Lifestyle Habits and Alternative Tobacco and Nicotine Products: Results from the MINERVA Project
by
, , , , , , and
Giulia Lorenzoni
1,†
,
Honoria Ocagli
1,†
,
Danila Azzolina
2,
Noor Muhammad Khan
1,
Francesca Angioletti
1,
Kostantina-Thaleia Pilali
1,
Aslihan Şentürk Acar
3,
Paola Berchialla
4
,
Matteo Martinato
1,‡ and
Dario Gregori
1,*,‡
1
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
2
Department of Translational Medicine, University of Naples Federico II, 80131 Naples, Italy
3
Department of Actuarial Sciences, Hacettepe University, 06800 Ankara, Turkey
4
Centre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10043 Orbassano, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors contributed equally to this work.
J. Clin. Med. 2026, 15(1), 389; https://doi.org/10.3390/jcm15010389
Submission received: 20 November 2025
/
Revised: 28 December 2025
/
Accepted: 29 December 2025
/
Published: 5 January 2026
(This article belongs to the Section Epidemiology & Public Health)
Abstract
Background/Objectives: Alternative Tobacco and Nicotine Products (ATNPs) have gained widespread popularity. Although they are often promoted as lower-risk alternatives to traditional tobacco products, concerns remain regarding their association with risky behaviors among adolescents and young adults. This study examines the relationship between dietary and lifestyle habits and both ATNP use and intention to use ATNP among Italian participants in the MINERVA (My changINg lifEstyles our Research and eVeryone heAlth) international project. Methods: MINERVA is an observational, international, prospective cohort study. A study-specific questionnaire was administered to participants, who were recruited through informal snowball sampling. The questionnaire collected information on sociodemographic characteristics, lifestyle factors, dietary habits, and the use of both traditional tobacco products and ATNPs. Predictors of ATNP use and intention to use were assessed using logistic regression models. Results: Data from 7535 Italian participants were analyzed. Overall, 48% reported having ever used ATNP, and 14% of non-smokers and non-users expressed an intention to try these products. Significant predictors of ATNP use and intention to use included prior smoking, lower age, and having family members who smoke. Lifestyle factors such as frequent consumption of fast food, junk food, and alcoholic beverages were positively associated with both ATNP use and intention to use. Conversely, daily fruit and vegetable consumption was inversely associated with these outcomes. Conclusions: ATNP use and intention to use were associated with unhealthy dietary and lifestyle patterns. These findings highlight the importance of integrated public health strategies addressing substance use alongside broader lifestyle behaviors among adolescents and young adults.
1. Introduction
Alternative tobacco and nicotine products (ATNPs), i.e., electronic nicotine delivery systems (ENDS) like e-cigarettes and vape devices, heat-not-burn (HNB) tobacco, and oral nicotine, have gained great popularity in recent years [1,2]. They have been proposed as healthier alternatives to conventional tobacco products, because their use is associated with lower health risk compared to combustible products [3,4]. Additionally, they have the potential to serve as transition products to help with smoking cessation [5].
However, in a short time, these products have become very popular among adolescents and young adults [6] who do not necessarily smoke conventional tobacco products. It is noteworthy that, although their use has been associated with lower health risks than traditional products, they do not come without risks, especially for young people [7]. They are associated with nicotine dependence, to which adolescents seem to be particularly susceptible [8]. In addition, the use of these products in adolescents and young adults has been associated with traditional tobacco smoking progression and risky behaviors [9]. ATNP use is associated with alcohol consumption, heavy drinking, and marijuana use in adolescents and young adults [10].
Considering the potential for ATNP use to be associated with, or a predictor of, risky behaviors, there is an increasing interest in the literature in profiling ATNP users and people who intend to try ATNP.
It is not easy to summarize the literature in this field because of the methodological differences in the conduct of the published studies [11]. Nevertheless, all studies seem to agree on the predictors of ATNP use, regardless of the criteria used to select the sample and the instruments employed for assessment. Male gender, young age, and low socio-economic status (including low educational level) seem to be associated with a higher probability of using ATNP [12,13]. Among psychological factors, impulsivity traits and psychological distress have been suggested to be predictors of ATNP use [14].
Given the high prevalence of ATNP use in the adolescent age [6], several studies have concentrated on identifying predictors of use in this age group, showing that having already tried to smoke conventional products and exposure to family and friends who smoke are the strongest predictors of ATNP use [15,16]. Similar predictors have been identified regarding the intention to use ATNP [17].
Interestingly, the literature analysis highlights a knowledge gap. Recent studies have focused on socio-demographic characteristics and psychological factors. In contrast, only a few studies have attempted to characterize lifestyle habits according to ATNP use (or intention to use), with preliminary suggestions of an association between unhealthy eating and lifestyle habits [16,18]. Such a relationship must be better characterized, given the well-known strict mutual influence of dietary habits, lifestyle habits (including addictive behaviors), and socio-demographics. Literature suggests that eating patterns often coexist with other lifestyle behaviors. Young people who report irregular or unbalanced diets—including a higher consumption of protein-rich foods (PRFs) such as processed meats—also tend to show greater impulsivity, lower attention to overall diet quality, and a higher likelihood of engaging in risk behaviors, including smoking or vaping. These behaviors often cluster within the same family or peer environment, where dietary choices and experimentation with nicotine products may be shaped by similar social and behavioral influences [19].
