The Effects of Scheduled Smoking Reduction and Precessation Nicotine Replacement Therapy on Smoking Cessation: Randomized Controlled Trial With Compliance
Bibliographic record
Abstract
BACKGROUND: Smoking remains a major public health problem, and it is important to provide a variety of efficacious and appealing options to encourage smokers to quit smoking. Scheduled smoking is a method of gradual reduction, preparing smokers to quit by systematically reducing cigarette consumption according to a predetermined schedule that increases the time between cigarette consumption. Gradual reduction may be preferred to abrupt quitting, but the efficacy of this cessation approach is unclear. OBJECTIVE: This study aims, first, to evaluate the overall effectiveness of scheduled smoking alone, or in combination with precessation nicotine replacement therapy (NRT), versus standard NRT starting on the quit date with no prior smoking reduction and, second, to evaluate the impact of schedule compliance on the effectiveness of the intervention. METHODS: A total of 916 participants recruited from the Houston metropolitan area were randomly assigned to 1 of the following 3 groups: scheduled smoking plus a precessation nicotine patch (n=306, 33.4%), scheduled smoking only with no precessation patch (n=309, 33.7%), and enhanced usual care (n=301, 32.9%) control. The primary abstinence outcomes were carbon monoxide-verified, self-reported, 7-day point prevalence abstinence at 2 and 4 weeks after the quit date. Unadjusted and adjusted logistic regression analyses were performed to evaluate the intervention effect. Scheduled smoking was implemented using a handheld device for 3 weeks before quitting. This trial was not registered because data collection began before July 1, 2005. RESULTS: Results for the first aim showed no overall differences in abstinence among the 3 groups in both the unadjusted and adjusted models. However, the results for the second aim showed a clear effect on abstinence by schedule compliance at 2 and 4 weeks and 6 months after quitting (odds ratio [OR] 2.01, 95% CI 1.31-3.07), 4 weeks (OR 1.58, 95% CI 1.05-2.38), and 6 months (OR 1.68, 95% CI 1.04-2.64), with the differences at 2 and 4 weeks after quitting being the most robust. We also found that scheduled smoking was related to a reduction in nicotine withdrawal, negative affect, and craving when compared with the controls. CONCLUSIONS: Scheduled smoking, when combined with precessation use of NRT, can result in significantly higher abstinence rates than usual care (abrupt quitting with NRT), particularly in the early postquit phase (2 and 4 weeks after cessation) when smokers are compliant with the procedure. Scheduled smoking also produced a better overall quitting experience by reducing symptoms of nicotine withdrawal and craving, in comparison with usual care, which could encourage future quit attempts. Studies in this area should focus on the use of counseling or other methods to improve adherence.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".