Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments.

ABSTRACT: Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO₂max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third ext

SUBMITTER: Spathis D 

PROVIDER: S-EPMC9718831 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature