- Human Kinetics Journals

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- Human Kinetics Journals
International Journal of Sports Physiology and Performance, 2013, 8, 312-318
© 2013 Human Kinetics, Inc.
www.IJSPP-Journal.com
ORIGINAL INVESTIGATION
The Effects of Game and Training Loads on Perceptual
Responses of Muscle Soreness in Australian Football
Paul G. Montgomery and Will G. Hopkins
Australian Football is an intense team sport played over ~120 min on a weekly basis. To determine the effects
of game and training load on muscle soreness and the time frame of soreness dissipation, 64 elite Australian
Football players (age 23.8 ± 1.8 y, height 183.9 ± 3.8 cm, weight 83.2 ± 5.0 kg; mean ± SD) recorded perceptions of muscle soreness, game intensity, and training intensity on scales of 1–10 on most mornings for up to
3 competition seasons. Playing and training times were also recorded in minutes. Data were analyzed with a
mixed linear model, and magnitudes of effects on soreness were evaluated by standardization. All effects had
acceptably low uncertainty. Game and training-session loads were 790 ± 182 and 229 ± 98 intensity-minutes
(mean ± SD), respectively. General muscle soreness was 4.6 ± 1.1 units on d 1 postgame and fell to 1.9 ± 1.0
by d 6. There was a small increase in general muscle soreness (0.22 ± 0.07–0.50 ± 0.13 units) in the 3 d after
high-load games relative to low-load games. Other soreness responses showed similar timelines and magnitudes of change. Training sessions made only small contributions to soreness over the 3 d after each session.
Practitioners should be aware of these responses when planning weekly training and recovery programs, as it
appears that game-related soreness dissipates after 3 d regardless of game load and increased training loads
in the following week produce only small increases in soreness.
Keywords: DOMS, monitoring, player management
Paramount in the management of team-sport athletes is the understanding of competition demands and
how players respond to and recover from these demands
in preparation for the subsequent week’s training and
competition. Indeed, the next competition for some team
sports may be within 2 to 4 days, contributing to the
overall demands and limiting the opportunity for optimal
recovery and regeneration.
Australian Football is an intense team sport comprising long periods of steady running combined with intermittent bouts of high-intensity running and impact trauma
from opponents and ground contact. Game demands have
been described1 and reviewed2 previously. These demands
provide practitioners with the challenge of managing the
physical and physiological outcomes players experience
during a weekly competition cycle. The management of
these demands over a 23-week season, plus 4 weeks of
finals if successful, is an additional challenge; therefore,
the weekly management in Australian Football and similar team sports revolves around maximizing recovery and
minimizing excessive training stress when the competition itself is a sufficient exercise stimulus.
In team sport, one of the primary aims is to keep
player availability high; this ensures healthy competition
Montgomery is with St Kilda Football Club, St Kilda, VIC,
Australia. Hopkins is with Sport and Recreation, Auckland
University of Technology, Auckland, New Zealand.
312
between players for selection and that the best players
are available for the greatest chance of team success. The
importance of this management was insightfully shown
by Gabbett and Jenkins.3 In rugby league, however, the
outcomes showed that increases in training load can have
detrimental effects on injury rates. In addition, Coutts and
Reaburn4 proposed that the 72-hour period after a rugby
league game is critical for ensuring appropriate recovery
for the next game, and small doses of additional training
during this period can negatively influence performance.
