1. Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation.
- Author
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Presseller EK, Velkoff EA, Riddle DR, Liu J, Zhang F, and Juarascio AS
- Subjects
- Humans, Female, Adult, Male, Machine Learning, Blood Glucose metabolism, Middle Aged, Blood Glucose Self-Monitoring, Sensitivity and Specificity, Ecological Momentary Assessment, Continuous Glucose Monitoring, Vomiting, Binge-Eating Disorder diagnosis, Binge-Eating Disorder classification, Binge-Eating Disorder blood, Bulimia classification, Bulimia diagnosis, Bulimia blood
- Abstract
Objective: Binge eating and self-induced vomiting are common, transdiagnostic eating disorder (ED) symptoms. Efforts to understand these behaviors in research and clinical settings have historically relied on self-report measures, which may be biased and have limited ecological validity. It may be possible to passively detect binge eating and vomiting using data collected by continuous glucose monitors (CGMs; minimally invasive sensors that measure blood glucose levels), as these behaviors yield characteristic glucose responses., Method: This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting., Results: The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71., Discussion: Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications., (© 2024 Wiley Periodicals LLC.)
- Published
- 2024
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