Personalized chat‐based support for smoking cessation among smokers with mental health symptoms in the workplace: A randomized controlled trial
Bibliographic record
Abstract
AIMS: Smoking rates are disproportionately high among people with mental health issues. We evaluated the effectiveness of a chat-based intervention paired with psychological support for workplace smokers with mental health symptoms. DESIGN AND SETTING: A pragmatic, two-arm, randomized controlled trial in 65 companies in Hong Kong, China. PARTICIPANTS: 300 employees who smoked daily (84.3% male) with stress (Perceived Stress Scale-4 ≥ 6), anxiety (General Anxiety Disorder-2 ≥ 3) or depressive symptoms (Patient Health Questionnaire-2 ≥ 3). INTERVENTIONS: Participants were randomly allocated to either the intervention group (n = 144) or the control group (n = 156). The intervention group received 20 instant messages and chat-based support delivered by trained counselors over 3 months, using Acceptance and Commitment Therapy. The control group received 6 messages on general health. Both groups received a health talk and self-help booklet at baseline, brief phone advice at follow-ups and full-course nicotine replacement therapy for continuing smokers at 6 months. MEASUREMENTS: The primary outcome was self-reported 7-day point prevalence abstinence (PPA) at 6 months after intervention initiation. Secondary outcomes included PPA at 1, 3, 9 and 12 months, changes in mental health symptoms and intervention engagement (chatting with counsellors). FINDINGS: By intention to treat, there was no evidence of a difference in 7-day PPA between intervention and control group at 6 months [16.0% vs. 13.5%, odds ratio (OR) = 1.22, 95% confidence interval (CI) = 0.64-2.32]. At 12 months, there was weak evidence of an intervention benefit (21.5% vs. 13.5%, 1.76, 0.96-3.24), which was statistically significant after adjusting for baseline factors (adjusted OR = 2.61, 1.22-5.58, P = 0.01). A greater reduction in anxiety symptoms over a 12-month period in the intervention group was observed (adjusted b = -0.28, 95% CI = -0.56 to -0.00, P = 0.05). CONCLUSIONS: There appears to be weak evidence for a benefit of a personalised chat-based smoking cessation intervention plus psychological support on promoting smoking abstinence among workplace smokers with mental health symptoms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".