Mediational Effects on Motivation to Quit Smoking After Exposure to a Cigarette Pictorial Warning Label Among Young Adults

Document Type

Journal Article

Publication Date



Annals of behavioral medicine : a publication of the Society of Behavioral Medicine








Young adults; affect; emotion; mediation; pictorial warning; risk perceptions


BACKGROUND: Young adults are vulnerable to cigarette package marketing. Pictorial warning labels are recommended for tobacco control. Research should address questions raised in legal challenges including causal mechanisms. Evidence is mixed and understudied among young adults (e.g., discrete emotions and risk perceptions). PURPOSE: This study investigated mediators of pictorial warning label effects on motivation to quit smoking among young adult smokers. METHODS: This study analyzed data from a randomized trial with a 4 week exposure to a cigarette pictorial warning among young adult smokers (N = 229) aged 18-30 with assessments at baseline, immediately post-intervention, and 3 months. Mediation analyses used latent change scores to test the effects post-intervention on fear, anger, and risk perceptions. We also examined whether post-intervention measures predicted change in motivation to quit smoking at 3 months. The first model assessed aggregate risk perceptions and the second model assessed discrete risk perceptions (deliberative, affective). RESULTS: Pictorial warning label exposure led to increases in fear which led to increased motivation to quit smoking for the first (B = 0.12, 95% CI = 0.04, 0.26) and second (B = 0.12, 95% CI = 0.03, 0.25) model. Exposure modestly increased motivation to quit by way of fear and affective risk perceptions (B = 0.01, 95% CI = 0.00, 0.04). Exposure had a direct relationship on increased motivation to quit as well. CONCLUSIONS: Findings demonstrate factors contributing to change in motivation to quit smoking among young adult smokers after pictorial warning label exposure. Affective processes are mediators of pictorial warning label effects.


Biostatistics and Bioinformatics