Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors

Abstract

Background Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO 2 max), or indirectly assessed using heart rate response to a standard exercise test. However, such testing is costly and burdensome, limiting its utility and scalability. Fitness can also be approximated using resting heart rate and self-reported exercise habits but with lower accuracy. Modern wearables capture dynamic heart rate data which, in combination with machine learning models that find latent patterns in high-dimensional sensor signals, could improve fitness prediction. Methods In this work, we analyze movement and heart rate signals from wearable sensors in free-living conditions from 11,059 participants who also underwent a standard exercise test, along with a longitudinal repeat cohort of 2,675 participants. We design algorithms and models that convert raw sensor data into cardio-respiratory fitness estimates, and validate these estimates' ability to capture fitness profiles in a longitudinal cohort over time while subjects engaged in real-world (non-exercise) behavior. Additionally, we validate our methods with a third external cohort of 181 participants who underwent maximal VO 2 max testing, which is considered the gold standard measurement because it requires reaching one's maximum heart rate and exhaustion level. Findings Our results show that the developed models yield high correlation (r = 0.82, 95CI 0.80-0.83), when compared to the ground truth in a holdout sample. These models outperform conventional non-exercise fitness models and traditional bio-markers using measurements of normal daily living without the need of a specific exercise test. Additionally, we show the adaptability and applicability of this approach for detecting fitness change over time in the longitudinal subsample who repeated measurements after 7 years. Interpretation These results demonstrate the value of wearables for the longitudinal assessment of fitness that today can be measured only with laboratory tests.