|dc.contributor.advisor||Hunter, Angus M.||-|
|dc.contributor.advisor||Galloway, Stuart D. R.||-|
|dc.contributor.author||Neal, Craig M.||-|
|dc.description.abstract||Exercise intensity and its distribution is probably the most important and most heavily debated variable of endurance training. Training induces adaptation but also induces stress responses. Controlling the training-intensity distribution may provide a mechanism for balancing these two effects. It has been reported that elite endurance athletes train with a high volume and load, relative to the sport. These athletes spend the vast majority (>80%) of training time at relatively low intensities (lower than the lactate threshold, zone one), and therefore <20% of training time above the lactate threshold (zones two and three). Experimental studies support the beneficial effects of a high training volume in zone one, and show detrimental effects of replacing zone one training with training in zone two. This is likely due to enhanced recovery from training in zone one compared with training in zone two. The acute recovery following training sessions in zones two and three has been reported to not be different, but the recovery following training in zone one has been reported to be faster. Improvements in physiological adaptation and endurance performance have been reported to be greater following training programmes with higher exercise intensities. Therefore, it has been suggested that a polarised training model, which includes ~80% of training in zone one with ~20% of training in zone three is more beneficial than a threshold training model, with the majority of training in zone two. However, research into an optimal training-intensity distribution is limited. Therefore, the aims of this thesis were to assess the effectiveness of training-intensity distribution on the improvements in physiological adaptation, endurance performance and assess if manipulating training-intensity distribution had an effect on immune function.
Study one revealed that the lactate threshold, the lactate turnpoint and maximal performance measures in swimming, cycling and running, assessed using the methods outlined in the study, are reproducible in trained endurance athletes. These tests can therefore be used by trained endurance athletes as part of a physiological testing programme to assess not only endurance performance, but also to demarcate training intensity zones for exercise intensity prescription and monitor moderate to large adaptations to training. Practitioners should take care when deciding on the duration between tests to test for adaptations from training, as adaptations need to be greater than these detected test-retest variations to be considered physiologically meaningful.
To the best of the author’s knowledge, study two was the first study to have assessed training-intensity distribution in a group of multisport athletes. Training was monitored over a 6-month period, and testing took place every two months to assess the effect of the training on physiological adaptation. Although speculative due to the number of variables involved, the results suggest that a greater proportion of training time spent in zone one and a lower proportion of training time spent in zone two is beneficial to physiological adaptation. However, given the number of variables associated with assessing the training-intensity distribution in multisport athletes, it is not easy to draw conclusions as to the effectiveness of the training in the different disciplines on the key measures of adaptation in the different disciplines. Study two highlighted the need for future research to focus on experimental manipulation of training-intensity distribution and thus improve our understanding of its impact on the training-induced adaptations in endurance athletes.
Study three manipulated the training-intensity distribution in trained endurance athletes in just one discipline, to reduce the number of variables involved. A polarised training model was compared to a threshold training model on the effectiveness to improve physiological adaptation and endurance performance. Results revealed that a polarised training model is recommended for trained cyclists wishing to maximally improve performance and physiological adaptation over a short-term (six week) training period.
The first part of study four assessed the effect of a polarised and a threshold training model on immune function markers in trained cyclists. Both endurance training programmes had similar volume, and were sufficient to induce improvements in performance and physiological adaptation. However, despite likely differences in recovery, both training programmes had no effect on the proportion of low or high differentiated or senescent CD8+ or CD4+ T-cells in blood. Therefore, training adaptation was achieved at no cost to this particular aspect of immune function. From these results and evidence from previous studies, it seems likely that athletes need to be overreached to induce any change in immune function following a period of intensified training.
The second part of study four assessed the impact of an ironman triathlon race on Epstein-Barr virus (EBV) and Varicella-Zoster virus (VZV) antibody titres and the frequency of low and high differentiated and senescent blood T-cells in trained endurance athletes. Previous work has revealed that an ironman triathlon race increases the proportion of senescent CD4+ T cells and decreases the proportion of naive CD4+ T cells, and thus induces changes the immune space which could leave an individual at a greater risk of infection. This study however, did not find any changes in the proportions of these T cell subsets following an ironman triathlon race. The mean results of this study suggest that there is no relationship between EBV and VZV-specific antibody concentrations and the proportion of senescent, low and highly differientiated T cells. However, on analysis of individual subject data, it seems possible that subjects with a high antibody titre for EBV or VZV 3 wks before a competition might be more at risk of infection post race. A greater subject number would be needed in order to make a more conclusive statement about this relationship.
The results of this thesis suggest that future research is required in the area of training-intensity distribution. Firstly, our understanding of the physiological mechanisms responsible for the effectiveness of a polarised training model in trained endurance athletes is limited, and thus studies should attempt to address this issue. Our current knowledge on the mechanisms underlying a blunted T cell response following strenous exercise is also limited. A change in the immune space to a greater proportion of senescent T cells and a lower proportion of naive T cells might contribute to this blunted response. In the current thesis however, the proportions of these T cell markers were unchanged following the training/racing interventions. It is possible that with a higher training load, there could be changes in these markers, and thus this is an exciting area that could have potential implications on athlete health. Finally, testing for antibody titres in endurance athletes is possibly an avenue to detect individuals at the greatest risk of infection if subjected to a large physical and/or mental stress. This could have implications on maintaining athlete health and therefore, allowing athletes to train consistently.||en_GB|
|dc.publisher||University of Stirling||en_GB|
|dc.subject||Training intensity distribution||en_GB|
|dc.subject.lcsh||Endurance sports Physiological aspects||en_GB|
|dc.title||Training intensity distribution, physiological adaptation and immune function in endurance athletes||en_GB|
|dc.type||Thesis or Dissertation||en_GB|
|dc.type.qualificationname||Doctor of Philosophy||en_GB|
|dc.contributor.affiliation||School of Sport||en_GB|
|Appears in Collections:||Faculty of Health Sciences and Sport eTheses|