Identifying high risk loading conditions for in-season injury in elite Australian football players
- Stares, Jordan, Dawson, Brian, Peeling, Peter, Heasman, Jarryd, Rogalski, Brent, Drew, Michael, Colby, Marcus, Dupont, Gregory, Lester, Leanne
- Authors: Stares, Jordan , Dawson, Brian , Peeling, Peter , Heasman, Jarryd , Rogalski, Brent , Drew, Michael , Colby, Marcus , Dupont, Gregory , Lester, Leanne
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 21, no. 1 (2018), p. 46-51
- Full Text: false
- Reviewed:
- Description: Objectives To examine different timeframes for calculating acute to chronic workload ratio (ACWR) and whether this variable is associated with intrinsic injury risk in elite Australian football players. Design Prospective cohort study. Methods Internal (session rating of perceived exertion: sRPE) and external (GPS distance and sprint distance) workload and injury data were collected from 70 players from one AFL club over 4 seasons. Various acute (1–2 weeks) and chronic (3–8 weeks) timeframes were used to calculate ACWRs: these and chronic load categories were then analysed to determine the injury risk in the subsequent month. Poisson regression with robust errors within a generalised estimating equation were utilised to determine incidence rate ratios (IRR). Results Altering acute and/or chronic timeframes did not improve the ability to detect high injury risk conditions above the commonly used 1:4 week ACWR. Twenty-seven ACWR/chronic load combinations were found to be “high risk conditions” (IRR > 1, p < 0.05) for injury within 7 days. Most (93%) of these conditions occurred when chronic load was low or very low and ACWR was either low (<0.6) or high (>1.5). Once a high injury risk condition was entered, the elevated risk persisted for up to 28 days. Conclusions Injury risk was greatest when chronic load was low and ACWR was either low or high. This heightened risk remained for up to 4 weeks. There was no improvement in the ability to identify high injury risk situations by altering acute or chronic time periods from 1:4 weeks.
Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers
- Colby, Marcus, Dawson, Brian, Peeling, Peter, Heasman, Jarryd, Rogalski, Brent, Drew, Michael, Stares, Jordan, Zouhal, Hassane, Lester, Leanne
- Authors: Colby, Marcus , Dawson, Brian , Peeling, Peter , Heasman, Jarryd , Rogalski, Brent , Drew, Michael , Stares, Jordan , Zouhal, Hassane , Lester, Leanne
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 20, no. 12 (2017), p. 1068-1074
- Full Text:
- Reviewed:
- Description: Objectives: To assess the association between workload, subjective wellness, musculoskeletal screening measures and non-contact injury risk in elite Australian footballers. Design: Prospective cohort study. Methods: Across 4 seasons in 70 players from one club, cumulative weekly workloads (acute; 1 week, chronic; 2-, 3-, 4-week) and acute:chronic workload ratio’s (ACWR: 1-week load/average 4-weekly load) for session-Rating of Perceived Exertion (sRPE) and GPS-derived distance and sprint distance were calculated. Wellness, screening and non-contact injury data were also documented. Univariate and multivariate regression models determined injury incidence rate ratios (IRR) while accounting for interaction/moderating effects. Receiver operating characteristics determined model predictive accuracy (area under curve: AUC). Results: Very low cumulative chronic (2-, 3-, 4- week) workloads were associated with the greatest injury risk (univariate IRR = 1.71–2.16, 95% CI = 1.10–4.52) in the subsequent week. In multivariate analysis, the interaction between a low chronic load and a very high distance (adj-IRR = 2.60, 95% CI = 1.07–6.34) or low sRPE ACWR (adj-IRR = 2.52, 95% CI = 1.01–6.29) was associated with increased injury risk. Subjectively reporting “yes” (vs. “no”) for old lower limb pain and heavy non-football activity in the previous 7 days (multivariate adj-IRR = 2.01–2.25, 95% CI = 1.02–4.95) and playing experience (>9 years) (multivariate adj- IRR = 2.05, 95% CI = 1.03–4.06) was also associated with increased injury risk, but screening data were not. Predictive capacity of multivariate models was significantly better than univariate (AUCmultivariate = 0.70, 95% CI 0.64–0.75; AUCunivariate range = 0.51–0.60). Conclusions: Chronic load is an important moderating factor in the workload–injury relationship. Low chronic loads coupled with low or very high ACWR are associated with increased injury risk.
- Description: Objectives: To assess the association between workload, subjective
- Authors: Colby, Marcus , Dawson, Brian , Peeling, Peter , Heasman, Jarryd , Rogalski, Brent , Drew, Michael , Stares, Jordan , Zouhal, Hassane , Lester, Leanne
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 20, no. 12 (2017), p. 1068-1074
- Full Text:
- Reviewed:
- Description: Objectives: To assess the association between workload, subjective wellness, musculoskeletal screening measures and non-contact injury risk in elite Australian footballers. Design: Prospective cohort study. Methods: Across 4 seasons in 70 players from one club, cumulative weekly workloads (acute; 1 week, chronic; 2-, 3-, 4-week) and acute:chronic workload ratio’s (ACWR: 1-week load/average 4-weekly load) for session-Rating of Perceived Exertion (sRPE) and GPS-derived distance and sprint distance were calculated. Wellness, screening and non-contact injury data were also documented. Univariate and multivariate regression models determined injury incidence rate ratios (IRR) while accounting for interaction/moderating effects. Receiver operating characteristics determined model predictive accuracy (area under curve: AUC). Results: Very low cumulative chronic (2-, 3-, 4- week) workloads were associated with the greatest injury risk (univariate IRR = 1.71–2.16, 95% CI = 1.10–4.52) in the subsequent week. In multivariate analysis, the interaction between a low chronic load and a very high distance (adj-IRR = 2.60, 95% CI = 1.07–6.34) or low sRPE ACWR (adj-IRR = 2.52, 95% CI = 1.01–6.29) was associated with increased injury risk. Subjectively reporting “yes” (vs. “no”) for old lower limb pain and heavy non-football activity in the previous 7 days (multivariate adj-IRR = 2.01–2.25, 95% CI = 1.02–4.95) and playing experience (>9 years) (multivariate adj- IRR = 2.05, 95% CI = 1.03–4.06) was also associated with increased injury risk, but screening data were not. Predictive capacity of multivariate models was significantly better than univariate (AUCmultivariate = 0.70, 95% CI 0.64–0.75; AUCunivariate range = 0.51–0.60). Conclusions: Chronic load is an important moderating factor in the workload–injury relationship. Low chronic loads coupled with low or very high ACWR are associated with increased injury risk.
- Description: Objectives: To assess the association between workload, subjective
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