Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge‐Spectrum Eating Disorders: A Preliminary Investigation.
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 p...
| Publicado en: | International Journal of Eating Disorders Vol. 57; no. 11; pp. 2285 - 2292 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Nov2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180851295&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180851295 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02763478 IJD jtl: International Journal of Eating Disorders issn: 02763478 maglogo: Y pubinfo: dt: Nov2024 vid: 57 iid: 11 pid: 480 pub: Wiley-Blackwell artinfo: ui: 180851295 10.1002/eat.24266 ppf: 2285 ppct: 7 formats: tig: atl: Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge‐Spectrum Eating Disorders: A Preliminary Investigation. aug: au: Presseller, Emily K. Velkoff, Elizabeth A. Riddle, Devyn R. Liu, Jianyi Zhang, Fengqing Juarascio, Adrienne S. affil: Department of Psychological and Brain Sciences, Drexel University, Philadelphia Pennsylvania,, USA Center for Weight, Eating, and Lifestyle Science, Drexel University, Philadelphia Pennsylvania,, USA su: Pennsylvania Bulimia Binge-eating disorder Research funding Descriptive statistics Blood sugar Continuous glucose monitoring Vomiting Machine learning Confidence intervals Algorithms Sensitivity & specificity (Statistics) sug: subj: Bulimia Binge-eating disorder Pennsylvania Research funding Descriptive statistics Blood sugar Continuous glucose monitoring Vomiting Machine learning Confidence intervals Algorithms Sensitivity & specificity (Statistics) keyword: binge eating binge eating disorder bulimia nervosa continuous glucose monitor purging vomiting binge eating binge eating disorder bulimia nervosa continuous glucose monitor purging vomiting ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|