Prevalence of Emotional and Binge Eating Among Patients With Obesity Attending a Specialist Weight Management Service for Bariatric Surgery in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Emotional eating (EE) is a tendency to consume food in response to positive or negative emotions, leading to obesity and an increased Body Mass Index (BMI). Evidence supports the positive association between EE and binge eating disorder (BED), but little is known about its prevalence among patients referred for bariatric surgery and the psychological characteristics of this patient population. We aim to examine (i) the prevalence of binge eating and EE, (ii) its association with the prevalence of anxiety, depression, diabetes and hypertension and (iii) the correlation between anxiety and depression with emotional and binge eating behaviours among patients attending a regional bariatric service in the UK. METHOD: A cross-sectional case file design involving 285 participants (mean age = 43.88 ± 11.5, female (80.7%) and male (19.3%)) was used. Outcome measures included body weight, BMI, the Weight Loss Readiness (WLR) Questionnaire, Generalised Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire (PHQ-9) and Alcohol Use Disorders Identification Test- Consumption (AUDIT-C). RESULTS: Within this patient group, the prevalence of binge eating and EE were 28.8% and 22.1% respectively. Among these, 19.3% had diabetes mellitus, 24.8% hypertension, 21% harmful alcohol use, 65% had high anxiety score and 77% high depression scores. Most correlations between body weight and variables like AUDIT-C, GAD-7, PHQ-9 scores and WLR scores for hunger, binge eating and EE were not significant. A positive association was observed between depression and anxiety with binge eating, and EE behaviours. CONCLUSION: Patients awaiting bariatric surgery have a wide range of mental and physical health comorbidities, with evidence of positive associations between higher depression and anxiety levels with abnormal eating behaviours. These findings highlight the need for screening for comorbidities in this patient population to optimise patient outcomes postbariatric surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".