Coopmans, S.C. (2020) Monitoring of internal training load in speed skating. thesis, Sport Sciences.
Full text available on request.Abstract
Monitoring of the training load is essential for coaches and athletes to ensure that athletes receive the right amount of training to optimise performance. The TRIMP method is proven to be good measure of internal training load and is used in multiple sports. Despite being proposed before, no research has been done on TRIMP methods in ice speed skating. The first aim of this study was therefore to analyse whether the TRIMP method is a valid tool to monitor internal training load in speed skating. The second aim was to develop a new TRIMP method specific for speed skating using machine learning algorithms. During the training sessions of fifteen speed skaters (aged +18y), who rode at least at national level, heart rate data and session rate of perceived exertion (sRPE) where recorded. Several established TRIMP score were calculated to validate their usefulness for speed skating. Correlations between the different TRIMP methods and sRPE are moderate to strong, with Spearman’s. However, the Edwards’ TRIMP, which had the highest correlation with sRPE, was able to explain only 30% of the variance of sRPE. Using different machine learning approaches, none of the prediction models were able to predict sRPE scores reliably in the test set. However, predicted sRPE scores resulting from the prediction models correlated moderate to very strong with the measured sRPE, with the decision tree model based on time in zones of the Edwards’ TRIMP correlating the strongest. This study was the first to assess whether the TRIMP method is also a valid method to measure internal training load in speed skating. This study showed that previously developed TRIMP methods might not be suitable for speed skating. New methods, using prediction algorithms to predict internal training load based on time in heart rate zones, are very promising for the use of sport specific internal load measures.
| Item Type: | Thesis (UNSPECIFIED) |
|---|---|
| Supervisor name: | Kempe, dr. M. |
| Faculty: | Medical Sciences |
| Date Deposited: | 16 May 2022 09:49 |
| Last Modified: | 16 May 2022 09:49 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/3340 |
Actions (login required)
 |
View Item |