International Journal of Sports Physiology and Performance, 2015, 10, 458 -463 http://dx.doi.org/10.1123/ijspp.2014-0257 © 2015 Human Kinetics, Inc.
Original Investigation
Increase in Injury Risk With Low Body Mass and Aerobic-Running Fitness in Elite Australian Football Paul B. Gastin, Denny Meyer, Emy Huntsman, and Jill Cook Purpose: To assess the relationships between player characteristics (including age, playing experience, ethnicity, and physical fitness) and in-season injury in elite Australian football. Design: Single-cohort, prospective, longitudinal study. Methods: Player characteristics (height, body mass, age, experience, ethnicity, playing position), preseason fitness (6-min run, 40-m sprint, 6 × 40-m sprint, vertical jump), and in-season injury data were collected over 4 seasons from 1 professional Australian football club. Data were analyzed for 69 players, for a total of 3879 player rounds and 174 seasons. Injury risk (odds ratio [OR]) and injury severity (matches missed; rate ratio [RR]) were assessed using a series of multilevel univariate and multivariate hierarchical linear models. Results: A total of 177 injuries were recorded with 494 matches missed (2.8 ± 3.3 matches/injury). The majority (87%) of injuries affected the lower body, with hamstring (20%) and groin/hip (14%) most prevalent. Nineteen players (28%) suffered recurrent injuries. Injury incidence was increased in players with low body mass (OR = 0.887, P = .005), with poor 6-min-run performance (OR = 0.994, P = .051), and playing as forwards (OR = 2.216, P = .036). Injury severity was increased in players with low body mass (RR = 0.892, P = .008), tall stature (RR = 1.131, P = .002), poor 6-min-run (RR = 0.990, P = .006), and slow 40-m-sprint (RR = 3.963, P = .082) performance. Conclusions: The potential to modify intrinsic risk factors is greatest in the preseason period, and improvements in aerobic-running fitness and increased body mass may protect against in-season injury in elite Australian football. Keywords: injury prevention, fitness testing, contact sports, sporting injuries Sports injuries are associated with considerable personal and economic burden with both short- and long-term consequences,1,2 including treatment costs, time lost to further participation, reduced performance in subsequent activity, and increased risk of chronic musculoskeletal conditions.3 Team ball sports are generally associated with a higher risk of injury than individual sports, with the risks further increased in contact sports3 and at the elite level compared with other levels of participation.1 In team sports, injuries to key players or a high proportion of players unavailable to play can influence match and season outcomes.4 Sports-injury risk factors are generally categorized as intrinsic (internal, personal) or extrinsic (external, environmental) and whether these risk factors are modifiable or nonmodifiable.3,5 The cause of sports injury is multifactorial and a result of the interaction of several risk factors or events.3 Injury prevention and individual player management are increasingly prioritized as strategies to minimize injury time loss in professional team sport. Training load and practices, playing schedules and rotations, and interventions that target identified risk factors (eg, physical fitness, flexibility, landing and balance, running technique, coordination, and hamstring function6–8) are important considerations when planning and implementing a periodized training and competition program. The potential to modify risk factors is greatest in the preseason period when the focus is on conditioning and skill development without the need to prepare for, and recover from, regular match play. During the Gastin and Huntsman are (and Cook was) with the Centre for Exercise and Sport Science, Deakin University, Burwood, VIC, Australia. Meyer is with the Faculty of Life and Social Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia. Cook is currently with the Dept of Physiotherapy, Monash University–Peninsula Campus, Australia. Address author correspondence to Paul Gastin at [email protected]. 458
competition phase, the opportunity to increase fitness is diminished and the emphasis is typically on maintenance. As such, the level of player fitness attained during the preseason period represents not only an important foundation for player performance9 but also a time to assess and address injury risk factors.2 The Australian Football League (AFL) injury survey is the world’s longest-running publicly released injury survey in sport.10 Despite this comprehensive level of documentation of injury incidence and injury severity, data contributing to the understanding of injury risk factors in elite Australian football remain limited. One exception relates to hamstring strain, which is the number-1 injury in Australian football.10 Identified risk factors for hamstring injury in Australian football include older age, previous hamstring injury, past history of other injuries, strength deficits, leg stiffness, and indigenous (Aboriginal) descent.11–14 Hamstring injury in high-speed intermittent-running sports such as Australian football, soccer, and rugby occurs predominantly during running and, to a lesser degree, kicking, with a number of factors likely acting together.15,16 Risk factors for hip and groin injuries have also been investigated in Australian football and include previous injury, hip range of motion, and indigenous descent.8,17 In elite junior Australian football, Chalmers et al18 recently demonstrated that injury is related to lower preseason aerobic endurance, faster 5-m sprint, and faster planned agility. In subelite rugby league players, Gabbett and Domrow19 found injury risk to be higher in players with low speed and aerobic fitness, while heavier players had a greater risk of sustaining a lower-limb injury and lighter players a greater risk of sustaining a severe injury. Injury-prevention strategies and modifications to training practices are in part dependent on the identification of risk factors associated with injury. Despite suggestions that various components of physical fitness may either protect or predispose a player to injury,
the links between player size and physical fitness and injury risk in Australian football are relatively unexplored. The purpose of this study therefore was to assess the relationships between individual player characteristics (including age, size, playing experience, indigenous descent, and physical fitness) and in-season injury incidence and severity in elite Australian football.
Methods Participants
Statistical Analyses
Sixty-nine male AFL players (mean ± SD age 22.8 ± 3.6 y, AFL experience 65 ± 71 matches), including 7 indigenous players, participated in the study. Player and club consent were obtained for the study along with institutional ethics approval in accordance with the Helsinki Declaration.
Behavioral data such as those collected in sport over time can be hierarchical and commonly have a nested structure, as measurement occasions and the number of repeated observations on each individual are not identical.22,23 Multilevel linear-modeling techniques were therefore used to analyze the data, with each submodel representing the structural relations and residual variability at that level. The data had 3 nested layers (round, season, and player). Data for each round (level 1) recorded whether an injury occurred (injury incidence) and how many matches were missed as a result (injury severity). Season data (level 2) recorded player characteristics such as age, body mass, previous AFL and VFL experience, preseason fitness (40 m-sprint, 6 × 40-m sprint, 6-min run, jump variables), and playing position. The third level of data referred to individual player variables that did not change between seasons (height and ethnicity). A series of multilevel hierarchical linear models (HLM6.02, Scientific Software International Inc, Lincolnwood, IL) were developed that allowed for the nested structure of the data.24 For injury risk (yes or no in any given week), a binary logistic regression was used with a logit link function, and for injury severity (matches missed) a Poisson regression was used with a log link function. A sequence of models was developed that began simply (eg, interceptonly base model) and then became more complex (eg, univariate and multivariate conditional linear models). Descriptive statistics are presented as means, standard deviations, and ranges. Independent-sample t tests were used to compare the indigenous subgroup with the rest of the playing group. For all multilevel models significance values of below .10 were identified, based on the notion that the traditional P < .05 may be too conservative to detect important practical effects in elite sport research, given relatively small sample sizes and large measurement variability.25 Although P values are important in that they indicate which relationships are significant, odds and rate ratios provide an indication of the expected magnitude (meaningfulness) of the effects. Odds ratios (OR) are presented for injury risk and rate ratios (RR) for injury severity, with values above 1 generally indicating greater risk/severity and those below 1 indicating less risk/severity. The ratios do need to be interpreted in relation to the unit of measure for each predictor variable and whether an increase in the variable is associated with positive performance (eg, distance in the 6-min run) or negative performance (eg, time in the sprint). The precision of the ratios is demonstrated by 95% confidence intervals.
