Habit Reversal Training and Variants of Decoupling for Use in Body-Focused Repetitive Behaviors. A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background Behavioral interventions hold promise in improving body-focused repetitive behaviors (BFRBs), such as hair pulling and skin picking. The effect of combining different treatment techniques is currently unknown. Methods In the framework of a randomized controlled crossover trial, 334 individuals with at least one BFRB were allocated either to a waitlist control or to three experimental conditions (1:1:1:1). Participants in the experimental condition received self-help manuals teaching habit reversal training (HRT), decoupling (DC) and decoupling in sensu (DC-is) during a six-week period. Treatment conditions differed only in the order of manual presentation. We examined whether applying more than one technique would lead either to add-on or interference effects. Results The three treatment conditions were significantly superior to the waitlist control group in the improvement of BFRBs according to intention-to-treat analyses at a medium effect size (all p ≤ 0.002, d = 0.52 – 0.54). The condition displaying DC first significantly reduced depressive symptoms (p = 0.003, d = 0.47) and improved quality of life (p = 0.011, d = 0.39) compared to the waitlist control. Those using more techniques concurrently showed the strongest decline in BFRB symptoms, even after controlling for days practiced. Participants rated all manuals favorably, with standard DC and HRT yielding greatest acceptability. Discussion Results tentatively suggest the concurrent application of different behavioral treatments for BFRBs leads to add-on effects. Results were superior when DC was practiced first, with positive effects extending to depressive symptoms and quality of life. Integrating the three techniques into one self-help manual or video along with other treatment procedures (e.g., stimulus control techniques) is recommended.
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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 | 0.004 | 0.005 |
| 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) | 0.006 | 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".