Hair Cortisol as a Biomarker of Stress in Mindfulness Training for Smokers
Bibliographic record
Abstract
OBJECTIVES: Stress is a well-known predictor of smoking relapse, and cortisol is a primary biomarker of stress. The current pilot study examined changes in levels of cortisol in hair within the context of two time-intensity matched behavioral smoking cessation treatments: mindfulness training for smokers and a cognitive-behavioral comparison group. PARTICIPANTS: Eighteen participants were recruited from a larger randomized controlled trial of smoking cessation. OUTCOME MEASURES: Hair samples (3 cm) were obtained 1 month after quit attempt, allowing for a retrospective analysis of hair cortisol at preintervention and post-quit attempt time periods. Self-reported negative affect was also assessed before and after treatment. INTERVENTION: Both groups received a 7-week intensive intervention using mindfulness or cognitive-behavioral strategies. RESULTS: Cortisol significantly decreased from baseline to 1 month after quit attempt in the entire sample (d=-0.35; p=.005). In subsequent repeated-measures analysis of variance models, time by group and time by quit status interaction effects were not significant. However, post hoc paired t tests yielded significant pre-post effects among those randomly assigned to the mindfulness condition (d=-0.48; p=.018) and in those abstinent at post-test (d=-0.41; p=.004). Decreased hair cortisol correlated with reduced negative affect (r=.60; p=.011). CONCLUSIONS: These preliminary findings suggest that smoking cessation intervention is associated with decreased hair cortisol levels and that reduced hair cortisol may be specifically associated with mindfulness training and smoking abstinence. RESULTS support the use of hair cortisol as a novel objective biomarker in future research.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".