Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

Autor: Presseller EK; Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, Pennsylvania, USA.; Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA., Parker MN; Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.; Section on Growth and Obesity, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), Bethesda, Maryland, USA., Zhang F; Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA., Manasse S; Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, Pennsylvania, USA.; Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA., Juarascio AS; Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, Pennsylvania, USA.; Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: European eating disorders review : the journal of the Eating Disorders Association [Eur Eat Disord Rev] 2024 Jul; Vol. 32 (4), pp. 828-837. Date of Electronic Publication: 2024 Apr 03.
DOI: 10.1002/erv.3094
Abstrakt: Objective: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning.
Method: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes.
Results: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94).
Conclusions: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.
(© 2024 Eating Disorders Association and John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz: