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

Year: 2025
Type: article
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 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: = 3375). Participants who frequently logged their weight, exercise, meals, and snacks were more likely to achieve weight loss. The model achieved an ROC AUC of 0.830 [95% confidence interval 0.819-0.840] and PRC AUC of 0.727 [0.707-0.746] in the validation set, and an ROC AUC of 0.821 [0.806-0.835] and PRC AUC of 0.717 [0.689-0.744] in the test set, using only the first four weeks of lifelog data. Early weight change and initial weight were the most important features as determined by the integrated gradients. 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.
DOI: 10.1177/20552076251406656
Language: English
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W7117330586
FWCI:
Cites: 33
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