Emotion-Focused Therapy for Binge-Eating Disorder: A Pilot Randomized Control Trial
Bibliographic record
Abstract
Abstract Background:Research into psychotherapy for binge-eating disorder (BED) has focused mainly on cognitive behavioral therapies, but efficacy, failure to abstain, and dropout rates continue to be problematic. The experience of negative emotions is among the most accurate predictors for the occurrence of binge eating episodes in BED, suggesting benefits to exploring other psychological treatments with a more specific focus on the role of emotion. The present study aimed to build upon the emerging evidence for emotion-focused therapy (EFT) as a treatment for BED by examining the outcomes of a pilot randomized waitlist-controlled trial of individual EFT for BED.Methods:Twenty-one participants were assessed on the primary outcome measures of objective binge episodes, the number of days on which objective binge episodes occurred, and binge eating symptoms and the secondary outcome measures of anxiety and depressive symptoms. The treatment consisted of 12 weekly one-hour sessions of EFT for maladaptive emotions over three months. A series of between groups repeated measures analyses of variance (ANOVA) was used to test the hypothesis that those receiving the treatment would demonstrate a greater degree of improvement in primary outcome measures compared to participants on the waitlist. A series of within-groups repeated-measures ANOVA was then used to test the hypothesis that participation in the EFT intervention would result in significant improvements in the primary and secondary outcome measures from pre to post-therapy, and then maintained at each follow-up period.Results:Participants receiving the EFT demonstrated a greater degree of improvement in primary outcome measures compared to participants on the waitlist. Participation in the EFT intervention resulted in significant improvements in all primary outcome measures and anxiety, but not depressive symptoms. The intervention also demonstrated a relatively low dropout rate when compared to other psychological therapy interventions for BED.Conclusions:These findings provide further preliminary evidence that individual EFT may be an efficacious treatment for BED and provide support for more extensive randomized control trials to test the efficacy and effectiveness of EFT for BED further.Trial registration: The study was retrospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000563965) on the 14 May 2020https://www.anzctr.org.au/ACTRN12620000563965.aspx
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".