Subsequent Injury Risk Is Elevated Above Baseline After Return to Play: A 5-Year Prospective Study in Elite Australian Football.

BACKGROUND: The risk of sustaining a subsequent injury is elevated in the weeks after return to play (RTP) from an index injury. However, little is known about the magnitude, duration, and nature by which subsequent injury risk is increased. PURPOSE: To quantify and describe the risk of injury in a 12-week period after RTP from an index injury in Australian football players. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: Injury data were collected from 79 players over 5 years at 1 Australian Football League club. Injuries were classified with the Orchard Sports Injury Classification System and by side of the body. Furthermore, injury severity was classified as time loss (resulting in ≥1 matches being missed) or non-time loss (no matches missed). Subsequent injury was categorized with the SIC-2.0 model and applied to the data set via an automated script. The probability of a time loss subsequent injury was calculated for in-season index injuries for each week of a 12-week period after RTP via a mixed effect logistic regression model. RESULTS: Subsequent injury risk was found to be highest in the week of RTP for both time loss injuries (9.4%) and non-time loss injuries (6.9%). Risk decreased with each week survived after RTP; however, it did not return to baseline risk of participation (3.6%). CONCLUSION: These findings demonstrate that athletes returning to play are at an increased risk of injury for a number of weeks, thus indicating the requirement for tertiary prevention strategies to ensure that they survive this period.

Improvement of Prediction of Noncontact Injury in Elite Australian Footballers With Repeated Exposure to Established High-Risk Workload Scenarios.

OBJECTIVES: To assess the effect of multiple high-risk-scenario (HRS) exposures on noncontact injury prediction in elite Australian footballers. DESIGN: Retrospective cohort study. METHODS: Sessional workload data (session rating of perceived exertion, global positioning system-derived distance, sprint distance, and maximum velocity) from 1 club (N = 60 players) over 3 seasons were collated; several established HRSs were also defined. Accumulated HRS sessional exposures were calculated retrospectively (previous 1-8 wk). Noncontact injury data were documented. Univariate and multivariate Poisson regression models determined injury incidence rate ratios (IRRs) while accounting for moderating effects (preseason workload volume and playing experience). Model performance was evaluated using receiver operating characteristics (area under curve). RESULTS: Very low (0-8 sessions: IRR = 5.76; 95% confidence interval [CI], 1.69-19.66) and very high (>15 sessions: IRR = 4.70; 95% CI, 1.49-14.87) exposures to >85% of an individual's maximal velocity over the previous 8 wk were associated with greater injury risk compared with moderate exposures (11-12 sessions) and displayed the best model performance (area under curve = 0.64). A single session corresponding to a very low chronic load condition over the previous week for all workload variables was associated with increased injury risk, with sprint distance (IRR = 3.25; 95% CI, 1.95-5.40) providing the most accurate prediction model (area under curve = 0.63). CONCLUSIONS: Minimal exposure to high-velocity efforts (maximum speed exposure and sprint volume) was associated with the greatest injury risk. Being underloaded may be a mediator for noncontact injury in elite Australian football. Preseason workload and playing experience were not moderators of this effect.

Identifying high risk loading conditions for in-season injury in elite Australian football players.

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.

How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury.

OBJECTIVES: Examine the influence of rehabilitation training loads on return to play (RTP) time and subsequent injury in elite Australian footballers. DESIGN: Prospective cohort study. METHODS: Internal (sessional rating of perceived exertion: sRPE) and external (distance, sprint distance) workload and lower limb non-contact muscle injury data was collected from 58 players over 5 seasons. Rehabilitation periods were analysed for running workloads and time spent in 3 rehabilitation stages (1: off-legs training, 2: non-football running, 3: group football training) was calculated. Multi-level survival analyses with random effects accounting for player and season were performed. Hazard ratios (HR) and 95% confidence intervals (CI) for each variable were produced for RTP time and time to subsequent injury. RESULTS: Of 85 lower limb muscle injuries, 70 were rehabilitated to RTP, with 30 cases of subsequent injury recorded (recurrence rate=11.8%, new site injury rate=31.4%). Completion of high rehabilitation workloads delayed RTP (distance: >49,775m [reference: 34,613-49,775m]: HR 0.12, 95%CI 0.04-0.36, sRPE: >1266AU [reference: 852-1266AU]: HR 0.09, 95%CI 0.03-0.32). Return to running within 4days increased subsequent injury risk (3-4days [reference: 5-6 days]: HR 25.88, 95%CI 2.06-324.4). Attaining moderate-high sprint distance (427-710m) was protective against subsequent injury (154-426m: [reference: 427-710m]: HR 37.41, 95%CI 2.70-518.64). CONCLUSIONS: Training load monitoring can inform player rehabilitation programs. Higher rehabilitation training loads delayed RTP; however, moderate-high sprint running loads can protect against subsequent injury. Shared-decision making regarding RTP should include accumulated training loads and consider the trade-off between expedited RTP and lower subsequent injury risk.

Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers.

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.