UNASSIGNED: Accelerating smoking cessation, particularly among young adults, is a national priority for decreasing tobacco-related disease. Healthcare providers play a critical role in delivering tobacco treatment interventions to this population. This study examined associations of demographic and tobacco use characteristics with young adults\' self-reported past-year clinical encounters to identify opportunities to facilitate cessation.
UNASSIGNED: We conducted cross-sectional, secondary analyses on a sample of 831 young adults aged 18-34 participating in the first wave of the National Young Adult Health Survey (NYAHS 2018-2019). Demographic and tobacco use characteristics were participants\' sex, age, race, current cigarette use, and current other tobacco use. Clinical encounter outcomes were past-year self-report of (1) seeing a clinician, (2) being asked about tobacco use, and among those currently smoking, (3) being advised to quit smoking.
UNASSIGNED: After adjustment for covariates, women (vs. men) had 2.16 times greater odds of reporting seeing a clinician, while Non-White (vs. White) young adults and those currently (vs. never) smoking had 69% and 47% lower odds. Women and those currently smoking had 2.98 and 2.66 times greater odds, respectively, of being asked about tobacco use. Among those who currently smoked, being not confident (vs. confident) about quitting smoking was associated with 69% lower odds of being advised to quit; those who reported moderate (vs. low) nicotine dependence had 3.11 times higher odds of being advised to quit.
UNASSIGNED: Sex, racial, and smoking status differences in young adults\' clinical encounter outcomes suggest multiple opportunities for future smoking prevention and cessation intervention efforts.

BACKGROUND: The net benefit of aspirin cessation in older adults remains uncertain. This study aimed to use observational data to emulate a randomized trial of aspirin cessation versus continuation in older adults without cardiovascular disease (CVD).
METHODS: Post hoc analysis using a target trial emulation framework applied to the immediate post-trial period (2017-2021) of a study of low-dose aspirin initiation in adults aged ≥ 70 years (ASPREE; NCT01038583). Participants from Australia and the USA were included if they were free of CVD at the start of the post-trial intervention period (time zero, T0) and had been taking open-label or randomized aspirin immediately before T0. The two groups in the target trial were as follows: aspirin cessation (participants who were taking randomized aspirin immediately before T0; assumed to have stopped at T0 as instructed) versus aspirin continuation (participants on open-label aspirin at T0 regardless of their randomized treatment; assumed to have continued at T0). The outcomes after T0 were incident CVD, major adverse cardiovascular events (MACE), all-cause mortality, and major bleeding during 3, 6, and 12 months (short-term) and 48 months (long-term) follow-up. Hazard ratios (HRs) comparing aspirin cessation to continuation were estimated from propensity-score (PS) adjusted Cox proportional-hazards regression models.
RESULTS: We included 6103 CVD-free participants (cessation: 5427, continuation: 676). Over both short- and long-term follow-up, aspirin cessation versus continuation was not associated with elevated risk of CVD, MACE, and all-cause mortality (HRs, at 3 and 48 months respectively, were 1.23 and 0.73 for CVD, 1.11 and 0.84 for MACE, and 0.23 and 0.79 for all-cause mortality, p > 0.05), but cessation had a reduced risk of incident major bleeding events (HRs at 3 and 48 months, 0.16 and 0.63, p < 0.05). Similar findings were seen for all outcomes at 6 and 12 months, except for a lowered risk of all-cause mortality in the cessation group at 12 months.
CONCLUSIONS: Our findings suggest that deprescribing prophylactic aspirin might be safe in healthy older adults with no known CVD.

UNASSIGNED: Early smoking initiation has been associated with a higher risk of developing long-term smoking habit. There is a growing global consensus that demands raising the minimum legal age (MLA) for smoking as an approach to address this problem. Singapore successfully raised the MLA from 18 to 21 years in 2021. This study aimed to evaluate the awareness and attitude of multi-ethnic Asian youth (aged 15-24) on raising MLA to 21 and passive smoking.
UNASSIGNED: A cross-sectional survey comprising of 23 items was circulated via a secure internet-based platform, FORMSG between September and November 2022. Data were analyzed for descriptive statistics. Categorical variables were compared for association with receptivity toward change in MLA using Chi-Squared test and multivariable logistic regression analysis using Rstudio. Post-hoc Bonferroni correction were further utilized for pairwise comparison.
UNASSIGNED: Majority (80.3%) of the 608 participants expressed their support for MLA 21 implementation. Participants\' age was a significant variable as those aged 15-17 years old (OR = 2.1, 95%CI = 1.01-4.32, p = 0.048) showed a higher likelihood of supporting MLA implementation compared to those aged 21 and above. In addition, majority (89.8%) of them were also aware of the harmful effects of passive smoking. When it came to discouraging smoking among youth, family influence (64%) and school education (55.6%) emerged as the top strategies.
UNASSIGNED: Most of the youth express strong support for raising the MLA to 21, with over 80% in favor of such change, reflects a significant harmony among youth in favor of tobacco-free environment.

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.

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.

BACKGROUND: Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.
OBJECTIVE: We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot.
METHODS: The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat\'s persona (\"personality\"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs.
RESULTS: We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute\'s SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants\' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants\' frustration with QuitBot\'s inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft\'s Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs.
CONCLUSIONS: The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions.
BACKGROUND: ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231.

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: 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.

Background: Tobacco cannabis co-use is common and becoming more prevalent. Frequent and heavy users of cannabis may struggle to quit smoking. Quitlines offer free cessation treatment in the United States and 25% of quitline callers may also be cannabis users. The present paper describes a randomized pilot study of a tailored intervention for cannabis and cigarette co-users. The intervention combines the quitline smoking cessation treatment with a motivational enhancement therapy-based cannabis intervention. Methods: The randomized pilot study was conducted within four state-funded quitlines with quitline coaches as interventionists. 102 quitline callers who were cannabis and cigarette co-users were randomized to receive treatment as usual (TAU) or the new Quitline Check-Up (QLCU) intervention. Outcomes were collected 90 days post-randomization. Primary outcomes included feasibility and acceptability of delivering the QLCU in the quitline setting. Secondary outcomes included 7-day point prevalence tobacco abstinence, past 30-day cannabis use, and Cannabis Use Disorder Identification Test scores. Results: Study participants were heavy cannabis users, averaging 25 days of use in the past 30; nearly 70% used at a level considered hazardous. Fidelity ratings indicated coaches were successful at delivering the intervention. Treatment engagement was high for both groups (TAU m = 3.4 calls; QLCU m = 3.6 calls) as was treatment satisfaction. Intent-to-treat quit rates (with survey non-responders classified as smokers) were 28.6% for the TAU control group and 24.5% for the QLCU group (P = .45). Discussion: Hazardous cannabis use rates were high in this sample of tobacco cannabis co-users calling quitlines to quit smoking. The intervention for co-users was acceptable and feasible to deliver. No improvements in tobacco cessation outcomes were observed. Pragmatic intervention development within a real-world clinical setting can streamline the intervention development process. More research is needed on tobacco cannabis co-users and who can benefit from a tailored intervention. Registered: ClinicalTrials.gov NCT04737772, February 4, 2021.