Long‐term impact of a behavioral weight management program on depression and anxiety symptoms: 5‐year follow‐up of the <scp>WRAP</scp> trial
Bibliographic record
Abstract
OBJECTIVE: Behavioral weight management programs may support short-term mental health; however, limited evidence reports the long-term impacts. This study investigated the impact of behavioral weight management programs on depression and anxiety symptoms at 5 years from baseline. METHODS: to a brief intervention (BI) or commercial behavioral weight management program (WW; formerly Weight Watchers) for 12 or 52 weeks (CP12 and CP52, respectively). Linear regression was used to separately compare 5-year changes in depression and anxiety symptoms (by Hospital Anxiety and Depression Scale) between randomized groups, adjusting for baseline depression/anxiety symptoms, gender, and research center. RESULTS: A total of 643 (51%) participants attended the 5-year study follow-up visit. There was no evidence of a difference between the randomized groups for 5-year changes in depression (BI: -0.08 ± 3.29; CP12: 0.02 ± 3.01; CP52: -0.09 ± 3.41) or anxiety (BI: 0.16 ± 3.50; CP12: -0.05 ± 3.55; CP52: -0.66 ± 3.59) symptoms. CONCLUSIONS: This study found no evidence that commercial weight management programs differed in 5-year changes in depression and anxiety symptoms, compared with BI. These are average effects; some individuals experienced increases or decreases in symptoms. Future research should investigate who is at most risk of mental health declines and investigate how to support them. Future trials should transparently report long-term mental health outcomes to strengthen understanding.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".