Justin Crow

Prevention and management of osteitis pubis in the Australian Football League: a qualitative analysis.

OBJECTIVES To examine current practices and develop a set of recommendations for the management of osteitis pubis in the Australian Football League (AFL). DESIGN A qualitative study using in-depth interviews to gather data and thematic coding to analyze findings. SETTING Participants were interviewed in their workplace or at a convenient meeting point. PARTICIPANTS Thirty-six medical and fitness staff from the 16 AFL clubs. RESULTS Respondents from all clubs viewed osteitis pubis as an overuse injury and recognized that the key to prevention is balancing pelvic integrity and load. Osteitis pubis was described as the end result of a continuum of groin pathology, and recognition of predisposing factors and early detection were identified as the key elements of optimal management. Management strategies included rest, training modification, cross-training, correction of predisposing factors, physical therapy and a progression back to competition. Most clubs also conduct generic prevention and education programs. CONCLUSIONS Overall, respondents perceived that awareness and management of osteitis pubis is currently at a high level in the AFL. Management of osteitis pubis requires the balancing of pelvic integrity and mechanical load through the pelvis and the early identification of warning signs.

Low load exercises targeting the gluteal muscle group acutely enhance explosive power output in elite athletes

Crow, JF, Buttifant, D, Kearny, SG, and Hrysomallis, C. Low load exercises targeting the gluteal muscle group acutely enhance explosive power output in elite athletes. J Strength Cond Res 26(2): 438–442, 2012—The purpose of this study was to investigate the acute effect of 3 warm-up protocols on peak power production during countermovement jump (CMJ) testing. The intention was to devise and compare practical protocols that could be applied as a warm-up immediately before competition matches or weight training sessions. A group of 22 elite Australian Rules Football players performed 3 different warm-up protocols over 3 testing sessions in a randomized order. The protocols included a series of low load exercises targeting the gluteal muscle group (GM-P), a whole-body vibration (WBV) protocol (WBV-P) wherein the subjects stood on a platform vibrating at 30 Hz for 45 seconds, and a no-warm-up condition (CON). The CMJ testing was performed within 5 minutes of each warm-up protocol on an unloaded Smith machine using a linear encoder to measure peak power output. Peak power production was significantly greater after the GM-P than after both the CON (p < 0.05) and WBV-P (p < 0.01). No significant differences in peak power production were detected between the WBV-P and CON. These results have demonstrated that a low load exercise protocol targeting the gluteal muscle group is effective at acutely enhancing peak power output in elite athletes. The mechanisms for the observed improvements are unclear and warrant further investigation. Coaches may consider incorporating low load exercises targeting the gluteal muscle group into the warm-up of athletes competing in sports requiring explosive power output of the lower limbs.

Muscle onset can be improved by therapeutic exercise: A systematic review

OBJECTIVES To determine whether therapeutic exercise can improve the timing of muscle onset following musculoskeletal pathology, and examine what exercise prescription parameters are being used to achieve these effects. PARTICIPANTS People with a musculoskeletal pathology. MAIN OUTCOME MEASURE Muscle onset timing as measured by electromyography. RESULTS Sixteen investigations were identified containing 19 therapeutic exercise groups. Three exercise modes were identified including: isolated muscle training, instability training, and general strength training. Isolated muscle training is consistently shown to have a positive effect on the muscle onset timing of transversus abdominus in people with low back pain. There is some evidence from cohort studies that instability training may change muscle onset timing in people with functional ankle instability, however controlled trials suggest that no effect is present. General strength training shows no effect on muscle onset timing in people with low back or neck pain, although one cohort study suggests that a positive effect on gluteus maximus may be present in people with low back pain. CONCLUSION Therapeutic exercise training is likely to improve muscle onset timing. Additionally, isolated muscle training appears to be the best exercise mode to use to achieve these effects.

Training loads and injury risk in Australian football—differing acute: chronic workload ratios influence match injury risk

Aims (1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood. Methods Workload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2–9 days) and 7 chronic time windows (14–35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R2). Results The ratio of moderate speed running workload (18–24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R2=0.79) and in the immediate 2 or 5 days following matches (R2=0.76–0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98–2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule. Conclusions Daily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk.

Predictive Modelling of Training Loads and Injury in Australian Football

Abstract To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day. Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were then generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC<0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting limited application as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training observations may improve predictive models for injury prevention

Modelling Training Loads and Injuries: The Dangers of Discretization.

Purpose To evaluate common modeling strategies in training load and injury risk research when modeling continuous variables and interpreting continuous risk estimates; and present improved modeling strategies. Method Workload data were pooled from Australian football (n = 2550) and soccer (n = 23,742) populations to create a representative sample of acute:chronic workload ratio observations for team sports. Injuries were simulated in the data using three predefined risk profiles (U-shaped, flat and S-shaped). One-hundred data sets were simulated with sample sizes of 1000 and 5000 observations. Discrete modeling methods were compared with continuous methods (spline regression and fractional polynomials) for their ability to fit the defined risk profiles. Models were evaluated using measures of discrimination (area under receiver operator characteristic [ROC] curve) and calibration (Brier score, logarithmic scoring). Results Discrete models were inferior to continuous methods for fitting the true injury risk profiles in the data. Discrete methods had higher false discovery rates (16%–21%) than continuous methods (3%–7%). Evaluating models using the area under the ROC curve incorrectly identified discrete models as superior in over 30% of simulations. Brier and logarithmic scoring was more suited to assessing model performance with less than 6% discrete model selection rate. Conclusions Many studies on the relationship between training loads and injury that have used regression modeling have significant limitations due to improper discretization of continuous variables and risk estimates. Continuous methods are more suited to modeling the relationship between training load and injury. Comparing injury risk models using ROC curves can lead to inferior model selection. Measures of calibration are more informative judging the utility of injury risk models.

Predicting ratings of perceived exertion in Australian football players: methods for live estimation

Abstract The ability of machine learning techniques to predict athlete ratings of perceived exertion (RPE) was investigated in professional Australian football players. RPE is commonly used to quantifying internal training loads and manage injury risk in team sports. Data from global positioning systems, heart-rate monitors, accelerometers and wellness questionnaires were recorded for each training session (n=3398) from 45 professional Australian football players across a full season. A variety of modelling approaches were considered to investigate the ability of objective data to predict RPE. Models were compared using nested cross validation and root mean square error (RMSE) on RPE predictions. A random forest model using player normalised running and heart rate variables provided the most accurate predictions (RMSE ± SD = 0.96 ± 0.08 au). A simplification of the model using only total distance, distance covered at speeds between 18-24 km·h−1, and the product of total distance and mean speed provided similarly accurate predictions (RMSE ± SD = 1.09 ± 0.05 au), suggesting that running distances and speeds are the strongest predictors of RPE in Australian football players. The ability of non-linear machine learning models to accurately predict athlete RPE has applications in live player monitoring and training load planning.