The prognostic role of food addiction for weight loss treatment outcomes in individuals with overweight and obesity: A systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Food addiction (FA) could be a potential prognostic factor of weight loss intervention outcomes. This systematic review with meta‐analysis aimed to (1) estimate this prognostic effect of FA diagnosis and symptom count in individuals with overweight or obesity and (2) explore potential sources of heterogeneity based on properties of the weight loss intervention, study, and sample (e.g., age, gender, ethnicity). We searched PubMed, PsycINFO, and Web of Science for studies reporting on associations between pre‐intervention FA (assessed with the Yale Food Addiction Scale) and weight outcomes after weight loss intervention in individuals with overweight or obesity without a medically diagnosed eating disorder. Twenty‐five studies met inclusion criteria, including 4904 individuals (71% women, M age = 41 years, BMI = 40.82 kg/m 2 ), k = 18 correlations of weight loss with FA symptom count, and k = 21 mean differences between FA diagnosis groups. Pooled estimates of random‐effects meta‐analyses found limited support for a detrimental effect of FA symptom count and diagnosis on weight loss intervention outcomes. Negative associations with FA increased for behavioral weight loss interventions and among more ethnically diverse samples. More research on the interaction of FA with pre‐existing mental health problems and environmental factors is needed.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".