Advances in Physical Education
2013. Vol.3, No.1, 36-42
Published Online February 2013 in SciRes (http://www.scirp.org/journal/ape) http://dx.doi.org/10.4236/ape.2013.31006
36
Masters Athletes: No Evidence of Increased Incidence of Injury in
Football Code Athletes
Joe Walsh1, Mike Climstein2, Ian T. Heazlewood3, Mark DeBeliso4, Jyrki Kettunen5,
Trish G. Sevene6, Kent J. Adams6
1Independent Researcher, Sydney, Australia
2Faculty of Health Sciences & Medicine,
Bond University Research Centre for Health, Exercise and Sports Science, Gold Coast, Australia
3Faculty Education, Health and Science, School of Environmental and Life Sciences, Charles Darwin University,
Darwin, Australia
4Department of Physical Education and Human Performance, Southern Utah University, Cedar City, USA
5Arcada University of Applied Sciences, Helsinki, Finland
6Kinesiology Department, California State University Monterey Bay, Seaside, USA
Received October 26th, 2012; revised November 30th, 2012; accepted December 10th, 2012
The World Masters Games, held quadrennially, is the largest international sporting competition in terms
of participant numbers. Yet this cohort remains proportionately under investigated. An online survey us-
ing an open-source specialized survey application software program was utilized to investigate the 2009
Sydney World Masters Games (SWMG) football code athletes (association football, touch football, rugby
union). A total of 931 masters athletes (28.2% response rate, aged 29 - 72 yrs, mean = 47.6, SD ± 7.1,
52.5% male) completed the survey, with touch football reporting the highest incidence of injury (29.2%)
followed by rugby (27.0%) and soccer (21.2%). Analyzing injury data (t-tests, chi square) identified pat-
terns in injury location (legs (11.2%, p < 0.01) followed by knees, feet and ankles) and significant (p <
0.01) classification patterns (muscle/tendon strain/tear (13.0%, p < 0.05), inflammation (6.1%), joint pain
(6.0%) and ligament sprain/tear (5.8%)). There were also significant differences (p < 0.01) compared to
general and elite sporting population data. For masters football athletes at the SWMG, the injury inci-
dence during preparation for the tournament has similarities to, but is in fact significantly less than for
these other sporting populations. Some gender and sport based differences in injury location and classifi-
cation type were identified. There were also no significant age related changes in injury nature (classifica-
tion type, location, incidence, time off work or training). Therefore these findings do not support the
premise of masters football code athletes having a higher incidence of injury as compared to younger ath-
letes.
Keywords: Sports; Exercise; Masters Athletes; Injury
Introduction
Masters athletes are those individuals systematically training
for and competing in organized sporting events designed spe-
cifically for older adults (Raeburn & Dascombe, 2008). Masters
athletes have either pursued a physically active lifestyle for an
extended period of time or have initiated exercise/sport in later
life. Regardless of their motivation(s) for participation, this
unique cohort of middle to older-aged adults remains propor-
tionately under investigated with regard to the incidence of di-
verse chronic diseases and disorders as well as associated phy-
siological measures of health.
