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Adolescent school connectedness generally protects from risk behaviors such as tobacco use; however, its relationship to e-cigarette use is unclear. This study examines the relationship between adolescent school connectedness and e-cigarette susceptibility in a diverse longitudinal sample. This secondary analysis of a school-based intervention surveyed 608 middle (66%) and high school (34%) students from 10 schools at 3 time points over 1 year. At baseline, respondents had a mean age of 14 years, 54% were female, and 71% were BIPOC (Black, Indigenous, People of Color). Logistic regression models examined unadjusted and adjusted associations between school connectedness (both baseline and concurrent) and e-cigarette susceptibility over time. E-cigarettes represented the most prevalent form of current nicotine-containing product use in spring 2019 (2.3%), and most respondents reported no e-cigarette susceptibility (69%). E-cigarette susceptibility remained relatively stable during the study. Higher baseline school connectedness levels were associated with lower odds of e-cigarette susceptibility over time. Similarly, higher concurrent school connectedness scores were associated with lower odds of e-cigarette susceptibility over time: spring 2019 (OR, 0.39; 95% CI, 0.32, 0.47), fall 2019 (OR, 0.49; 95% CI, 0.34, 0.72), and spring 2020 (OR, 0.64; 95% CI, 0.47, 0.87). Findings were similar for middle and high school students and did not differ significantly after adjusting for other covariates. Adolescents' school connectedness appears to protect from e-cigarette susceptibility over time, underscoring the importance of promoting positive school experiences to reduce adolescent risk e-cigarette use. [ABSTRACT FROM AUTHOR]

M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking. [ABSTRACT FROM AUTHOR]