The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes

Abstract

Aim: Relationships between athlete monitoring-derived variables and injury risk have been investigated predominantly in isolation. The aim of this study was to evaluate the individual and combined effects of multiple factors on the risk of soft-tissue non-contact injuries in elite team sport athletes.Methods: Fifty-five elite Australian footballers were prospectively monitored over two consecutive seasons. Internal and external training load was quantified using the session rating of perceived exertion and GPS/accelerometry, respectively. Cumulative load and acute-to-chronic workload ratios were derived using rolling averages and exponentially weighted moving averages. History of injuries in the current and previous seasons was recorded along with professional experience, weekly musculoskeletal screening, and subjective wellness scores for individual athletes. Individual and combined effects of these variables on injury risk were evaluated with generalized linear mixed models.Results: High cumulative loads and acute-to-chronic workload ratios were associated with increased risk of injuries. The effects for measures derived using exponentially weighted moving averages were greater than those for rolling averages. History of a recent injury, long-term experience at professional level, and substantial reductions in a selection of musculoskeletal screening and subjective wellness scores were associated with increased risk. The effects of high cumulative loads were underestimated by ~20% before adjusting for previous injuries, whereas the effects of high acute-to-chronic workload ratios were overestimated by 10–15%. Injury-prone players, identified via player identity in the mixed model, were at > 5 times higher risk of injuries compared to robust players (hazard ratio 5.4, 90% confidence limits 3.6–12) despite adjusting for training load and previous injuries. Combinations of multiple risk factors were associated with extremely large increases in risk; for example, a hazard ratio of 22 (9.7–52) was observed for the combination of high acute load, recent history of a leg injury, and a substantial reduction in the adductor squeeze test score.Conclusion: On the basis of our findings with an elite team of Australian footballers, the information from athlete monitoring practices in team sports should be interpreted collectively and used as a part of the injury prevention decision-making process along with consideration of individual differences in risk.

<p>The Relationship Between Acute: Chronic Workload Ratios and Injury Risk in Sports: A Systematic Review</p>

“…The most common timeframe used was a 1-week acute training load and 4-week chronic training load. [9][10][11]17,18,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Other timeframes included a 3-day acute load and a 21 daychronic load, 19 or varying timeframes. [37][38][39][40] One study examined various timeframes for calculating ACWR, 37 utilizing 2-9 days for acute loads, and chronic loads varying from 14 to 35 days, while the other study ranged from 1 to 2 weeks for acute loads, and 3-8 weeks for chronic loads, but only utilized the 1:4 week ratio for relationship to injury.…”

“…[37][38][39][40] One study examined various timeframes for calculating ACWR, 37 utilizing 2-9 days for acute loads, and chronic loads varying from 14 to 35 days, while the other study ranged from 1 to 2 weeks for acute loads, and 3-8 weeks for chronic loads, but only utilized the 1:4 week ratio for relationship to injury. 38 Five studies utilized exponentially weighted moving averages (EWMA) for their calculation of ACWR 17,27,29,36,39 and four articles studied a combination of ACWR and chronic workload. 11,30,38,39 Lastly, one study examined ACWR in combination with recent lower limb injuries, 36 while another evaluated the effects of fitness on the relationship between ACWR and injury risk.…”

“…38 Five studies utilized exponentially weighted moving averages (EWMA) for their calculation of ACWR 17,27,29,36,39 and four articles studied a combination of ACWR and chronic workload. 11,30,38,39 Lastly, one study examined ACWR in combination with recent lower limb injuries, 36 while another evaluated the effects of fitness on the relationship between ACWR and injury risk. 23…”

“…All seven studies who reported total distance (TD) 11,18,22,27,34,36,38 used ACWR. Three studies reported the ACWR of total distance and its relation to varying chronic workloads.…”

“…22 The study by Cummins et al also demonstrated a significant relationship between TD and injury risk (AUC = 0.580, p = 0.09). 34 Esmaeili et al 36 compared ACWR and EWMA-based ACWR for TD, and later combined these results with previous leg injuries. This article found that using EWMA-based ACWR demonstrated increases in hazard ratios (HR) (HR range from 21.0 to 6.80), compared to ACWR on its own (HR range from 0.92 to 2.40), and that the combination of a recent leg injury lead to higher hazard ratios for both methods (EWMA-based ACWR HR=16.00, ACWR HR=5.80).…”