A novel stress-based intervention reduces cigarette use in non-treatment seeking smokers
Bibliographic record
Abstract
Tobacco use is the leading cause of preventable mortality worldwide. Since current smoking cessation aids show only modest efficacy, new interventions are needed. Given the evidence that stress is a potent trigger for smoking, the present randomized clinical trial tested whether stress could augment the effects of a memory updating (retrieval-extinction) intervention. Non-treatment seeking smokers (n = 76) were assigned to one of four conditions composed of either a stressful or non-stressful psychosocial challenge followed by either smoking or neutral cues. Ten minutes after this manipulation, all underwent a 60-minute extinction procedure during which they viewed smoking-related videos and images and manipulated smoking paraphernalia. Compared to participants who were not exposed to the laboratory stressor, the stressor-exposed groups exhibited greater psychophysiological responses during their intervention and greater decreases in cigarette use at two- and six-weeks follow-up independent of smoking cue exposure. Together, these findings suggest that the ability of stress to activate cigarette seeking processes can be exploited to decrease cigarette use. With replication, the stress-based intervention could become a novel strategy for decreasing cigarette use in non-treatment seeking smokers.Clinicaltrials.gov identifier: NCT04843969.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".