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Introduction: Tobacco companies frequently distribute coupons for their products. This marketing tactic may be particularly effective among young adults, who tend to be especially price-sensitive. Young adulthood is also a stage during which many individuals initiate established cigarette smoking and are especially vulnerable to the effects of tobacco marketing., Methods: We used five waves of data from the US Population Assessment on Tobacco and Health Study (2013-2019) to assess the longitudinal relationship between cigarette coupon receipt and initiation of established cigarette smoking among young adults (18-24 years) who did not report current smoking and had smoked <100 cigarettes in their lifetime at baseline. Initiation of established cigarette smoking was defined as reporting current cigarette use and having smoked ≥100 cigarettes at follow-up. To test this relationship, we fit four discrete time survival models to an unbalanced person-period data set. The first model included our time-varying coupon receipt variable, which was lagged one wave. Subsequent models added sociodemographic, cigarette smoking exposure and other tobacco use variables., Results: Adopting the model adjusting for sociodemographic variables, respondents who received a coupon were found to be more likely to initiate established cigarette smoking at follow-up (adjusted HR (aHR): 2.31, 95% CI 1.41 to 3.80). This relationship remained significant when controlling for all covariates in the fully adjusted model (aHR: 1.96, 95% CI 1.18 to 3.26)., Conclusions: These findings show that receiving tobacco coupons may increase the likelihood that young adults will initiate established cigarette smoking, underscoring the need to address the effects of this tobacco marketing tactic., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., Trial Registration: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736., (©Leeann Nicole Siegel, Kara P Wiseman, Alex Budenz, Yvonne Prutzman. Originally published in JMIR AI (https://ai.jmir.org), 22.05.2024.)