-intensity intermittent running ability and injury risk is not known in team-sport athletes. Wearable microtechnology enables practitioners to easily quantify external workloads of multiple players. 10 , 11 As a means of assessing changes in high-intensity running ability, regular monitoring of heart rate responses
Billy T. Hulin, Tim J. Gabbett, Nathan J. Pickworth, Rich D. Johnston and David G. Jenkins
Robert McCunn, Hugh H.K. Fullagar, Sean Williams, Travis J. Halseth, John A. Sampson and Andrew Murray
professional playing experience, highlighting the potential influence of this factor on injury risk. In addition to this challenge, American football is characterized by disparate playing positions and athlete somatotypes, 8 further complicating the issue of training program design. 9 Unsurprisingly, playing
Marcus J. Colby, Brian Dawson, Peter Peeling, Jarryd Heasman, Brent Rogalski, Michael K. Drew and Jordan Stares
Australian football (AF) is a physical game involving large running volumes, rapid directional changes, and high-velocity running efforts. Minimizing injury risk is a priority for sports medicine/science staff as injuries have a detrimental impact on team and individual success. 1 An increased
Matthew J. Cross, Sean Williams, Grant Trewartha, Simon P.T. Kemp and Keith A. Stokes
To explore the association between in-season training-load (TL) measures and injury risk in professional rugby union players.
This was a 1-season prospective cohort study of 173 professional rugby union players from 4 English Premiership teams. TL (duration × session-RPE) and time-loss injuries were recorded for all players for all pitch- and gym-based sessions. Generalized estimating equations were used to model the association between in-season TL measures and injury in the subsequent week.
Injury risk increased linearly with 1-wk loads and week-to-week changes in loads, with a 2-SD increase in these variables (1245 AU and 1069 AU, respectively) associated with odds ratios of 1.68 (95% CI 1.05–2.68) and 1.58 (95% CI 0.98–2.54). When compared with the reference group (<3684 AU), a significant nonlinear effect was evident for 4-wk cumulative loads, with a likely beneficial reduction in injury risk associated with intermediate loads of 5932–8651 AU (OR 0.55, 95% CI 0.22–1.38) (this range equates to around 4 wk of average in-season TL) and a likely harmful effect evident for higher loads of >8651 AU (OR 1.39, 95% CI 0.98–1.98).
Players had an increased risk of injury if they had high 1-wk cumulative loads (1245 AU) or large week-to-week changes in TL (1069 AU). In addition, a U-shaped relationship was observed for 4-wk cumulative loads, with an apparent increase in risk associated with higher loads (>8651 AU). These measures should therefore be monitored to inform injury-risk-reduction strategies.
Paul B. Gastin, Denny Meyer, Emy Huntsman and Jill Cook
To assess the relationships between player characteristics (including age, playing experience, ethnicity, and physical fitness) and in-season injury in elite Australian football.
Single-cohort, prospective, longitudinal study.
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.
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.
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.
Lina E. Lundgren, Tai T. Tran, Sophia Nimphius, Ellen Raymond, Josh L. Secomb, Oliver R.L. Farley, Robert U. Newton, Julie R. Steele and Jeremy M. Sheppard
To develop and evaluate a multifactorial model based on landing performance to estimate injury risk for surfing athletes.
Five measures were collected from 78 competitive surfing athletes and used to create a model to serve as a screening tool for landing tasks and potential injury risk. In the second part of the study, the model was evaluated using junior surfing athletes (n = 32) with a longitudinal follow-up of their injuries over 26 wk. Two models were compared based on the collected data, and magnitude-based inferences were applied to determine the likelihood of differences between injured and noninjured groups.
The study resulted in a model based on 5 measures—ankle-dorsiflexion range of motion, isometric midthigh-pull lower-body strength, time to stabilization during a drop-and-stick (DS) landing, relative peak force during a DS landing, and frontal-plane DS-landing video analysis—for male and female professional surfers and male and female junior surfers. Evaluation of the model showed that a scaled probability score was more likely to detect injuries in junior surfing athletes and reported a correlation of r = .66, P = .001, with a model of equal variable importance. The injured (n = 7) surfers had a lower probability score (0.18 ± 0.16) than the noninjured group (n = 25, 0.36 ± 0.15), with 98% likelihood, Cohen d = 1.04.
The proposed model seems sensitive and easy to implement and interpret. Further research is recommended to show full validity for potential adaptations for other sports.
Shane Malone, Mark Roe, Dominic A. Doran, Tim J. Gabbett and Kieran D. Collins
To examine the association between combined session rating of perceived exertion (RPE) workload measures and injury risk in elite Gaelic footballers.
Thirty-seven elite Gaelic footballers (mean ± SD age 24.2 ± 2.9 y) from 1 elite squad were involved in a single-season study. Weekly workload (session RPE multiplied by duration) and all time-loss injuries (including subsequent-wk injuries) were recorded during the period. Rolling weekly sums and wk-to-wk changes in workload were measured, enabling the calculation of the acute:chronic workload ratio by dividing acute workload (ie, 1-weekly workload) by chronic workload (ie, rolling-average 4-weekly workload). Workload measures were then modeled against data for all injuries sustained using a logistic-regression model. Odds ratios (ORs) were reported against a reference group.
High 1-weekly workloads (≥2770 arbitrary units [AU], OR = 1.63–6.75) were associated with significantly higher risk of injury than in a low-training-load reference group (<1250 AU). When exposed to spikes in workload (acute:chronic workload ratio >1.5), players with 1 y experience had a higher risk of injury (OR = 2.22) and players with 2–3 (OR = 0.20) and 4–6 y (OR = 0.24) of experience had a lower risk of injury. Players with poorer aerobic fitness (estimated from a 1-km time trial) had a higher injury risk than those with higher aerobic fitness (OR = 1.50–2.50). An acute:chronic workload ratio of (≥2.0) demonstrated the greatest risk of injury.
These findings highlight an increased risk of injury for elite Gaelic football players with high (>2.0) acute:chronic workload ratios and high weekly workloads. A high aerobic capacity and playing experience appears to offer injury protection against rapid changes in workload and high acute:chronic workload ratios. Moderate workloads, coupled with moderate to high changes in the acute:chronic workload ratio, appear to be protective for Gaelic football players.
Karim Chamari and Roald Bahr
David L. Carey, Justin Crow, Kok-Leong Ong, Peter Blanch, Meg E. Morris, Ben J. Dascombe and Kay M. Crossley
Training-load prescription in team-sport athletes is a balance between performance improvement 1 , 2 and injury-risk reduction. 3 – 6 The manipulation of training intensity, duration, and frequency to induce improvements in athletic performance is a fundamental objective of training
Robert Ahmun, Steve McCaig, Jamie Tallent, Sean Williams and Tim Gabbett
It is well established that injury rates can influence the success of a team, 1 and consequently, managing loads appears to be an essential part of reducing injury risk. Training loads comprise both internal and external loads. External load relates to the amount of work completed, while internal