Relation between estimated cardiorespiratory fitness and running performance in free-living: An analysis of HRV4Training data

In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representative of fitness as lower HR at a given speed is expected for more fit individuals. Then, we analyze the relation between estimated CRF and running performance for 10 km, half marathon and full marathon runs. CRF estimation models were developed using lab-based VO
₂
max measurements. CRF estimates were obtained from data collected in unsupervised free-living in a sample of 532 runners for a period ranging between 1 and 8 months using the HRV4Training app. During the same period, running performance was determined for all runners. We show that the speed to HR ratio provides higher accuracy in CRF estimation compared to resting HR or no-physiological data (15% to 18% reduction in RMSE for person-independent models). Secondly, we found moderate to strong correlations between CRF estimated from free-living data and running performance (Pearson's r = 0.56 - 0.61). We conclude that estimating CRF in free-living using mobile technology and data integration can be a valuable tool to support individualized training plans and to track fitness and performance outside laboratory settings.