Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach

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.

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

“…Firstly, despite the recommendation to assess both internal and external training load parameters for insights into training stress ( Halson, 2014 ), there is currently no standardized direct coupling of internal and external training loads ( van der Zwaard et al, 2023 ). Using weightlifting athlete training data as an example, this study quantifies athletes’ external load and analyzes their training duration.…”

Research application of session-RPE in monitoring the training load of elite endurance athletes

“…One studies have proposed the use of plotting heart rate and velocity sequences (2-dimensional kernel density estimation-KDE) to couple internal and external loads, and found that ‘all training session types differed when comparing KDEs for heart rate and velocity’ (both p < 0.001; Van der Zwaard et al, 2023 ). The speed skating athletes investigated in this study applied various types of variable intensity training such as Extensive endurance, Extensive interval, Intensive endurance, Tempo, Sprint, etc.…”