There is growing evidence that regular exercise across the
lifespan is beneficial for improved health and decreased inci-
dence of various diseases and disorders (Liubicich, Magistro,
Candela, Rabaglietti, & Ciairano, 2012; Sawyer & Castaneda-
Sceppa, 2010; Williamson & Pahor, 2010; Ryan, 2010). The
genetic mechanisms that cause the aging process are uncertain
(Kenyon, 2010) however its existence and the declines associ-
ated with aging are well established. Masters athletes may dis-
play an age related increase to the range of pathologies present
in this population (e.g., heart disease) as well as physiological
changes due to the aging process (e.g., sarcopenia) (Williamson
& Pahor, 2010; King & Guralnik, 2010; Doherty, 2003). Due to
these types of changes, it has been proposed that there is a pre-
disposition to increased risk of injury associated with high level
competitive sports performance, such as those required in foot-
ball codes as well as other ball games (Kallinen & Markku,
1995). Furthermore, an increased prevalence of injuries may be
a factor contributing to reduced training intensity and volume,
leading to reduced exercise performance in masters’ endurance
athletes (Tanaka & Seals, 2008). Sports participation entails an
increased risk of injury for elite, as well as recreational athletes
(Bahr & Krosshaug, 2005). Structural and functional changes
may not only increase incidence of injury in older athletes, but
also healing may be delayed in aged individuals (Kallinen &
Alén, 1994). Promisingly, no evidence of either achilles tendi-
nopathy (Longo et al., 2009) or patella tendinopathy (Longo et
al., 2011), being influenced by advancing age was found in
masters track and field athletes. In contrast: “The adjusted risk
for shoulder region (odds ratio 2.84, p < 0.03) and Achilles
tendon rupture (14.87, p < 0.01) after the age of 45 years was

J. WALSH ET AL.
higher in the athletes than in the controls” (Kettunen, Kujala,
Kaprio, & Sarna, 2006). In the lay press, exercise at older ages
is promoted as beneficial for an array of reasons, but in contrast
it is often seen as deleterious to one’s joints (Hunter & Eckstein,
2009), with an associated supposed proneness to joint injuries
(Marks, 2006). It has been stated that current management of
knee pain in primary care tacitly confirms negative beliefs
whilst also failing to promote the positive benefits of exercise
(Hurley, Walsh, Bhavnani, Britten, & Stevenson, 2010). This
confusion as to the best approach to take can lead mature age
individuals to avoid exercise (Hurley et al., 2010). There is no
strong evidence indicating increased development of osteo-
arthritis in vigorous, low impact activities (Hunter and Eckstein,
2009). Physical activity plays an important role in the manage-
ment and also pathogenesis of osteoarthritis, with the con-
comitant presence of joint injury increasing the risk of devel-
oping osteoarthritis in elite athletes (Hunter & Eckstein,
2009).
Determining whether increased risk of acute or chronic in-
jury legitimately exists and its magnitude is an important step
in ascertaining the risk of participation in competitive exercise
and thus may assist in developing a best practice model for
optimizing health in the aging individual. One such best prac-
tice model (O’Shea, 2000) suggested that his proposed “all
season, multi-activity cross-training prescription for the masters
athlete” was a “passport to a healthy, active lifestyle.”
The World Masters Games (WMG) is the largest interna-
tional sporting competition in terms of participant numbers. In
2009, the Sydney World Masters Games (SWMG) attracted
28,676 competitors who represented 95 countries and competed
in 28 sports (Sydney World Masters Games Organizing Com-
mittee, 2010). Previous research on the SWMG athletes has
examined the health of North American competitors (Debeliso
et al., 2011), as well as motivations for competition (Heazlewood
et al., 2011; Adams et al., 2011) finding that socialising with
other participants is a primary motivating factor. Provisional
analysis of BMI of these athletes (Debeliso et al., 2012; Walsh
et al., 2011a, 2011b) has shown promising trends.
Aims of the Study
There is concern that due to age-related changes, increased
risk of injury will be associated with highly competitive sport
for the aging athlete. The aim of this study was to provisionally
investigate whether the frequency of injury in football code
athletes was higher compared to previously reported data in the
literature on either the general population, or amateur or elite
sporting populations (Ekstrand, Hägglund, & Waldén, 2011;
King et al., 2010; Schneider, 2006). It was hypothesized that
injury incidence rate per year would be less than professional
sporting populations due to a presumption of lower playing and
training hours compared to professional athletes. It was further
hypothesized that injuries would be similar in location to a
comparative (elite association football) population (Ekstrand et
al., 2011).
Methods
Nature of the Study and Settings
This was a cross-sectional descriptive survey design. Three
football codes from the SWMG were investigated: rugby union
(rugby), association football (soccer) and touch football (touch).