MacLean et al5 also showed that neuromuscular performance and perception of fatigue are reduced for at least
48 hours after competition but with appropriate training
are recovered to baseline levels within 4 days. A recovery
time between 2 football (soccer) matches of 72 to 96
hours appears sufficient to maintain the level of physical performance in sport-specific testing but is too short
to maintain a low injury rate.6 In Australian Football,
countermovement-jump assessments of neuromuscular
function were disrupted in the 72 hours after a game
and compromised at 60% of weekly assessments over
a competitive season.7,8 A moderate increase (10%) in
training load when assessed by the session-rating of
perceived exertion (RPE) method was associated with
40% of injuries during 15 weeks of Australian Football
preseason training.9
Exercise-induced muscle damage is caused by activities that induce eccentric actions of active musculature,
causing a disruption to the sarcolemma. A consequence of
Soreness Responses in Australian Football 313
the structural change is an increase of circulating enzymes
and protein concentrations.10 A perceptual response to
muscle damage is delayed-onset muscle soreness, which
peaks within 72 hours.11,12 The exact cause of the soreness
response is not known but may involve an inflammatory
reaction to the damage.13 Circulating creatine kinase has
been used as an indirect marker of muscle damage during
elite junior Australian Football play, differentiating fast
running and moderate to high acceleration/deceleration
profiles.14 It appears that there is a window of ~3 days in
which the responses of players to game demands requires
careful consideration and monitoring to ensure that training loads are suitable to minimize injury risk and maintain
performance and ensure that optimal recovery strategies
are in place to enhance regeneration.
Athlete monitoring has become an area of high interest and development recently, as practitioners increase
their efforts to understand psychophysiological responses
to sporting demands. If the demands of a sport are known,
and the athlete responses to those demands are quantified
and understood, there is an expectation that appropriate action can be taken in the areas of musculoskeletal
recovery, metabolic regeneration, and psychological
rejuvenation to maintain training capacity and hence competition performance. Emerging GPS technology has the
ability to quantify loads through algorithms combining
accelerometer vectors15,16 and appears to be a progressive
practice to assess physical training load. The monitoring
of competition and training load has generally been completed in team sports through the use of the session-RPE
method.4 Use of this uncomplicated classification tool is
well supported in the literature,17 yet, given the simplistic
nature of this method, there has been little investigation
into the correlations between the outcomes of this method
and player responses to game demands.
When daily responses from team-sport athletes
regarding their competition-related soreness, fatigue,
and wellness are required, the investigation tool needs
to be easily administered and time efficient for both
the athlete and the practitioner collating data. Questions need to be unambiguous in relation to soreness of
specific muscle groups and have responses that indicate
the type and effectiveness of recovery for various psychophysiological parameters. The most comprehensive
assessment tool for determining athlete recovery is the
Recovery-Stress Questionnaire for Athletes (RESTQSport).18 The extensive nature of the test is not applicable
for daily assessments, but when it is used over a 4- to
6-week period it can describe the responses of team-sport
athletes to training,4 the training relationships to injury,
and the impact of recovery on illness rates.19 Recently,
a Perceived Recovery Status scale to subjectively assess
an individual’s level of recovery in relation to exercise
performance was proposed.20 This scale is similar to the
Borg CR-10 scale, which allows direct correlations to
training when a similar scale is used to determine training intensity. This philosophy follows from the Total
Quality Recovery (TQR) scale developed previously
by Kenttä and Hassmén,21 but, unfortunately, the TQR
program received little acceptance due to its complexity
and untested validity.
Given that many team sports collect extensive data
related to training and competition loads and monitor
players on a daily basis to determine the responses to
these loads, the aim of this retrospective analysis was to
determine the effect of weekly game load and the combined effect of game and training loads when assessed
by the session-RPE method on subjective measures of
muscle soreness in the week following competition, as
well as to quantify the rate of soreness changes over the
subsequent training week.
Methods
Participants
Participants for this analysis were 64 (mean ± SD; 23.8
± 1.8 y, 183.9 ± 3.8 cm, 83.2 ± 5.0 kg) elite Australian
Football players competing in the Australian Football
League. All players were aware that scientific investigation and analysis may occur under their contractual
agreements, and the study conformed to the Declaration
of Helsinki. Only data from players who were training
for and competing in the 2008–2010 seasons were used
in the analysis.
