Sleep Disturbance During Smoking Cessation: Withdrawal or Side Effect of Treatment?
Bibliographic record
Abstract
Introduction: The nicotine–metabolite ratio (NMR) predicts treatment response and is related to treatment side effect severity. Sleep disturbance may be one important side effect, but understanding sleep disturbance effects on smoking cessation is complicated by the fact that nicotine withdrawal also produces sleep disturbance. Aims: To evaluate the effects of withdrawal and treatment side effects on sleep disturbance. Methods: This is a secondary analysis of data from a clinical trial (Lerman et al., 2015) of 1,136 smokers randomised to placebo (n = 363), transdermal nicotine (TN; n = 381), or varenicline (n = 392) and stratified based on NMR (559 slow metabolisers; 577 normal metabolisers). Sleep disturbance was assessed at baseline and at 1-week following the target quit date (TQD). We also examined whether sleep disturbance predicted 7-day point-prevalence abstinence at end-of-treatment (EOT). Results: The varenicline and TN groups exhibited greater increases in sleep disturbance (vs. placebo; treatment × time interaction; p = 0.005), particularly among those who quit smoking at 1-week post-TQD. There was a main effect of NMR (p = 0.04), but no interactions with treatment. TN and varenicline attenuated withdrawal symptoms unrelated to sleep (vs. placebo). Greater baseline sleep disturbance predicted relapse at EOT (p = 0.004). Conclusions: Existing treatments may not mitigate withdrawal-related sleep disturbance and adjunctive treatments that target sleep disturbance may improve abstinence rates.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".