The three sports investigated are all played internationally,
however touch football has its greatest density of participants in
Australasia, where it is a major amateur sport (Walsh, 2010).
The survey was activated on the 15 October, 2009 and closed to
participants on 26 February, 2010.
Ethics approval for the study was granted in accordance with
the ethical standards of the Helsinki Declaration of 1975 (re-
vised in 2008). The 2009 Sydney World Masters Games Or-
ganising Committee approved the project, stipulating the sur-
vey could only be provided in an online format. Accordingly,
an online survey was created using LimesurveyTM, an open-
source, web based application. The online survey was utilized
to investigate participants’ demographics, medical health histo-
ries and injuries sustained in the preparation for the SWMG.
Following pilot testing by investigators, electronic invitations
were sent by the SWMG registration team to masters games
athletes who provided a valid email address upon registration.
The survey consisted of several sections related to the following
areas: information for participants, a privacy statement, partici-
pant demographics, participant medical history (personal and
family), past surgical procedures, prescribed medications, phy-
siologic data, injuries sustained in the years of preparation for
the tournament and psychological participation factors. Filters
were used in the participant questionnaires to abbreviate re-
sponse times. The 2009 SWMG survey process is illustrated in
Figure 1.
Data Analysis
Statistical analysis was assisted with use of the software pac-
kage SPSS (Ver. 18.0.0). Comparisons of classification count
data was conducted using Pearson’s chi square statistic. This
was deemed appropriate and reliable due to the large sample
sizes involved. Significant differences between groups were at-
tained using an independent t-test or an appropriate non-pa-
rametric alternative, dependent on parameters for the applica-
tion of a t-test not being met. Normality was assessed by inves-
tigating kurtosis, skewness, Q-Q plots, as well as the Kolmo-
gorov-Smirnov test with the Lilliefors significance correction.
Heteroscedasticity was assessed using Levene’s test for the
equality of variances.
Results and Discussion
A total of 931 SWMG athletes competing in football codes
(which this paper is restricted to) completed the online survey.
Of the respondents from this sub-sample, 702 (75.4%) indi-
cated they were registered for association football, 77 (8.3%),
rugby union and 155 (16.6%) touch football. One of the 702
association football masters athletes also competed in rugby
union, whilst two of the 702 association football athletes also
competed in touch football. These participants were included in
the analysis.
A total of 489 (52.5%) of the participants were male, while
442 (47.5%) were female. The age range of the participants was
29 - 72 yrs (mean = 47.6, SD ± 7.1). A total of 67.2% of the
football code athletes indicated they were representing Austra-
lia at the SWMG, whilst 32.8% indicated that they were repre-
senting other countries. Subjects whom did not include their
age and gender were excluded from analysis. Athletes who
reported suffering an injury are represented in Figure 2. A total

J. WALSH ET AL.
38
Australian (national) participants
(n = 20,089)
Non-Australian (International)
participants (n = 8587)
Actual SWMG Participants
(n = 28,676)
Participants eligible to complete
survey via electronic
registrations (n = 26,676)
Electronic invitations to participate
in survey sent
(n = 24,528)
Invalid email address, no age/gender
specified (n = 1808)
Emails bounced (n = 248)
Participants unsubscribed emails
(n = 88)
Invite marked as Spam (n = 4)
SWMG participants completing
survey (n = 8072)
Expression of interest in
SWMG (n = 56,980)
Non-football code athletes
(n = 6279)
SWMG Football code participants
completing survey (n = 931) Australian participants (n = 625)
International participants (n = 306)
Figure 1.
Sydney world masters games survey overview.
of 67.2% of the football code athletes indicated they were rep-
resenting Australia at the SWMG, whilst 32.8% indicated that
they were representing other countries.
