Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling.
Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling.
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DOI:
10.3390/ijerph182211814
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发表时间:
2021-11-11
影响因子:
--
通讯作者:
Mays D
中科院分区:
文献类型:
--
作者:
Stevens EM;Villanti AC;Leshner G;Wagener TL;Keller-Hamilton B;Mays D
Background: 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. Methods: 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. Results: 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. Discussion: 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.
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影响因子:
4.2
作者:
Leshner G;Stevens EM;Cohn AM;Kim S;Kim N;Wagener TL;Villanti AC
通讯作者:
Villanti AC
影响因子:
4.7
作者:
Cobb, Caroline O.;Shihadeh, Alan;Eissenberg, Thomas
通讯作者:
Eissenberg, Thomas
DOI:
10.1056/nejmsa1607538
发表时间:
2017-01-26
期刊:
The New England journal of medicine
影响因子:
--
作者:
Kasza KA;Ambrose BK;Conway KP;Borek N;Taylor K;Goniewicz ML;Cummings KM;Sharma E;Pearson JL;Green VR;Kaufman AR;Bansal-Travers M;Travers MJ;Kwan J;Tworek C;Cheng YC;Yang L;Pharris-Ciurej N;van Bemmel DM;Backinger CL;Compton WM;Hyland AJ
通讯作者:
Hyland AJ
影响因子:
33.9
作者:
Farrelly, Matthew C.;Duke, Jennifer C.;Allen, Jane A.
通讯作者:
Allen, Jane A.
影响因子:
5.2
作者:
El-Zaatari ZM;Chami HA;Zaatari GS
通讯作者:
Zaatari GS