Abstract
Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.
Highlights
Wearable exercise trackers provide data that encode information on individual running performance
A linear relation p(v) maps running velocity v to relative power with p(vm) = 1 defining vm as an aerobic power index associated with maximal aerobic power (MAP) beyond which anaerobic energy supply can yield p > 1 for a short time only
Anaerobic supply contributes to maximal exercise shorter than a crossover time tc which in our model is the longest time over which MAP can be sustained
Summary
Wearable exercise trackers provide data that encode information on individual running performance. Endurance athletes like runners and cyclists currently upload from GPS enabled sensors more than a billion activities per year worldwide[6] In principle, these data provide an exciting opportunity to monitor human physiology noninvasively under real-world conditions outside the laboratory. The undeniable fact that the best test of running performance is an actual race and not laboratory tests highlights the need for models constructed to extract performance indices of an athlete from their regular exercise performance For these reasons, models that can utilize data from wearable devices and turn those into meaningful performance parameters may offer a cost effective alternative approach to laboratory testing. The main aim of our work is to demonstrate the feasibility of extracting performance indices from real-world racing results in a big population of runners and to use these indices to predict accurate race times and evaluate the effect and efficiency of training. Our approach represents a potentially powerful platform to enlarge dramatically the number of tested subjects in sports science by extending performance index acquisition from conventional laboratory testing to real-world conditions with the aid of mathematical modeling and wearable technology
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