Analyzing injury demographics indicated that 215 football
code athletes, namely 23.1% of respondents suffered at least
one injury in the preparatory exercise training for SWMG. The
highest percentage of injured players per sport was 29.2% in
touch football, 27.0% in rugby and 21.2% in soccer. The ele-
vated incidence of injury in touch football (χ2 = 3.84) fell below
the critical value for statistical significance of difference (χ2 =
5.99), meaning this difference was not statistically significant.
Touch players reporting an injury in preparation for the games
were significantly older (t = 2.43, p = 0.016) than the rugby
union and soccer players who reported injuries. Injury inci-
dence might thus be proposed to be related to age, however
although the 155 touch players were not significantly older
(mean age of touch players 47.6 years, mean age of the other
two football code athletes as a group 47.4 years, t = 1.56, p =
0.120), the participants were not matched (age, gender) be-
tween the three sport groups, thus it would be inadvisable to
draw any strong conclusions from this finding. Similarly, when
examining the sub-sample in its entirety, injured males were
significantly older than injured females (50.1 years vs. 45.5
years, t = 4.631, p < 0.001), however this may have been re-
Figure 2.
Population pyramid displaying age and gender of athletes who have had
an injury. Lines of normality illustrated.

J. WALSH ET AL.
lated to the age of males in the football code sample being sig-
nificantly higher than for females (49.7 years vs. 45.2 years, t =
10.18, p < 0.001). There was no statistical difference in injuries
sustained by age, for the football codes sub-sample as a whole
or for either gender analyzed in isolation.
Of the thirteen classifications of injuries reported, the most
common type of injury was muscle/tendon strain (13.0%, χ2 =
232.8, p < 0.0001), followed by inflammation (6.1%, χ2 = 17.2,
p > 0.05), joint pain (6.0%, χ2 = 15.7, p > 0.05) and ligament
sprain/tear (5.8%, χ2 = 13.1, p > 0.05). Analysis also indicated a
muscle/tendon sprain was significantly the most common injury
(p < 0.05), while comparatively punctures (0.1%, χ2 = 31.2),
dehydration (0.1%, χ2 = 31.2), lacerations (0.3%, χ2 = 27.4) (all
p < 0.01) and dislocation (0.6%, χ2 = 22.2, p < 0.05) were sig-
nificantly less common.
In total, across all three football codes, 346 of the injuries
were also designated by the survey participants according to
specific body locations. Analysis indicated the most common
site of injury were the legs (11.2%, χ2 = 37.2, p < 0.01), this
figure excluded injuries to knees, feet and ankles, which were
not significantly different to expected values (using a Pearson
Chi Square statistic). From a perspective of comparing the 13
injury locations (face, head (other), chest, eye, abdomen/pelvis,
hand, spine/back, hip and shoulder) were all proportionately
under represented (p < 0.01) below the Pearson Chi Square
expected value given by even distribution of injuries across
location categories. Distribution of injuries sustained via classi-
fication of injury location on the body is illustrated in Figure 3.
Females reported a lower incidence of injuries than males
(20.1% vs. 25%, χ2 = 16.21, p < 0.001). It should be noted how-
ever that males in the football code sub-sample were older and
the 77 rugby players were all male. One might assume that
these may have been confounding factors and influenced this
gender difference in injury incidence, with a higher proportion
of males involved in this collision sport and the greater mean
Figure 3.
Heat map of injury location including percentages of total injuries.
age of males in the sample. This was unlikely though as there
were no statistical increases in reported injuries amongst the
rugby competitors. Analysis also indicated no significant dif-
ference in age between those athletes indicating they either did
or did not suffer from an injury.
Analyzing differences in injury type and location revealed
some additional gender differences. Males were more likely to
suffer a muscle/tendon strain than females (16.4% vs. 9.3%, χ2
= 8.96, p < 0.01) and also more likely to sustain a leg (15.3% vs.
6.6%, χ2 = 16.01, p < 0.001) chest (1.0% vs. 0%, χ2 = 4.52, p <
0.05) or hand (1.4% vs. 0.2%, χ2 = 3.92, p < 0.05) injury.
