Early Detection of the Marathon Wall to Improve Pacing Strategies in Recreational Marathoners - Mohamad-Medhi El Dandachi; Veronique Billat; Florent Palacin; Vincent Vigneron (AI, 2025)
The individual marathon optimal pacing sparring the runner to hit the “wall” after 2 h of running remain unclear. In the current study we examined to what extent Deep neural Network contributes to identify the individual optimal pacing training a Variational Auto Encoder (VAE) with a small dataset of nine runners. This last one has been constructed from an original one that contains the values of multiple physiological variables for 10 different runners during a marathon. We plot the Lyapunov exponent/Time graph on these variables for each runner showing that the marathon wall could be anticipated. The pacing strategy that this innovative technique sheds light on is to predict and delay the moment when the runner empties his reserves and ’hits the wall’ while considering the individual physical capabilities of each athlete. Our data suggest that given that a further increase of marathon runner using a cardio-GPS could benefit of their pacing run for optimizing their performance if AI would be used for learning how to self-pace his marathon race for avoiding hitting the wall.