Discrete Choice Experiment on Financial Incentives for Engaging Young Adults in Vaping Cessation Programs

一项关于经济激励措施对吸引年轻人参与戒烟计划的离散选择实验

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Abstract

INTRODUCTION: Contingency management involves rewarding individuals based on objective evidence of behavioral changes. This study explores preferences for financial incentives in vaping cessation programs. METHODS: A discrete choice experiment (DCE) was conducted among young adult current e-cigarette users aged 19-29. DCE attributes (and levels) included reward amount ($100, $300), reward schedule (consistent value, escalating value), reward procedure (gain-framed, loss-framed), vaping education modules (yes, no), text messaging support (yes, no), and cost ($30, $100). Participants were randomized into one of the four blocks, each containing four choice sets. Each choice set presented two hypothetical vaping cessation programs. Choice data (n × choice sets × alternative programs = 154 × 4 × 2 = 1,232 observations) were analyzed using generalized estimation equation models. RESULTS: Participants showed a preference for vaping cessation programs offering a higher reward amount (odds ratio [OR] = 11.5; 95% CI,7.0-19.2), gain-framed rewards for sustained abstinence (OR = 1.4; 95% CI,1.0-1.8), text messaging support (OR = 1.9; 95% CI,1.4-2.5), and a lower program cost (OR = 2.6; 95% CI,1.8-3.8). Interaction tests showed significant effects of the rewards procedure among daily e-cigarette users (adjusted OR [AOR] = 2.1; 95% CI,1.3-3.5), but not some-day users; and among those with no quit attempts in the past six months (AOR = 2.7; 95% CI,1.5-4.7), but not those with previous quit attempts. Female participants preferred text messaging support (AOR = 3.6; 95% CI,2.3-5.4), whereas male participants did not show this preference. CONCLUSION: A multifaceted vaping cessation intervention augmented with financial incentives might improve participation and engagement among young adults. Future studies should investigate how these attributes can enhance program reach and vaping abstinence outcomes in real-world settings.

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