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to being rested (no specific injury listed but player advised not to play) or illness were not counted as injuries. As such, each player was coded as either playing AFL or VFL, injured, or “other” for each week during the competitive season. An injury was not included in the analysis if it occurred during the preseason and resulted in the player missing the opening round of the season. Injuries were recorded by the club’s doctor in accordance with procedures used for data reporting to the AFL’s annual injury survey.10
Data were collected over 4 seasons (2007–2010). Each week of the competitive season, players played either in the AFL team competing in the national competition or in the state-level league (Victorian Football League [VFL]), unless they were injured or otherwise unavailable. The study followed a prospective longitudinal design, with preseason fitness data (6-min run, 40-m sprint, 6 × 40-m, vertical jump) and in-season injury data (incidence, severity) collected each season for each player. Player characteristics (height, body mass, fitness) were assessed during the latter part of each preseason period (January/February) and before the commencement of the 22-round AFL season (March to August). Age on the first of January and football experience (AFL and VFL matches played) were recorded before the commencement of each season. Playing position (back, forward, midfield, or ruck20) was classified for each season, with some players changing roles from season to season or being classified twice if they performed a dual role (eg, midforward players were classified as both midfield and forward). Ethnicity was dichotomized as indigenous or nonindigenous.
Fitness Player-fitness characteristics were assessed using previously described procedures for aerobic-running fitness (6-min run), speed (40-m sprint), and repeated-sprint ability (6 × 40-m sprints).9 A linear-position transducer and encoder was used to assess jump performance.21 Concentric and eccentric mean power and contact time were measured during sets of 5 consecutive vertical jumps (ie, weighted squat jumps). A transducer and encoder (GymAware, Kinetic Performance Technology, Australia) were located on the ground and attached via a perpendicular cable to a 20.5-kg squat bar that the players held across their upper back.21 Players were instructed to squat to approximately 90° of knee flexion and then jump as high as possible 5 times without rest between the concentric and eccentric phases. Each player completed 3 sets with 2 minutes rest between sets. Due to the controlled nature of the first jump, only jumps 2 to 5 of each set were analyzed. Values for these jumps were then averaged and the set with the highest value recorded. The concentric- and eccentric-power measures were divided by the player’s body mass for standardization.
Injury Injury was defined as any musculoskeletal injury that caused a player to miss a match.10 Cases where players missed a game due
Results Preseason fitness data and in-season injury data were collected over 4 seasons, with data presented for 69 players (Table 1). Ten percent of the playing group (n = 7) were of indigenous descent, with this subgroup being younger (19.8 ± 2.3 vs 23.2 ± 3.6 y, P < .001) and lighter (78.1 ± 3.3 vs 89.8 ± 8.5 kg, P < .001) and having less AFL experience (14.4 ± 22.9 vs 71.1 ± 71.8 matches, P < .001). Injury
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data were collected for a total of 3879 player rounds and 174 seasons, although missing preseason testing results for some players, variables, and seasons meant that not all these data could be used in the multivariate analyses. In total, 57% of rounds were played in the AFL and 28% were played in the VFL; the remainder were missed due to injury or other reasons (eg, illness, suspension, rested). Over the 4 seasons, 177 rounds were associated with the first week of an injury (Table 2), with a mean of 2.8 matches missed, ranging from 1 week (49% of all injuries) to a season-ending 23 weeks. The vast majority (87%) of injuries were to the lower body, with hamstring (20%) and groin/hip (14%) being most prevalent. Nineteen players (28%) suffered a recurrent injury (34 instances, 19%). An approximately equal proportion of muscle/tendon-strain (46%) and bone-stress or joint-sprain (42%) injuries was observed. Univariate and multivariate regression analyses are presented for the risk of injury incidence (Table 3) and injury severity (Table 4). Several variables identified as predictors in the univariate analyses either disappeared (injury incidence: vertical-jump contact time; injury severity: 6 × 40-m sprint, vertical-jump contact time, verticaljump concentric mean power, playing as a forward, indigenous descent) or were reversed (injury incidence: indigenous descent) in the multivariate analyses. The multivariate analyses identified body mass (OR = 0.887, P = .005; ie, for every additional 1 kg of body mass, the risk of injury decreased by 11.3%), 6-minute-run performance (OR = 0.994, P = .051; ie, for every additional 1 m run, the risk decreased by 0.6%), and playing as a forward (OR = 2.216, P = .036; ie, forwards had a 222% increased risk of injury) as risk factors of injury incidence. As such, players of low body mass, poor 6-minute-run performance, and forwards were at greater risk of sustaining an injury. Injury severity was greatest for players with low body mass (RR = 0.892, P = .008), tall stature (RR = 1.131, P
= .002), poor 6-minute-run (RR = 0.990, P = .006), and slow sprint (RR = 3.963, P = .082) performance.