However, males were less likely to sustain an injury to the
foot/feet (1.8% vs. 5.0%, χ2 = 6.86, p < 0.01). Whilst there were
clearly some gender based trends in injury type and location,
due to the low cell counts in the Pearson Chi Square contin-
gency table and the necessary adjustments by the number of
comparisons made (thirteen classifications each of injury/injury
locations by two genders), it would be advised to focus on
gender differences in scores for muscle/tendon strain for type of
injury and legs and foot/feet for location as more reliable pat-
terns.
Tables 1-4 list the injury locations and classification types
by sport and gender. Due to overlap between different age
groups, genders and sports caution was taken not to draw con-
clusions from any apparent patterns, due to multiple variables
affecting these trends (for example rugby athletes were exclu-
sively male in this sample and males were predominantly older,
influencing results by sport, gender and age).
Table 1.
Injury type classification by sports. Significant differences between
codes of football were present, percentage and Pearson chi square val-
ues are highlighted in italics.
Injury
classification
Soccer
(n = 702)
(%)
Touch
(n = 155)
(%)
Rugby
(n = 77)
(%)
Σ χ2
Abrasion/
cuts 2.1 1.8 2.3 24.5***
Concussion 0.0 0.0 0.0 0.0
Contusion/
bruising 4.9 0.7 1.4 5.1
Dehydration 0.0 0.2 0.0 5.0
Dental injury 0.0 0.0 0.0 0.0
Dislocation 0.6 0.5 0.2 2.0
Fracture 0.6 0.7 0.2 4.2
Inflammation 7.6 2.5 2.0 5.0
Joint pain 6.5 3.6 1.8 9.7**
Laceration 0.0 0.0 0.7 33.4***
Ligament
sprain/tear 8.6 1.4 1.4 1.6
Muscle/tendon
strain 17.0 5.9 2.7 2.9
Muscle pain 5.5 2.9 0.7 5.8
Puncture 0.0 0.0 0.2 11.1**
Sprain 2.5 0.9 0.0 2.0
**indicates significance at the 0.01 level, ***indicates significance at the 0.001
level.

J. WALSH ET AL.
Table 2.
Injury location by sports. Where significant differences between codes
of football were present, percentage and Pearson chi square values are
highlighted in italics.
Injury location
Soccer
(n = 702)
(%)
Touch
(n = 155)
(%)
Rugby
(n = 77)
(%)
Σ χ2
Abdomen/pelvis 0.1 0.7 0.5 11.3**
Ankle 7.8 2.0 0.7 0.4
Arm 0.2 0.7 0.0 9.9**
Chest 0.2 0.7 0.2 8.5*
Eye 0.0 0.0 0.2 11.1**
Face 0.2 0.0 0.2 4.7
Foot/feet 4.9 1.1 0.5 0.1
Hand(s) 0.4 0.9 0.5 10.8**
Head 0.0 0.0 0.5 22.3***
Hip 2.7 0.7 0.2 0.1
Knees 10.4 3.8 2.0 3.3
Leg(s) 14.5 4.8 2.7 2.8
Neck 0.0 0.0 0.0 0.0
Shoulder 1.6 0.9 1.6 21.8***
Spine/back 1.4 1.6 0.5 9.6**
*indicates significance at the 0.05 level, **indicates significance at the 0.01 level,
***indicates significance at the 0.001 level.
Table 3.
Injury classification by gender. Where significant differences between
genders were present, percentage and Pearson Chi Square values are
highlighted in italics.