In this context, the MINERVA (My changINg lifEstyles our Research and eVeryone heAlth) international project is ongoing to evaluate the association between dietary and lifestyle habits and the use of, and intention to use, ATNP. The present work, conducted within the context of the MINERVA study, focuses on Italian data to identify predictors of ATNP use and intention to use among non-smokers of traditional tobacco products.
2. Materials and Methods
The MINERVA is an observational, international, prospective cohort study. The project aims to evaluate the relationship between dietary and lifestyle factors with smoking habits, focusing specifically on ATNP use and intention to use.
To be eligible for inclusion, individuals must be aged 18–99 years, be able to read and complete the questionnaire, and accept the study’s privacy and participation policy. Individuals who experience difficulties in accessing, reading, or completing the questionnaire, or who are unable to provide the required consent, are not eligible for participation.
Study participants are enrolled employing an informal snowball sampling technique and administered with an online baseline questionnaire, following a standard methodology [20]. The questionnaire comprises various sections that assess socio-demographic characteristics, lifestyle habits, dietary behaviors, and smoking habits related to traditional smoking and ATNP use. The questionnaire is administered via LimeSurvey©, an open-source survey tool.
At the end of the questionnaire, subjects are asked to consent to being recontacted to be included in the cohort. If they consent to be involved in the cohort, they are asked to provide their email address and will be contacted twice a year to complete a follow-up questionnaire that records any changes in their smoking habits.
The study is international, involving Mediterranean countries (starting with Italy, then Turkey, and Greece), and it is ongoing. It follows the Declaration of Helsinki guidelines and was approved by the Bioethics Committee of the University of Turin (protocol 0075071) on 2 February 2024. All participants provide informed consent before participation, with the option to withdraw at any time. Consent to participate and to collect and process personal data is given electronically (by ticking the corresponding box) on the project website, after reading the detailed information on data collection and processing, and before accessing the questionnaire, in accordance with the European Commission General Data Protection Regulation (679/2016). Both the informed consent form for study participation and the data processing consent form have been approved by the institutional ethics committee.
The present work focuses on baseline Italian data, where follow-up is ongoing. The published study protocol details the subjects’ enrollment, questionnaire development, and distribution [21].
During the preparation of this manuscript, the authors used ChatGPT (OpenAI; GPT-4o) for the purposes of formal language revision, including grammar and syntax correction, improvement of clarity, and refinement of academic style.
Statistical Analysis
Descriptive statistics were presented as median (I, III quartiles) for continuous variables and absolute numbers (percentages) for categorical variables.
Univariable logistic regression models were used to examine the relationship of socio-demographic characteristics, lifestyle habits, and dietary patterns with the ATNP use and intention to use. If the association was nonlinear, restricted cubic splines were used to estimate the models, and the change point was identified [22].
Complete case analysis was conducted, and statistical significance was defined as a p-value < 0.05. Analyses were performed using R [23] with the rms library.
3. Results
The MINERVA enrolled 7535 subjects in Italy (Table 1), and 4862 (65%) consented to be enrolled in the follow-up cohort. The median age was 42 years, with a slightly higher prevalence of females than males (53% vs. 47%). The analysis of socio-economic status revealed that 48% of the subjects held a high school diploma, while another 41% possessed a university degree. Three-fourths of the subjects (74%) were employed. The yearly income was generally medium-low, with 40% of the subjects reporting an annual income between 15,000 € and 30,000 €, and 30% reporting an annual income below 15,000 €.
Half of the subjects (46%) smoked traditional tobacco products daily, and 34% were past smokers. The median age of starting smoking among current and past smokers was 18 years (I–III quartiles: 15–20).
Regarding ATNP use, 48% (3645 subjects) have used an ATNP, with a median starting age of 32 years (I–III quartiles: 24–42 years). Of these, 2246 subjects reported being current users. The main reasons for using ATNP were to quit smoking tobacco (827 subjects) and the pleasure of using ATNP (855 subjects).
Among the 5289 subjects who were not current ATNP users, 25% stated that they were willing to try an ATNP product.
3.1. Predictors of ATNP Use Intention
The analysis involved 3214 subjects who did not smoke traditional tobacco products daily and did not currently use ATNP. Among them, 447 (14%) indicated they were open to using ATNP (Table 2).
Among socio-demographic characteristics, age was significantly associated with the intention of trying. Interestingly, this association was nonlinear (p-value < 0.001), with a change point at 45 years of age (Figure 1A). Specifically, the likelihood of trying ATNP increased with age up to age 45.
As concerns smoking history, past smokers were significantly more likely to be interested in trying ATNP (OR 1.48, 95% CI 1.09–1.99), and the older they had started to smoke, the more likely they were to use ATNP (OR 1.04, 95% CI 1.01–1.06). Not least, the smoking family environment was found to be a relevant predictor of interest in ATNP, with subjects having family members who smoke found to have a higher likelihood of trying ATNP (OR 4.39, 95% CI 3.55–5.44).
Lifestyle and dietary habits were significant predictors of the intention to try ATNPs. Being unaware of the impact of the diet on physical health and not paying attention to the diet were significant predictors of the intention to use ATNP (ORs: 3.79, 95% CI 1.50–8.97, and 2.13, 95% CI 1.11–3.87, respectively). Drinking alcoholic beverages and eating junk food, e.g., French fries, cookies, and candies, as snacks were associated with a higher likelihood of trying ATNP (ORs: 1.77, 95% CI 1.32–2.42, and 1.61, 95% CI 1.29–2.02, respectively). Similarly, eating fast food at least once a week was associated with interest in ATNP (OR 2.67, 95% CI 2.01–3.52). Conversely, daily vegetable intake was protective against the intention to use ATNP (OR 0.75, 95% CI 0.60–0.93).
3.2. Predictors of ATNP Use
The analysis involved 4104 subjects who did not smoke traditional tobacco products daily (Table 3). Among them, 890 (22%) were ATNP users.
The predictors of ATNP use were similar to ATNP intention to use. A significant, non-linear relationship (p-value < 0.001) with age was found (Figure 1B), with a change point identified at 42 years. Interestingly, even though ATNP users were significantly younger than non-ATNP users, i.e., a median of 38 (I–III quartiles: 29–46) vs. 43 (I–III quartiles: 29–56) years of age, respectively, they were significantly more likely to be past smokers (84% vs. 26%).
Among dietary habits, consuming junk food snacks (OR 1.51, 95% CI 1.28, 1.79) and alcohol (OR 3.18, 95% CI 2.42, 4.27), and eating food from fast food restaurants at least once a week (2.22, 95% CI 1.80, 2.73) were significantly associated with a higher likelihood of ATNP use. Conversely, people who ate fruit regularly were less likely to use ATNPs.
4. Discussion
In light of the recent spread of ATNPs among teenagers and young adults, characterizing ATNP use and intention to use would be helpful from a public health perspective. It is noteworthy that ATNPs come with fewer health risks compared to traditional smoking. However, it has been suggested that they could act as an intermediate step to the use of conventional tobacco products in non-smokers. Studies have shown that adolescents and young adults who use e-cigarettes or other ATNPs are more likely to subsequently start smoking traditional cigarettes than non-users [24], even after adjusting for baseline susceptibility and psychosocial determinants. This transition is often interpreted within the gateway hypothesis, whereby early exposure to nicotine, normalization of smoking-related behaviors, and reinforcement through peer contexts may facilitate the transition to combustible tobacco use. In parallel, ATNP use has been repeatedly linked to other risk behaviors [25,26], suggesting that vaping and similar products may be embedded in broader behavioral trajectories. These findings provide a conceptual basis for interpreting ATNP use not as an isolated behavior but as part of a broader set of risk patterns emerging among young adults.
The results of the present study showed that predictors of ATNP use and intention to use are similar to each other and include young age, unhealthy eating habits, and a positive family or personal smoking history. Evidence from Italian and European studies aligns with the present findings, showing that ATNP use is common in younger populations: Italian and European surveys have reported high prevalence of vaping among adolescents and young adults [27,28].
It would not be easy to compare results from different studies. Each published research study has analyzed different populations, e.g., including only non-smokers, or both smokers and non-smokers. Furthermore, each study defines the outcome and the products under study differently. Some studies focused solely on ATNP use, while others examined the willingness to try ATNPs. Furthermore, most studies evaluated heated tobacco products, while others assessed e-cigarettes. Despite these differences, some common predictors of ATNP use or intention to use could be identified, such as young age, male gender, and being a smoker (or having family members who smoke) [15,16]. These findings are consistent with those of the present study, showing a strong, nonlinear, significant relationship with age. In contrast, no significant associations with gender were detected. Together with socio-demographic characteristics, past smoking history was also a significant predictor of both ATNP use and intention to use, with a markedly more pronounced effect for ATNP use.
Regarding the relationship between ATNP use and dietary domains, the literature provides limited evidence. A few recent studies have suggested that vaping seems to be used as a weight control measure among young people [29] and seems to be associated with eating disorders, including binge eating, anorexia nervosa, and bulimia [30,31], even though the evidence is controversial [32]. The main novelty of the present study was the analysis of diet quality in relation to ATNPs. Interestingly, junk food, alcohol, and fast-food consumption were all associated with a higher likelihood of trying and using ATNPs. These findings seem to reinforce the hypothesis that the use of ATNPs is related to unhealthy behaviors, including eating behaviors, as suggested by previous studies [16].
The associations observed between ATNP use, intention to use them, and unhealthy eating habits and lifestyles are plausible from a behavioral perspective. First, these behaviors may share common psychological determinants, such as impulsivity, sensation seeking, or reduced self-regulation, which may predispose individuals to both risky eating patterns and experimentation with nicotine products. Second, family and peer environments that normalize smoking or ATNP use may also promote less structured or less healthy eating habits, reinforcing the clustering of these behaviors. Furthermore, alcohol and fast food consumption are often associated with social occasions where tobacco and ATNP use are more likely, providing additional opportunities for co-occurrence. Although a direct biological link between specific dietary components and ATNP use cannot be established in this study, the observed pattern is consistent with a broader risk-behavior profile in which dietary choices, substance use, and lifestyle habits are interconnected.
4.1. Public Health Implications
These findings suggest that ATNP prevention should not be considered separately from other lifestyle behaviors. School-based prevention programs that address smoking, ATNP use, and nutrition jointly, rather than in separate educational tracks, may be more effective in reaching youth who tend to accumulate multiple risk behaviors. Integrating nutrition education with tobacco and nicotine prevention through coordinated programs, counseling, and peer-led initiatives could help identify and support individuals with overall “unhealthy” lifestyles. Furthermore, the clustering of ATNP use with other unhealthy behaviors reinforces the importance of comprehensive tobacco control policies, including strict regulation of marketing and flavors, enforcement of age limits, and sustained public information campaigns that explicitly address ATNPs alongside conventional tobacco products. Such integrated strategies may be particularly relevant in settings where ATNPs are perceived as less harmful and are widely accessible to young adults.
4.2. Study Limitations
The study’s main limitation is the cross-sectional nature of the current data analysis, which is also a limitation of most research investigating potential predictors of ATNP use. The topic is relatively new, and cohort studies require time to develop. Furthermore, the use of a self-administered questionnaire may introduce recall and social desirability biases, particularly for sensitive behaviors such as smoking and eating habits. Although large, the study population is not representative, as recruitment was conducted using an informal snowball sampling method. Finally, the questionnaire did not include a qualitative assessment of motivations or intentions to use ATNPs, limiting the ability to explore the contextual or psychosocial factors underlying these behaviors.
5. Conclusions
This study provides evidence about a general “unhealthy” environment characterizing ATNP use or willingness to use. Therefore, a clear separation of health determinants among ATNP use, diet, physical activity, and other demographic and lifestyle aspects is complex. In this context, the cross-sectional design limits the ability to draw causal conclusions. Future longitudinal follow-up within the MINERVA cohort will provide a better understanding of how these factors evolve in relation to ATNP use.
For public health implications, these findings highlight the need for prevention strategies that address nutrition, lifestyle behaviors, and tobacco and nicotine use in an integrated manner, rather than treating these domains in isolation. Combining nutritional education with tobacco prevention initiatives may be particularly effective for reaching individuals who tend to engage in multiple risk-prone behaviors.
Author Contributions
Conceptualization, D.G. and M.M.; methodology, D.A., P.B. and A.Ş.A.; formal analysis, G.L. and H.O.; investigation, K.-T.P. and F.A.; data curation, N.M.K.; writing—original draft preparation, G.L. and H.O.; writing—review and editing, M.M. and D.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was approved by the Bioethics Committee of the University of Turin (protocol 0075071) on 2 February 2024.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The datasets presented in this article are not readily available because the data are part of an ongoing study.
Acknowledgments
During the preparation of this manuscript, the authors used ChatGPT (OpenAI; GPT-4o) for the purposes of formal language revision, including grammar and syntax correction, improvement of clarity, and refinement of academic style.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Relationship between age and Alternative Tobacco and Nicotine Products intention to use (A) and Alternative Tobacco and Nicotine Products use (B). The association was estimated using univariable logistic regression model with restricted cubic splines.
Figure 1.
Relationship between age and Alternative Tobacco and Nicotine Products intention to use (A) and Alternative Tobacco and Nicotine Products use (B). The association was estimated using univariable logistic regression model with restricted cubic splines.
Table 1.
Sample characteristics. Data are absolute numbers (percentages) and median (I, III quartiles).
Table 1.
Sample characteristics. Data are absolute numbers (percentages) and median (I, III quartiles).
| Characteristic | N | N = 7535 |
|---|---|---|
| Socio-demographic characteristics | ||
| Gender | 7524 | |
| Female | 3953 (53%) | |
| Male | 3571 (47%) | |
| Age | 7535 | 42 (32, 52) |
| Education | 7535 | |
| High school diploma | 3604 (48%) | |
| Primary/Middle school | 843 (11%) | |
| University degree | 3088 (41%) | |
| Job | 7535 | |
| Unemployed | 1404 (19%) | |
| Student | 540 (7.2%) | |
| Employed | 5591 (74%) | |
| Yearly gross income | 7480 | |
| >50,000 | 621 (8.3%) | |
| 0–15,000 | 2213 (30%) | |
| 15,000–30,000 | 2970 (40%) | |
| 30,000–50,000 | 1676 (22%) | |
| Number of adult family members | 7535 | 2.00 (2.00, 3.00) |
| Children in the family | 7535 | |
| No | 5501 (73%) | |
| Yes | 2034 (27%) | |
| Teenagers in the family | 7535 | |
| No | 5915 (79%) | |
| Yes | 1620 (21%) | |
| Smoking and ATNP use habits | ||
| Smoker | 7535 | |
| No | 4104 (54%) | |
| Yes | 3431 (46%) | |
| Smokers in the family | 7535 | |
| No | 2225 (30%) | |
| Yes | 5310 (70%) | |
| Past smoker | 2992 | |
| No | 1982 (66%) | |
| Yes | 1010 (34%) | |
