While cigarette smoking is highly comorbid with stimulant use disorder (SUD), the relationship is rarely evaluated concurrently to better understand the association between the two and how they influence one another over time. The overarching research question posed was, do patterns of cigarette smoking and stimulant use co-vary (both at baseline and throughout treatment) with one another during the testing of a combined treatment for people who smoking and use stimulants, and do those changes depend on the experimental treatment being tested? Participants (n = 538, 52% male) were randomly assigned to the experimental group [smoking cessation and treatment-as-usual (TAU)] or placebo group (TAU; a minimum of one treatment session per week over 10 weeks). A parallel growth model was applied to determine whether initial smoking levels predicted stimulant use growth trajectories (and vice versa), and whether initial levels and growth trajectories of each were related. A significant treatment effect on the targeted disorder (smoking; B = .667, p < .001) and no significant effect on the non-targeted disorder (stimulant use; B = .007, p = .948) were found. In addition, there was a negative relationship between the slope of smoking and stimulant use (r = −.117, p = .208), however, it was not statistically significant. Clinical significance from the original study was replicated. Using parallel growth modeling, researchers can test hypotheses about off-target treatment effects, particularly when the effect is routed through change in the targeted disorder. This technique allows researchers to advance methodological procedures in the field, while better understanding the comorbidity between two disorders.
Public Health SignificanceThis study replicated the effect of a novel, previously reported treatment for smokers who also reported a stimulant use disorder, but did so in the context of a parallel growth model that also allowed this treatment to impact patient's stimulant use. This modeling method provides an analytic framework for being able to examine how treatments impact non-targeted outcomes of interest.