Injury classification
Male
(n = 489)
(%)
Female
(n = 442)
(%)
Σ χ2
Abrasion/cuts 4.1 2.9 0.9
Concussion 0.0 0.0 0.0
Contusion/bruising 3.1 4.1 0.7
Dehydration 0.2 0.0 0.9
Dental injury 0.0 0.0 0.0
Dislocation 1.0 0.2 2.3
Fracture 0.8 0.7 0.1
Inflammation 5.9 6.3 0.1
Joint pain 6.1 5.9 0.0
Laceration 0.6 0.0 2.7
Ligament sprain/tear 5.3 6.3 0.4
Muscle/tendon strain 16.4 9.3 9.0***
Muscle pain 4.9 4.3 0.2
Puncture 0.2 0.0 0.9
Sprain 1.0 2.5 2.9
***indicates significance at the 0.001 level.
Table 4.
Injury type location by gender. Where significant differences between
genders were present, percentage and Pearson chi square values are
highlighted in italics.
Injury location
Male
(n = 489)
(%)
Female
(n = 442)
(%)
Σ χ2
Abdomen/pelvis 1.0 0.2 2.3
Ankle 4.3 6.6 2.2
Arm 0.6 0.2 0.8
Chest 1.0 0.0 4.5*
Eye 0.2 0.0 0.9
Face 0.4 0.0 1.8
Foot/feet 1.8 5.0 6.9**
Hand(s) 1.4 0.2 3.9*
Head 0.4 0.0 1.8
Hip 1.4 2.3 0.9
Knees 7.0 9.7 2.2
Leg(s) 15.3 6.6 16.0***
Neck 0.0 0.0 0.0
Shoulder 2.9 1.1 3.4
Spine/back 2.0 1.4 0.6
*indicates significance at the 0.05 level, **indicates significance at the 0.01 level,
***indicates significance at the 0.001 level.
In an attempt to investigate whether injury classification and
location were dependent upon age, the database was split into
those under 50 years old (<50 yrs, n = 578) and those 50 years
of age and older (>50 yrs, n = 353). Using Pearson Chi Square
tests surprisingly there was no difference in the incidence of
injury between those <50 or >50. There were also no differ-
ences in the 13 locations or 13 classification types for the inju-
ries sustained based upon age categories. Collectively, these
results suggest that injury patterns between football code ath-
letes <50 yrs and >50 yrs, were not significantly different in
incidence rate, location or classification, which is a promising
finding. Further investigation was performed, analyzing trends
in total injuries per decade. No statistical differences in the
distribution over each decade were observed when compared to
the Pearson Chi Square expected values. An independent t-test
also indicated no significant difference in age between those
athletes who were or were not injured. Therefore there was no
significantly increased risk of injury for older masters athletes
from preparation for the world masters games.
These findings are in agreement with previous research con-
ducted by Ekstrand et al., 2011, who investigated high level
open age sport. Ekstrand and colleagues’ study was conducted
on 23 teams selected by the Union of European Football Asso-
ciations as being in the top 50 European association football
clubs, finding that between 2001 and 2008 a thigh strain was
the most common injury (17% of all injuries). This injury loca-
tion (specifically legs, excluding knees, feet and ankles) and
type (classification as muscle/tendon strain) is therefore consis-
tently the highest occurring in both SWMG athletes prior to the
games and open age professional athletes.
A total of 172 participants (18.5%) indicated that they had
40

J. WALSH ET AL.
been unable to complete exercise training on a number of days
(range 1 - 365, mean = 53.8, SD ± 79.2) due to injury in prepa-
ration for the SWMG. The statistical range of the impact of
these injuries was thus quite large. A total of 41 (4.4%) partici-
pants had been unable to attend work over the same period due
to sporting injuries (range 1 - 90, mean = 11.9, SD ± 18.3). This
equates to the sample population on average missing an average
of 2.5 training days and 0.1 work days per athlete per year.
Comparatively, in 2010 the average Australian worker had an
average absence rate of 9.9 days (at an average employer cost
of $385/d). Therefore, preparation for the SWMG did not sig-
nificantly (p < 0.001) negatively impact employment absentee-
ism in context of total absences for all other reasons (Direct
Health Solutions, 2011).
