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Longitudinal workload monitoring to keep athletes healthy and performing : conceptual, methodological, and applied considerations in sports injury aetiology

University of British Columbia

Sports injury aetiology is a process in which internal and external risk factors contribute to an inciting event. The last decade has seen a rapid growth in research that identified how training and competition workloads relate to sports injury risk. My literature review highlighted that existing aetiology models do not describe how workloads contribute to injury. Furthermore, injury risk fluctuates on a time-scale in parallel to these workloads (e.g. daily), which creates several methodological and statistical challenges that have largely been ignored. If researchers are to understand how athletes’ workloads relate to injury risk, their conceptual aetiological models must be updated to incorporate training and competition workloads, and they should use appropriate statistical analyses. After my literature review, I divide this dissertation into three parts. In Part 1 I discuss how workloads relate to injury. I present a novel workload—injury aetiology model, which expands on previous aetiological frameworks and details 3 ways that workloads contribute to injury: 1) exposing athletes to external risk factors and potential inciting events, 2) reducing injury risk through beneficial physiological changes, and 3) increasing injury risk through transient negative changes in athletes’ internal risk profiles. I then present mediation and moderation as potential causal approaches to understand how athlete risk factors interact with workload changes to alter injury risk. In Part 2 I tackle the methodological challenges of analysing workload—injury data. I reviewed prospective cohort studies that reported intensive longitudinal data to analyse workload—injury data in team sports. I identified that few studies utilised statistical approaches that align with theoretical aetiology models or addressed the methodological challenges associated with longitudinal data. My analysis leads me to recommend mixed modeling as one advance, and I exemplify how it can be used by studying how player unavailability affects player outputs. In Part 3 I integrate the conceptual and methodological considerations into two applied settings. First, I describe how a methodological/mathematical concern (mathematical coupling) may influence applied practice (multifaceted player load management) and research (explicitly reporting calculations). Finally, I use mixed modeling to examine pre-season workload and in-season injury risk, controlling for athletes’ weekly workloads.

Text

2018-12-18

Attribution-NonCommercial-ShareAlike 4.0 International

http://hdl.handle.net/2429/68093

Experimental Medicine

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-sa/4.0/