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Related Experiment Video

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Vapes, E-cigs, and Mods: What Do Young Adults Call E-cigarettes?

Jennifer L Pearson1,2, Domonique M Reed3,4, Andrea C Villanti2,5

  • 1Division of Social and Behavioral Health/Health Administration and Policy, School of Community Health Sciences, University of Nevada, Reno, NV.

Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco
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Summary

Young adults often misidentify e-cigarette subtypes, posing challenges for health surveillance. Using specific images in surveys can improve accurate reporting of e-cigarette use among this population.

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Area of Science:

  • Public Health
  • Behavioral Science
  • Survey Methodology

Background:

  • E-cigarette (e-cig) products present surveillance and regulatory challenges due to nonstandard terminology.
  • Occasional e-cig use is most prevalent among young adults, a demographic critical for public health monitoring.
  • Accurate classification of e-cig subtypes is essential for understanding health behaviors and outcomes.

Purpose of the Study:

  • To assess young adults' ability to correctly identify various e-cigarette subtypes from images.
  • To identify characteristics associated with accurate identification of e-cigarette devices among young adults.
  • To inform best practices for survey design to improve e-cig use reporting.

Main Methods:

  • A cohort of 3,364 young adults viewed images of five common e-cig products (three first-generation, one second-generation, one third-generation).
  • Participant responses were qualitatively coded into nine classifications, including "e-cigarette," "vape-related," and "misidentified."
  • Multivariable logistic regression analyzed participant characteristics linked to correct device identification, using weighted data for the US young adult population in 2016.

Main Results:

  • The majority identified devices as some type of e-cig (57.7%-83.6%).
  • White first-generation, second-, and third-generation e-cigs caused significant confusion, with high "don't know" (12.2%-25.1%) or misidentification rates (e.g., as non-e-cig tobacco products: 1.4%-14.6%).
  • Specific device colors and generations influenced identification accuracy.

Conclusions:

  • Accurate e-cig subtype data collection is crucial for effective public health surveillance and analysis.
  • Carefully selected images in surveys can enhance the accuracy of e-cig use reporting in population studies.
  • Recommendations are provided for image selection and terminology use in surveys targeting young adults to improve e-cig subtype identification.