Coordinating smoking cessation treatment with menstrual cycle phase to improve quit outcomes (MC-NRT): study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Women experience greater difficulty achieving smoking abstinence compared to men. Recent evidence suggests that hormonal fluctuations during different phases of the menstrual cycle can contribute to lower smoking abstinence rates following a quit attempt among women. However, these findings are limited by small sample sizes and variability among targeted smoking quit dates. This clinical trial aims to clarify whether targeting the quit date to the follicular or luteal phase of the menstrual cycle can improve smoking abstinence. Methods Participants will enroll in an online smoking cessation program providing nicotine replacement therapy (NRT) and behavioral support. We will randomize 1200 eligible individuals to set a target quit date: (1) during the mid-luteal phase, (2) during the mid-follicular phase, or (3) 15–30 days after enrollment with no regard to the menstrual cycle phase (usual practice). Participants will receive a 6-week supply of combination NRT consisting of a nicotine patch plus their choice of nicotine gum or lozenge. Participants will be instructed to start using NRT on their target quit date. Optional behavioral support will consist of a free downloadable app and brief videos focusing on building a quit plan, coping with cravings, and relapse prevention, delivered via e-mail. Smoking status will be assessed via dried blood spot analysis of cotinine concentration at 7 days, 6 weeks, and 6 months post-target quit date. Discussion We aim to overcome the limitations of previous studies by recruiting a large sample of participants and assigning target quit dates to the middle of both the follicular and luteal phases. The results of the trial can further elucidate the effects of the menstrual cycle on smoking cessation outcomes and whether it is beneficial to combine menstrual cycle phase timing strategies with accessible and low-cost NRT. Trial registration ClinicalTrials.gov NCT05515354. Registered on August 23, 2022.
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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 | 0.046 | 0.043 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.017 |
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".