Data Collection
All data were collected on custom-designed electronic
questionnaires using the KineticAthlete program (Kinetic
Pty Ltd, Canberra, Australia). Questionnaire responses
to muscle soreness were reported on a scale adapted
from the Borg CR-10 ranging from 1 (no soreness) to 10
(extremely sore). All players were required to complete
the forms each morning before any musculoskeletal
treatment or training. Quantification of game load was
performed using the session-RPE method, previously
reviewed as a valid measure of exercise intensity.17,22
In brief, this process involves players providing a rating
of the game and training intensity on a previously validated CR-10 scale,23 and this unit is multiplied by each
player’s individual game or training time to obtain an
overall value. The only verbal prompt given was “How
hard was that game/session?” Each player provided his
game rating ~24 hours postgame or posttraining rating
~15 to 30 minutes posttraining. The postgame rating
was collected at ~24 hours to ensure that players could
provide an accurate reflection of intensity, rather than
being influenced by postgame requirements. During
games, only data for players who played for more than
30 minutes were used, ensuring that low values due to
early substitution were excluded.
Statistical Analysis
All estimations were made using the Statistical Analysis
System (Version 8.2, SAS Institute, Cary, NC, USA).
Each measure of soreness was analyzed separately as the
314 Montgomery and Hopkins
dependent variable in a mixed linear model. The data
were processed to give each player a single observation
for each occasion on which soreness was measured.
Each observation had values for variables representing
the season of play (3 levels), identity of the player (64
levels), the number of days since the last game (6 levels,
represented by integer values of 1–6), the game load
(previously described), the date of the game (80 levels),
the presence or absence of training up to 6 days prior (6
dummy variables coded 1 or 0), and the training load
on each of those days (6 variables calculated as for the
game load). The fixed effects in the model were all these
variables (except for player identity), with game date
nested within season and with an interaction between
days since the last game and game load (to estimate
the linear effect of game load on days subsequent to
the game).
To model the repeated measurements on players
within and between seasons, the random effects were
subject identity, interactions of subject identity with
season and game date, and the residual. The mean soreness on each of the 6 days after a game was estimated as
the least-squares means for the variable representing days
since the last game. The effect of training on each of the
6 days before the measurement of soreness was estimated
as the coefficients of the training dummy variables. The
moderating effects of training load and game load on
each of the 6 days prior were estimated from the coefficients for the corresponding variables by multiplying
them by a load equal to 2 within-subject SDs of the load
on each day.24 The within-subject SDs were calculated
by rescaling each player’s load to a mean of zero before
grouping the data for each of the 6 days; days on which
training did not occur were treated as missing, not zero.
The effect of a difference in load of 2 SDs represents the
difference in soreness after typically low and typically
high game or training loads.
Magnitudes of effects were evaluated via standardization. For this purpose, a between-subjects SD
representing the typical variation in soreness between
players on any given day was derived as the square
root of the sum of the variances of the random effects
in the model. The effects were divided by this SD and
their magnitudes interpreted with the following scale:
<0.2, trivial; 0.2 to 0.6, small; 0.6 to 1.2, moderate; and
>1.2, large.24 Uncertainty in the effects was expressed
as 90% confidence limits. Inferences about magnitudes
were mechanistic: If the confidence interval overlapped
thresholds for substantial positive and negative values (±
0.20 standardized units), the effect was deemed unclear;
effects were otherwise deemed clear.24
during the 72 hours postgame, with little further effect
thereafter (Figure 1). Training load contributed small
amounts of ~0.4 units to the general soreness profile in
the following training week, and additional training load
promoted further small increases in soreness up to ~0.4
units in the 4 days after any training session (Figure 2).
Similar effects of training load were observed for hamstring, calf, groin, and quadriceps muscle soreness, but
there was little effect of training on the soreness of the
other muscle groups (Table 1).
Figure 1 — General muscle soreness in the 6 days after a game
(black circles) and the additional soreness associated with a
2-SD difference in game load (white boxes). Values are means,
bars are between-players SDs. Soreness responses of specific
muscle groups showed similar time lines and magnitudes of
change (lines without symbols). The effects of game load on
soreness were clear for all measures of muscle soreness for all
days postgame (range of confidence limits 0.05–0.11).
