Abstract
BACKGROUND: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. AIM: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. METHODS: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively. RESULTS: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. CONCLUSION: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.
Original language | English |
---|---|
Pages (from-to) | 634-642 |
Number of pages | 9 |
Journal | International Journal of Sports Physiology and Performance |
Volume | 18 |
Issue number | 6 |
Early online date | 20 Apr 2023 |
DOIs | |
Publication status | Published - Jun 2023 |
Funding
The authors would like to thank the speed skaters and coaches for their support and participation in this study. In addition, the authors would like to thank Wouter van der Ploeg and the Royal Dutch Skating Federation (KNSB) for their support with data collection and to acknowledge the contribution of Sport Data Valley to this study. This research was co-funded by regieorgaan SIA RAAK publiek, RAAK.PUB06.043.
Funders | Funder number |
---|---|
KNSB | |
Royal Dutch Skating Federation | |
Sport Data Valley | RAAK.PUB06.043 |
Keywords
- big data
- data science
- heart rate
- KDE
- kernel density estimation
- speed skating
- training load monitoring
- velocity