Physical activity as an aid to smoking cessation during pregnancy: Two feasibility studies
Bibliographic record
Abstract
BACKGROUND: Pharmacotherapies for smoking cessation have not been adequately tested in pregnancy and women are reluctant to use them. Behavioural support alone has a modest effect on cessation rates; therefore, more effective interventions are needed. Even moderate intensity physical activity (e.g. brisk walk) reduces urges to smoke and there is some evidence it increases cessation rates in non-pregnant smokers. Two pilot studies assessed i) the feasibility of recruiting pregnant women to a trial of physical activity for smoking cessation, ii) adherence to physical activity and iii) women' perceptions of the intervention. METHODS: Pregnant smokers volunteered for an intervention combining smoking cessation support, physical activity counselling and supervised exercise (e.g. treadmill walking). The first study provided six weekly treatment sessions. The second study provided 15 sessions over eight weeks. Physical activity levels and continuous smoking abstinence (verified by expired carbon monoxide) were monitored up to eight months gestation. RESULTS: Overall, 11.6% (32/277) of women recorded as smokers at their first antenatal booking visit were recruited. At eight months gestation 25% (8/32) of the women achieved continuous smoking abstinence. Abstinent women attended at least 85% of treatment sessions and 75% (6/8) achieved the target level of 110 minutes/week of physical activity at end-of-treatment. Increased physical activity was maintained at eight months gestation only in the second study. Women reported that the intervention helped weight management, reduced cigarette cravings and increased confidence for quitting. CONCLUSION: It is feasible to recruit pregnant smokers to a trial of physical activity for smoking cessation and this is likely to be popular. A large randomised controlled trial is needed to examine the efficacy of this intervention.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".