Results
Perceptual soreness responses peaked immediately
postgame, then decreased gradually (Figure 1). Games
of greater intensity, as determined by additional game
load, promoted a clear small increase of up to 0.5 units in
general soreness and calf, groin, and quadriceps soreness
Figure 2 — Contribution to general muscle soreness in the
6 days after a training session due to the mean training load
(black boxes) and a 2-SD difference in training load (white
boxes). The effects were clear for all measures (confidence
limits 0.04–0.09).
Soreness Responses in Australian Football 315
Table 1 Changes in Soreness Responses in the Week Following Competition Due to Additional Game
Load
General
soreness
(4.6 ± 0.4)
Hamstring
(3.2 ± 0.4)
Calf
(3.5 ± 0.4)
Gluteals
(3.1 ± 0.4)
Groin
(3.0 ± 0.4)
Quadriceps
(3.0 ± 0.4)
Low back
(3.3 ± 0.9)
1
0.50 ± 0.13,
0.37, small ↑
0.23 ± 0.11,
0.23, small ↑
0.35 ± 0.12,
0.31, small ↑
0.12 ± 0.11
0.27 ± 0.11,
0.25, small ↑
0.27 ± 0.11,
0.26, small ↑
0.03 ± 0.11
2
0.34 ± 0.08,
0.31, small ↑
0.17 ± 0.07
0.28 ± 0.08,
0.24, small ↑
0.14 ± 0.07
0.28 ± 0.07,
0.26, small ↑
0.28 ± 0.07,
0.27, small ↑
0.21 ± 0.07,
0.21,
small ↑
3
0.22 ± 0.07,
0.21, small ↑
0.12 ± 0.06
0.21 ± 0.07,
0.20, small ↑
0.12 ± 0.06
0.21 ± 0.06,
0.20, small ↑
0.13 ± 0.07
0.08 ± 0.06
Days
postgame
4
0.12 ± 0.07
0.07 ± 0.07
0.10 ± 0.08
0.11 ± 0.06
0.11 ± 0.06
0.15 ± 0.07
0.04 ± 0.07
5
0.04 ± 0.08
–0.04 ± 0.07
0.03 ± 0.08
0.03 ± 0.07
0.10 ± 0.07
0.04 ± 0.08
–0.01 ± 0.07
6
0.00 ± 0.13
–0.02 ± 0.12
–0.08 ± 0.12
0.12 ± 0.11
0.11 ± 0.07
0.07 ± 0.12
–0.07 ± 0.11
Note: Values in parentheses in table heads are daily soreness 1 day postgame ± SD. Values in table are soreness change ± 90% confidence limit (CL), effect size
(ES), qualitative outcome. The magnitudes of difference were classified using the criteria trivial < 0.2, small 0.2–0.6, moderate 0.6–1.2, large 1.2–2.0, and very
large >2. A substantial increase or decrease was classified as a ≥75% likelihood of the effect being greater than or equal to the ES 0.2 reference value (small).
Effects were classified as unclear if the ± 90% CL of the ES crossed the boundaries of ES ± 0.2. CLs for effects ranged from 0.06 to 0.12.
All effects in this study had sufficiently low uncertainty to be clear. For the 3-year observation period,
there were 2568 responses to the soreness questionnaire.
Average weekly game load, training load, and general
muscle soreness were 790 ± 182, 229 ± 98, and 3.1 ±
1.3, respectively. There was no substantial difference in
the average yearly game load between years. The combined training and game loads (mean ± SD) during the
precompetition and early- and late-competition seasons
across the analysis period were 1138 ± 274, 967 ± 187,
and 1041 ± 124, respectively, in Year 1; 1152 ± 323, 1012
± 159, and 928 ± 210 in Year 2; and 1408 ± 420, 1085
± 191, and 1012 ± 307 in Year 3. Main training sessions
over the study period were on days 2 and 4 before the
game; due to scheduling and player requirements some
sessions were on other days, but those sessions were
consistent in load with main sessions (Figure 3). Typical
error measures for the reliability of the RPE and musclesoreness scales were 0.95(14%); 90% confidence limits
0.82–1.16, and 0.51(25%); ± 0.20, respectively.