| Age started smoking | 5553 | 18.0 (15.0, 20.0) |
| Ever used ATNP | 7535 | |
| No | 3890 (52%) | |
| Yes | 3645 (48%) | |
| Current ATNP user | 3645 | |
| No | 1399 (38%) | |
| Yes | 2246 (62%) | |
| Age started using ATNP | 3645 | 32 (24, 42) |
| Reasons for ATNP use (actual users only) * | 3902 | |
| To quit smoking tobacco. | 827 (100%) | |
| To avoid returning to smoking tobacco | 473 (100%) | |
| Enjoy it | 855 (100%) | |
| Addicted to it | 168 (100%) | |
| Use it in situations where smoking tobacco is not allowed | 507 (100%) | |
| Believe it is less harmful than smoking tobacco | 637 (100%) | |
| Prefer the available flavors | 356 (100%) | |
| Influenced by a friend or family member | 79 (100%) | |
| Intention to try ATNP (only for non-users) | 5289 | |
| No | 3967 (75%) | |
| Yes | 1322 (25%) | |
| Eating habits | ||
| Junk food snacks | 7535 | |
| No | 2283 (30%) | |
| Yes | 5252 (70%) | |
| Alcoholic beverages consumption | 7535 | |
| No | 900 (12%) | |
| Yes | 6635 (88%) | |
| Daily fruit consumption | 7535 | |
| No | 2471 (33%) | |
| Yes | 5064 (67%) | |
| Daily vegetables consumption | 7535 | |
| No | 2104 (28%) | |
| Yes | 5431 (72%) | |
| Weekly fast food visit | 7535 | |
| No | 6483 (86%) | |
| Yes | 1052 (14%) | |
| Number of daily meals | 7535 | |
| 1 | 432 (5.7%) | |
| 2 | 747 (9.9%) | |
| 3 | 3155 (42%) | |
| 4 | 1815 (24%) | |
| 5 | 1386 (18%) | |
| Eating habits description | 7535 | |
| I try to be careful about what I eat | 2637 (35%) | |
| I try to be careful, but I don’t always succeed | 3934 (52%) | |
| I don’t particularly worry about what I eat | 793 (11%) | |
| I eat everything without thinking | 171 (2.3%) | |
| Impact of eating habits on physical shape | 7535 | |
| Very much | 3366 (45%) | |
| Enough | 3822 (51%) | |
| Little | 298 (4.0%) | |
| Not at all | 49 (0.7%) | |
| Lifestyle habits | ||
| Wake-up time | 7535 | |
| At 6.30 or earlier | 2495 (33%) | |
| Around 7.00 | 2062 (27%) | |
| Around 7.30 | 1449 (19%) | |
| After 8.00 | 1032 (14%) | |
| I don’t have a specific time | 497 (6.6%) | |
| Bedtime | 7535 | |
| At 10.30 pm or earlier | 1554 (21%) | |
| Around 11.00 pm | 3197 (42%) | |
| After midnight | 2196 (29%) | |
| I don’t have a specific time | 588 (7.8%) | |
| Effect of sleep on physical health | 7535 | |
| Very much | 3760 (50%) | |
| Enough | 3471 (46%) | |
| Little | 268 (3.6%) | |
| Not at all | 36 (0.5%) | |
| Attention to physical shape | 7535 | |
| Very much | 1349 (18%) | |
| Enough | 4579 (61%) | |
| Little | 1488 (20%) | |
| Not at all | 119 (1.6%) | |
| Regular physical activity | 7535 | |
| No | 3068 (41%) | |
| Yes | 4467 (59%) |
ATNP: Alternative Tobacco and Nicotine Product. * The percentages do not sum to 100% because respondents could select more than one reason.
Table 2.
Characteristics of the subjects who did not smoke traditional tobacco products daily and did not currently use Alternative Tobacco and Nicotine Products according to Alternative Tobacco and Nicotine Products intention to use and results of the univariable logistic regression analysis. Data are absolute numbers (percentages) and median (I, III quartiles). Results of the regression analysis are reported as OR, 95% CI, and p-value.
Table 2.
Characteristics of the subjects who did not smoke traditional tobacco products daily and did not currently use Alternative Tobacco and Nicotine Products according to Alternative Tobacco and Nicotine Products intention to use and results of the univariable logistic regression analysis. Data are absolute numbers (percentages) and median (I, III quartiles). Results of the regression analysis are reported as OR, 95% CI, and p-value.
| Characteristic | N | No Intention to Use ATNP N = 2767 | Intention to Use ATNP N = 447 | OR | 95% CI | p-Value |
|---|---|---|---|---|---|---|
| Socio-demographic characteristics | ||||||
| Gender | 3208 | |||||
| Female | 1511 (55%) | 235 (53%) | ||||
| Male | 1252 (45%) | 210 (47%) | 1.08 | 0.88, 1.32 | 0.5 | |
| Age | 3214 | 44 (30, 57) | 37 (28, 47) | 0.97 | 0.97, 0.98 | <0.001 |
| Education | 3214 | |||||
| High school diploma | 1315 (48%) | 198 (44%) | ||||
| Primary/Middle school | 309 (11%) | 43 (9.6%) | 0.92 | 0.64, 1.30 | 0.7 | |
| University degree | 1143 (41%) | 206 (46%) | 1.2 | 0.97, 1.48 | 0.094 | |
| Job | 3214 | |||||
| Unemployed | 641 (23%) | 66 (15%) | ||||
| Student | 308 (11%) | 43 (9.6%) | 1.36 | 0.90, 2.03 | 0.14 | |
| Employed | 1818 (66%) | 338 (76%) | 1.81 | 1.38, 2.40 | <0.001 | |
| Yearly gross income | 3180 | |||||
| >50,000 | 204 (7.5%) | 27 (6.1%) | ||||
| 0–15,000 | 872 (32%) | 142 (32%) | 1.23 | 0.81, 1.94 | 0.4 | |
| 15,000–30,000 | 1060 (39%) | 164 (37%) | 1.17 | 0.77, 1.84 | 0.5 | |
| 30,000–50,000 | 600 (22%) | 111 (25%) | 1.4 | 0.90, 2.23 | 0.14 | |
| Number of adult family members | 3214 | 2.00 (2.00, 3.00) | 2.00 (2.00, 3.00) | 1.01 | 0.95, 1.07 | 0.6 |
| Children in family | 3214 | |||||
| No | 2265 (82%) | 284 (64%) | ||||
| Yes | 502 (18%) | 163 (36%) | 2.59 | 2.08, 3.21 | <0.001 | |
| Teenagers in family | 3214 | |||||
| No | 2312 (84%) | 334 (75%) | ||||
| Yes | 455 (16%) | 113 (25%) | 1.72 | 1.35, 2.17 | <0.001 | |
| Smoking and ATNP use habits | ||||||
| Smokers in family | 3214 | |||||
| No | 1876 (68%) | 145 (32%) | ||||
| Yes | 891 (32%) | 302 (68%) | 4.39 | 3.55, 5.44 | <0.001 | |
| Past smoker | 2576 | |||||
| No | 1773 (75%) | 142 (67%) | ||||
| Yes | 591 (25%) | 70 (33%) | 1.48 | 1.09, 1.99 | 0.011 | |
| Age started smoking | 1299 | 17.0 (15.0, 20.0) | 18.0 (16.0, 20.0) | 1.04 | 1.01, 1.06 | 0.002 |
| Ever used ATNP | 3214 | |||||
| No | 2401 (87%) | 296 (66%) | ||||
| Yes | 366 (13%) | 151 (34%) | 0.3 | 0.24, 0.37 | <0.001 | |
| Age started using ATNP | 517 | 26 (20, 39) | 25 (20, 35) | 0.99 | 0.97, 1.01 | 0.3 |
| Eating habits | ||||||
| Junk food snacks | 3214 | |||||
| No | 1012 (37%) | 118 (26%) | ||||
| Yes | 1755 (63%) | 329 (74%) | 1.61 | 1.29, 2.02 | <0.001 | |
| Alcoholic beverages consumption | 3214 | |||||
| No | 523 (19%) | 52 (12%) | ||||
| Yes | 2244 (81%) | 395 (88%) | 1.77 | 1.32, 2.42 | <0.001 | |
| Daily fruit consumption | 3214 | |||||
| No | 769 (28%) | 135 (30%) | ||||
| Yes | 1998 (72%) | 312 (70%) | 0.89 | 0.72, 1.11 | 0.3 | |
| Daily vegetables consumption | 3214 | |||||
| No | 705 (25%) | 140 (31%) | ||||
| Yes | 2062 (75%) | 307 (69%) | 0.75 | 0.60, 0.93 | 0.009 | |
| Weekly fast food visit | 3214 | |||||
| No | 2558 (92%) | 367 (82%) | ||||
| Yes | 209 (7.6%) | 80 (18%) | 2.67 | 2.01, 3.52 | <0.001 | |
| Number of daily meals | 3.00 (3.00, 4.00) | 3.00 (3.00, 4.00) | 0.85 | 0.77, 0.93 | <0.001 | |
| Eating habits description | 3214 | |||||
| I try to be careful about what I eat | 1053 (38%) | 147 (33%) | ||||
| I try to be careful, but I don’t always succeed | 1410 (51%) | 241 (54%) | 1.22 | 0.98, 1.53 | 0.071 | |
| I don’t particularly worry about what I eat | 257 (9.3%) | 45 (10%) | 1.25 | 0.87, 1.79 | 0.2 | |
| I eat everything without thinking | 47 (1.7%) | 14 (3.1%) | 2.13 | 1.11, 3.87 | 0.017 | |
| Impact of eating habits on physical shape | 3214 | |||||
| Very much | 1233 (45%) | 186 (42%) | ||||
| Enough | 1395 (50%) | 238 (53%) | 1.13 | 0.92, 1.39 | 0.2 | |
| Little | 125 (4.5%) | 15 (3.4%) | 0.8 | 0.44, 1.35 | 0.4 | |
| Not at all | 14 (0.5%) | 8 (1.8%) | 3.79 | 1.50, 8.97 | 0.003 | |
| Lifestyle habits | ||||||
| Wake-up time | 3214 | |||||
| At 6.30 or earlier | 886 (32%) | 121 (27%) | ||||
| Around 7.00 | 768 (28%) | 127 (28%) | 1.21 | 0.93, 1.58 | 0.2 | |
| Around 7.30 | 543 (20%) | 97 (22%) | 1.31 | 0.98, 1.74 | 0.067 | |
| After 8.00 | 393 (14%) | 67 (15%) | 1.25 | 0.90, 1.72 | 0.2 | |
| I don’t have a specific time | 177 (6.4%) | 35 (7.8%) | 1.45 | 0.95, 2.16 | 0.076 | |
| Bedtime | 3214 | |||||
| At 10.30 pm or earlier | 633 (23%) | 114 (26%) | ||||
| Around 11.00 pm | 1209 (44%) | 179 (40%) | 0.82 | 0.64, 1.06 | 0.13 | |
| After midnight | 695 (25%) | 114 (26%) | 0.91 | 0.69, 1.21 | 0.5 | |
| I don’t have a specific time | 230 (8.3%) | 40 (8.9%) | 0.97 | 0.65, 1.42 | 0.9 | |
| Effect of sleep on physical health | 3214 | |||||
| Very much | 1371 (50%) | 213 (48%) | ||||
| Enough | 1270 (46%) | 217 (49%) | 1.1 | 0.90, 1.35 | 0.4 | |
| Little | 114 (4.1%) | 11 (2.5%) | 0.62 | 0.31, 1.12 | 0.14 | |
| Not at all | 12 (0.4%) | 6 (1.3%) | 3.22 | 1.11, 8.38 | 0.021 | |
| Attention to physical shape | 3214 | |||||
| Very much | 424 (15%) | 104 (23%) | ||||
| Enough | 1731 (63%) | 273 (61%) | 0.64 | 0.50, 0.83 | <0.001 | |
| Little | 569 (21%) | 63 (14%) | 0.45 | 0.32, 0.63 | <0.001 | |
| Not at all | 43 (1.6%) | 7 (1.6%) | 0.66 | 0.27, 1.43 | 0.3 | |
| Regular physical activity | 3214 | |||||
| No | 1190 (43%) | 161 (36%) | ||||
| Yes | 1577 (57%) | 286 (64%) | 1.34 | 1.09, 1.65 | 0.006 |
ATNP: Alternative Tobacco and Nicotine Product.
Table 3.
Characteristics of the subjects who did not smoke traditional tobacco products daily according to Alternative Tobacco and Nicotine Products use and results of the univariable logistic regression analysis. Data are absolute numbers (percentages) and median (I–III quartiles). Results of the regression analysis are reported as OR, 95% CI, and p-value.
Table 3.
Characteristics of the subjects who did not smoke traditional tobacco products daily according to Alternative Tobacco and Nicotine Products use and results of the univariable logistic regression analysis. Data are absolute numbers (percentages) and median (I–III quartiles). Results of the regression analysis are reported as OR, 95% CI, and p-value.
| Characteristic | N | No ATNP Users N = 3214 | ATNP Users N = 890 | OR | 95% CI | p-Value |
|---|---|---|---|---|---|---|
| Socio-demographic characteristics | ||||||
| Gender | 4097 | |||||
| Female | 1746 (54%) | 501 (56%) | ||||
| Male | 1462 (46%) | 388 (44%) | 0.92 | 0.80, 1.07 | 0.3 | |
| Age | 4104 | 43 (29, 56) | 38 (29, 46) | 0.98 | 0.97, 0.98 | <0.001 |
| Education | 4104 | |||||
| High school diploma | 1513 (47%) | 403 (45%) | ||||
| Primary/Middle school | 352 (11%) | 92 (10%) | 0.98 | 0.76, 1.26 | 0.9 | |
| University degree | 1349 (42%) | 395 (44%) | 1.1 | 0.94, 1.29 | 0.2 | |
| Job | 4104 | |||||
| Unemployed | 707 (22%) | 127 (14%) | ||||
| Student | 351 (11%) | 72 (8.1%) | 1.14 | 0.83, 1.56 | 0.4 | |
| Employed | 2156 (67%) | 691 (78%) | 1.78 | 1.45, 2.20 | <0.001 | |
| Yearly gross income | 4062 | |||||
| >50,000 | 231 (7.3%) | 55 (6.2%) | ||||
| 0–15,000 | 1014 (32%) | 280 (32%) | 1.16 | 0.85, 1.61 | 0.4 | |
| 15,000–30,000 | 1224 (38%) | 374 (42%) | 1.28 | 0.94, 1.77 | 0.12 | |
| 30,000–50,000 | 711 (22%) | 173 (20%) | 1.02 | 0.73, 1.44 | 0.9 | |
| Number of adult family members | 4104 | 2.00 (2.00, 3.00) | 2.00 (2.00, 3.00) | 0.98 | 0.93, 1.02 | 0.3 |
| Children in family | 4104 | |||||
| No | 2549 (79%) | 588 (66%) | ||||
| Yes | 665 (21%) | 302 (34%) | 1.97 | 1.67, 2.32 | <0.001 | |
| Teenagers in family | 4104 | |||||
| No | 2646 (82%) | 700 (79%) | ||||
| Yes | 568 (18%) | 190 (21%) | 1.26 | 1.05, 1.52 | 0.013 | |
| Smoking and ATNP use habits | ||||||
| Smokers in family | 4104 | |||||
| No | 2021 (63%) | 100 (11%) | ||||
| Yes | 1193 (37%) | 790 (89%) | 13.4 | 10.8, 16.8 | <0.001 | |
| Past smoker | 2992 | |||||
| No | 1915 (74%) | 67 (16%) | ||||
| Yes | 661 (26%) | 349 (84%) | 15.1 | 11.5, 20.0 | <0.001 | |
| Age started smoking | 2122 | 18.0 (16.0, 20.0) | 17.0 (15.0, 20.0) | 0.98 | 0.96, 1.00 | 0.025 |
| Eating habits | ||||||
| Junk food snacks | 4104 | |||||
| No | 1130 (35%) | 235 (26%) | ||||
| Yes | 2084 (65%) | 655 (74%) | 1.51 | 1.28, 1.79 | <0.001 | |
| Alcoholic beverages consumption | 4104 | |||||
| No | 575 (18%) | 57 (6.4%) | ||||
| Yes | 2639 (82%) | 833 (94%) | 3.18 | 2.42, 4.27 | <0.001 | |
| Daily fruit consumption | 4104 | |||||
| No | 904 (28%) | 314 (35%) | ||||
| Yes | 2310 (72%) | 576 (65%) | 0.72 | 0.61, 0.84 | <0.001 | |
| Daily vegetable consumption | 4104 | |||||
| No | 845 (26%) | 230 (26%) | ||||
| Yes | 2369 (74%) | 660 (74%) | 1.02 | 0.87, 1.21 | 0.8 | |
| Weekly fast-food visit | 4104 | |||||
| No | 2925 (91%) | 730 (82%) | ||||
| Yes | 289 (9.0%) | 160 (18%) | 2.22 | 1.80, 2.73 | <0.001 | |
| Number of daily meals | 4104 | 3.00 (3.00, 4.00) | 3.00 (3.00, 4.00) | 0.9 | 0.84, 0.96 | 0.001 |
| Eating habits description | 4104 | |||||
| I try to be careful about what I eat | 1200 (37%) | 301 (34%) | ||||
