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Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling

Author

Listed:
  • Elise M. Stevens

    (Department of Population and Quantitative Health Sciences, Division of Preventative and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA)

  • Andrea C. Villanti

    (Vermont Center on Behavior and Health, Department of Psychiatry, University of Vermont, Burlington, VT 05405, USA)

  • Glenn Leshner

    (Gaylord College of Journalism and Mass Communication, University of Oklahoma, Norman, OK 73019, USA)

  • Theodore L. Wagener

    (Center for Tobacco Research, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43214, USA
    Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA)

  • Brittney Keller-Hamilton

    (Center for Tobacco Research, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43214, USA
    Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA)

  • Darren Mays

    (Center for Tobacco Research, The Ohio State University James Comprehensive Cancer Center, Columbus, OH 43214, USA
    Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA)

Abstract

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.

Suggested Citation

  • Elise M. Stevens & Andrea C. Villanti & Glenn Leshner & Theodore L. Wagener & Brittney Keller-Hamilton & Darren Mays, 2021. "Integrating Self-Report and Psychophysiological Measures in Waterpipe Tobacco Message Testing: A Novel Application of Multi-Attribute Decision Modeling," IJERPH, MDPI, vol. 18(22), pages 1-12, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:11814-:d:676705
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    References listed on IDEAS

    as
    1. Platts, K. W. & Probert, D. R. & Canez, L., 2002. "Make vs. buy decisions: A process incorporating multi-attribute decision-making," International Journal of Production Economics, Elsevier, vol. 77(3), pages 247-257, June.
    2. Lilianna Phan & Andrea C. Villanti & Glenn Leshner & Theodore L. Wagener & Elise M. Stevens & Andrea C. Johnson & Darren Mays, 2020. "Development and Pretesting of Hookah Tobacco Public Education Messages for Young Adults," IJERPH, MDPI, vol. 17(23), pages 1-14, November.
    Full references (including those not matched with items on IDEAS)

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