Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation.

Autor: Presseller EK; Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania, USA.; Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, Pennsylvania, USA., Velkoff EA; Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, Pennsylvania, USA., Riddle DR; Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, Pennsylvania, USA., Liu J; Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania, USA.; Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, Pennsylvania, USA., Zhang F; Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania, USA., Juarascio AS; Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania, USA.; Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: The International journal of eating disorders [Int J Eat Disord] 2024 Nov; Vol. 57 (11), pp. 2285-2291. Date of Electronic Publication: 2024 Jul 19.
DOI: 10.1002/eat.24266
Abstrakt: 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.)
Databáze: MEDLINE
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