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NAQC Newsroom: Research

A Method for Classifying User-Reported Electronic Cigarette Liquid Flavors.

Monday, November 13, 2017  
Posted by: Natalia Gromov
Jessica M Yingst, Susan Veldheer, Erin Hammett, Shari Hrabovsky, Jonathan Foulds.
A Method for Classifying User-Reported Electronic Cigarette Liquid Flavors.
Nicotine & Tobacco Research, Volume 19, Issue 11, 1 November 2017, Pages 1381–1385, https://doi.org/10.1093/ntr/ntw383
 
Along with the growth in popularity of electronic cigarette devices (e-cigs), the variety of e-cig liquids (e-liquid) available to users has also grown. Although some studies have published data about the use of flavored e-liquid, there is no standardized way to group flavors, making it difficult to interpret the data and replicate results across studies. The current study describes a method to classify user-reported e-liquid flavors and presents the resulting proportion of users in each flavor group in a large online survey of e-cig users. Three thousand seven hundred sixteen participants completed an online survey about their e-cig use and responded to the following open-ended question regarding their use of e-liquid, “What is your favorite flavor and what brand of flavored liquid do you prefer?” Researchers used a 3 step method to determine the flavor attributes present in the e-liquids reported using an online search engine. Once all flavor attributes were identified, researchers used the constant comparative method to group the flavor attributes and delineate how to classify flavors with mixed components (eg, cinnamon Red Hots as a candy not a spice). The resulting classification scheme and proportions of e-liquids in each category were as follows: Tobacco (23.7%), Menthol/mint (14.8%), Fruit (20.3%), Dessert/sweets (20.7%), Alcohol (2.8%), Nuts/spices (2.0%), Candy (2.1%), Coffee/tea (4.3%), Beverage (3.1%), Unflavored (0.4%), and Don’t Know/Other (5.8%). To better understand the use of flavored e-liquids, standardized methods to classify the flavors could facilitate data interpretation and comparison across studies. This study proposes a method for classifying the characterizing flavors in e-liquids used most commonly by experienced e-cig users. Current studies on the use of flavored e-liquid have used unclear methods to collect and report information on the use of flavors. This study adds a proposed method for classifying the flavors in the e-liquids used most commonly by experienced e-cig users. With a clear and explicit method for classifying self-reported flavors, future study results may be more easily compared.


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