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

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 loss ≥5% in three months as the primary outcome. We evaluated the model performance using the area under the receiver operating characteristic curve (ROC AUC) and precision-recall curve (PRC AUC). Results: This study utilized a large-scale dataset ( Conclusion: The proposed model predicted early outcomes in weight management and might contribute to developing effective intervention methods for participants at risk of failure and reducing the burden of frequent self-reporting.