Monitoring Training Intensity; Using a Tri-Phasic Model
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2018, Vol 5, Issue 5
Abstract
Introduction: Different training models can produce the same outcome in competition. As the magnitude of change is dependent on the characteristics of the training programs, the duration of the intervention, and the gender, it is important to identify the training stimulus and how that relates to performance. The aim was to investigate the utility of a new model to describe training adaptions and outcomes. Methods: 12 Swimming Australia Scholarship holders were followed over a 6-week initial training period. A range of sub maximal and maximal swims were used to assess their training state in week 0 week 3 and week 7. We used a novel tri-phasic model to monitor and assess athlete conditioning. Results: Aerobic contribution increase between test 1 and 3 was 8.25% (±19.74%;). Glycolytic metabolism showed a reduction in contribution of -20.40% (±10.82%) that was significant (P = 0.04). Anaerobically, there was an overall gain of 12.15% (±11.70%; P = 0.01) Findings. We demonstrated how the use of a new model of metabolic capability may help to explain physiological changes made by the athlete in response to the coaches intended outcome. In particular, we observed that Malate Aspartate (MAS) has a far greater role to play in retaining the equilibrium within skeletal muscle that allows higher intensity of aerobic exercise to continue in less aerobically friendly conditions. Introduction Performance at major championships requires good rates of improvement by the swimmer both during the year leading up to the meet, and during the competition itself [1]. An analysis of medal winners’ times in both semi-finals and finals reveals that medalling requires a further improvement between semi and final also. To identify what the swimmer needs, and why it is needed, coaches seek to monitor training performance by a number of methods (heart rate, swimming speed, stroke rate, lactate). When, what, and duration of specific training, are also important elements of how the training program is put together. Measurement of training indices, however, even when used with multiple regression techniques, still do not appear to produce good markers of performance [2,3]. Different training models can produce the same outcome in competition [4] and, if a positive effect is to be maintained, the specificity of training requirements of international athletes needs to be retained in all training sessions at least 70% of the time [5]. As the magnitude of change is dependent on the characteristics of the training programs, the duration of the intervention, and the gender, [6] it is important to identify what stimulus is created by the training and how that relates to performance. Physiological monitoring showed little relationship between the use of blood lactate testing and competition performance, [7] although monitoring in the final 4 weeks before a major competition could improve resilience of the training observation in relation to performance [8]. These changes, however, need to be relatively large [2,9]. Some researchers have suggested that training pace is a good marker of training intensity, [10] although later observers found that critical velocity was no different in the first and last week of a 14-week training program [11]. Swimmers appear to adhere closely to all aspects of training load, except intensity [12], which is not helped if coaches do not comply with scientific direction as the communication between the sport scientist and the coach is not forthcoming [13]. Wallace et al. [14] suggested that there were few practical methods available to evaluate training load during swimming, and showed that use of RPE heart-rate based methods were not consistent, and were generally a poor estimate of training load. Further, the training load identified by the coach and swimmer were not in agreement. Estimations of maximal heart rate also appear to be an imprecise guide to training pace in cycling and running, [15] however cardiac drift changes the heart rate power relationship significantly [16]. Although heart rate variability has been used in moderate fitness situations with some success [17], these authors also found it was very difficult to draw conclusions from this method in elite athletes [18].
Authors and Affiliations
Emma Swanwick, David Pyne, Martyn Matthews
Keywords
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- EP ID EP591848
- DOI 10.26717/BJSTR.2018.05.001276
- Views 189
- Downloads 0
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