Association Between Smoking Abstinence and Depression and Anxiety Symptoms After Hospital Discharge: The Helping HAND 4 Trial
Bibliographic record
Abstract
OBJECTIVES: Some people who stop smoking experience improved mood, but few studies have examined this relationship after hospitalization or accounted for concomitant substance use and psychological factors. We examined associations between smoking abstinence after a hospital discharge and change in depression and anxiety symptoms. METHODS: We conducted a secondary analysis of data from the Helping HAND 4 smoking cessation trial, which enrolled people who used tobacco when admitted to three academic medical center general hospitals. Participants (n = 986) were categorized as continuously abstinent (CA) or not. We used linear and logistic regression to model continuous and binary measures of depression (Patient Health Questionnaire [PHQ-8] ≥/<10), and anxiety (Generalized Anxiety Disorder Assessment [GAD-7], ≥/<8) over 6 months, adjusting for baseline mood, psychological factors, and substance use. Binary outcomes were defined using established clinical thresholds to aid in the clinical interpretation of the results. RESULTS: Mean age was 52.3 years, 56.5% were female, and the baseline mean cigarettes/day was 16.2 (SD: 3.2). In the adjusted analyses, depression and anxiety scores improved more in CA than non-CA participants over 6 months (difference-in-improvement, 2.43 [95% CI: 1.50-3.36] for PHQ-8; 3.04 [95% CI: 2.16-3.93] for GAD-7). At 6 months, CA participants were more likely to have a PHQ-8 score <10 (aOR = 2.07 [95% CI: 1.36-3.16]) and a GAD-7 score <8 (aOR = 2.90 [95% CI: 1.91-4.39]). CONCLUSIONS: Individuals who were CA, compared to those who were not, had fewer depression and anxiety symptoms at 6 months, and were twice as likely to score below the population screening thresholds for major depression and anxiety disorders. Clinicians should emphasize the association between continuous abstinence and improved mood symptoms after hospital discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".