Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

ABSTRACT: Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of p

SUBMITTER: Ravindra NG 

PROVIDER: S-EPMC10539360 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature