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DC Field | Value | Language |
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dc.contributor.advisor | Hunter, Angus | - |
dc.contributor.advisor | Macgregor, Lewis | - |
dc.contributor.author | Wilson, Matthew Thomas | - |
dc.date.accessioned | 2021-06-16T09:13:57Z | - |
dc.date.available | 2021-06-16T09:13:57Z | - |
dc.date.issued | 2020-09 | - |
dc.identifier.uri | http://hdl.handle.net/1893/32723 | - |
dc.description.abstract | The implementation of strength training interventions within performance sport requires practitioners to have high levels of confidence in the training’s efficacy to produce transferable increases in strength for improved sporting performance. Therefore, it is important to understand neural and morphological mechanisms of adaptation which lead to strength gain. Technological advancements now enable researchers to investigate specific sites of adaptation, however the peripheral and central regions of the neuromuscular system are typically investigated in isolation. The lack of integrated neuromuscular assessment within literature and practice represents a knowledge gap as to the inter-relationship between peripheral contractile properties and centrally governed adaptations to strength training. Whilst information regarding the specific mechanisms of adaptation can contribute to a practitioner’s confidence in a training intervention, the method of inferencing applied to quantify training effects can be of equal importance. Traditional frequentist inferencing does not provide sufficient information to answer pertinent practitioner questions. Thus, an alternative method of inferencing may be more applicable within strength training contexts. As such, the overall aim of this thesis is to determine the role played by skeletal muscle contractile properties in adaptations to strength training, in relation to other peripheral and central regions of the neuromuscular system. This thesis aims to contribute to the literary gap of integrated neuromuscular assessment and outline a practically applicable method of inferencing within the context of strength training, in order to contribute to the confidence levels with which practitioners employ specific strength training interventions. Chapter 1 reviews the existing literature surrounding neuromuscular adaptations to strength training, their methods of non-invasive assessment, and inferential methods used to quantify training effects. Contractile mechanics adaptations to strength training, and their relationship with other adaptations were identified as areas requiring further investigation. Chapter 2 demonstrated the application of non-invasive contractile mechanics assessments in the context of strength training, as well as a level of construct validation for such contractile mechanics assessment; through associations between contractile properties and muscle architecture parameters. Subsequently, it was observed that contractile properties were altered prior to any other measured neuromuscular adaptation following strength training (chapter 3), and that there was no modulation effect between peripheral and central adaptations leading to strength gain. Furthermore, it was observed that firing rates of peripheral motor units did not appear to adapt following strength training, suggesting the early neural responses leading to strength gain appear to come from changes in spinal excitability. Chapter 4 confirmed the aforementioned absence of motor unit firing rate adaptions, despite being assessed in training-specific manner, using a dynamic strength test. However, this pilot study did demonstrate the applicability of motor unit behaviour assessment during dynamic movement, providing information of high practical relevance within the context of strength training. Finally, chapter 5 demonstrated the successful application of Bayesian inferencing to quantify the certainty/uncertainty surrounding performance outcome effects following three different strength training interventions. This demonstrated analytical method provides directly interpretable information to answer the aforementioned practitioner questions and was capable of providing meaningful inferences in a situation where frequentist significance testing was unable too. This thesis demonstrates the adaptive responses of skeletal muscle contractile properties following strength training, and their relationship with other peripheral and central neuromuscular adaptations. The information provided within this thesis regarding the integrated assessment of multiple regions of neuromuscular adaptation, and the demonstrated method Bayesian inferencing, can provide practitioners with directly interpretable information upon the efficacy of employed strength training interventions, designed for improving athletic performance. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | University of Stirling | en_GB |
dc.subject | Resistance exercise | en_GB |
dc.subject | Training | en_GB |
dc.subject | Tensiomyography | en_GB |
dc.subject | Electromyography | en_GB |
dc.subject | Trans-cranial Stimulation | en_GB |
dc.subject | Decomposition electromyography | en_GB |
dc.subject | Ultrasound | en_GB |
dc.subject | muscle hypertrophy | en_GB |
dc.subject | strength | en_GB |
dc.subject | performance | en_GB |
dc.subject | Bayesian statistics | en_GB |
dc.subject | sport | en_GB |
dc.title | The role of muscle contractile mechanics in neuromuscular control and performance adaptations to resistance exercise | en_GB |
dc.type | Thesis or Dissertation | en_GB |
dc.type.qualificationlevel | Doctoral | en_GB |
dc.type.qualificationname | Doctor of Philosophy | en_GB |
dc.rights.embargodate | 2022-01-31 | - |
dc.rights.embargoreason | I wish to delay public access to my thesis for 6 months. This is due to two experimental chapters currently being prepared for publication. | en_GB |
dc.contributor.funder | Sports Scotland; Scottish Institute of Sport | en_GB |
dc.author.email | wilsonm901@gmail.com | en_GB |
dc.rights.embargoterms | 2022-02-01 | - |
dc.rights.embargoliftdate | 2022-02-01 | - |
Appears in Collections: | Faculty of Health Sciences and Sport eTheses |
Files in This Item:
File | Description | Size | Format | |
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Matthew Wilson Thesis.pdf | 3.85 MB | Adobe PDF | View/Open |
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