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• W3211877185 abstract "Waterpipe (i.e., hookah) tobacco smoking (WTS) is one of the most prevalent types of smoking among young people, yet there is little public education communicating the risks of WTS to the population. Using self-report and psychophysiological measures, this study proposes an innovative message testing and data integration approach to choose optimal content for health communication messaging focusing on WTS.In a two-part study, we tested 12 WTS risk messages. Using crowdsourcing, participants (N = 713) rated WTS messages based on self-reported receptivity, engagement, attitudes, and negative emotions. In an in-lab study, participants (N = 120) viewed the 12 WTS risk messages while being monitored for heart rate and eye-tracking, and then completed a recognition task. Using a multi-attribute decision-making (MADM) model, we integrated data from these two methods with scenarios assigning different weights to the self-report and laboratory data to identify optimal messages.We identified different optimal messages when differently weighting the importance of specific attributes or data collection method (self-report, laboratory). Across all scenarios, five messages consistently ranked in the top half: four addressed harms content, both alone and with themes regarding social use and flavors and one addiction alone message.Results showed that the self-report and psychophysiological data did not always have the same ranking and differed based on weighting of the two methods. These findings highlight the need to formatively test messages using multiple methods and use an integrated approach when selecting content." @default.
• W3211877185 created "2021-11-22" @default.
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• W3211877185 date "2021-11-11" @default.
• W3211877185 modified "2023-09-30" @default.
• W3211877185 title "Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling" @default.
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• W3211877185 doi "https://doi.org/10.3390/ijerph182211814" @default.
• W3211877185 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8617707" @default.
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• W3211877185 hasPublicationYear "2021" @default.
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