A deep learning-based early prediction framework for weight management using real-world lifelog data: GRU-ODE-Bayes model development and validation study.

ABSTRACT:

Objective

Although the growing prevalence of obesity has led to an increased reliance on health-tracking apps for weight management, their effectiveness remains limited owing to missing data and irregular sampling of user-reported records. These issues highlight the need for more sophisticated predictive models to address real-world data limitations and offer personalized interventions. This study developed a gated recurrent unit-ordinary differential equation (GRU-ODE)-Bayes-based deep learning framework to predict successful weight loss using real-world lifelog data.

Methods

We analyzed a retrospective cohort of Noom Coach users, who logged data at least twice a month for six months between 2012 and 2014. We included demographic and self-monitoring variables, with weight l

SUBMITTER: Choi Y 

PROVIDER: S-EPMC12745516 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature