalski et al., 2013), (ii) l'évolution des performances (Akubat et al., 2012;Gastin et al., 2013) ...
Ce travail de thèse avait pour objectif d'analyser l'influence d'une saison sportive sur les caractéristiques physiques, physiologiques et psychologiques des joueurs de handball du club de Montpellier Agglomération Handball, un des meilleurs clubs européens. Dans un premier temps (Etude 1), nous nous sommes intéressés à l'évolution du profil musculaire isocinétique des membres inférieurs pendant la phase de préparation pré-compétitive (Pc2P). Bien que cette période soit courte (8 semaines), nos résultats montrent que la plupart des valeurs de force, de puissance (à 30±.s¡1, 60±.s¡1 et 240±.s¡1, en concentrique et en excentrique), et des différents ratios (agoniste vs antagoniste, dominant vs non dominant ainsi que le ratio mixte) augmentent significativement pendant Pc2P. Dans un deuxième temps (Etude 2), nous nous sommes intéressés à l'évolution du profil musculaire isocinétique des membres inférieurs pendant la période de compétition. Nos résultats montrent qu'une saison de compétition n'impacte pas significativement l'évolutionde la plupart des paramètres isocinétiques suscités. Enfin, au cours de notre 3e travail, nous avons étudié l'évolution de certains marqueurs (biologiques, physiologiques et psychologiques) au cours d'une saison sportive. Les principaux résultats de nos travaux montrent (i) une baisse des valeurs moyennes de VFC concernant les valeurs de HF et de RMSSD, couplée à une légère augmentation de FC en T4, laissant supposer une baisse de l'activité parasympathique en position couchée, (ii) une augmentation des valeurs au questionnaire d'état de forme en T4 et (iii) une diminution des valeurs de [C]sg , [F]sg , IGF-1 et Hématocrite,respectivement en T5 et T4. Les résultats des valeurs de Testostérone montrent une augmentation significative en T5. Ils ne montrent aucune modification significative des valeurs de CPK et d'IGFBP-3. Ces travaux soulignent la nécessité de développer les qualités de force et de puissance le plus efficacement possible pendant Pc2P et de cibler les marqueurs les plus pertinents pour le suivi longitudinal des joueurs de handball
... Monitoring athlete running load is an important role of professional team sport coaches, and is commonly performed using technology such as Global Positioning Systems (GPS; Cummins et al., 2013). Effective management of running load is not only able to improve athletic performance but greatly reduce injury risk (Colby et al., 2014;Hulin et al., 2016;Rogalski et al., 2013). However, external load as measured by GPS is not representative of the mechanical forces exerted on the lower limb while running (Glassbrook et al., 2020). ...
The purpose of this study was to investigate the behaviour of physiological load measures as well as ground reaction forces (GRF) and acceleration load during a prolonged running task that simulated the running demands of an intermittent team sport. Nineteen males completed a maximal aerobic fitness test and an extended running protocol across two sessions. Participants wore a portable metabolic system, and four inertial measurement units (IMU), one on each foot, the lower back and upper back. GRF were measured via an instrumented treadmill. Change in metabolic, IMU and GRF variables across five blocks during the running protocol were assessed using a one-way repeated measures ANOVA. The running protocol elicited large increases in heart rate and oxygen consumption over time. No statistically significant changes in any peak impact accelerations were observed. Resultant acceleration area under the curve (AUC) increased at the lower and upper back locations but was unchanged at the foot. GRF active peak but not impact peak increased during the prolonged run. The results of this study indicate that the effect of an extended running task on IMU measures of external mechanical load is manifested in the upper body, and is effectively measured by AUC.
... Athletes who are subject to large fluctuations in the amount of soft tissue loadbearing sustained over a short period of time (e.g., NFL player starting the regular season without a proper preseason) may be subject to increased microdamage to soft tissues, altered kinematics during high-intensity gameplay, and decreased joint stability, leading to an increased incidence of soft tissue injuries [4][5][6][7][8]. Identifying modifiable factors that may contribute to increased soft tissue injuries could help mitigate these injuries, which has implications for athletes, teams, and medical personnel. ...
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Introduction The purpose of this study is to evaluate the rates of regular season soft tissue injuries in National Football League (NFL) players during the 2020 season, which had a canceled preseason due to the COVID-19 pandemic. Methods This study retrospectively reviewed the injury rates of the 2020-2021 NFL regular season in comparison to the 2018-2019 NFL regular season using publicly available injury data. The focus of our analysis was comparing the following soft tissue injuries: hamstring, groin, calf, quadriceps, thigh, knee - anterior cruciate ligament (ACL), pectoral, and Achilles. The week of injury occurrence, duration of injury in weeks, position of the injured player, and age of the NFL player at injury were obtained. Injury rates were calculated per 1000 athletic exposures with 95% confidence intervals (CIs). A chi-square test and Student’s t-test were utilized as appropriate. Results There were 1370 total injuries in the 2018-2019 regular NFL season and 2086 total injuries reported in the 2020-2021 regular NFL season. The total number of injuries per 1000 athletic exposures was significantly higher in the 2020-2021 NFL season compared to the 2018-2019 NFL season (88.57 versus 58.17, p < 0.001). The rates of injuries per 1000 athletic exposures for hamstring (9.98 versus 5.31, p = 0.043), groin (5.56 versus 2.46, p = 0.007), calf (4.08 versus 1.61, p = 0.006), quadriceps (2.00 versus 0.72, p = 0.030), and thigh (1.23 versus 0.30, p = 0.012) injuries were significantly higher in the 2020-2021 regular NFL season compared to the 2018-2019 NFL regular season. Conclusions The 2020-2021 NFL season had a significantly higher incidence of soft tissue injuries compared to the 2018- 2019 regular NFL season, which may have been associated with the absent preseason due to the COVID-19 pandemic and an abrupt increase in the athletic workload of players.
... The mechanism is when a soccer player naturally acquired a non-contact or contact injury. (Rogalski et al., 2013) In addition, the week in which the injury occurred was recorded. ...
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Purpose: It has been hypothesized that sports injury risk is explained by muscle metabolism. The objective was to evaluate the muscle oxygen saturation slopes (ΔSmO2 slopes) and muscle oxygenation asymmetry (MO2Asy) at rest and to study their associations with injuries during the pre-season. Methods: A total of 16 male and 10 female footballers participated in this study. Injuries were diagnosed and classified by level of severity during the pre-season. The workload was also evaluated using the rate of perceived exertion × training time, from which the accumulated loads. The SmO2 was measured at rest in the gastrocnemius muscle using the arterial occlusion method in the dominant and non-dominant legs. The repeated measures ANOVA, relative risk, and binary logistic regression were applied to assess the probability of injury with SmO2 and workload. Results: Higher MO2Asy and ΔSmO2 Slope 2 were found among footballer who suffered high-severity injuries and those who presented no injuries. In addition, an MO2Asy greater than 15% and an increase in accumulated load were variables that explained a greater probability of injury. Conclusion: This study presents the new concept of muscle oxygenation asymmetry in sports science and its possible application in injury prevention through the measurement of SmO2 at rest.
... The competition calendar determines team periodization (Ireland et al., 2019), with a very high traveling frequency required to compete in elite professional sport (Calleja-Gonzalez et al., 2020). In this scenario, the design of both strength programs and their workload should be adapted to each player's individual needs and characteristics (Rogalski et al., 2013). Concretely, during the six seasons analyzed, the players studied were distributed in three types of strength programs precisely based on their individual profiles . ...
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This study aims to determine possible associations between strength parameters, injury rates, and performance outcomes over six seasons in professional basketball settings. Thirty-six male professional basketball players [mean ± standard deviation (SD): age, 30.5 ± 4.7 years; height, 199.5 ± 9.5 cm; body mass, 97.9 ± 12.9 kg; BMI 24.6 ± 2.5 kg/m 2 ] participated in this retrospective observational study, conducted from the 2008-09 to the 2013-14 season. According to their epidemiological records, each player followed an individual plan designed within different strength training programs: Functional (n = 16), Eccentric (n = 8), or Resistance (n = 12). Seven hundred and fourteen valid records were obtained from 170 individual strength tests during 31 sessions. Tests performed were leg press, squat, and jerk. Parameters recorded were force, power, velocity, peak velocity, and time to peak velocity for strength; time loss injury and muscle injury for injury rate; and games won, games lost, and championships for performance outcomes. All the strength variables and injuries are independent of the strength programs (p < 0.01). The correlation analysis showed very significant relationships between muscular injuries and time to peak velocity (r = 0.94; p < 0.01), significant relationships between force and games lost (r = 0.85; p < 0.05), and muscular injuries with games lost (r =-0.81; p < 0.05) per season. Mean values per season described a possible association of force, time to peak velocity, and muscular injuries with performance outcomes (R 2 = 0.96; p < 0.05). In this specific context, strength variables and injury rate data show no association with a single type of strength training program in this cohort of high-performance basketball players.
The aim of this study was to determine if the quantity of running load performed in pre-season affects the incidence of injury in elite Gaelic footballers. It was hypothesized that a greater quantity of running loads completed will reduce the incidence rate of injury. A cohort of elite male Gaelic football players (n = 25) was prospectively monitored throughout one full season. This longitudinal study involved the collection of GPS data and injury data. Participants were retrospectively divided into two groups and assigned to a high (HTL, completed >50% of pre-season sessions, n = 13) or low (LTL, completed <50% of pre-season sessions, n = 12) training load group based on the percentage of pre-season sessions completed. Data for total distance, PlayerLoad™, meters covered at running speeds (4.0–4.84 m/s), meters covered at high running speeds (4.85–6.39 m/s), meters covered at sprint speeds (>6.4 m/s) and high-intensity running meters (high-speed running meters and sprint meters combined) were collected during all sessions. A one-way analysis of variance (ANOVA) was completed to understand the variation of external training load data across the different phases of the season. A series of repeated measures of ANOVA were subsequently completed to understand the variation of external training load data across seasonal phases within the training groups. Although the LTL group had a higher incidence rate of non-contact injuries (large effect size) per 1000 h of exposure in each phase of the season, statistical analysis revealed that there was no significant difference (F = 4.32, p = 0.173, partial η2 = 0.684, large) between the HTL (14.9 ± 4.17/1000 h) and the LTL (24.5 ± 7.36/1000 h) groups. Additionally, the HTL group was able to sustain greater running loads in the competitive phases of the season compared to the LTL group, total distance (F = 8.10, p < 0.001, partial η2 = 0.299, large), high-speed running distance (F = 8.74, p < 0.001, partial η2 = 0.304, large) and high-intensity running distance (F = 7.63, p < 0.001, partial η2 = 0.276, large). Furthermore, players who complete a greater proportion of running loads in pre-season may alter their body composition levels to more optimal levels, which in turn may reduce the risk of injury while also helping increase or maintain performance-related fitness markers such as aerobic fitness.
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Perceptions of wellness are often used by athletes and coaches to assess adaptive responses to training. The purpose of this research was to describe how players were coping with the demands of elite level Australian football over a competitive season using subjective ratings of physical and psychological wellness and to assess the ecological validity of such a monitoring approach. Twenty seven players completed ratings for nine items (fatigue, general muscle, hamstring, quadriceps, pain/stiffness, power, sleep quality, stress, wellbeing). Players subjectively rated each item as they arrived at the training or competition venue on a 1 - 5 visual analog scale, with 1 representing the positive end of the continuum. A total of 2,583 questionnaires were analysed from completions on 183 days throughout the season (92 ± 24 per player, 103 ± 20 per week; mean ± SD). Descriptive statistics and multi-level modelling were used to understand how player ratings of wellness varied over the season and during the week leading into game day and whether selected player characteristics moderated these relationships. Results indicated that subjective ratings of physical and psychological wellness were sensitive to weekly training manipulations (i.e., improve steadily throughout the week to a game day low, p < 0.001), to periods of unloading during the season (i.e., a week of no competition, p < 0.05) and to individual player characteristics (e.g., muscle strain after a game was poorer in players with high maximum speed, p < 0.01). It is concluded that self-reported player ratings of wellness provide a useful tool for coaches and practitioners to monitor player responses to the rigorous demands of training, competition and life as a professional athlete.
