Paris: Scientists have developed a mathematical model that promises to optimize training for athletes in the 400m and 1500m track and field events, according to a study published on Tuesday.
The model is based on performance data collected from elite athletes including Olympic 1500m champion Jakob Ingebrigtsen of Norway, Dutch indoor 400m world record holder Femke Bol and Britain’s Matthew Hudson-Smith at the 2022 European Championships in Munich.
“We wanted to understand what happens at a physiological level in the 400 meters, which is a sprint, and in the 1,500 meters, which is the first endurance race,” Amandine Aftalion, co-author of the study published in the journal Frontiers in Sport and Active Living, told AFP.
Thanks to the new technology of GPS sensors placed under the athletes’ jerseys, the scientists were able to precisely monitor the speed of each athlete, with their position indicated ten times per second.
They integrated equations to calculate physiological variables — energy expenditure during exercise, maximal oxygen consumption (VO2), running economy and motor control — in other words, the brain’s role in the movement process, such as motivation, which plays a role in the delay in action.
The data was later examined by scientists from the French National Center for Scientific Research (CNRS), who looked at how it affected the speed of the champions.
“By quantifying costs and benefits, the model provides immediate access to the best strategy, so that the runner ‘serves’ in an optimized way,” CNRS said in a statement.
The study shows the importance of starting quickly in the first 50 meters for reasons related to the rate of oxygen consumption or slowing down less at the end of 400 meters.
In particular, the simulations explained middle-distance runner Ingebrigtsen’s performance by his ability to quickly reach maximal oxygen consumption (VO2) and maintain it throughout the race.
A quirk that allows the Olympic champion to “run faster than his competitors during the race, even though we see him start less strongly,” Aftalion explained.
The model could lead to performance support software so that coaches can “refine race strategy in relation to a runner’s physiological profile”, the researcher concluded.
