Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders

Glycemic variability and weight-focused eating behaviors among adolescents and young adults with type 1 diabetes†

OBJECTIVE

Type 1 diabetes (T1D) disease management and associated glycemic fluctuations can disrupt experiences of hunger and satiety, which may increase risk for disordered eating behaviors. Glycemic variability may be a useful trigger for just-in-time interventions for disordered eating behaviors. In this exploratory study, we hypothesized that two metrics of glycemic variability would be associated with greater hunger and predict eating behaviors for weight loss or maintenance in adolescents and young adults with T1D.

METHODS

Individuals with T1D were recruited from a university diabetes clinic (N = 34; 50% female, Mage = 19.53 years, MHbA1c = 7.98%; 29% Hispanic/Latinx; 79% White). Participants wore a blinded continuous glucose monitor for 5 days, and completed ecological momentary assessments (four prompts per day) that included measures of hunger and eating behaviors for weight loss or maintenance purposes. Generalized and linear mixed models were used to test hypotheses.

RESULTS

Approximately 30% of participants were at risk for an eating disorder based on Diabetes Eating Problem Survey-Revised scores. Greater glycemic variability did not predict hunger, but was associated with increased odds of endorsing any eating behavior for weight loss or maintenance purposes within-person (ps<.05). Greater hunger was associated with increased odds of endorsing disordered eating behavior within-person (p<.05).

CONCLUSIONS

This exploratory study provides some preliminary evidence that times of greater hunger and glycemic variability may be when individuals with T1D may be at higher risk of engaging in behaviors to lose or maintain weight.

Effectiveness of written emotional disclosure interventions for eating disorders: a systematic review and meta-analysis

Background

Eating disorders are illnesses that can seriously impair the health and wellbeing of patients. Written emotional disclosure has been recognized as a therapeutic technique that may be helpful in aiding patients' emotional and psychological adjustment. However, it is unclear what favorable effects eating disorder patients can derive from written emotional disclosure therapy. This study aimed to review the effectiveness of written emotional disclosure in treating eating disorders using a systematic review and meta-analysis.

Objective

This study was to examine the validity of written emotional disclosure intervention for eating disorders as well as to provide guidelines for the management of eating disorders in patients.

Methods

Researchers independently developed inclusion and exclusion criteria according to the PICOS principle and systematically searched English literature databases such as PubMed, Medline, Web of Science, Cochrane Library, CINAHL, EBSCO, Embase, and so on, from the time of library construction to December 2023. Cochrane Risk of Bias version 2 (RoB 2) was used to evaluate studies included in this review. All the studies included in this study were randomized controlled trials. Accurate information was extracted and then subjected to meta-analysis with Review Manager 5.4 software. The credibility of the evidence of the studies was assessed using GRADEprofiler 3.6 software.

Results

The final analysis included 13 randomized controlled trials involving 1,444 participants. The written emotional disclosure intervention can decrease eating disorder symptoms scores (SMD = -0.20, 95% CI [-0.34, -0.05], Z = 2.59, p = 0.01), body dissatisfaction scores (SMD = 0.37, 95% CI [0.21, 0.52], Z = 4.59, p < 0.001), and thin ideal internalization score (SMD = 0.42, 95% CI [0.22, 0.62], Z = 4.12, p < 0.001). Anxiety scores (MD = 0.43, 95% CI [-0.77, 1.63], Z = 0.70, p = 0.48), depression scores (MD = -0.66, 95% CI [-1.78, 0.47], Z = 1.14, p = 0.25) and negative affect scores (SMD = 0.51, 95% CI [-0.24, 1.27], Z = 1.33, p = 0.18), with no statistically significant differences.

Conclusion

In conclusion, this systematic review analyzed the existing literature and showed that written emotional disclosure intervention is effective in alleviating eating disorder symptoms and patients' body image problems, but there is insufficient evidence in alleviating depression, anxiety, and negative affect. However, the evidence is limited. Therefore, more research is needed in the future to further enrich the evidence for written emotional disclosure intervention in the field of eating disorders.

Systematic review registration

https://www.crd.york.ac.uk/prospero/,CRD42023445577.

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 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.

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

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.