A brief mindfulness-based intervention reduces eating disorder symptoms and improves eating self-efficacy and emotion regulation among adults seeking bariatric surgery
Bibliographic record
Abstract
Background Up to 64% of bariatric (weight-loss) surgery-seeking adults report eating disorder (ED) symptoms (i.e., binge eating, emotional eating, addictive-like eating, and grazing) that can interfere with surgery outcomes. Well-designed pre-surgical interventions targeting eating behaviours may reduce ED symptoms and protect against suboptimal surgery outcomes. Objectives Provide proof-of-concept data to inform the design and optimization of a pre-surgical mindfulness-based intervention (MBI) for ED symptoms. Evaluate whether the MBI produces meaningful improvements in ED symptoms and clarify the mechanisms-of-action by which the MBI impacts ED symptoms. Methods Twenty-one pre-surgical patients with obesity and ED symptoms referred to a MBI completed self-report measures of addictive-like eating, binge eating, emotional eating, grazing, mindful eating, eating self-efficacy, and emotion regulation pre-(T1) and post-(T2) MBI. Results Repeated-measures ANOVAs revealed improvements in binge eating symptoms (F (1,20) = 30.38, ηp2 = .60, p < .001) and grazing (F (1,20) = 7.57, ηp2 = .28, p = .012), pre- to post-MBI. Adjusting for multiple comparisons, no significant improvements were found for addictive-like eating or emotional eating. Eating self-efficacy (F (1,20) = 29.70, ηp2 = .60, p < .001) and emotion regulation (F (1,20) = 7.18, ηp2 = .26, p = .014) improved, while mindful eating decreased (F (1,20) = 16.25, ηp2 = .45, p = .001), following the MBI. Bivariate correlations found associations between improvements in the mechanism of eating self-efficacy and improvements in the ED symptom of grazing pre- to post-MBI (r = 0.46, p < .05). As well, improvements in emotion regulation were associated with positive changes in binge and emotional eating and grazing (r = 0.55, p < .001, r = 0.66, p < .001, r = 0.61, p < .05, respectively). Conclusions After participating in the MBI binge eating, grazing, eating self-efficacy, and emotion regulation abilities improved. Further work is needed to understand and mitigate deterioration in mindful eating. Moreover, acceptability and feasibility of the MBI should be assessed prior to testing the MBI in a large-scale efficacy trial. Future research should assess the the impact of this intervention on post-surgery weight-loss, weight-loss maintenance, and maintenance of improvements in ED symptoms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".