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Training Loads and Injury Risk in Australian Football-differing Acute: Chronic Workload Ratios Influence Match Injury Risk

Overview
Journal Br J Sports Med
Specialty Orthopedics
Date 2016 Nov 1
PMID 27789430
Citations 31
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Abstract

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 R).

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 (R=0.79) and in the immediate 2 or 5 days following matches (R=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.

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References
1.
Boyd L, Ball K, Aughey R . The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011; 6(3):311-21. DOI: 10.1123/ijspp.6.3.311. View

2.
Varley M, Fairweather I, Aughey R . Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J Sports Sci. 2011; 30(2):121-7. DOI: 10.1080/02640414.2011.627941. View

3.
Rampinini E, Alberti G, Fiorenza M, Riggio M, Sassi R, Borges T . Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int J Sports Med. 2014; 36(1):49-53. DOI: 10.1055/s-0034-1385866. View

4.
Lovell R, Abt G . Individualization of time-motion analysis: a case-cohort example. Int J Sports Physiol Perform. 2012; 8(4):456-8. DOI: 10.1123/ijspp.8.4.456. View

5.
Rogalski B, Dawson B, Heasman J, Gabbett T . Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport. 2013; 16(6):499-503. DOI: 10.1016/j.jsams.2012.12.004. View