There were no statistical differences in the amount of days
off work or off training between the injured athletes across the
three codes. As expected there was a correlation between days
lost training and days of lost work within the sample (ρ = 0.31,
p < 0.01). There were also no significant differences in the
numbers of days of lost work or training between genders.
There were also no correlations between days off work or train-
ing and age.
When compared to other studies, these findings indicate in-
juries are significantly lower in incidence than those for both
professional athletes in professional football codes (Ekstrand et
al., 2011; King et al., 2010) as well as amateur athletes in foot-
ball codes (King et al., 2010) (p < 0.01). Our findings also com-
pare favorably with data from national populations in terms of
the reported incidence of total injuries sustained (Schneider,
2006).
Causation was also considered, namely it was possible that
some masters athletes did not register to compete in the Sydney
WMG due to injuries sustained in preparation for the tourna-
ment. This would have affected these results, in effect remov-
ing an undetermined number of injured athletes from the survey.
Any causation of this nature however, was assumed to be slight,
with such a large sample number. This was logical because of
several factors. The sample number was large (namely with 346
injuries in 215 athletes from 931 football code athletes) which
minimized the likelihood of effects purely due to random sam-
pling. The nature of the most common injuries were found to be
of a non-serious nature (strains, inflammation, sprains, muscles
and tendons of the leg) and the incidence of injuries was re-
corded relatively long period meaning that with reasonable
distribution only a small fraction of the minor injuries suffered
would be close to the tournament, the majority happening with
sufficient time to recover for the Sydney WMG. This was
re-iterated by the mean number of days missed due to injury
(53.8 days) and its dispersion (SD ± 7.92). These values indi-
cated it was likely some athletes failed to compete due to injury,
but that proportionally, in context of the total number of inju-
ries sustained, this would not have been sufficient in magnitude
to reverse the trend. That is the trend of favorable injury rates in
the football codes sub-sample.
There was also the possibility that athletes with some inher-
ent ability to resist injury were able to continue physical activ-
ity across the lifespan and were therefore more likely to be
involved in masters sport. While many athletes did suffer inju-
ries, it was previously confirmed that this was not a sole moti-
vator for being involved in masters football (Heazlewood et al.,
2011).
In addition, it should be noted that care should be taken to
not read too much into gender based trends or sport based
trends because of exclusively male participation in rugby union
in addition to non-significant differences in age between sports
and also significant differences between genders. There is still
some uncertainty as to whether type of injury might be due to
gender or sports classification. For this reason sport based
trends should be considered in the context of a small sample
(215 athletes, of different ages, by two genders and three sports).
The purpose of this manuscript was primarily to evaluate injury
risks in context of reported data on comparative populations, in
order to assess the viability of the view that there would be
increased injury risk amongst competitive masters athletes. This
risk was not supported by this research and in fact reduced
injury risks were apparent.
Conclusion
It is a commonly held conception that adherence to exercise
reduces the incidence of chronic disorders as well as improving
indices of general health. There was concern that due to age
related changes, increased risk of injury will be associated with
highly competitive sport for the aging athlete. For masters
football athletes at the SWMG, it was shown that injury during
the preparation for the tournament had similarities, but was in
fact significantly less in incidence than for many other compet-
ing football populations as well as open age general sporting
populations. There was also no increase in injury incidence,
days of training missed due to injury or days of work missed
due to injury as age was increased across the sub-sample from
the SWMG. Nor were there any changes in injury location or
classification type with advancing age. Therefore evidence
indicated that these concerns are unfounded for athletes com-
peting in soccer, rugby union and touch football.
Acknowledgements
The authors would like to thank those masters athletes who
donated their time to complete the survey. We would also like
to acknowledge the support provided by the Sydney World
Masters Games Organizing Committee and the information
technology assistance provided by Evan Wills which was in-
strumental in the successful completion of this project. The
authors would also like to thank Paul Dundon of Direct Health
Solutions for providing the comparative data on workplace
absenteeism in Australia.
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