Discussion
Game loads from Australian Football promote musclesoreness responses in players that dissipate over the
remainder of a weekly competition cycle. The contribution to this soreness from games that are harder, from a
perceptual load perspective, is small early in the week
and practically nonexistent by the end of the week. The
contribution to soreness from training load is also small
at the beginning of the week, and by the second training
session this contribution is negligible, even when training
loads are increased. It would appear from the analysis of
these data that increasing the training load in any given
training session by up to one-half during the week following a game will have no substantial contribution to
Figure 3 — Training load (black boxes) and the percentage of
squad participating (white circles) in training sessions in the 6
days before a game. Values are means, bars are SDs.
soreness responses in the days following the training
session. To our knowledge, this is the first attempt to
statistically quantify the changes in perceptual soreness
responses in team sport after competition by combining
the weekly demands of games and training.
Differences between days for muscle-soreness
responses during the week following competition showed
small immediate improvements in the first days of the
week. This improvement continued for 3 days after a
game, which supports the findings of previous physical
316 Montgomery and Hopkins
outcomes of performance and neuromuscular function
being compromised ~3 days postcompetition. Immediate postgame recovery strategies such as cold-water
immersion and massage,25,26 often replicated in the days
following competition, could influence this outcome and
reduce the soreness expected after this type of exercise.
These athletes used cold- and contrast-water immersion
posttraining and postgame consistently over the analysis
period and also received massage 2 or 3 times per week
during the training and competition period. Previous
claims discount an ability of either strategy to accelerate
structural or functional recovery and found that they may
negatively influence the inflammatory process delaying
recovery.27 In this instance it appears that consistent
cold-water and massage therapies could have played a
beneficial role in minimizing soreness responses. Ideally,
the quantification of remedial therapies during the competition week would provide insight as to the impact of
these treatments on diminishing soreness responses and
improvements in function compared with no treatment.
In any event, as the use of these treatments was consistent
across the subject group and the analysis period, any benefit would also have been consistent within the training
and competition cycles. It would also appear that some
players carry over some residual muscle soreness from
the previous game into the next week’s competition. A
comparison of short and long weekly competition cycles
against soreness and performance would provide valuable information to assess changes in game intensity with
decreased recovery and preparation time.
Of particular interest to practitioners will be the
contribution of these soreness changes and training
loads with respect to injury risk. During the early- and
late-competition training phases of a rugby league
season, increases in training load of 175 to 620 and 145
to 410 intensity-minutes (respectively) resulted in no
further increase in injury incidence compared with the
preseason phase, when increases between 155 and 590
would increase injury risk.28 Those training loads are
substantially higher than those presented herein, which
may be related to the differences in physical contact
between sports, as well as longer training sessions.
Recent injury-risk data describe previous injury and age
as dominant risk factors for soft-tissue injury, particularly
hamstring injury in Australian Football.29 Although the
Hamstring Outcome Score test has questions related to
soreness, this along with performance and strength tests,
anthropomorphic characteristics, and clinical findings
does not predict risk for hamstring injury.30 Hamstring
soreness has previously been found as a predisposing
factor to muscle injury in soccer,31 but we observed that
hamstring soreness was not the highest of responses and
was not influenced by additional game load as heavily as
calf, groin, and quadriceps muscle groups. Further work
is required to determine any relationship between muscle
soreness and injury risk.