| I try to be careful, but I don’t always succeed | 1651 (51%) | 482 (54%) | 1.16 | 0.99, 1.37 | 0.066 | |
| I don’t particularly worry about what I eat | 302 (9.4%) | 93 (10%) | 1.23 | 0.94, 1.59 | 0.13 | |
| I eat everything without thinking | 61 (1.9%) | 14 (1.6%) | 0.91 | 0.49, 1.61 | 0.8 | |
| Impact of eating habits on physical shape | 4104 | |||||
| Very much | 1419 (44%) | 391 (44%) | ||||
| Enough | 1633 (51%) | 448 (50%) | 1 | 0.85, 1.16 | >0.9 | |
| Little | 140 (4.4%) | 45 (5.1%) | 1.17 | 0.81, 1.65 | 0.4 | |
| Not at all | 22 (0.7%) | 6 (0.7%) | 0.99 | 0.36, 2.31 | >0.9 | |
| Lifestyle habits | ||||||
| Wake-up time | 4104 | |||||
| At 6.30 or earlier | 1007 (31%) | 248 (28%) | ||||
| Around 7.00 | 895 (28%) | 243 (27%) | 1.1 | 0.90, 1.34 | 0.3 | |
| Around 7.30 | 640 (20%) | 186 (21%) | 1.18 | 0.95, 1.46 | 0.13 | |
| After 8.00 | 460 (14%) | 149 (17%) | 1.32 | 1.04, 1.66 | 0.02 | |
| I don’t have a specific time | 212 (6.6%) | 64 (7.2%) | 1.23 | 0.89, 1.67 | 0.2 | |
| Bedtime | 4104 | |||||
| At 10.30 pm or earlier | 747 (23%) | 162 (18%) | ||||
| Around 11.00 pm | 1388 (43%) | 381 (43%) | 1.27 | 1.03, 1.56 | 0.024 | |
| After midnight | 809 (25%) | 277 (31%) | 1.58 | 1.27, 1.97 | <0.001 | |
| I don’t have a specific time | 270 (8.4%) | 70 (7.9%) | 1.2 | 0.87, 1.63 | 0.3 | |
| Effect of sleep on physical health | 4104 | |||||
| Very much | 1584 (49%) | 441 (50%) | ||||
| Enough | 1487 (46%) | 422 (47%) | 1.02 | 0.88, 1.19 | 0.8 | |
| Little | 125 (3.9%) | 26 (2.9%) | 0.75 | 0.47, 1.14 | 0.2 | |
| Not at all | 18 (0.6%) | 1 (0.1%) | 0.2 | 0.01, 0.97 | 0.12 | |
| Attention to physical shape | 4104 | |||||
| Very much | 528 (16%) | 179 (20%) | ||||
| Enough | 2004 (62%) | 543 (61%) | 0.8 | 0.66, 0.97 | 0.024 | |
| Little | 632 (20%) | 157 (18%) | 0.73 | 0.57, 0.93 | 0.012 | |
| Not at all | 50 (1.6%) | 11 (1.2%) | 0.65 | 0.31, 1.23 | 0.2 | |
| Regular physical activity | 4104 | |||||
| No | 1351 (42%) | 332 (37%) | ||||
| Yes | 1863 (58%) | 558 (63%) | 1.22 | 1.05, 1.42 | 0.011 |
ATNP: Alternative Tobacco and Nicotine Product.
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Lorenzoni, G.; Ocagli, H.; Azzolina, D.; Khan, N.M.; Angioletti, F.; Pilali, K.-T.; Şentürk Acar, A.; Berchialla, P.; Martinato, M.; Gregori, D. Lifestyle Habits and Alternative Tobacco and Nicotine Products: Results from the MINERVA Project. J. Clin. Med. 2026, 15, 389. https://doi.org/10.3390/jcm15010389
AMA Style
Lorenzoni G, Ocagli H, Azzolina D, Khan NM, Angioletti F, Pilali K-T, Şentürk Acar A, Berchialla P, Martinato M, Gregori D. Lifestyle Habits and Alternative Tobacco and Nicotine Products: Results from the MINERVA Project. Journal of Clinical Medicine. 2026; 15(1):389. https://doi.org/10.3390/jcm15010389
Chicago/Turabian StyleLorenzoni, Giulia, Honoria Ocagli, Danila Azzolina, Noor Muhammad Khan, Francesca Angioletti, Kostantina-Thaleia Pilali, Aslihan Şentürk Acar, Paola Berchialla, Matteo Martinato, and Dario Gregori. 2026. "Lifestyle Habits and Alternative Tobacco and Nicotine Products: Results from the MINERVA Project" Journal of Clinical Medicine 15, no. 1: 389. https://doi.org/10.3390/jcm15010389
APA StyleLorenzoni, G., Ocagli, H., Azzolina, D., Khan, N. M., Angioletti, F., Pilali, K.-T., Şentürk Acar, A., Berchialla, P., Martinato, M., & Gregori, D. (2026). Lifestyle Habits and Alternative Tobacco and Nicotine Products: Results from the MINERVA Project. Journal of Clinical Medicine, 15(1), 389. https://doi.org/10.3390/jcm15010389
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