ArticleJMIR AI2025
AI Awareness and Tobacco Policy Messaging Among US Adults: Electronic Experimental Study.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Despite public health efforts, tobacco use remains the leading cause of preventable death in the United States and continues to disproportionately affect underrepresented populations. Public policies are needed to improve health equity in tobacco-related health outcomes. One strategy for promoting public support for these policies is through health messaging. Improvements in artificial intelligence (AI) technology offer new opportunities to create tailored policy messages quickly; however, there is limited research on how the public might perceive the use of AI for public health messages. Objective: This study aimed to examine how knowledge of AI use impacts perceptions of a tobacco control policy video. Methods: A national sample of US adults (N=500) was shown the same AI-generated video that focused on a tobacco control policy. Participants were then randomly assigned to 1 of 4 conditions where they were (1) told the narrator of the video was AI, (2) told the narrator of the video was human, (3) told it was unknown whether the narrator was AI or human, or (4) not provided any information about the narrator. Results: Perceived video rating, effectiveness, and credibility did not significantly differ among the conditions. However, the mean speaker rating was significantly higher (P=.001) when participants were told the narrator of the health message was human (mean 3.65, SD 0.91) compared to the other conditions. Notably, positive attitudes toward AI were highest among those not provided information about the narrator; however, this difference was not statistically significant (mean 3.04, SD 0.90). Conclusions: Results suggest that AI may impact perceptions of the speaker of a video; however, more research is needed to understand if these impacts would occur over time and after multiple exposures to content. Further qualitative research may help explain why potential differences may have occurred in speaker ratings. Public health professionals and researchers should further consider the ethics and cost-effectiveness of using AI for health messaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.