Real world data, such as that collected routinely in hospitals and point-of-care institutions, but also those now available from wearable devices, is projected to change healthcare dramatically. The data collected by wearable exercise trackers hold the potential to enhance our understanding of the interplay between training and performance. Our results are reported in a paper in Nature Communications.
In this work with Polar Electro Oy we linked smartwatch-collected running data from approximately 14,000 runners to their individual physiological parameters, via a mathematical model of human running performance. We used this framework to predict marathon race times accurately and identify key predictive parameters of running performance, such as a version of lactate threshold, using only information on the distance and times of their training runs (around 1.6 million exercise sessions in total). Moreover, we provide insights into how features of training sessions are associated with race performance.
Invasive laboratory tests are considered the gold standard for accurately measuring important physiological parameters like oxygen consumption, maximal aerobic speed, lactate threshold. But because these analyses are done in an artificial environment, the results do not always transfer to the real-world. Our approach complements such lab tests and can assess important parameters such endurance that are very difficult to measure in a lab.
Our model might be useful for beginners as well as professional athletes looking for insight into how their training is affecting their performance. A runner who has completed some races on shorter distances, say 5km and 10km, could use our approach to estimate maximal aerobic speed and endurance, and from these two parameters get a good estimate for what to expect in a marathon race, and how to train for a marathon. Other applications, for runners that like only shorter distances, would be tracking over time of the two model parameters and hence potential race performance or health status.
Press information and first media coverage:
CNRS Press Alert
New Scientist (6 October 2020)
Inverse (6 October 2020)