Weight Gain After Smoking Cessation and Lifestyle Strategies to Reduce it
Bibliographic record
Abstract
BACKGROUND: Weight gain following smoking cessation reduces the incentive to quit, especially among women. Exercise and diet interventions may reduce postcessation weight gain, but their long-term effect has not been estimated in randomized trials. METHODS: We estimated the long-term reduction in postcessation weight gain among women under smoking cessation alone or combined with (1) moderate-to-vigorous exercise (15, 30, 45, 60 minutes/day), and (2) exercise and diet modification (≤2 servings/week of unprocessed red meat; ≥5 servings/day of fruits and vegetables; minimal sugar-sweetened beverages, sweets and desserts, potato chips or fried potatoes, and processed red meat). RESULTS: Among 10,087 eligible smokers in the Nurses' Health Study and 9,271 in the Nurses' Health Study II, the estimated 10-year mean weights under smoking cessation were 75.0 (95% CI = 74.7, 75.5) kg and 79.0 (78.2, 79.6) kg, respectively. Pooling both cohorts, the estimated postcessation mean weight gain was 4.9 (7.3, 2.6) kg lower under a hypothetical strategy of exercising at least 30 minutes/day and diet modification, and 5.9 (8.0, 3.8) kg lower under exercising at least 60 minutes/day and diet modification, compared with smoking cessation without exercising. CONCLUSIONS: In this study, substantial weight gain occurred in women after smoking cessation, but we estimate that exercise and dietary modifications could have averted most of it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".