Discussion The incidence of injuries at this club was similar to that reported for all clubs in the AFL competition during the same 4-year period10 (44 compared with 42 injuries per club per year, respectively). Injury severity was lower (2.8 vs 4.0 matches per injury), while injury recurrence was higher (19% vs 13%). The most important findings were that aerobic-running fitness and greater body mass were found to have a protective effect against injury incidence and severity. These 2 player characteristics represent modifiable risk factors and should be assessed and managed during the preseason training period, and likely over multiple years in players who are identified as at risk. Australian football is a field team sport that involves highintensity running, jumping, landing, and player contact including tackling and bumping.20,26 The current study showed that players with lower body mass experienced greater injury incidence and severity. In subelite rugby league, players with low body mass have also been found to be at a greater risk of severe injury.19 As force is directly proportional to mass, players with a greater body mass exert a greater impact force when colliding with another player (assuming a similar velocity at impact). The mean body mass of players was 88.5 kg; however, the range spanned 38 kg. As such, when 2 players collide, the player with lower body mass will need to absorb a greater impact force and do this with less muscle and fat mass. The increasing speed and intensity of Australian football20 and the increased number of tackles26 have been linked to the rising number of collision injuries, including shoulder and soft-tissue injuries.27 In other collision sports such as rugby league and rugby
Table 1 Sample Sizes and Descriptive Statistics for Individual Player Characteristics Player characteristic Age (y) Body mass (kg) Height (cm) Previous Australian Football League matches (n) Previous Victorian Football League matches (n) 40-m sprint (s) 6 × 40-m sprint (s) 6-min run (m) Vertical-jump contact time (s) Vertical-jump concentric mean power (W/kg) Vertical-jump eccentric mean power (W/kg)
Rounds 3879 3879 3879 3879 3879 2596 2545 3333 2836 2836 2836
Seasons 174 174 174 174 174 112 111 148 125 125 125
Players 69 69 69 69 69 58 57 65 57 57 57
Mean ± SD 22.8 ± 3.6 88.5 ± 8.9 188.7 ± 7.1 65.2 ± 71.0 25.8 ± 23.3 5.49 ± 0.22 35.6 ± 1.4 1722 ± 78 0.48 ± 0.13 31.5 ± 3.91 34.9 ± 5.48
Range 17–32 71–109 175–206 0–256 0–183 4.98–6.22 29.9–39.9 1478–1863 0.21–0.84 22.8–41.1 22.1–47.2
2010 550 268 44 142 3.2 (± 3.9)
2007–2010 2200 1104 177 494 2.8 (± 3.3)
Table 2 Injury Statistics for Each Season and Over the Entire 2007–2010 Period Australian Football League matches played Victorian Football League matches played Injuries Matches missed due to injury Mean matches missed per injury (± SD)
2007 550 317 40 117 2.9 (± 2.1)
2008 550 269 39 81 2.1 (± 1.2)
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2009 550 250 54 154 2.9 (± 4.3)
Table 3 Odds Ratios for the Risk of Sustaining an Injury (Injury Incidence) Associated With Individual Player Characteristics
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Univariate Regressiona Season Variables age (y) body mass (kg) previous Australian Football League matches previous Victorian Football League matches 40 m sprint (s) 6 × 40 m sprint (s) 6 min run (m) vertical-jump contact time (s) vertical-jump concentric mean power (W/kg) vertical-jump eccentric mean power (W/kg) back forward midfield ruck Player Variables height Aboriginal
Multivariate Regressionb
Odds ratio
95% CI
P
Odds ratio
95% CI
P
1.015 0.979* 1.001 0.999 1.062 1.076 0.999 0.245** 0.990 1.004 0.834 1.617** 0.988 0.677
0.963–1.070 0.959–1.000 0.998–1.003 0.992–1.005 0.368–3.062 0.927–1.250 0.996–1.002 0.071–0.843 0.951–1.031 0.978–1.031 0.554–1.256 1.100–2.360 0.682–1.430 0.336–1.368
.568 .055 .530 .703 .910 .328 .424 .026 .627 .750 .369 .025 .947 .274
1.078 0.887*** 0.999 1.002 2.977 1.036 0.994* 0.344 0.934 1.015 0.893 2.216** 1.258 1.787
0.835–1.393 0.821–0.957 0.986–1.103 0.981–1.022 0.692–12.81 0.761–1.410 0.988–1.000 0.015–7.802 0.835–1.044 0.939–1.097 0.413–1.930 1.061–4.629 0.624–2.537 0.403–7.929
.534 .005 .928 .877 .129 .807 .051 .471 .206 .686 .754 .036 .489 .413
0.985 1.783*
0.958–1.011 0.943–3.372
.250 .074
1.049 0.262*
0.961–1.146 0.058–1.191
.277 .082
a Univariate, single-predictor analyses; all available data used (3879 rounds, 174 seasons, 69 players; see Table 1 for each variable). b Multivariate analyses; data reduced to 1699 rounds, 73 seasons, and 47 players. *P < .10. **P < .05. ***P < .01.
Table 4 Rate Ratios for Injury Severity (Matches Missed) Associated With Individual Player Characteristics Univariate Regressiona Season Variables age (y) body mass (kg) previous Australian Football League matches previous Victorian Football League matches 40 m sprint (s) 6 × 40 m sprint (s) 6 min run (m) vertical-jump contact time (s) vertical-jump concentric mean power (W/kg) vertical-jump eccentric mean power (W/kg) back forward midfield ruck Player Variables height Aboriginal
Multivariate Regressionb
Rate ratio
95% CI
P
Rate ratio
95% CI
P
1.001 0.982 1.000 0.990** 2.001 1.201* 0.997* 0.147*** 0.963* 0.994 0.636 2.497** 0.631 0.814
0.812–1.098 0.956–1.000 0.995–1.004 0.981–0.999 0.466–8.584 0.973–1.483 0.993–1.000 0.036–0.608 0.922–1.006 0.961–1.027 0.336–1.205 1.173–5.315 0.350–1.139 0.320–2.073
.989 .193 .884 .024 .344 .087 .052 .009 .090 .702 .163 .018 .125 .663
0.970 0.892*** 1.005 1.016 3.963* 0.770 0.990*** 0.322 1.033 0.976 0.524* 2.332 1.815 0.973
0.720–1.307 0.824–0.965 0.989–1.021 0.991–1.041 0.817–19.23 0.525–1.130 0.983–0.996 0.038–2.743 0.881–1.211 0.898–1.061 0.244–1.127 0.748–7.274 0.759–4.337 0.152–6.240
.828 .008 .506 .198 .082 .164 .006 .272 .667 .545 .091 .131 .162 .975
1.015 1.749*
0.975–1.056 0.936–3.334
.474 .078
1.131*** 1.959
1.049–1.219 0.263–14.57
.002 .503
a Univariate, single-predictor analyses; all available data used (3879 rounds, 174 seasons, 69 players; see Table 1 for each variable). b Multivariate analyses; data reduced to 1699 rounds, 73 seasons, and 47 players. *P < .10. **P < .05. ***P < .01.
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union, a high incidence of injury has been directly linked to the frequency and nature of tackling and heavy collisions.1,28 While tackling in Australian football is less frequent and forceful than that in rugby, considerable force and movement velocity at impact are still evident.26 To protect players at risk for collision injuries, training strategies should therefore look to increase muscle mass in those with low body mass, most likely newly drafted players who are known to have lower mean body mass than their AFL peers.20 Performance in running-based assessment tasks that target aerobic or endurance characteristics has consistently been associated with playing performance in Australian football9,29 and other field team sports.30 The findings of the current study further support the importance of developing aerobic-running fitness, given that players with lower 6-minute-run performance were at increased risk of sustaining an injury and that these injuries resulted in a greater number of matches missed. Chalmers et al18 recently demonstrated a link between lower levels of aerobic endurance (20-m shuttle run) and a greater risk of shin/ankle/foot injuries in elite junior Australian football, suggesting a reduced ability to cope with repetitive loading and/or avoid potentially dangerous situations as a result of fatigue. In rugby league, players with poor aerobic fitness are at a greater risk of contact injuries,19 while the increased incidence of injury in the second half of matches or latter stages of training has been linked to player fatigue.1 Impaired decision making and movement response as a result of fatigue31,32 may place a player in a vulnerable position or render the player unable to respond quickly to avoid a collision. The number of matches missed, as defined here and in other AFL research,18 is a reflection of the severity of the injury; however, other factors may also influence this variable. Coaching and medical staff need to balance the benefits of an early return to competition against the risk of recurrence and may or may not take a conservative approach, depending on individual and team considerations. It is also possible that players with a low conditioning base take longer to recover and return to an acceptable level of perceived match fitness. This is supported by the finding that players with poor 6-minute-run performance, slow 40-m-sprint time, and poor repeated-sprint ability (univariate analysis only) demonstrated increased injury severity. Match and work demands in Australian football vary with playing position.20 The high running demands and game activities of midfield players compared with other positions33 did not seem to influence injury risk in this group of players. Midfield players had significantly greater 6-minute-run performances (P < .001; data not presented) than all other players, which suggests that enhanced aerobic fitness not only allows them to meet the challenging physical demands of the position20 but may also provide a protective effect against injury. Playing as a forward, however, was associated with a significant increase in injury incidence, while playing as a back resulted in reduced injury severity. Forwards are required to lead to receive the football, to contest marking duels from the front, and to move the ball forward to create scoring opportunities.20 Intense defensive pressure (eg, spoil, bump, tackle) from the opposition to prevent scoring may expose forwards to increased risk from collisions. Other possibilities include rapid accelerations when leading or the frequent change of direction when transitioning from moving forward to receive the ball and then rapidly turn toward goal. Univariate analyses of vertical-squat-jump data indicated that a long ground contact time between jumps resulted in reduced injury incidence and that a long contact time and large concentric mean power resulted in a reduced injury severity. These findings were not confirmed in the multivariate analyses, supporting the need for dataanalysis techniques that statistically control for other variables16 and cautioning against conclusions derived from univariate analyses.
Indigenous players represent a disproportionate number of those playing elite-level Australian football (approximately 10–11% compared with 2% of the national population) and are regarded as fast and highly skilled.17 Previous research has indicated that indigenous players have a greater risk of hamstring, anterior cruciate ligament, quadriceps, and calf-strain injuries compared with nonindigenous players.11,12 Taylor et al17 found that indigenous elite junior Australian football players displayed less hip range of motion, reduced adductor strength, and higher levels of groin pain during a knee-squeeze test and hypothesized that these characteristics may precede future groin injury. They did not, however, find any difference in past hip or groin injury. The results of the current study, albeit in a small number of aboriginal players (7/69), support the contention that injury cause is complex and multifactorial.3 As a single predictor, indigenous descent was associated with a higher overall injury incidence, yet this was reversed in the multivariate analysis when other variables were controlled. Hence, characteristics in this subgroup, such as being lighter in body mass, having lower aerobic fitness, and/or playing as a forward, in specific individuals are more likely to contribute to injury risk than ethnicity per se. Several limitations exist in the analysis of the data in this study, mostly related to sample size. Missing values reduced the sample to only 47 players and 73 seasons for the multivariate analyses. These results should therefore be treated with some caution, although controlling for the effect of other variables does enhance their interpretability. Furthermore, different types of injury could not be analyzed individually, such that all injuries were considered collectively and overall risk factors identified. A limitation to this approach is that different types of injuries may have some unique or different risk factors. Finally, the assessment of fitness at the commencement of the season may not reflect the fitness status of a player at the time of injury. It is acknowledged that fitness may change over the course of the season, and an assessment closer to the incidence of an injury could influence the analysis. However, the purpose of this study and our interest in analyzing the data in this way was to consider the influence of player characteristics at the commencement of a season on in-season injury risk (ie, how does player status at the end of a 3-month preparation period influence injury risk during the competition season?). As preseason is a critically important time to lay a foundation for the in-season phase, individual weaknesses and development priorities should be identified. Player assessments and identified risk factors can therefore be used to guide preseason programming.
Conclusion The identification of injury risk factors that are modifiable is important in individual player management, while the potential to modify these risk factors is greatest in the preseason period. Aerobic-running fitness and increased body mass appear to have a protective effect against injury incidence and severity in elite Australian football and represent important characteristics for targeted development in susceptible players. Age, previous AFL experience, ethnicity, and leg power were not important predictors of injury in this playing group.
Practical Implications • Injury-prevention strategies in Australian football should target low body mass and aerobic-running fitness. • Risk factors common to all players, rather than ethnicity, explain injury risk in indigenous players.
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• Multivariate rather than univariate analysis techniques are recommended when assessing injury risk factors. • Team-sport athletes should be managed on an individual basis given the complex and multifactorial nature of injury risk. Acknowledgments The authors would like to acknowledge the support and contributions of players and staff from the AFL club that participated in the research.
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References 1. Gabbett TJ. Incidence of injury in junior and senior rugby league players. Sports Med. 2004;34(12):849–859. PubMed doi:10.2165/00007256-200434120-00004 2. Smith AM. Psychological impact of injuries in athletes. Sports Med. 1996;22(6):391–405. PubMed doi:10.2165/00007256-19962206000006 3. Dennis R, Finch C. Sports injuries. In: Heggenhougen K, ed. International Encyclopedia of Public Health. Oxford, UK: Academic Press; 2008:206–211. 4. Arnason A, Sigurdsson SB, Gudmundsson A, Holme I, Engebretsen L, Bahr R. Physical fitness, injuries, and team performance in soccer. Med Sci Sports Exerc. 2004;36(2):278–285. PubMed doi:10.1249/01. MSS.0000113478.92945.CA 5. Hrysomallis C. Injury incidence, risk factors and prevention in Australian Rules Football. Sports Med. 2013;43(5):339–354. PubMed doi:10.1007/s40279-013-0034-0 6. Cameron ML, Adams RD, Maher CG, Misson D. Effect of the HamSprint Drills training programme on lower limb neuromuscular control in Australian football players. J Sci Med Sport. 2009;12(1):24–30. PubMed doi:10.1016/j.jsams.2007.09.003 7. Scase E, Cook J, Makdissi M, Gabbe B, Shuck L. Teaching landing skills in elite junior Australian football: evaluation of an injury prevention strategy. Br J Sports Med. 2006;40(10):834–838. PubMed doi:10.1136/bjsm.2006.025692 8. Verrall GM, Hamilton IA, Slavotinek JP, et al. Hip joint range of motion reduction in sports-related chronic groin injury diagnosed as pubic bone stress injury. J Sci Med Sport. 2005;8(1):77–84. PubMed doi:10.1016/S1440-2440(05)80027-1 9. Gastin PB, Fahrner B, Meyer D, Robinson D, Cook JL. Influence of physical fitness, age, playing experience and weekly training load on match performance in elite Australian football. J Strength Cond Res. 2013;27(5):1272–1279. PubMed doi:10.1519/JSC.0b013e318267925f 10. Orchard J, Seward DH, Orchard J. 2012 AFL injury report. March 6, 2013. 11. Orchard J. Soft Tissue Injuries in the Australian Football League: Seasons 1992–2008. Saarbrucken, Germany: Lambert Academic; 2009. 12. Verrall GM, Slavotinek JP, Barnes PG, Fon GT, Spriggins AJ. Clinical risk factors for hamstring muscle strain injury: a prospective study with correlation of injury by magnetic resonance imaging. Br J Sports Med. 2001;35(6):435–439. PubMed doi:10.1136/bjsm.35.6.435 13. Watsford ML, Murphy AJ, McLachlan KA, et al. A prospective study of the relationship between lower body stiffness and hamstring injury in professional Australian Rules footballers. Am J Sports Med. 2010;38(10):2058–2064. PubMed doi:10.1177/0363546510370197 14. Orchard J, Orchard J, Seward H. A comparison between Australian Football League (AFL) injuries in Australian indigenous versus non-indigenous players. Sports. 2013;1(3):69–77. doi:10.3390/ sports1030069 15. Liu H, Garrett WE, Moorman CT, Yu B. Injury rate, mechanism, and risk factors of hamstring strain injuries in sports: a review of
the literature. J Sport Health Sci. 2012;1(2):92–101. doi:10.1016/j. jshs.2012.07.003 16. van Beijsterveldt AMC, van de Port IGL, Vereijken AJ, Backx FJG. Risk factors for hamstring injuries in male soccer players: a systematic review of prospective studies. Scand J Med Sci Sports. 2013;23(3):253–262. PubMed doi:10.1111/j.1600-0838.2012.01487.x 17. Taylor CJ, Pizzari T, Ames N, Orchard JW, Gabbe BJ, Cook JL. Groin pain and hip range of motion is different in Indigenous compared to non-indigenous young Australian football players. J Sci Med Sport. 2011;14(4):283–286. PubMed doi:10.1016/j.jsams.2011.02.002 18. Chalmers S, Magarey ME, Esterman A, Speechley M, Scase E, Heynen M. The relationship between pre-season fitness testing and injury in elite junior Australian football players. J Sci Med Sport. 2013;16(4):307–311. PubMed doi:10.1016/j.jsams.2012.09.005 19. Gabbett TJ, Domrow N. Risk factors for injury in subelite rugby league players. Am J Sports Med. 2005;33(3):428–434. PubMed doi:10.1177/0363546504268407 20. Gray AJ, Jenkins DG. Match analysis and the physiological demands of Australian football. Sports Med. 2010;40(4):347–360. PubMed doi:10.2165/11531400-000000000-00000 21. Crewther BT, Kilduff LP, Cunningham DJ, Cook C, Owen N, Yang GZ. Validating two systems for estimating force and power. Int J Sports Med. 2011;32(4):254–258. PubMed doi:10.1055/s-0030-1270487 22. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. 