Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population.

ABSTRACT: Accelerometers are widely used to measure physical activity behaviour, including in children. The traditional method for processing acceleration data uses cut points to define physical activity intensity, relying on calibration studies that relate the magnitude of acceleration to energy expenditure. However, these relationships do not generalise across diverse populations and hence they must be parametrised for each subpopulation (e.g., age groups) which is costly and makes studies across diverse populations and over time difficult. A data-driven approach that allows physical activity intensity states to emerge from the data, without relying on parameters derived from external populations, offers a new perspective on this problem and potentially improved results. We applied an unsupervised

SUBMITTER: Thornton CB 

PROVIDER: S-EPMC10075441 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature