Efficacy of the Ascure Smoking Cessation Program: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Smoking cessation helps extend a healthy life span and reduces medical expenses. However, the standard 12-week smoking cessation program in Japan has several notable problems. First, only 30% of participants complete this program. Second, participants may choose not to participate unless they have a strong motivation to quit smoking, such as health problems. Third, the program does not provide enough support during the period between clinical visits and after 12 weeks. OBJECTIVE: This study examined the efficacy of the 24-week ascure program to address the problems of accessibility and continuous support. The program combines online mentoring, over-the-counter pharmacotherapy, and a smartphone app. METHODS: Using a retrospective study design, we investigated data for 177 adult smokers who were enrolled in the ascure smoking cessation program between August 2017 and August 2018. The primary outcomes were continuous abstinence rates (CARs) during weeks 9-12 and weeks 21-24. To confirm smoking status, we performed salivary cotinine testing at weeks 12 and 24. We also evaluated the program adherence rate. Finally, we performed exploratory analysis to determine the factors associated with continuous abstinence at weeks 21-24 to provide insights for assisting with long-term continuous abstinence. RESULTS: The CARs of all participants for weeks 9-12 and weeks 21-24 were 48.6% (95% CI 41.2-56.0) and 47.5% (95% CI 40.0-54.8), respectively. Program adherence rates were relatively high throughout (72% at week 12 and 60% at week 24). In the analysis of the factors related to the CAR at weeks 21-24, the number of entries in the app's digital diary and number of educational videos watched during the first 12 weeks were significant factors. CONCLUSIONS: The ascure program achieved favorable CARs, and participants showed high adherence. Proactive usage of the smartphone app may help contribute to smoking cessation success in the long-term.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".