Individual participant data meta-analysis of eating behaviour traits as effect modifiers in acceptance and commitment therapy-based weight management interventions
Bibliographic record
Abstract
BACKGROUND: Obesity care may benefit from precision approaches, matching patients to treatment types based on their individual characteristics, including eating behaviour traits (EBTs) like emotional eating, uncontrolled eating, external eating, internal disinhibition and restraint. Initial evidence suggests that Acceptance and Commitment Therapy (ACT)-based interventions might address dysregulated EBTs more effectively than standard behavioural treatments. However, it is unclear if ACT is more effective for certain EBT levels. METHODS AND ANALYSIS: . Unlike traditional meta-analyses, IPD meta-analyses re-analyse existing data to answer novel research questions. We identified 16 eligible trials through a systematic search of eight databases until June 20, 2022. We obtained, checked, and harmonised data from 15 trials (N = 2535). We used mixed regression models to investigate both continuous and categorical interaction effects. RESULTS: We found no evidence of interactions between ACT vs. control and baseline EBTs as continuous variables on percentage weight change. However, we found evidence to suggest an added difference in weight change of -4.47% (95%CI -1.15, -7.73) from baseline to 12-months after intervention end in participants with medium levels of internal disinhibition compared to those with high levels. Sensitivity analyses similarly indicated a greater intervention benefit for participants with medium, rather than high, emotional eating levels (in trials that reduced experiential avoidance and in trials using the three-factor eating questionnaire) and internal disinhibition (in analyses of participants with at least 60% attendance). Given the exploratory nature of analyses, results should be interpreted with caution. CONCLUSION: Findings suggest potential non-linear interaction effects of ACT with internal disinhibition but require replication in confirmatory trials. These results may help guide further research on precision approaches based on EBTs.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".