Fully Funded PhD Studentship - Carnegie School of Sport & Rotherham United Football Club - Measurement and modelling of modifiable and non-modifiable risk factors for hamstring adaptation and injury in professional football

Measurement and modelling of modifiable and non-modifiable risk factors for hamstring adaptation and injury in professional football

Hamstring strain injuries (HSI) remain the most prevalent non-contact lower limb injury in professional soccer (Ekstrand et al. 2016) with prevalence greater in matches than training. Given this and the subsequent negative impacts on team and individual performance and its financial implications, practitioners are still seeking for mitigation strategies in the gym and on the pitch (Buckthorpe et al. 2019). 

A number of non-modifiable risk factors have been identified in previous research, primarily increasing age and previous HSI. Although, modifiable risk factors are arguably more important so that mitigation strategies and interventions can be developed to potentially reduce an athletes risk of HSI. Developing eccentric hamstring strength (Bourne et al. 2018) and fascicle length of the biceps femoris long head (BFlh) (McGrath et al., 2020) and managing high-speed running prescription (Duhig et al. 2016) during training are considered important modifiable risk factors (Buckthorpe et al. 2019). Both under- and over exposures to (close to) maximal speed runs have been reported to be associated with higher HSI risk providing some suggestion of an optimal chronic ‘dose’ that balances performance and injury prevention goals (e.g., frequency of maximal speed exposure [Malone et al. 2017; Colby et al. 2018] and/or monthly cumulative distance [O’Connor et al. 2019]). However, most investigations have analysed running exposure using arbitrary thresholds (e.g., distance within fixed high-speed speed bands) without taking into consideration players’ individual physiological and strength profiles (e.g., MSS) (Mendez-Villanueva et al. 2013). Equally, the independent use of running variables to estimate the likelihood of future HSI and/or hamstring strength adaptation likely has limited clinical value as there are multiple factors (including eccentric strength, fascicle length, aerobic fitness) that mediate this relationship. Recently, McGrath et al. (2020) identified 56 day high-speed running exposure and eccentric hamstring strength to provide the largest contribution to the 50% explained variance in changes in fascicle length of the BFlh in professional rugby league players. Noteworthy, it is also important to consider that HSI prevalence is greater in matches than training (Ekstrand et al., 2001) and so investigating whether differences in the frequency, intensity and duration of physical activities completed by players during matches contributes to HSI is also warranted. Therefore, the aim of the project is to explore the holistic measurement and modelling of modifiable (e.g., eccentric hamstring strength, fascicle length) and non-modifiable risk factors (e.g., age, previous HSI injury) to hamstring adaptation and injury in professional football. 

Desirable Applied Skills

• Highly competent using programming languages, ideally R.

• Highly competent using data visualisation software, ideally Power BI.

• Previous experience using GPS technology to monitor locomotor demands of training and matches, ideally Statsports.

• Previous experience using the following hardware/software: ForceDecks, GroinBar, Nordbord, and the ValdHub.

• Previous experience using athlete monitoring systems, ideally Smartabase.

• A strong interest in football, the ideal candidate will have a sound technical and tactical understanding of the sport (strategies, systems, philosophies, game models).

• Sound inter-personal skills in order to develop good relationships with both playing staff and members of the multi-disciplinary team.

To discuss your application and project proposal please contact Dr Stacey Emmonds [Email Address Removed] 

Information on how to apply and an application form can be found here