Athletic groin pain (part 2): a prospective cohort study on the biomechanical evaluation of change of direction identifies three clusters of movement patterns

Background: Athletic groin pain (AGP) is prevalent in sports involving repeated accelerations, decelerations, kicking and change-of-direction movements. Clinical and radiological examinations lack the ability to assess pathomechanics of AGP, but three-dimensional biomechanical movement analysis may be an important innovation. Aim: The primary aim was to describe and analyse movements used by patients with AGP during a maximum effort change-of-direction task. The secondary aim was to determine if specific anatomical diagnoses were related to a distinct movement strategy. Methods: 322 athletes with a current symptom of chronic AGP participated. Structured and standardised clinical assessments and radiological examinations were performed on all participants. Additionally, each participant performed multiple repetitions of a planned maximum effort change-of-direction task during which whole body kinematics were recorded. Kinematic and kinetic data were examined using continuous waveform analysis techniques in combination with a subgroup design that used gap statistic and hierarchical clustering. Results: Three subgroups (clusters) were identified. Kinematic and kinetic measures of the clusters differed strongly in patterns observed in thorax, pelvis, hip, knee and ankle. Cluster 1 (40%) was characterised by increased ankle eversion, external rotation and knee internal rotation and greater knee work. Cluster 2 (15%) was characterised by increased hip flexion, pelvis contralateral drop, thorax tilt and increased hip work. Cluster 3 (45%) was characterised by high ankle dorsiflexion, thorax contralateral drop, ankle work and prolonged ground contact time. No correlation was observed between movement clusters and clinically palpated location of the participant's pain. Conclusions: We identified three distinct movement strategies among athletes with long-standing groin pain during a maximum effort change-of-direction task. These movement strategies were not related to clinical assessment findings but highlighted targets for rehabilitation in response to possible propagative mechanisms. Trial registration number NCT02437942, pre results.

Studies relating to the population dynamics of the European badger (Meles meles) in Ireland

European badgers (Meles meles) are an important part of the Irish ecosystem; they are a component of Ireland’s native fauna and are afforded protection by national and international laws. The species is also a reservoir host for bovine tuberculosis (bTB) and implicated in the epidemiology of bTB in cattle. Due to this latter point, badgers have been culled in the Republic of Ireland (ROI) in areas where persistent cattle bTB outbreaks exist. The population dynamics of badgers are therefore of great pure and applied interest. The studies within this thesis used large datasets and a number of analytical approaches to uncover essential elements of badger populations in the ROI. Furthermore, a review and meta-analysis of all available data on Irish badgers was completed to give a framework from which key knowledge gaps and future directions could be identified (Chapter 1). One main finding suggested that badger densities are significantly reduced in areas of repeated culling, as revealed through declining trends in signs of activity (Chapter 2) and capture numbers (Chapter 2 and Chapter 3). Despite this, the trappability of badgers was shown to be lower than previously thought. This indicates that management programmes would require repeated long-term efforts to be effective (Chapter 4). Mark-recapture modelling of a population (sample area: 755km2) suggested that mean badger density was typical of continental European populations, but substantially lower than British populations (Chapter 4). Badger movement patterns indicated that most of the population exhibited site fidelity. Long-distance movements were also recorded, the longest of which (20.1km) was the greatest displacement of an Irish badger currently known (Chapter 5). The studies presented in this thesis allows for the development of more robust models of the badger population at national scales (see Future Directions). Through the use of large-scale datasets future models will facilitate informed sustainable planning for disease control.