A six stage operational framework for individualising ...

1 COMMENTARYOpen AccessA six stage operational framework forindividualising injury risk management insportMark Roe1,2*, Shane Malone2, Catherine Blake1, Kieran Collins2, Conor Gissane3, Fionn B ttner1,John C. Murphy4and Eamonn Delahunt1,5 AbstractManaging injury risk is important for maximising athlete availability and performance. Although athletes are inherentlypredisposed to musculoskeletal injuries by participating in sports, etiology models have illustrated how susceptibility isinfluenced by repeat interactions between the athlete ( intrinsic factors) and environmental stimuli ( extrinsicfactors). Such models also reveal that the likelihood of an injury emerging across time is related to the interconnectednessof multiple factors cumulating in a pattern of either positive ( increased fitness) or negative adaptation ( injury).

2 The process of repeatedly exposing athletes to workloads in order to promote positive adaptations whilst minimisinginjury risk can be difficult to manage. Etiology models have highlighted that preventing injuries in sport, as opposed toreducing injury risk, is likely impossible given our inability toappreciate the interactions of the factors at play. Given theseuncertainties, practitioners need to be able to design, deliver, and monitor risk management strategies that ensure a lowsusceptibility to injury is maintained during pursuits to enhance performance. The current article discusses previousetiology and injury prevention models before proposing a new operational :Injury risk management, operational framework , Etiology model, Injury prevention, Athletic performance,Athlete managementBackgroundManaging injury risk is important for maximising athleteavailability and performance.

3 Although athletes areinherently predisposed to musculoskeletal injuries by par-ticipating in sports, etiology models have illustrated howsusceptibility is influenced by repeat interactions betweenthe athlete ( intrinsic factors) and environmental stimuli( extrinsic factors) (Meeuwisse 1994; Meeuwisse et ). Such models also reveal that the likelihood of aninjury emerging across time is related to the interconnect-edness of multiple factors cumulating in a pattern of eitherpositive ( increased fitness) or negative adaptation ( ) (Bittencourt et al. ; Windt and Gabbett 2016).The process of repeatedly exposing athletes to work-loads in order to promote positive adaptations whilstminimising injury risk can be difficult to models have highlighted that preventing injuriesin sport, as opposed to reducing injury risk, is likely im-possible given our inability to appreciate the interactionsof the factors at play.

4 Thus, practitioners must acceptsome degree of uncertainty despite their best efforts tominimise injury risk (Windt and Gabbett 2016). Giventhese uncertainties, practitioners need to be able todesign, deliver, and monitor risk management strategiesthat ensure a low susceptibility to injury is maintainedduring pursuits to enhance performance. The currentarticle discusses previous etiology and injury preventionmodels before proposing a new operational modelsIn 1994 Meeuwisse proposed a linear, causal pathway to il-lustrate the onset of injury. This involved a predisposedathlete characterised by intrinsic factors ( age, previous* of Public Health, Physiotherapy and Sports Science, UniversityCollege Dublin, Dublin 4, Ireland2 Gaelic Sports Research Centre, Department of Science, Institute ofTechnology Tallaght, Dublin, IrelandFull list of author information is available at the end of the article The Author(s).)

5 2017 open AccessThis article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were al. Injury Epidemiology (2017) 4:26 DOI , neuromuscular control level) becoming suscep-tible to injury via interactions with extrinsic risk factors( game conditions, playing equipment) (Meeuwisse1994). It was proposed that these risk factors wouldinfluence the athlete s tolerance to inciting events and tothe mechanism attributable to the onset of et al. later recognised that a linear ap-proach containing a start and an end point does notreflect the true onset of injury in sport and proposed arecursive cycle where repeated participation occurs inthe absence of injury (Meeuwisse et al.

6 2007). Thisrevised model more accurately reflected the frequent ex-posures to activities associated with sporting seasonswhilst illustrating that the same factors and mechanismsmay have different outcomes ( injury or continuedparticipation) for different et al. expanded upon the dynamic natureof injury risk in a conceptual framework to counteractthe reductionist approach of simplifying the many fac-tors surrounding the onset of injury into separate units( biomechanical, behavioral, physiological and psycho-logical) (Bittencourt et al. ). It was proposed thatunits of varying magnitudes of influence interact andcollectively create a web of determinants . In turn, thiswould influence the athlete s response to their environ-ment leading to the emergence of an injury or positiveadaptation.

7 The recursive elements of these modelshighlight the influence of positive ( increased aerobiccapacity) and negative ( injury) responses on anathlete s ever-evolving injury risk in sports. However,despite physiological systems underpinning many areasof human performance, and injury healing, Bittencourtwas the first to place great emphasis on supercompensa-tion ( positive physiological changes associated withexposures to stressful stimuli and recovery).Simultaneously, Windt and Gabbet expanded on theBannister fitness-fatigue model proposed as previous eti-ology models failed to adequately account for the work-loads associated with training and competition (Windtand Gabbett 2016). Indeed, the workload injuryaetiology model illustrated a paradox that workloads canboth decrease injury risk by increasing fitness, or in-crease injury risk by inducing fatigue or was proposed that the careful application of appropri-ate workload and recovery were required to manageinjury risk and optimise risk management modelsAlthough these models help illustrate the elements influen-cing the onset of injury they do not, by design, promote thedevelopment of injury risk management strategies.

8 In 1987vanMechelenoutlinedafour-stagesequen ceforprevent-ing injuries. These included establishing the extent of theproblem using epidemiology data (step 1), establishing thecause and mechanism of injury (step 2), introducing pre-ventative measures (step 3), and assessing intervention effi-cacy by repeating step 1 (step 4) (van Mechelen et al. 1992).Finch later added additional steps to assist in the trans-lation of research into injury prevention practise (TRIPP model) to include a description of the intervention con-text to inform implementation strategies (step 5) andevaluation of the intervention via real-world , as opposedto solely scientific analytics (step 6) (Finch 2006).From etiology and prevention models to anoperational frameworkInterventions derived from current etiology models tendto be group-based such as standardised warm-ups or rulechanges.

9 Although some group-based initiatives have beenshown to be effective it is possible that injury risk manage-ment may be enhanced with personalised interventions(Al Attar et al. 2016). For instance, diversity within a teamsquad means each athlete presents with unique character-istics which modifies their susceptibility to injury. Hence,implementing a generic injury prevention protocol, orclustering athletes into groups based on the presence orabsence of certain variables, may not reduce injury risk ifcertain factors unique to each athlete are not operational framework to guide practitioners incontinuously managing injury risk whilst consideringfactors unique to the athlete s sport and profile has yetto be proposed in a manner facilitating supercompensa-tion. Thus, the current article builds on previousetiology and prevention models to propose a novel para-digm (Fig.)

10 1). The six stage operational frameworkoutlines how awareness of injury trends and risk factors( stage 1 and 2), profiling the demands of a sport and thecapabilities of the athlete ( stage 3 and 4), and monitoringthe athlete s responses to evidence-based interventions( stage 5 and 6) can guide practitioners in managinginjury risk. The authors propose that this novel frame-work can build on the success of group based interven-tions (Fig. 2). stage 1 injury trends: when, where, and how docertain athletes sustain certain injury?Stakeholders need to understand the incidence ( rateper 1000 exposure hours, number of injuries per athleteeach season) and prevalence (proportion of populationaffected) of common medical attention and time-lossinjuries across different stages of the season.