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Background Although the incidence of football injuries should relate to team success there is little empirical evidence. Objective We investigated the relationship between injury incidence and team success in Qatar first-division football clubs. Methods Using a prospective cohort study design, we captured exposure and injuries in Qatar male elite football for a season. Club performance was measured by total league points, ranking, goal scored, goals conceded and number of matches won, drawn or lost. Results Lower injury incidence was strongly correlated with team ranking position (r=0.929, p=0.003), more games won (r=0.883, p=0.008), more goals scored (r=0.893, p=0.007), greater goal difference (r=0.821, p=0.003) and total points (r=0.929, p=0.003). Conclusions Lower incidence rate was strongly correlated with team success. Prevention of injuries may contribute to team success.
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The purpose of this study was to examine and compare the criterion validity and test-retest reliability of the CR10 and CR100 rating of perceived exertion (RPE) scales for team sport athletes that undertake high intensity, intermittent exercise. Twenty one male Australian football (AF) players (age: 19.0 ± 1.8 years, body mass: 83.92 ± 7.88 kg) participated the first part (Part A) of this study which examined the construct validity of the session-RPE (sRPE) method for quantifying training load in AF. Ten male athletes (age: 16.1 ± 0.5 years) participated in the second part of the study (Part B), which compared the test-retest reliability of the CR10 and CR100 RPE scales. In Part A, the validity of the sRPE method was assessed by examining the relationships between sRPE, and objective measures of internal (i.e. heart rate) and external training load (i.e. distance travelled), collected from AF training sessions. Part B of the study assessed the reliability of sRPE through examining the test-retest reliability of sRPE during three different intensities of controlled intermittent running (10 km·hr, 11.5 km·hr and 13 km·hr). Results from Part A demonstrated strong correlations for CR10- and CR100-derived sRPE with measures of internal training load (Banisters TRIMP and Edwards TRIMP) (CR10: r = 0.83 and 0.83, and CR100: r = 0.80 and 0.81, p<0.05). Correlations between sRPE and external training load (Distance, higher speed running and player load) for both the CR10 (r = 0.81, 0.71 and 0.83) and CR100 (r = 0.78, 0.69 and 0.80) were significant (p<0.05). Results from Part B demonstrated poor reliability for both the CR10 (31.9% CV) and CR100 (38.6% CV) RPE scales following short bouts of intermittent running. Collectively, these results suggest both CR10 and CR100 derived sRPE methods have good construct validity for assessing training load in AF. The poor levels of reliability revealed under field testing indicate that the sRPE method may not be sensible to detecting small changes in exercise intensity during brief intermittent running bouts. Despite this limitation, the sRPE remains a valid method to quantify training loads in high intensity, intermittent team sport.
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Although the potential link between running loads and soft-tissue injury is appealing, the evidence supporting or refuting this relationship in high-performance team sport athletes is nonexistent, with all published studies using subjective measures (e.g., ratings of perceived exertion) to quantify training loads. The purpose of this study was to investigate the risk of low-intensity (e.g., walking, jogging, total distances) and high-intensity (e.g., high acceleration and velocity efforts, repeated high-intensity exercise bouts) movement activities on lower body soft-tissue injury in elite team sport athletes. Thirty-four elite rugby league players participated in this study. Global positioning system data and the incidence of lower body soft-tissue injuries were monitored in 117 skill training sessions during the preseason and in-season periods. The frailty model (an extension of the Cox proportional regression model for recurrent events) was applied to calculate the relative risk of injury after controlling for all other training data. The risk of injury was 2.7 (95% confidence interval 1.2-6.5) times higher when very high-velocity running (i.e., sprinting) exceeded 9 m per session. Greater distances covered in mild, moderate, and maximum accelerations and low- and very low-intensity movement velocities were associated with a reduced risk of injury. These results demonstrate that greater amounts of very high-velocity running (i.e., sprinting) are associated with an increased risk of lower body soft-tissue injury, whereas distances covered at low and moderate speeds offer a protective effect against soft-tissue injury. From an injury prevention perspective, these findings provide empirical support for restricting the amount of sprinting performed in preparation for elite team sport competition. However, coaches should also consider the consequences of reducing training loads on the development of physical qualities and playing performance.
