Analysis of changing risk factors and explanation of risk predictions with machine learning for improved hamstring strain injury prevention in Australian football

Professional athletes and organizations can face significant consequences as a result of injury incidents in sports. Therefore, an abundance of studies has been conducted to identify the risk factors in the hope of preventing injuries from occurring in the first place. Hamstring strain injuries (HSIs) are the most frequent injuries in Australian Football League (AFL). Many studies had shown that there are several prominent risk factors for HSIs. However, this finding cannot be identified with any consistency through assessing the risk factors at a single time point, typically the beginning of a season (e.g., in the pre-season) or more frequently throughout the season (e.g., in the pre-season, early in-season and late in-season). Nonetheless, these studies did not consider the potential variability of risk factors across the season. In light of this, it was hypothesised that risk factors may vary depending on the time of the season. This thesis aims to answer if the risk of hamstring strain injuries in Australian Football can be reduced through a better understanding of the changing risk factors over the course of the season. Despite the study, identifying HSI risk at individual-level remains a challenge. This study aims to explore whether the risk of HSI for individual players can be better understood by explaining the predictions of machine learning (ML) models. The study utilised recursive feature selection and cross-validation to provide a holistic understanding of important risk factors at different points. Subsequently, counterfactual explanations were effectively generated for players at risk of sustaining HSI. The study found that non-modifiable risk factors were primarily linked to pre-season injuries, whereas modifiable risk factors were mostly associated with early in-season injuries. Counterfactual explanations and ML models offer a novel perspective in interpreting risk and finding potential solutions. Overall, this study provides new insights into risk factors associated with HSIs at different time points, as well as offers a solution for interpreting risk at individual-level using ML models and counterfactual explanations. The findings have important implications for researchers and practitioners who seek to mitigate the risk of HSI in the future

Lower-limb injury in elite Australian football: A narrative review of kinanthropometric and physical risk factors

Objective This review aims to provide a succinct and critical analysis of the current physical and mechanical demands of elite Australian football while examining lower-limb injury and the associated physical and kinanthropometric risk factors. Methods MEDLINE, PubMed, Web of Science and SPORTSDiscus electronic databases were searched for studies that investigated the playing demands, injury trends, and physical and kinanthropometric injury risk factors of elite Australian football. Articles from similar team sports including soccer and rugby (union and league) were also included. Results While the physical demands of elite AF have steadied over the past decade, injury rates continue to rise with more than two-thirds of all injuries affecting the lower-limbs. Body composition and musculoskeletal morphological assessments are regularly adopted in many sporting settings with current research suggesting high and low body mass are both associated with heightened injury risk. However, more extensive investigations are required to determine whether the proportions of muscle and fat are linked. Repeated assessment of musculoskeletal morphology may also provide further insight into stress fracture rates. Conclusions While kinanthropometric and physical attributes are highly valued within elite sporting environments, establishing a deeper connection with injury may provide practitioners with more insight into current injury trends

Session availability as a result of prior injury impacts the risk of subsequent injury in elite male Australian footballers

Prior injury is a commonly identified risk factor for subsequent injury. However, a binary approach to classifying prior injury (i.e., yes/no) is commonly implemented and may constrain scientific findings, as it is possible that variations in the amount of time lost due to an injury will impact subsequent injury risk to differing degrees. Accordingly, this study investigated whether session availability, a surrogate marker of prior injury, influenced the risk of subsequent non-contact lower limb injury in Australian footballers. Data were collected from 62 male elite Australian footballers throughout the 2015, 2016, and 2017 Australian Football League seasons. Each athlete’s participation status (i.e., full or missed/modified) and any injuries that occurred during training sessions/matches were recorded. As the focus of the current study was prior injury, any training sessions/matches that were missed due to reasons other than an injury (e.g., load management, illness and personal reasons) were removed from the data prior to all analyses. For every Monday during the in-season periods, session availability (%) in the prior 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84 days was determined as the number of training sessions/matches fully completed (injury free) relative to the number of training sessions/matches possible in each window. Each variable was modeled using logistic regression to determine its impact on subsequent injury risk. Throughout the study period, 173 non-contact lower limb injuries that resulted in at least one missed/modified training session or match during the in-season periods occurred. Greater availability in the prior 7 days increased injury probabilities by up to 4.4%. The impact of session availability on subsequent injury risk diminished with expanding windows (i.e., availability in the prior 14 days through to the prior 84 days). Lesser availability in the prior 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the prior 7 days. Session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by coaches and clinicians to guide the progression of training, particularly for athletes that are returning from long periods of injury

Relationship between GPS workload and injury risk in elite rugby league players

A systematically planned and distributed training program is required for elite athletes to have positive adaptations to training workloads, with minimal risk of injury. Workload-injury investigations in team sports typically quantify workload in absolute terms, for example the workload performed in a week versus injury. However, workload-performance investigations have examined absolute workload performed in one week (referred to as acute workload) relative to four-week chronic workload (i.e. four-week average acute workload). The logic behind this comparison of workloads is the provision of a workload index, which provides an indication of whether the athlete’s recent acute workload is greater, less than or equal to the workload that the athlete has been prepared for during the preceding chronic period. This method is referred to as the acute:chronic workload ratio. The purpose of this thesis was to investigate whether acute workload and chronic workload could be mapped and modelled to predict injury in elite rugby league players

A new statistical approach to training load and injury risk: separating the acute from the chronic load

The relationship between recent (acute) training load relative to long-term (chronic) training load may be associated with sports injury risk. We explored the potential for modelling acute and chronic loads separately to address current statistical methodology limitations. We also determined whether there was any evidence of an interaction in the association between acute and chronic training loads and injury risk in football. A men’s Qatar Stars League football cohort (1 465 players, 1 977 injuries), where training load was defined as the number of minutes of activity, and a Norwegian elite U-19 football cohort (81 players, 60 injuries), where training load was defined as the session rating of perceived exertion (sRPE). Mixed logistic regression was run with training load on the current day (acute load) and cumulative past training load estimated by distributed lag non-linear models (chronic load) as independent variables. Injury was the outcome. An interaction between acute and chronic training load was modelled. In both football populations, we observed that the risk of injury on the current day for different values of acute training load was highest for players with low chronic load, followed by high and then medium chronic load. The slopes varied substantially between different levels of chronic training load, indicating an interaction. Modelling acute and chronic loads separately in regression models is a suitable statistical approach for analysing the association between relative training load and injury risk in injury prevention research. Sports scientists should also consider the potential for interactions between acute and chronic load.publishedVersio