Lessons Learned from an ACT-Based Physician-Delivered Weight Loss Intervention: A Pilot RCT Demonstrates Limits to Feasibility
Bibliographic record
Abstract
Abstract Background: Acceptance and Commitment Therapy (ACT) interventions have shown to be effective in facilitating weight loss for emotional eaters, however, the lack of accessibility of these interventions limits their impact. The present study aimed to increase the accessibility of an ACT intervention for emotional eaters through delivery by physicians. Methods: This two-arm pilot randomized controlled trial tested the effectiveness and feasibility of a brief ACT intervention for emotional eaters compared with standard care at a weight loss clinic in Toronto, Canada. Primary outcomes were changes in weight and emotional eating. Treatment satisfaction was also assessed. Results: Participants in neither condition lost weight. Both conditions displayed decreases in emotional eating, but no condition interaction was found. Both patients and physicians reported high treatment satisfaction with the ACT intervention. However, there were high attrition rates and variability in intervention completion times. Conclusion: The ACT intervention led to reductions in emotional eating and was well received by patients and physicians alike. However, the present study identified high attrition as a limitation to the feasibility of this mode of intervention delivery. Future interventions may be more effectively delivered in primary care settings by encouraging further brevity and exploring delivery by other health professionals. Trial registration: ClinicalTrials.gov NCT03611829. Registered 26 July 2018. Retrospectively registered.
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".