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There has been no previous investigation of the concurrent validity and reliability of the current 5 Hz global positioning system (GPS) to assess sprinting speed or the reliability of integrated GPS-accelerometer technology. In the present study, we wished to determine: (1) the concurrent validity and reliability of a GPS and timing gates to measure sprinting speed or distance, and (2) the reliability of proper accelerations recorded via GPS-accelerometer integration. Nineteen elite youth rugby league players performed two over-ground sprints and were simultaneously assessed using GPS and timing gates. The GPS measurements systematically underestimated both distance and timing gate speed. The GPS measurements were reliable for all variables of distance and speed (coefficient of variation [CV] = 1.62% to 2.3%), particularly peak speed (95% limits of agreement [LOA] = 0.00 ± 0.8 km · h(-1); CV = 0.78%). Timing gates were more reliable (CV = 1% to 1.54%) than equivalent GPS measurements. Accelerometer measurements were least reliable (CV = 4.69% to 5.16%), particularly for the frequency of proper accelerations (95% LOA = 1.00 ± 5.43; CV = 14.12%). Timing gates and GPS were found to reliably assess speed and distance, although the validity of the GPS remains questionable. The error found in accelerometer measurements indicates the limits of this device for detecting changes in performance.
Objectives: To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9±2.0 years). Design: Observational. Methods: Training load, perceived ratings of wellness (e.g., fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4 km h(-1), 4 times throughout, outdoor) were used as performance measures. Results: There were significant (P<0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6-18%), HRex (3.3%), LnSD1 (19.0%), but not cortisol (20.0%, P=0.60). While the overall wellness (+0.06, 90% CL (-0.14; 0.02) AU day(-1)) did not change substantially throughout the camp, HRex decreased (-0.51 (-0.58; -0.45)% day(-1)), and cortisol (+0.31 (0.06; 0.57) nmol L(-1)day(-1)), LnSD1 (+0.1 (0.04; 0.06) ms day(-1)), Yo-YoIR2 performance (+23.7 (20.8; 26.6) m day(-1), P<0.001), and HSR (+4.1 (1.5; 6.6) m day(-1), P<0.001) increased. Day-to-day ΔHRex (r=0.80, 90% CL (0.75; 0.85)), ΔLnSD1 (0.51 (r=0.40; 0.62)) and all wellness measures (0.28 (-0.39; -0.17)<r<0.25 (0.14; 0.36)) were related to Δtraining load. There was however no clear relationship between Δcortisol and Δtraining load. ΔYo-YoIR2 was correlated with ΔHRex (r=0.88 (0.84; 0.92)), ΔLnSD1 (r=0.78 (0.67; 0.89)), Δwellness (r=0.58 (0.41; 0.75), but not Δcortisol. ΔHSR was correlated with ΔHRex (r = -0.27 (-0.48; -0.06)) and Δwellness (r=0.65 (0.49; 0.81)), but neither with ΔLnSD1 nor Δcortisol. Conclusions: Training load, HRex and wellness measures are the best simple measures for monitoring training responses to an intensified training camp; cortisol post-exercise and LnSD1 did not show practical efficacy here.
The purpose of this research was to investigate the validity and the reliability of 5-Hz MinimaxX global positioning system (GPS) units measuring athlete movement demands. A team sport simulation circuit (files collected from each unit = 12) and flying 50-m sprints (files collected from each unit = 34) were undertaken, during which the total distance covered; peak speed; player load; the distance covered; time spent and number of efforts performed walking, jogging, running, high-speed running, and sprinting were examined. Movement demands were also separately categorized into low-intensity activity, high-intensity running, and very high-intensity running. The results revealed that GPS was a valid and reliable measure of total distance covered (p > 0.05, percentage typical error of measurement [%TEM] < 5%) and peak speed (p > 0.05, %TEM 5-10%). Further, GPS was found to be a reliable measure of player load (%TEM 4.9%) and the distance covered, time spent, and number of efforts performed at certain velocity zones (%TEM <5% to >10%). The level of GPS error was found to increase along with the velocity of exercise. The findings demonstrated that GPS is capable of measuring movement demands performed at velocities <20 km·h(-1), whereas more caution is to be exercised when analyzing movement demands collected by using GPS velocities >20 km·h(-1).