2nd ed. Thousand Oaks, CA: Sage; 2002. 23. Gastin PB, Meyer D, Robinson D. Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. J Strength Cond Res. 2013;27(9):2518–2526. PubMed doi:10.1519/JSC.0b013e31827fd600 24. Goldstein H. Multilevel Statistical Models. 4th ed. Chichester, UK: John Wiley & Sons; 2011. 25. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform. 2006;1(1):50–57. PubMed 26. Gastin PB, McLean O, Spittle M, Breed RVP. Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J Sci Med Sport. 2013;16:589–593. PubMed doi:10.1016/j.jsams.2013.01.007 27. Orchard J, Seward DH, Orchard J. AFL 2010 injury report. 19th ann ed. Australian Football League; 2011. 28. McIntosh AS, Savage TN, McCrory P, Fréchède BO, Wolfe R. Tackle characteristics and injury in a cross section of rugby union football. Med Sci Sports Exerc. 2010;42(5):977–984. PubMed doi:10.1249/ MSS.0b013e3181c07b5b 29. Mooney M, O’Brien B, Cormack S, Coutts A, Berry J, Young W. The relationship between physical capacity and match performance in elite Australian football: a mediation approach. J Sci Med Sport. 2011;14(5):447–452. PubMed doi:10.1016/j.jsams.2011.03.010 30. Helgerud J, Engen LC, Wisloff U, Hoff J. Aerobic endurance training improves soccer performance. Med Sci Sports Exerc. 2001;33(11):1925–1931. PubMed doi:10.1097/00005768-20011100000019 31. Borotikar BS, Newcomer R, Koppes R, McLean SG. Combined effects of fatigue and decision making on female lower limb landing postures: central and peripheral contributions to ACL injury risk. Clin Biomech (Bristol, Avon). 2008;23(1):81–92. PubMed doi:10.1016/j. clinbiomech.2007.08.008 32. Thomson K, Watt A, Liukkonen J. Differences in ball sports athletes speed discrimination skills before and after exercise induced fatigue. J Sports Sci Med. 2009;8(2):259–264. PubMed 33. Veale JP, Pearce AJ. Profile of position movement demands in elite junior Australian rules footballers. J Sports Sci Med. 2009;8(3):320– 326. PubMed
IJSPP Vol. 10, No. 4, 2015
Original Investigation
Increase in Injury Risk With Low Body Mass and Aerobic-Running Fitness in Elite Australian Football Paul B. Gastin, Denny Meyer, Emy Huntsman, and Jill Cook Purpose: To assess the relationships between player characteristics (including age, playing experience, ethnicity, and physical fitness) and in-season injury in elite Australian football. Design: Single-cohort, prospective, longitudinal study. Methods: Player characteristics (height, body mass, age, experience, ethnicity, playing position), preseason fitness (6-min run, 40-m sprint, 6 × 40-m sprint, vertical jump), and in-season injury data were collected over 4 seasons from 1 professional Australian football club. Data were analyzed for 69 players, for a total of 3879 player rounds and 174 seasons. Injury risk (odds ratio [OR]) and injury severity (matches missed; rate ratio [RR]) were assessed using a series of multilevel univariate and multivariate hierarchical linear models. Results: A total of 177 injuries were recorded with 494 matches missed (2.8 ± 3.3 matches/injury). The majority (87%) of injuries affected the lower body, with hamstring (20%) and groin/hip (14%) most prevalent. Nineteen players (28%) suffered recurrent injuries. Injury incidence was increased in players with low body mass (OR = 0.887, P = .005), with poor 6-min-run performance (OR = 0.994, P = .051), and playing as forwards (OR = 2.216, P = .036). Injury severity was increased in players with low body mass (RR = 0.892, P = .008), tall stature (RR = 1.131, P = .002), poor 6-min-run (RR = 0.990, P = .006), and slow 40-m-sprint (RR = 3.963, P = .082) performance. Conclusions: The potential to modify intrinsic risk factors is greatest in the preseason period, and improvements in aerobic-running fitness and increased body mass may protect against in-season injury in elite Australian football. Keywords: injury prevention, fitness testing, contact sports, sporting injuries Sports injuries are associated with considerable personal and economic burden with both short- and long-term consequences,1,2 including treatment costs, time lost to further participation, reduced performance in subsequent activity, and increased risk of chronic musculoskeletal conditions.3 Team ball sports are generally associated with a higher risk of injury than individual sports, with the risks further increased in contact sports3 and at the elite level compared with other levels of participation.1 In team sports, injuries to key players or a high proportion of players unavailable to play can influence match and season outcomes.4 Sports-injury risk factors are generally categorized as intrinsic (internal, personal) or extrinsic (external, environmental) and whether these risk factors are modifiable or nonmodifiable.3,5 The cause of sports injury is multifactorial and a result of the interaction of several risk factors or events.3 Injury prevention and individual player management are increasingly prioritized as strategies to minimize injury time loss in professional team sport. Training load and practices, playing schedules and rotations, and interventions that target identified risk factors (eg, physical fitness, flexibility, landing and balance, running technique, coordination, and hamstring function6–8) are important considerations when planning and implementing a periodized training and competition program. The potential to modify risk factors is greatest in the preseason period when the focus is on conditioning and skill development without the need to prepare for, and recover from, regular match play. During the Gastin and Huntsman are (and Cook was) with the Centre for Exercise and Sport Science, Deakin University, Burwood, VIC, Australia. Meyer is with the Faculty of Life and Social Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia. Cook is currently with the Dept of Physiotherapy, Monash University–Peninsula Campus, Australia. Address author correspondence to Paul Gastin at [email protected]. 458
competition phase, the opportunity to increase fitness is diminished and the emphasis is typically on maintenance. As such, the level of player fitness attained during the preseason period represents not only an important foundation for player performance9 but also a time to assess and address injury risk factors.2 The Australian Football League (AFL) injury survey is the world’s longest-running publicly released injury survey in sport.10 Despite this comprehensive level of documentation of injury incidence and injury severity, data contributing to the understanding of injury risk factors in elite Australian football remain limited. One exception relates to hamstring strain, which is the number-1 injury in Australian football.10 Identified risk factors for hamstring injury in Australian football include older age, previous hamstring injury, past history of other injuries, strength deficits, leg stiffness, and indigenous (Aboriginal) descent.11–14 Hamstring injury in high-speed intermittent-running sports such as Australian football, soccer, and rugby occurs predominantly during running and, to a lesser degree, kicking, with a number of factors likely acting together.15,16 Risk factors for hip and groin injuries have also been investigated in Australian football and include previous injury, hip range of motion, and indigenous descent.8,17 In elite junior Australian football, Chalmers et al18 recently demonstrated that injury is related to lower preseason aerobic endurance, faster 5-m sprint, and faster planned agility. In subelite rugby league players, Gabbett and Domrow19 found injury risk to be higher in players with low speed and aerobic fitness, while heavier players had a greater risk of sustaining a lower-limb injury and lighter players a greater risk of sustaining a severe injury. Injury-prevention strategies and modifications to training practices are in part dependent on the identification of risk factors associated with injury. Despite suggestions that various components of physical fitness may either protect or predispose a player to injury,
the links between player size and physical fitness and injury risk in Australian football are relatively unexplored. The purpose of this study therefore was to assess the relationships between individual player characteristics (including age, size, playing experience, indigenous descent, and physical fitness) and in-season injury incidence and severity in elite Australian football.
