Menstrual cycle influences on cue-induced smoking cravings and heart rate variability
Bibliographic record
Abstract
Women experience greater difficulties in quitting smoking than men, though the hormonal factors contributing to this sex difference remain to be clarified. The current study aimed to examine menstrual cycle effects on smoking cue-induced cravings as well as examine dynamic reproductive hormone change as a potential mediator underlying any cycle effects observed. Twenty-one women who smoke underwent two laboratory sessions - one in the mid-follicular phase and the other in the late luteal phase - involving an in-vivo smoking cue task, administered before and after exposure to a psychosocial laboratory stressor. Heart rate variability (HRV) and subjective smoking cravings were assessed in response to the cue task. The degree of change in the urinary metabolites of estradiol and progesterone from 2 days before to the day of each laboratory session was measured. Results revealed that both before and following exposure to psychosocial stress, highly nicotine-dependent women exhibited smaller cue-induced increases in HRV relative to the follicular phase. In contrast, less nicotine-dependent women exhibit an increase in HRV in both menstrual cycle phases. Results furthermore suggest that menstrual cycle effects seen in highly nicotine-dependent women are driven by the decline in estradiol and progesterone occurring in the late luteal phase. Though limited by a small sample size, this study suggests that withdrawal from reproductive hormones in the late luteal phase may alter highly nicotine-dependent women's physiological response to smoking cues, which may reflect greater difficulty resisting temptation. These findings may provide some insight regarding women's greater difficulty in maintaining abstinence after quitting smoking.
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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 | 0.001 |
| 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".