BACKGROUND: This study investigated the risks for all-cause death and death from cancer or cardiovascular diseases due to smoking status and behavior, focusing on differences in smoking duration and amount stratified by sex.
METHODS: The integrated Korean Genome and Epidemiology Study provided data for 209770 individuals who were classified as never, former, or current smokers, based on their current smoking status. Pack-years were computed using daily average smoking amount and total smoking duration, and were categorized into quantiles separately for men and women. Based on the number of deaths in 2018, hazard ratios (HRs) were estimated for all-cause mortality, as well as for death caused by all cancers, lung cancer, and cardiovascular diseases according to pack-years adjusted for age, household income, marital status, body mass index, physical activity, and alcohol consumption.
RESULTS: A significant increase in the risk of all-cause mortality was observed for current smokers (men HR=1.90; 95% CI: 1.69-2.14; women HR=2.25; 95% CI: 1.68-2.99) and former smokers (men HR=1.31; 95% CI: 1.17-1.47; women HR=2.35; 95% CI: 1.63-3.39) compared with that for those who had never smoked. Among men, HR for death from lung cancer was 3.13 (95% CI: 2.06-4.75) in former smokers and tended to increase with each pack-year quantile (range HR: 5.72-17.11). Among women, the HR was estimated to be 17.20 (95% CI: 6.22-47.57) only for >3rd quantile.
CONCLUSIONS: Smoking increases the risks of all-cause death. Considering the persistent risks post-smoking cessation, it is vital to focus on preventing smoking initiation and providing proactive support for successful smoking cessation and maintenance of a smoke-free lifestyle.

BACKGROUND: Little is known about the prevalence and predictors of adolescents\' intention to quit or reduce use of e-cigarettes and/or cannabis.
METHODS: Frequencies of intention to change (quit, reduce) e-cigarettes and/or cannabis use were examined among 23,915 surveyed middle and high school students with sole and co-use. Predictors of intention to change were identified via LASSO/multilevel logistic regression.
RESULTS: Among those with sole e-cigarette use (n = 543), 40.9 % intended to quit and 24.1 % intended to reduce; non-daily e-cigarette use predicted intention to quit and reduce e-cigarettes (p\'s < 0.03). Among those with sole cannabis use (n = 546), 10.6 % intended to quit and 25.1 % intended to reduce; absence of cannabis cravings predicted intention to reduce cannabis use (p < 0.01). Among those with co-use (n = 816), 26.2 % intended to either quit or reduce (quit/reduce) both substances, 27.5 % intended to quit/reduce e-cigarettes only, and 6.9 % intended to quit/reduce cannabis only. No predictors emerged for intention to change e-cigarette use among those with co-use (p\'s > 0.09), but younger age, lack of poly-tobacco use, and lack of cannabis craving predicted intention to quit/reduce cannabis use (p\'s < 0.04).
CONCLUSIONS: More than half of adolescents with past-month e-cigarette use, regardless of concurrent cannabis use, expressed interest in changing their use. However, only heaviness of e-cigarette use emerged as a predictor of intention to change suggesting. While fewer students expressed interest in changing their cannabis use, cannabis cravings and poly-tobacco use predicted intent to change. Overall, findings emphasize the need to tailor interventions towards adolescents engaging in more problematic substance use patterns.

OBJECTIVE: To pilot test QuitGuide for Natives, a culturally aligned version of the National Cancer Institute\'s QuitGuide smartphone app for smoking cessation.
METHODS: This randomised controlled trial was conducted remotely during 2022-2023. American Indian adults who smoked and resided in the Midwest (n=115) were randomised to QuitGuide for Natives or the general audience QuitGuide smartphone-based intervention. Group differences in feasibility (times the app was initiated), usability, acceptability (\'How likely would you be to recommend the app to a friend?\'), fit of app with culture and preliminary efficacy (24-hour quit attempts, cotinine-confirmed self-reported 7-day abstinence) outcomes were examined.
RESULTS: QuitGuide for Natives versus the general audience QuitGuide did not differ in the number of times the app was opened (adjusted incidence rate ratio 0.94 (95% CI 0.63 to 1.40); p=0.743) nor in usability score (adjusted mean difference (aMD) 0.73 (95% CI: -5.00 to 6.46); p=0.801) or likeliness of recommending the app to a friend (aMD 0.62 (95% CI -0.02 to 1.27); p=0.058). Differences were observed for all cultural fit outcomes such as \'The app fits my American Indian culture (aMD 0.75 (95% CI 0.35 to 1.16); p<0.001). QuitGuide for Natives versus the general audience QuitGuide resulted in an average of 6.6 vs 5.1 24-hour quit attempts (p=0.349) and cotinine-confirmed 7-day abstinence was achieved by 6.9% vs 3.5% (p=0.679).
CONCLUSIONS: Acceptability, cultural fit and preliminary efficacy findings are encouraging and will inform future, larger-scale evaluation of culturally aligned digital smoking cessation resources for American Indian adults.

BACKGROUND: A subpopulation of adults who smoke cigarettes use electronic nicotine device systems (ENDS) for cigarette cessation. This study examined the relationship between ENDS flavors, device types, and nicotine concentration with past month cigarette abstinence among adults using ENDS for cigarette cessation.
METHODS: We used the Population Assessment of Tobacco and Health (PATH) Study (waves 5 and 6) to identify adults who self-reported using ENDS to quit cigarettes at baseline (wave 5) and investigated their cigarette abstinence at follow-up (wave 6) [n = 1252]. Measures assessed include ENDS features (flavors, device types, nicotine concentration) at baseline and past-month abstinence from cigarette smoking at follow-up. Weighted descriptive analysis was used, and multivariable logistic regression models examined ENDS features associated with past-month cigarette abstinence, adjusting for demographic factors and tobacco dependence at baseline.
RESULTS: Most participants used disposable devices (37.2 %; 95 % CI:33.2-41.5), followed by refillable tanks (30.2 %; 95 % CI:26.2-34.5). Additionally, fruit (41.3 %; 95 % CI:37.3-45.5), followed by menthol (19.1 %; 95 % CI:16.2-22.4), and tobacco (18.5 %; 95 % CI:15.5-22.1) were the most common flavors. The most common nicotine concentration used was 1-6 mg/ml (38.8 %; 95 % CI:34.6-43.2). Furthermore, in the adjusted model, daily ENDS users at baseline had 86 % (95 % CI:1.08-3.18) higher odds of past month cigarette abstinence at follow-up, than individuals who indicated \'not at all\' to the current use of ENDS at baseline. There were no significant differences by preferred flavors, device type and nicotine concentrations (p-values > 0.05).
CONCLUSIONS: Daily ENDS users had higher odds of quitting cigarettes compared to those who stopped using ENDS. However, the type of device, flavoring, and nicotine concentration used by ENDS users were not associated with past-month cigarette abstinence at follow-up two years later.

OBJECTIVE: With the prevalence of e-cigarette use among Australian youth increasing significantly in recent years, greater attention is being paid to encouraging and supporting cessation. However, research to inform such efforts is lacking. The present study sought to (i) measure desire to quit e-cigarette use and actual quitting attempts among young Australians and (ii) explore correlates of quitting-related cognitions and behaviours.
METHODS: This was a cross-sectional on-line survey conducted in Australia. The participants were 14-25-year-old e-cigarette users (n = 602; 53% women).
METHODS: Desire to quit vaping and attempts to quit vaping were the primary dependent variables. The independent variables included several individual (e.g. harm perceptions, perceived appeal of vapes), social (descriptive norms) and environmental (e.g. ease of e-cigarette access) factors.
RESULTS: A majority of respondents (61%) expressed a desire to quit vaping, and just over half (55%) had made a quit attempt. Finding vapes easy to access was associated with both a lack of desire [odds ratio (OR) = 0.71] and attempts to quit (OR = 0.77), while self-reported addiction to vaping (OR = 1.42 and OR = 3.11) and perceiving vaping to be associated with mental health risks (OR = 1.30 and OR = 1.40) were positively correlated with these variables. Perceiving that vaping is common among people of one\'s age (OR = 0.82) and finding vapes appealing (OR = 0.55) were associated with a lack of desire to quit, while perceiving vaping to have physical health risks was positively associated with quitting desire (OR = 1.58). School-based education on vaping was associated with reporting an attempt/s to quit (OR = 0.47).
CONCLUSIONS: This survey of young Australian e-cigarette users suggests a high level of desire to quit using e-cigarettes and attempts to quit. Increasing knowledge regarding the physical and mental health risks associated with e-cigarette use may assist with promoting quitting-related intentions. Changing social norms, reducing the accessibility of e-cigarettes and reducing the appeal of the products also constitute potential means of increasing the desire to quit.

BACKGROUND: Recent studies have shown positive, though small, clinical effects of digital smoking cessation (SC) interventions for cancer survivors. However, research on associations among participant characteristics, intervention engagement, and outcomes is limited.
OBJECTIVE: This study aimed to explore the predictors and moderators of engagement and outcome of MyCourse-Quit Smoking (in Dutch: \"MijnKoers-Stoppen met Roken\"), a digital minimally guided intervention for cancer survivors.
METHODS: A secondary analysis of data from the randomized controlled trial was performed. The number of cigarettes smoked in the past 7 days at 6-month follow-up was the primary outcome measure. We analyzed interactions among participant characteristics (11 variables), intervention engagement (3 variables), and outcome using robust linear (mixed) modeling.
RESULTS: In total, 165 participants were included in this study. Female participants accessed the intervention less often than male participants (B=-11.12; P=.004). A higher Alcohol Use Disorders Identification Test score at baseline was associated with a significantly higher number of logins (B=1.10; P<.001) and diary registrations (B=1.29; P<.001). A higher Fagerström Test for Nicotine Dependence score at baseline in the intervention group was associated with a significantly larger reduction in tobacco use after 6 months (B=-9.86; P=.002). No other associations and no moderating effects were found.
CONCLUSIONS: Overall, a limited number of associations was found between participant characteristics, engagement, and outcome, except for gender, problematic alcohol use, and nicotine dependence. Future studies are needed to shed light on how this knowledge can be used to improve the effects of digital SC programs for cancer survivors.
BACKGROUND: Netherlands Trial register NTR6011/NL5434; https://onderzoekmetmensen.nl/nl/trial/22832.

BACKGROUND: Many people continue to smoke despite strong policies to deter use, thus stronger regulatory measures may be required. In four high-income countries, we examined whether people who smoke would support a total ban on smoked tobacco products under two differing policy scenarios.
METHODS: Data were from 14 363 adults (≥18) who smoked cigarettes (≥monthly) and participated in at least one of the 2018, 2020 or 2022 International Tobacco Control Four Country Smoking and Vaping Surveys in Australia, Canada, England and the USA. In 2018, respondents were asked whether they would support a law that totally bans smoked tobacco if the government provides smoking cessation assistance (Cessation Assistance scenario). In 2020 and 2022, respondents were asked a slightly different question as to whether they would support a law that totally bans smoked tobacco if the government encourages people who smoke to use alternative nicotine products like vaping products and nicotine replacement products instead (substitution scenario). Responses (support vs oppose/don\'t know) were estimated on weighted data.
RESULTS: Support was greater for the cessation assistance scenario (2018, 36.6%) than the nicotine substitution scenario (2020, 26.9%; 2022, 26.3%, both p<0.0001). In the longitudinal analysis, there was a significant scenario by country interaction effect with lower support in Canada, the USA and Australia under the substitution scenario than in the cessation scenario, but equivalent levels in England under both scenarios. The strongest correlates of support under both scenarios were planning to quit smoking within 6 months, wanting to quit smoking \'a lot\' and recent use of nicotine replacement therapy.
CONCLUSIONS: Opposition to banning smoked tobacco predominates among people who smoke, but less with a cessation assistance scenario than one encouraging nicotine substitution. Wanting to quit a lot was the strongest indicator of support.

BACKGROUND: Dual use of e-cigarettes and cigarettes is a growing usage pattern in adults, but little is known about the motivations underlying this trend. We investigated the reasons for e-cigarette use among adults who smoke, considering variation in sociodemographic subgroups.
METHODS: This repeated cross-sectional study analysed adults who smoked at least weekly and vaped at any frequency. Data were from the International Tobacco Control Korea Surveys conducted in 2016 (n=164) and 2020 (n=1088). Fourteen reasons for e-cigarette use were assessed in both waves. Subgroup analyses were performed by age, sex and educational level.
RESULTS: The top reasons for e-cigarette use in 2020 were curiosity (62.8%), less harmful than smoking (45.4%) and taste (43.2%). Curiosity was the most cited across age, sex and education subgroups. Significant differences were observed in 2020 compared with 2016, with lower percentages in goal-oriented reasons: helping quit smoking (36.3% vs 48.9%; p=0.017), helping cut down smoking (35.3% vs 52.7%; p=0.001), less harmful to others (39.0% vs 54.6%; p=0.003) and more acceptable (31.6% vs 61.2%; p<0.001). By contrast, non-goal-oriented reasons showed higher percentages in 2020, such as curiosity (62.8% vs 27.9%; p<0.001), taste (43.2% vs 22.1%; p<0.001) and enjoyment (26.8% vs 8.6%; p<0.001). In 2020, a majority of adults who smoked and vaped (53.3%) reported no intention to quit or reduce smoking.
CONCLUSIONS: E-cigarette use for curiosity and pleasure predominated among adults who smoked. The reasons for dual use in adults have shifted from goal-oriented to non-goal-oriented.

OBJECTIVE: Smoking is an important modifiable risk factor in all vascular diseases and verbal advice from providers has been shown to increase rates of tobacco cessation. We sought to identify factors that will improve tobacco cessation and recall of receiving verbal cessation advice in vascular surgery patients at a single institution.
METHODS: The study is a retrospective cohort study. Patients seen in outpatient vascular surgery clinic who triggered a tobacco Best Practice Advisory (BPA) during their office visits over a 10-month period were contacted post-clinic and administered surveys detailing smoking status, cessation advice recall, and validated scales for nicotine dependence and willingness to quit smoking. This BPA is a \"hard stop\" that requires providers to document actions taken. Charts were reviewed for tobacco cessation documentation. Nine-digit zip-codes identified the area deprivation index, a measure of socioeconomic status. Univariate analysis was used to identify factors associated with cessation and advice recall.
RESULTS: One hundred out of 318 (31.4%) patients responded to the survey. Epic Slicer Dicer found 97 BPA responses. To dismiss the BPA, 89 providers (91.8%) selected \"advised tobacco cessation\" and \"Unable to Advise\" otherwise. Of the 318 patients, 115 (36.1%) had cessation intervention documented in their provider notes and 151 (47.5%) received written tobacco cessation advice. Of survey respondents, 70 recalled receiving verbal advice, 27 recalled receiving written advice, 28 reported receiving offers of medication/therapy for cessation. 55 patients reported having tobacco cessation plans, and among those 17 reported having quit tobacco. Recall of receiving written advice (P < .001) and recall of receiving medication/therapy (P = .008) were associated with recall of receiving verbal cessation advice.
CONCLUSIONS: Providing patients with tobacco cessation medication/therapy and written tobacco cessation education during office visits is associated with increased patients\' recall of tobacco cessation advice. Vascular surgeons should continue to provide directed tobacco cessation advice.

BACKGROUND: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness.
OBJECTIVE: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors).
METHODS: Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute\'s quitSTART app. Participants\' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants\' probability of cessation from 28 variables reflecting participants\' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals\' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation.
RESULTS: The SML model\'s sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals\' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16).
CONCLUSIONS: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps.
BACKGROUND: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.