Multivariate dynamic association analysis between sprint interval training and adolescent freestyle swimming performance.

Complex network modeling has been applied in sports science research; yet few studies have applied it to capture the dynamic evolution of multivariate relationships within a single session of high-intensity training. This study is the first to use complex network modeling to examine the dynamic associations among kinematic (stroke rate, stroke length), metabolic (blood lactate), and perceived exertion (Rating of Perceived Exertion, RPE) variables and 100-meter freestyle performance. The analysis was conducted on 16 adolescent swimmers during a 6 × 50-meter sprint interval training (6 × 50 m SSIT) protocol. The findings showed that the overall network topology remained stable throughout the SSIT protocol, suggesting that multiple training bouts within the session collectively contributed to enhancing 100-meter freestyle performance (network density: from 42.65% to 49.17%; modularity: from 0.2 to 0.24). Nevertheless, the relative importance of individual variables shifted markedly during the training process. Specifically, the nodal centrality of swimming velocity, blood lactate, and RPE increased substantially, positioning them as central hubs mediating performance outcomes. In contrast, the influence of stroke rate progressively declined, whereas stroke length remained relatively stable. This research introduces a powerful analytical tool for the dynamic assessment of training processes and provides valuable insights into the adaptive mechanisms shaping sport-specific anaerobic capacity in competitive swimmers.