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Paper: Analysing the relation between training patterns and performance in over 2000 athletes

In the paper “Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living”, we analyse for the first time a unique database of over 2000 runners regarding their 10km performance in relation to anthropometrics, resting heart rate (HR) and heart rate variability (HRV), and training patterns regularly acquired over two years using smartphones.

The paper has is presented at the EBMC 2018 conference in Honolulu during July 17-21 and published in the Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’18).

Abstract

In this work, we use data acquired longitudinally, in free-living, to provide accurate estimates of running performance. In particular, we used the HRV4Training app and integrated APIs (e.g. Strava and TrainingPeaks) to acquire different sets of parameters, either via user input, morning measurements of resting physiology, or running workouts to estimate running 10 km running time. Our unique dataset comprises data on 2113 individuals, from world class triathletes to individuals just getting started with running, and it spans over 2 years. Analyzed predictors of running performance include anthropometrics, resting heart rate (HR) and heart rate variability (HRV), training physiology (heart rate during exercise), training volume, training patterns (training intensity distribution over multiple workouts, or training polarization) and previous performance. We build multiple linear regression models and highlight the relative impact of different predictors as well as trade-offs between the amount of data required for features extraction and the models accuracy in estimating running performance (10 km time). Cross-validated root mean square error (RMSE) for 10 km running time estimation was 2.6 minutes (4% mean average error, MAE, 0.87 R2), an improvement of 58% with respect to estimation models using anthropometrics data only as predictors. Finally, we provide insights on the relationship between training and performance, including further evidence of the importance of training volume and a polarized training approach to improve performance.

Reference

Marco Altini, Oliver Amft, "Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living", Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), IEEE, Jul 2018.

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