Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale.

ABSTRACT: Physical activity improves quality of life and protects against age-related diseases. With age, physical activity tends to decrease, increasing vulnerability to disease in the elderly. In the following, we trained a neural network to predict age from 115,456 one week-long 100Hz wrist accelerometer recordings from the UK Biobank (mean absolute error = 3.7±0.2 years), using a variety of data structures to capture the complexity of real-world activity. We achieved this performance by preprocessing the raw frequency data as 2,271 scalar features, 113 time series, and four images. We defined accelerated aging for a participant as being predicted older than one's actual age and identified both genetic and environmental exposure factors associated with the new phenotype. We performed a genome wid

SUBMITTER: Le Goallec A 

PROVIDER: S-EPMC9931315 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature