Adolescent smokers tend to have friends who also smoke. This association has been attributed to peer socialization and peer selection effects. However, evidence regarding timing and relative magnitude of these effects is mixed. Using a random-intercept cross-lagged panel model, we examined the reciprocal relations between adolescent cigarette use and perceptions of friends’ cigarette use in a sample of 387 adolescents, assessed annually for 4 years. Adolescent cigarette use predicted increases in perceived friend use before the reverse effect emerged. Further, some of the effect of early adolescent cigarette use on subsequent use was mediated by changes in perceived friend use. The results support a greater role for friend selection than socialization in predicting early adolescent cigarette use.

Aims To illustrate a machine learning-based approach for identifying and investigating moderators of alcohol use intervention effects in aggregate-data meta-analysis. Methods We illustrated the machine learning technique of random forest modeling using data from an ongoing meta-analysis of brief substance use interventions implemented in general healthcare settings. A subset of 40 trials testing brief alcohol interventions (BAIs) was used; these trials provided 344 estimates of post-intervention effects on participants’ alcohol use as well as data on 20 potential moderators of intervention effects. These candidate moderators included characteristics of trial methodology and implementation, intervention design and participant samples. Results The best-fitting random forest model identified 10 important moderators from the pool of 20 candidate moderators. Meta-regression utilizing the selected moderators found that inclusion of prescriptive advice in a BAI session significantly moderated BAI effects on alcohol use. Observed effects were also significantly moderated by several methodological characteristics of trials, including the type of comparison group used, the overall level of attrition and the strategy used to address missing data. In a meta-regression model that included all candidate moderators, fewer coefficients were found to be significant, indicating that the use of a preliminary data reduction technique to identify only important moderators for inclusion in final analyses may have yielded improved statistical power to detect moderation. Conclusions Machine learning methods can be valuable tools for clarifying the influence of trial, intervention and sample characteristics on alcohol use intervention effects, in particular when numerous candidate moderators are available.

Adolescent decisions, especially in novel contexts, are often guided by affective evaluations (i.e., feelings associated with a stimulus) rather than knowledge of the risks and probabilities of different outcomes. In this study, we used the affect-driven exploration (ADE) model to illustrate how affective evaluations can play a critical role in driving early use of cigarettes, as well as the adaptive function of the resulting experiential learning in informing future affect and cigarette use. We analyzed five waves of data collected from a large, diverse community sample of adolescents who were followed from early to late adolescence (N = 386; 50.9% female; Baseline age = 11.41 ± 0.88 years) during years 2004–2010 to model trajectories of positive affect and risk perceptions (associated with cigarette use) and examined the associations of these trajectories with their self-reported cigarette use and dependence symptoms. Consistent with the ADE model, early initiators reported higher levels of positive affect at baseline, which we argue may have led them to try cigarettes. Notably, most early initiators reported a decline in positive affect over time, suggesting an experience-based shift in affective evaluations associated with cigarette use. Risk perceptions associated with cigarette use did not emerge as a significant predictor of cigarette use or subsequent dependence. Therefore, for deterring adolescent cigarette use, efforts to influence affect (through graphic warning labels and other media) may be more effective than directly influencing risk perceptions. Despite the affective basis for initiating cigarette use, few adolescents engaged in early use (N = 20) or developed symptoms of dependence (N = 25). Majority of those who engaged in early cigarette use showed a decline in positive affect, with corresponding increase in risk perceptions over time. Some early users may indeed continue to engage in cigarette use, but this is likely driven by the addictive properties of the drug. Overall these findings challenge the popular stereotype of impulsive and emotionally reactive behaviors during adolescence, and suggest a more nuanced interpretation of adolescent risk behavior.

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