Methods Participants
Statistical Analyses
Sixty-nine male AFL players (mean ± SD age 22.8 ± 3.6 y, AFL experience 65 ± 71 matches), including 7 indigenous players, participated in the study. Player and club consent were obtained for the study along with institutional ethics approval in accordance with the Helsinki Declaration.
Behavioral data such as those collected in sport over time can be hierarchical and commonly have a nested structure, as measurement occasions and the number of repeated observations on each individual are not identical.22,23 Multilevel linear-modeling techniques were therefore used to analyze the data, with each submodel representing the structural relations and residual variability at that level. The data had 3 nested layers (round, season, and player). Data for each round (level 1) recorded whether an injury occurred (injury incidence) and how many matches were missed as a result (injury severity). Season data (level 2) recorded player characteristics such as age, body mass, previous AFL and VFL experience, preseason fitness (40 m-sprint, 6 × 40-m sprint, 6-min run, jump variables), and playing position. The third level of data referred to individual player variables that did not change between seasons (height and ethnicity). A series of multilevel hierarchical linear models (HLM6.02, Scientific Software International Inc, Lincolnwood, IL) were developed that allowed for the nested structure of the data.24 For injury risk (yes or no in any given week), a binary logistic regression was used with a logit link function, and for injury severity (matches missed) a Poisson regression was used with a log link function. A sequence of models was developed that began simply (eg, interceptonly base model) and then became more complex (eg, univariate and multivariate conditional linear models). Descriptive statistics are presented as means, standard deviations, and ranges. Independent-sample t tests were used to compare the indigenous subgroup with the rest of the playing group. For all multilevel models significance values of below .10 were identified, based on the notion that the traditional P < .05 may be too conservative to detect important practical effects in elite sport research, given relatively small sample sizes and large measurement variability.25 Although P values are important in that they indicate which relationships are significant, odds and rate ratios provide an indication of the expected magnitude (meaningfulness) of the effects. Odds ratios (OR) are presented for injury risk and rate ratios (RR) for injury severity, with values above 1 generally indicating greater risk/severity and those below 1 indicating less risk/severity. The ratios do need to be interpreted in relation to the unit of measure for each predictor variable and whether an increase in the variable is associated with positive performance (eg, distance in the 6-min run) or negative performance (eg, time in the sprint). The precision of the ratios is demonstrated by 95% confidence intervals.
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to being rested (no specific injury listed but player advised not to play) or illness were not counted as injuries. As such, each player was coded as either playing AFL or VFL, injured, or “other” for each week during the competitive season. An injury was not included in the analysis if it occurred during the preseason and resulted in the player missing the opening round of the season. Injuries were recorded by the club’s doctor in accordance with procedures used for data reporting to the AFL’s annual injury survey.10
Data were collected over 4 seasons (2007–2010). Each week of the competitive season, players played either in the AFL team competing in the national competition or in the state-level league (Victorian Football League [VFL]), unless they were injured or otherwise unavailable. The study followed a prospective longitudinal design, with preseason fitness data (6-min run, 40-m sprint, 6 × 40-m, vertical jump) and in-season injury data (incidence, severity) collected each season for each player. Player characteristics (height, body mass, fitness) were assessed during the latter part of each preseason period (January/February) and before the commencement of the 22-round AFL season (March to August). Age on the first of January and football experience (AFL and VFL matches played) were recorded before the commencement of each season. Playing position (back, forward, midfield, or ruck20) was classified for each season, with some players changing roles from season to season or being classified twice if they performed a dual role (eg, midforward players were classified as both midfield and forward). Ethnicity was dichotomized as indigenous or nonindigenous.
Fitness Player-fitness characteristics were assessed using previously described procedures for aerobic-running fitness (6-min run), speed (40-m sprint), and repeated-sprint ability (6 × 40-m sprints).9 A linear-position transducer and encoder was used to assess jump performance.21 Concentric and eccentric mean power and contact time were measured during sets of 5 consecutive vertical jumps (ie, weighted squat jumps). A transducer and encoder (GymAware, Kinetic Performance Technology, Australia) were located on the ground and attached via a perpendicular cable to a 20.5-kg squat bar that the players held across their upper back.21 Players were instructed to squat to approximately 90° of knee flexion and then jump as high as possible 5 times without rest between the concentric and eccentric phases. Each player completed 3 sets with 2 minutes rest between sets. Due to the controlled nature of the first jump, only jumps 2 to 5 of each set were analyzed. Values for these jumps were then averaged and the set with the highest value recorded. The concentric- and eccentric-power measures were divided by the player’s body mass for standardization.