The current analysis was undertaken over a period
where Australian Football game interchanges increased
by 22 per year on average. Performance indicators from
Australian Football League GPS data indicate that estimated game intensity increased by 17% in the period
from 2005 to 2010, with little difference from 2008 to
2010.32 Although average running velocity increased by
8.4% from 2005 to 2008,33 there was only a small increase
from 2008 to 2010, with no difference from 2009 to
2010.32 So it would appear that average weekly running
velocity in Australian Football competition was largely
unchanged over the study period; however, the nature of
the running demands may have affected game intensity.
Acceleration characteristics, continual (steady-state, >8
km ∙ h–1) running, and long continual efforts had small to
moderate increases from 2008 to 2010.32 We recognize
that these are average data from organizations that chose
to submit data and that the athletes in this study compose
a small sample of that analysis; however, we feel that
these demands would be experienced by the majority of
players. Injury data show an increase in the incidence
of injuries since 2007 and in the prevalence of injuries
overall since 2003.34 This included hamstring strains,
which were claimed to be the most significant; however,
the increase in new injury incidence for hamstrings
since 2007 was 0.5% and decreased by 1.1% from 2009
to 2010.34 Recently, Orchard et al35 concluded from an
analysis of player interchange rates and hamstring injury
that increasing the amount of individual rest during a
game may have a protective effect against hamstring
injury. It remains to be investigated if there is a link
between playing time (rotations), soreness responses, and
soft-tissue injury, as the influence of game demands on
soreness are small for most muscle groups and changes
in soft-tissue injury are small and variable between years.
The soreness associated with the running demands of
Australian Football after competition could be classified
as delayed-onset muscle soreness (DOMS). The eccentric
loads associated with the volume and type of running in a
game promote the perceptual responses observed in this
study. Previous research has demonstrated that repeatsprint exercise applicable to team-sport demands elevated
DOMS for 72 hours after exercise.11 A single soccer game
showed less response, with DOMS peaking at 24 hours,36
while DOMS peaked after a single basketball game but
was similar to baseline at 24 hours.37 It is more likely
that the initial outcomes seen here are acute responses
related to the demands of competition rather than a typical
DOMS profile, as the responses are greatest immediately
after a game, whereas DOMS is typically most severe
at ~72 hours. Even when additional game load occurs,
the profile changes are small and occur consistently in
general muscle soreness and calf and groin soreness over
the 72 hours postgame.
A limitation of the current analysis is that the study
cohort represented only 1 of the 16 teams in the Australian
Football League over the study period; other teams may
have had different training and game loads. There was
also no examination of blood markers of muscle damage.
There is evidence from Australian Football that creatine
kinase is useful for quantifying muscle damage based
on relationships between elevated concentrations and
Soreness Responses in Australian Football 317
high to low performers in fast running, accelerations,
and decelerations14; however, these observations were
not from elite players, and the use of creatine kinase in
these scenarios has been questioned previously.38 Other
markers such as circulating fatty-acid-binding protein and
myoglobin, have faster appearance and clearance rates39
and have been used successfully during Australian Football contact trials,40 college-level American football,41
and basketball.37 These and similar markers should be
included in future studies to increase the knowledge of
interactions between game loads, muscle damage, and
perceptual responses of muscle soreness in Australian
Football and other contact team sports. Many organizations may not have access to biochemical analysis; therefore, quantifying soreness responses with the approach
described herein has practical application.
Practical Application
The perceptual responses of elite players to combined
weekly game and training load shows that soreness of
several main locomotive muscle groups is elevated in the
immediate days after competition, and these increases
will take until the end of the subsequent week to dissipate. Additional game load and additional training
load have only a small impact on increasing these daily
soreness responses within a 3-day period immediately
after a game or training session. Practitioners should
consider that elevated training loads on main training
days appear tolerable, as the additional training load has
a small further impact on the small soreness responses
related to training. Future studies should combine injury
and GPS or accelerometer data to determine whether
compounding soreness correlates with injury prevalence
and muscle damage, particularly if a player’s game time
and game load change from previous years.
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