The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes
Authors: Andrew M Stewart
Alireza Esmaeili
William G Hopkins
George P Elias
Brendan H Lazarus
Robert J Aughey
Journal: Frontiers in Physiology (2018)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: athlete monitoring; injury history; injury prevention; musculoskeletal screening; professional experience
Affiliations: Institute for Health and Sport (iHeS), Victoria University, Melbourne, VIC, Australia.

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 &gt; 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. 

Retracted: No

PubPeer comments:
 In 2014, Alan Welch presented his work along with Emma Knight to the AIS where they looked into a method of statistics called Magnitude Based Inference (MBI). MBI has never been published in a statistical or mathematical journal but is heavily used by the sports science field. You can view the presentation here https: maths.anu.edu.au files AIS-slides.pdf . After presenting MBI was supposedly discontinued at the AIS after Alan and Emma showed the method to be fundamentally flawed. The authors made the interesting decision to instead of list Magnitude Based Inference as the method used, they decide to exclude the key phrase Magnitude Based Inference. I believe this is so that it avoids scrutiny as people were looking into exposing the method as fundamentally flawed. The thresholds for the smallest important hazard ratio representing increase and decrease in injury risk were 1.11 and 0.90, respectively (Hopkins, 2010). The uncertainty in all effects was expressed as 90% confidence limits, and qualitatively as chances that the true value of the effect was substantial for clear effects using the following scale: 0.5%, most unlikely; 0.5% to 5%, very unlikely; 5% to 25%, unlikely, 25% to 75%, possibly; 75% to 95%, likely; 95% to 99.5%, very likely; 99.5%, most likely. The effect was deemed unclear when both the lower confidence limit was 0.90 and the upper confidence limit was 1.11 (Hopkins et al., 2009). Results were rounded and reported to two significant digits (Hopkins et al., 2011). These thresholds are very MBI sounding things and sure enough lets check out the reference hopkins 2009, https: www.ugr.es ~fmocan MATERIALES%20DOCTORADO Progressive%20Statistics%20for%20Studies%20in%20Sports.pdf Such magnitude-based inferences about effects can be made more accurate and informative by qualifying them with probabilities that reflect the uncertainty in the true value: possibly harmful, very likely substantially positive, unclear but likely to be beneficial, and so on. The qualitative probabilistic terms can be assigned using the following scale (16): 0.5%, most unlikely, almost certainly not; 0.5- 5%, very unlikely; 5-25%, unlikely, probably not; 25-75%, possibly; 75-95%, likely, probably; 95-99.5%, very likely; 99.5%, most likely, almost certainly. Research on the perception of probability could result in small adjustments to this scale. Use of thresholds for moderate and large effects allows even more informative inferential assertions about magnitude, such as probably moderately positive, possibly associated with small increase in risk, almost certainly large gain, and so on. , 
 Magnitude-based inference, or as it is now known, magnitude-based decisions (MBD), does not give  false results . It turns out to be equivalent to several hypothesis tests with well-defined and acceptable Type-2 error rates (failed discovery of substantial effects, 5% for the non-clinical version used in this study), and the Type-1 rates (false discovery of substantial effects) for the non-clinical version are only ever slightly more than 5% and usually a lot less. An article substantiating these assertions first-authored by a mathematics professor and intended for a statistical journal is in preparation. I am also publishing something at Sportscience shortly that will be more accessible to researchers without substantial training in statistics. Meantime, for some recent background, see my posting and other postings each side of it in the datamethods forum: https: discourse.datamethods.org t what-are-credible-priors-and-what-are-skeptical-priors 580 53 It is worth noting one of statistician Sander Greenland s postings to that conversation in the datamethods forum:  Earlier applications of MBI did not necessarily reach incorrect conclusions. Instead when a report that used MBI is seen as supplying data relevant to a topic, that report needs to be scrutinized to see if its conclusions hold up or fail in light of critical insights about what MBI was actually producing (as opposed to the original MBI descriptions). Note that this advice involves study-specific examinations, and thus is a far cry from simply discarding everything that was published using MBI. Being able to follow it in a particular case hinges on how well the report described its design, execution and data, and if the needed summary statistics were given or computable from the report; if not, the report authors would need to supply those items. You will find that we supply compatibility limits and identify smallest important effects for the main outcomes in all our papers using MBI MBD, so it is a simple matter for readers to evaluate for themselves whether precision of the estimates is adequate for a probabilistic conclusion, in the manner that epidemiologist Ken Rothman and psychologist Geoff Cumming evaluate outcomes. Rothman uses 90% confidence compatibility limits in his introductory text on epidemiology three times more frequently than 95% CIs. Cumming s default is 95%. MBI MBD uses 90% CIs for non-clinical effects, and 99% on the harm side and 50% on the benefit side for clinical effects, all for very good reasons. ,