Injury Injury was defined as any musculoskeletal injury that caused a player to miss a match.10 Cases where players missed a game due
Results Preseason fitness data and in-season injury data were collected over 4 seasons, with data presented for 69 players (Table 1). Ten percent of the playing group (n = 7) were of indigenous descent, with this subgroup being younger (19.8 ± 2.3 vs 23.2 ± 3.6 y, P < .001) and lighter (78.1 ± 3.3 vs 89.8 ± 8.5 kg, P < .001) and having less AFL experience (14.4 ± 22.9 vs 71.1 ± 71.8 matches, P < .001). Injury
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data were collected for a total of 3879 player rounds and 174 seasons, although missing preseason testing results for some players, variables, and seasons meant that not all these data could be used in the multivariate analyses. In total, 57% of rounds were played in the AFL and 28% were played in the VFL; the remainder were missed due to injury or other reasons (eg, illness, suspension, rested). Over the 4 seasons, 177 rounds were associated with the first week of an injury (Table 2), with a mean of 2.8 matches missed, ranging from 1 week (49% of all injuries) to a season-ending 23 weeks. The vast majority (87%) of injuries were to the lower body, with hamstring (20%) and groin/hip (14%) being most prevalent. Nineteen players (28%) suffered a recurrent injury (34 instances, 19%). An approximately equal proportion of muscle/tendon-strain (46%) and bone-stress or joint-sprain (42%) injuries was observed. Univariate and multivariate regression analyses are presented for the risk of injury incidence (Table 3) and injury severity (Table 4). Several variables identified as predictors in the univariate analyses either disappeared (injury incidence: vertical-jump contact time; injury severity: 6 × 40-m sprint, vertical-jump contact time, verticaljump concentric mean power, playing as a forward, indigenous descent) or were reversed (injury incidence: indigenous descent) in the multivariate analyses. The multivariate analyses identified body mass (OR = 0.887, P = .005; ie, for every additional 1 kg of body mass, the risk of injury decreased by 11.3%), 6-minute-run performance (OR = 0.994, P = .051; ie, for every additional 1 m run, the risk decreased by 0.6%), and playing as a forward (OR = 2.216, P = .036; ie, forwards had a 222% increased risk of injury) as risk factors of injury incidence. As such, players of low body mass, poor 6-minute-run performance, and forwards were at greater risk of sustaining an injury. Injury severity was greatest for players with low body mass (RR = 0.892, P = .008), tall stature (RR = 1.131, P
= .002), poor 6-minute-run (RR = 0.990, P = .006), and slow sprint (RR = 3.963, P = .082) performance.
Discussion The incidence of injuries at this club was similar to that reported for all clubs in the AFL competition during the same 4-year period10 (44 compared with 42 injuries per club per year, respectively). Injury severity was lower (2.8 vs 4.0 matches per injury), while injury recurrence was higher (19% vs 13%). The most important findings were that aerobic-running fitness and greater body mass were found to have a protective effect against injury incidence and severity. These 2 player characteristics represent modifiable risk factors and should be assessed and managed during the preseason training period, and likely over multiple years in players who are identified as at risk. Australian football is a field team sport that involves highintensity running, jumping, landing, and player contact including tackling and bumping.20,26 The current study showed that players with lower body mass experienced greater injury incidence and severity. In subelite rugby league, players with low body mass have also been found to be at a greater risk of severe injury.19 As force is directly proportional to mass, players with a greater body mass exert a greater impact force when colliding with another player (assuming a similar velocity at impact). The mean body mass of players was 88.5 kg; however, the range spanned 38 kg. As such, when 2 players collide, the player with lower body mass will need to absorb a greater impact force and do this with less muscle and fat mass. The increasing speed and intensity of Australian football20 and the increased number of tackles26 have been linked to the rising number of collision injuries, including shoulder and soft-tissue injuries.27 In other collision sports such as rugby league and rugby
Table 1 Sample Sizes and Descriptive Statistics for Individual Player Characteristics Player characteristic Age (y) Body mass (kg) Height (cm) Previous Australian Football League matches (n) Previous Victorian Football League matches (n) 40-m sprint (s) 6 × 40-m sprint (s) 6-min run (m) Vertical-jump contact time (s) Vertical-jump concentric mean power (W/kg) Vertical-jump eccentric mean power (W/kg)
Rounds 3879 3879 3879 3879 3879 2596 2545 3333 2836 2836 2836
Seasons 174 174 174 174 174 112 111 148 125 125 125
Players 69 69 69 69 69 58 57 65 57 57 57
Mean ± SD 22.8 ± 3.6 88.5 ± 8.9 188.7 ± 7.1 65.2 ± 71.0 25.8 ± 23.3 5.49 ± 0.22 35.6 ± 1.4 1722 ± 78 0.48 ± 0.13 31.5 ± 3.91 34.9 ± 5.48
Range 17–32 71–109 175–206 0–256 0–183 4.98–6.22 29.9–39.9 1478–1863 0.21–0.84 22.8–41.1 22.1–47.2
2010 550 268 44 142 3.2 (± 3.9)
2007–2010 2200 1104 177 494 2.8 (± 3.3)
Table 2 Injury Statistics for Each Season and Over the Entire 2007–2010 Period Australian Football League matches played Victorian Football League matches played Injuries Matches missed due to injury Mean matches missed per injury (± SD)
2007 550 317 40 117 2.9 (± 2.1)
2008 550 269 39 81 2.1 (± 1.2)
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2009 550 250 54 154 2.9 (± 4.3)
Table 3 Odds Ratios for the Risk of Sustaining an Injury (Injury Incidence) Associated With Individual Player Characteristics
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Univariate Regressiona Season Variables age (y) body mass (kg) previous Australian Football League matches previous Victorian Football League matches 40 m sprint (s) 6 × 40 m sprint (s) 6 min run (m) vertical-jump contact time (s) vertical-jump concentric mean power (W/kg) vertical-jump eccentric mean power (W/kg) back forward midfield ruck Player Variables height Aboriginal
Multivariate Regressionb
Odds ratio
95% CI
P
Odds ratio
95% CI
P
1.015 0.979* 1.001 0.999 1.062 1.076 0.999 0.245** 0.990 1.004 0.834 1.617** 0.988 0.677
0.963–1.070 0.959–1.000 0.998–1.003 0.992–1.005 0.368–3.062 0.927–1.250 0.996–1.002 0.071–0.843 0.951–1.031 0.978–1.031 0.554–1.256 1.100–2.360 0.682–1.430 0.336–1.368
.568 .055 .530 .703 .910 .328 .424 .026 .627 .750 .369 .025 .947 .274
1.078 0.887*** 0.999 1.002 2.977 1.036 0.994* 0.344 0.934 1.015 0.893 2.216** 1.258 1.787
0.835–1.393 0.821–0.957 0.986–1.103 0.981–1.022 0.692–12.81 0.761–1.410 0.988–1.000 0.015–7.802 0.835–1.044 0.939–1.097 0.413–1.930 1.061–4.629 0.624–2.537 0.403–7.929
.534 .005 .928 .877 .129 .807 .051 .471 .206 .686 .754 .036 .489 .413
0.985 1.783*
0.958–1.011 0.943–3.372
.250 .074
1.049 0.262*
0.961–1.146 0.058–1.191
.277 .082
a Univariate, single-predictor analyses; all available data used (3879 rounds, 174 seasons, 69 players; see Table 1 for each variable). b Multivariate analyses; data reduced to 1699 rounds, 73 seasons, and 47 players. *P < .10. **P < .05. ***P < .01.
