Examining the role of emotion regulation in dialectical behaviour therapy self-help for binge-eating disorder
Bibliographic record
Abstract
Background: Dialectical behavior therapy (DBT) for binge-eating disorder (BED) is based on an emotion regulation (ER) model of BED. This model suggests that individuals with BED struggle with ER and engage in binge-eating in an attempt to manage difficult emotions (e.g., numbing, distracting). Thus, DBT for BED focuses on the development of more adaptive ER skills so that individuals are less likely to binge eat to cope with uncomfortable feelings. While DBT is an evidence-based treatment for BED, little research has examined whether ER acts as a mechanism of change in DBT for BED. The goal of the present study was to examine the role of ER in DBT self-help (DBT-SH) for BED outcome. Methods: A secondary analysis of data from a recent randomized controlled trial (RCT) of DBT-SH was conducted. A community sample of 71 adults diagnosed with BED took part in the trial. Participants were randomized to receive either DBT-SH or an active SH control condition for 12 weeks. Assessments of BED symptoms, difficulties in ER, and global distress were conducted at baseline and post-treatment. Results: Participants in both conditions demonstrated sizable improvements in binge-eating and medium-magnitude improvements ER from pre-to-post-treatment. However, there were no significant between-group differences in outcome and the associated effect sizes were small. Contrary to expectations, within the DBT-SH group, pre-post ER change did not significantly predict pre-post binge frequency change or post-treatment remission status and treatment condition did not significantly moderate the strength of this relationship. Effect sizes for these analyses were small. Discussion: The current study failed to provide evidence that ER is a mechanism of change in DBT-SH. Certain methodological limitations including small sample size and low statistical power should be considered when interpreting these results. However, it is also possible that other mechanisms of change besides ER explain how DBT-SH works for BED. Clinical implications and future directions are discussed.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".