The Healthy Weights Initiative: a community-based obesity reduction program with positive impact on depressed mood scores
Bibliographic record
Abstract
OBJECTIVES: The risk for many chronic diseases increases with obesity. In addition to these, the risk for depression also increases. Exercise interventions for weight loss among those who are not overweight or obese have shown a moderate effect on depression, but few studies have looked at those with obesity. The objectives of this study were to determine 1) the prevalence of depressed mood in obese participants as determined by the Beck Depression Inventory II at baseline and follow-up; 2) the change in depressed mood between those who completed the program and those who did not; and 3) the differences between those whose depressed mood was alleviated after the program and those who continued to have depressed mood. METHODS: Depressed mood scores were calculated at baseline and follow-up for those who completed the program and for those who quit. Among those who completed the program, chi-squares were used to determine the differences between those who no longer had depressed mood and those who still had depressed mood at the end of the program, and regression analysis was used to determine the independent risk factors for still having depressed mood at program completion. RESULTS: Depressed mood prevalence decreased from 45.7% to 11.7% (P<0.000) from baseline to follow-up among those who completed the program and increased from 44.8% to 55.6% (P<0.000) among those who quit. After logistic regression, a score of <40 in general health increased the risk of still having depressed mood upon program completion (odds ratio [OR] 3.39; 95% CI 1.18-9.72; P=0.023). CONCLUSION: Treating depressed mood among obese adults through a community-based, weight-loss program based on evidence may be an adjunct to medical treatment. More research 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".