Table 4 Rate Ratios for Injury Severity (Matches Missed) Associated With Individual Player Characteristics Univariate Regressiona Season Variables age (y) body mass (kg) previous Australian Football League matches previous Victorian Football League matches 40 m sprint (s) 6 × 40 m sprint (s) 6 min run (m) vertical-jump contact time (s) vertical-jump concentric mean power (W/kg) vertical-jump eccentric mean power (W/kg) back forward midfield ruck Player Variables height Aboriginal
Multivariate Regressionb
Rate ratio
95% CI
P
Rate ratio
95% CI
P
1.001 0.982 1.000 0.990** 2.001 1.201* 0.997* 0.147*** 0.963* 0.994 0.636 2.497** 0.631 0.814
0.812–1.098 0.956–1.000 0.995–1.004 0.981–0.999 0.466–8.584 0.973–1.483 0.993–1.000 0.036–0.608 0.922–1.006 0.961–1.027 0.336–1.205 1.173–5.315 0.350–1.139 0.320–2.073
.989 .193 .884 .024 .344 .087 .052 .009 .090 .702 .163 .018 .125 .663
0.970 0.892*** 1.005 1.016 3.963* 0.770 0.990*** 0.322 1.033 0.976 0.524* 2.332 1.815 0.973
0.720–1.307 0.824–0.965 0.989–1.021 0.991–1.041 0.817–19.23 0.525–1.130 0.983–0.996 0.038–2.743 0.881–1.211 0.898–1.061 0.244–1.127 0.748–7.274 0.759–4.337 0.152–6.240
.828 .008 .506 .198 .082 .164 .006 .272 .667 .545 .091 .131 .162 .975
1.015 1.749*
0.975–1.056 0.936–3.334
.474 .078
1.131*** 1.959
1.049–1.219 0.263–14.57
.002 .503
a Univariate, single-predictor analyses; all available data used (3879 rounds, 174 seasons, 69 players; see Table 1 for each variable). b Multivariate analyses; data reduced to 1699 rounds, 73 seasons, and 47 players. *P < .10. **P < .05. ***P < .01.
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union, a high incidence of injury has been directly linked to the frequency and nature of tackling and heavy collisions.1,28 While tackling in Australian football is less frequent and forceful than that in rugby, considerable force and movement velocity at impact are still evident.26 To protect players at risk for collision injuries, training strategies should therefore look to increase muscle mass in those with low body mass, most likely newly drafted players who are known to have lower mean body mass than their AFL peers.20 Performance in running-based assessment tasks that target aerobic or endurance characteristics has consistently been associated with playing performance in Australian football9,29 and other field team sports.30 The findings of the current study further support the importance of developing aerobic-running fitness, given that players with lower 6-minute-run performance were at increased risk of sustaining an injury and that these injuries resulted in a greater number of matches missed. Chalmers et al18 recently demonstrated a link between lower levels of aerobic endurance (20-m shuttle run) and a greater risk of shin/ankle/foot injuries in elite junior Australian football, suggesting a reduced ability to cope with repetitive loading and/or avoid potentially dangerous situations as a result of fatigue. In rugby league, players with poor aerobic fitness are at a greater risk of contact injuries,19 while the increased incidence of injury in the second half of matches or latter stages of training has been linked to player fatigue.1 Impaired decision making and movement response as a result of fatigue31,32 may place a player in a vulnerable position or render the player unable to respond quickly to avoid a collision. The number of matches missed, as defined here and in other AFL research,18 is a reflection of the severity of the injury; however, other factors may also influence this variable. Coaching and medical staff need to balance the benefits of an early return to competition against the risk of recurrence and may or may not take a conservative approach, depending on individual and team considerations. It is also possible that players with a low conditioning base take longer to recover and return to an acceptable level of perceived match fitness. This is supported by the finding that players with poor 6-minute-run performance, slow 40-m-sprint time, and poor repeated-sprint ability (univariate analysis only) demonstrated increased injury severity. Match and work demands in Australian football vary with playing position.20 The high running demands and game activities of midfield players compared with other positions33 did not seem to influence injury risk in this group of players. Midfield players had significantly greater 6-minute-run performances (P < .001; data not presented) than all other players, which suggests that enhanced aerobic fitness not only allows them to meet the challenging physical demands of the position20 but may also provide a protective effect against injury. Playing as a forward, however, was associated with a significant increase in injury incidence, while playing as a back resulted in reduced injury severity. Forwards are required to lead to receive the football, to contest marking duels from the front, and to move the ball forward to create scoring opportunities.20 Intense defensive pressure (eg, spoil, bump, tackle) from the opposition to prevent scoring may expose forwards to increased risk from collisions. Other possibilities include rapid accelerations when leading or the frequent change of direction when transitioning from moving forward to receive the ball and then rapidly turn toward goal. Univariate analyses of vertical-squat-jump data indicated that a long ground contact time between jumps resulted in reduced injury incidence and that a long contact time and large concentric mean power resulted in a reduced injury severity. These findings were not confirmed in the multivariate analyses, supporting the need for dataanalysis techniques that statistically control for other variables16 and cautioning against conclusions derived from univariate analyses.
Indigenous players represent a disproportionate number of those playing elite-level Australian football (approximately 10–11% compared with 2% of the national population) and are regarded as fast and highly skilled.17 Previous research has indicated that indigenous players have a greater risk of hamstring, anterior cruciate ligament, quadriceps, and calf-strain injuries compared with nonindigenous players.11,12 Taylor et al17 found that indigenous elite junior Australian football players displayed less hip range of motion, reduced adductor strength, and higher levels of groin pain during a knee-squeeze test and hypothesized that these characteristics may precede future groin injury. They did not, however, find any difference in past hip or groin injury. The results of the current study, albeit in a small number of aboriginal players (7/69), support the contention that injury cause is complex and multifactorial.3 As a single predictor, indigenous descent was associated with a higher overall injury incidence, yet this was reversed in the multivariate analysis when other variables were controlled. Hence, characteristics in this subgroup, such as being lighter in body mass, having lower aerobic fitness, and/or playing as a forward, in specific individuals are more likely to contribute to injury risk than ethnicity per se. Several limitations exist in the analysis of the data in this study, mostly related to sample size. Missing values reduced the sample to only 47 players and 73 seasons for the multivariate analyses. These results should therefore be treated with some caution, although controlling for the effect of other variables does enhance their interpretability. Furthermore, different types of injury could not be analyzed individually, such that all injuries were considered collectively and overall risk factors identified. A limitation to this approach is that different types of injuries may have some unique or different risk factors. Finally, the assessment of fitness at the commencement of the season may not reflect the fitness status of a player at the time of injury. It is acknowledged that fitness may change over the course of the season, and an assessment closer to the incidence of an injury could influence the analysis. However, the purpose of this study and our interest in analyzing the data in this way was to consider the influence of player characteristics at the commencement of a season on in-season injury risk (ie, how does player status at the end of a 3-month preparation period influence injury risk during the competition season?). As preseason is a critically important time to lay a foundation for the in-season phase, individual weaknesses and development priorities should be identified. Player assessments and identified risk factors can therefore be used to guide preseason programming.
Conclusion The identification of injury risk factors that are modifiable is important in individual player management, while the potential to modify these risk factors is greatest in the preseason period. Aerobic-running fitness and increased body mass appear to have a protective effect against injury incidence and severity in elite Australian football and represent important characteristics for targeted development in susceptible players. Age, previous AFL experience, ethnicity, and leg power were not important predictors of injury in this playing group.
Practical Implications • Injury-prevention strategies in Australian football should target low body mass and aerobic-running fitness. • Risk factors common to all players, rather than ethnicity, explain injury risk in indigenous players.
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• Multivariate rather than univariate analysis techniques are recommended when assessing injury risk factors. • Team-sport athletes should be managed on an individual basis given the complex and multifactorial nature of injury risk. Acknowledgments The authors would like to acknowledge the support and contributions of players and staff from the AFL club that participated in the research.
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