Enhancing the Salience of Future Health Risks to Promote Smoking Cessation: A Randomized Nudge 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.
Related
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- A Randomized Factorial Trial of Varenicline, Nicotine Lozenges, and Smartphone Medication Adherence Reminders for Smoking Cessation: Protocol for the COMBO II Study.
- Remote vs. In-Person Study Evaluation (RISE) Trial 1 of 3 to examine the impact of decentralized trial methods on accrual efficiency, retention, biospecimen capture, and treatment utilization: study protocol for a multi-center randomized controlled trial in the context of smoking cessation (RISE Above Smoking)
- An analysis of enrollment rates by admitting service for a secondary analysis of enrollment data from a randomized smoking-cessation trial (FIESTA)
BibTeX
@article{park2026enhancing,
title = {{Enhancing the Salience of Future Health Risks to Promote Smoking Cessation: A Randomized Nudge Trial}},
author = {JE Park and SH Park and JH Park and SY Kim and HJ Kim},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.329},
url = {https://doi.org/10.1093/eurpub/ckag169.329},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Enhancing the Salience of Future Health Risks to Promote Smoking Cessation: A Randomized Nudge Trial
AU - JE Park
AU - SH Park
AU - JH Park
AU - SY Kim
AU - HJ Kim
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.329
UR - https://doi.org/10.1093/eurpub/ckag169.329
AB - 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.
SN - 1101-1262
SN - 1464-360X
ER -