An individual participant data meta-analysis investigating the mediating role of eating behavior traits in Acceptance and Commitment Therapy-based weight management interventions
Bibliographic record
Abstract
BACKGROUND: Identifying mechanisms of action can aid the refinement of weight management interventions. Acceptance and Commitment Therapy (ACT)-based interventions may support long-term weight management by improving self-regulation of eating behavior traits (EBTs). However, it remains unclear if changing EBTs like emotional eating, external eating, internal disinhibition, and restraint during ACT causes improved weight management. METHODS: For this 1-stage Individual Participant Data (IPD) meta-analysis, we requested IPD from 9 trials identified through a systematic search of ACT-based interventions for adults with a body mass index >25 kg/m2 across 8 databases until June 20, 2022. We obtained, checked, and harmonized data from 8 of those trials (N = 1391) and conducted separate structural equation models with complex survey analysis to estimate short- and long-term mediating effects of changes in each EBT on percent weight change. RESULTS: In the short-term (ie, follow-up closest to intervention end), we found indirect effects of the intervention on percent weight change through changes in emotional eating, external eating, internal disinhibition, and restraint. Each 1-unit change in these EBTs led to a 0.02% (95% CI, 0.05-0.001), 0.03% (95% CI, 0.06-0.001), 0.05% (95% CI, 0.11-0.02), and 0.09% (95% CI, 0.14-0.04) decrease in weight, respectively. In the long term (ie, 12 months after intervention end), we found both indirect and total effects for emotional eating, internal disinhibition, and restraint, with EBT changes explaining 23.78%, 23.12%, and 25.64% of total effects. CONCLUSION: Findings suggest small partial mediating effects of ACT on weight through EBTs. Targeting EBTs may support improved weight management outcomes, particularly in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.061 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".