Enhancing the Salience of Future Health Risks to Promote Smoking Cessation: A Randomized Nudge Trial
Year: 2026
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
Abstract: Background Present bias, a central concept in behavioral economics, offers a useful framework for understanding why smokers often prioritize the immediate rewards of smoking over its long-term health consequences. We examined whether personalized disease-risk prediction information derived from health examination results could serve as a nudge-based intervention to reduce present bias and improve smoking cessation outcomes. Methods The study included 367 heavy smokers with a smoking history of at least 20 pack-years who were enrolled in a residential smoking cessation camp at a Regional Tobacco Control Centre in Cheongju, Korea, from 2021 to 2024. Participants were randomly assigned to receive either an AI-driven personalized report estimating their 4-year risks of developing nine major diseases (n = 171, 46.6%) or no report (n = 196, 53.4%). Smoking cessation was assessed through week 24. Results Although cessation rates declined over time in both groups, they remained higher in the intervention group, with the largest between-group difference observed at week 12 (82.5% vs. 64.8%, p < 0.001). The between-group difference remained significant through week 18 but was not significant thereafter in the overall sample. In subgroup analyses, a significant difference persisted through week 24 only among participants aged <65 years (p = 0.035). In fully adjusted logistic regression models, the intervention was associated with higher odds of smoking cessation at week 12 (OR = 2.49, 95% CI, 1.47–4.21) and week 16 (OR = 1.74, 95% CI, 1.08–2.77). Perceived importance of quitting and cessation self-efficacy were also higher in the intervention group. Conclusions These findings suggest that personalized disease-risk prediction information may support smoking cessation by increasing smokers’ perceived susceptibility to smoking-related diseases and the salience of future health consequences, thereby shifting their decisional balance toward quitting. Key messages • Among heavy smokers who intended to quit, a personalized disease-risk prediction report was associated with significantly higher short- to mid-term smoking cessation success than in the control group. • Personalized disease-risk information may promote quitting by increasing the salience of future health consequences, enhancing perceived susceptibility, and helping counteract present bias.
Institution: Chungbuk National University
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,181
OpenAlex ID: W7223675862