Attitudes Toward Alternative Tobacco and Nicotine Products and Their Association With Lifestyle Habits: Protocol for the MINERVA International Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Traditional smoking is widely recognized as a public health issue, with established risks that include cardiovascular, respiratory, and cancer-related health impacts. Recently, alternative tobacco and nicotine products (ATNPs), such as electronic cigarettes, have gained popularity as potentially safer substitutes for traditional tobacco use. However, there remains uncertainty regarding the health effects of ATNPs, which require comprehensive research to evaluate their risks and potential benefits compared to conventional smoking. OBJECTIVE: The aim of this multicenter observational study, MINERVA (My Changing Lifestyles Our Research and Everyone Health), is to examine the interplay between traditional smoking and the potential effect of switching to ATNPs in culturally diverse European countries. METHODS: Data collection will be conducted through a structured web-based questionnaire and administered using a snowball sampling technique, which relies on the referrals and network connections of initial participants. A link will be provided for participants to access the questionnaire. Responders who express a willingness to participate in the study cohort will be recontacted at least twice a year to ensure continuity in their participation, and they will be asked to complete the same questionnaire during these follow-up assessments. This approach allows for the gathering of useful information over an extended period and the assessment of any changes or trends in the data that may emerge over time. Using social media for data collection, the research explores real-time attitudes and experiences related to smoking, with a particular focus on participants' receptiveness to transitioning to nonconventional tobacco products. Statistical analysis will include descriptive statistics, and logistic regression models will examine associations. Nonlinear trends will be assessed using restricted cubic splines. Analyses will be conducted in R software with relevant statistical packages. RESULTS: The MINERVA study began in June 2023, and data collection is still in progress. As of January 2025, 7535 participants had been enrolled in Italy, of whom 4862 have consented to a follow-up. Preliminary analyses are currently being conducted. CONCLUSIONS: The study underscores the importance of ongoing research to inform evidence-based policies and interventions, emphasizing the role of social media surveys in capturing diverse perspectives and facilitating longitudinal studies for a deeper understanding of the health implications of transitioning from traditional smoking to ATNPs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67163.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".