12 Citations (Scopus)

Abstract

Background: 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. Methods: 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). Results: 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%). Conclusion: 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. Implications: 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.

Original languageEnglish (US)
Article numberntw383
Pages (from-to)1381-1385
Number of pages5
JournalNicotine and Tobacco Research
Volume19
Issue number11
DOIs
StatePublished - Nov 1 2017

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Candy
Spices
Research Personnel
Cinnamomum zeylanicum
Mentha
Menthol
Electronic Cigarettes
Search Engine
Nuts
Coffee
Beverages
Tea
Tobacco
Fruit
Alcohols
Equipment and Supplies
Growth
Surveys and Questionnaires

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health

Cite this

@article{e9b3c6df4efb4a6391c9dbff5be7239b,
title = "A method for classifying user-reported electronic cigarette liquid flavors",
abstract = "Background: 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. Methods: 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). Results: 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{\%}). Conclusion: 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. Implications: 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.",
author = "Yingst, {Jessica M.} and Susan Veldheer and Erin Hammett and Shari Hrabovsky and Jonathan Foulds",
year = "2017",
month = "11",
day = "1",
doi = "10.1093/ntr/ntw383",
language = "English (US)",
volume = "19",
pages = "1381--1385",
journal = "Nicotine and Tobacco Research",
issn = "1462-2203",
publisher = "Oxford University Press",
number = "11",

}

A method for classifying user-reported electronic cigarette liquid flavors. / Yingst, Jessica M.; Veldheer, Susan; Hammett, Erin; Hrabovsky, Shari; Foulds, Jonathan.

In: Nicotine and Tobacco Research, Vol. 19, No. 11, ntw383, 01.11.2017, p. 1381-1385.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A method for classifying user-reported electronic cigarette liquid flavors

AU - Yingst, Jessica M.

AU - Veldheer, Susan

AU - Hammett, Erin

AU - Hrabovsky, Shari

AU - Foulds, Jonathan

PY - 2017/11/1

Y1 - 2017/11/1

N2 - Background: 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. Methods: 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). Results: 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%). Conclusion: 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. Implications: 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.

AB - Background: 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. Methods: 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). Results: 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%). Conclusion: 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. Implications: 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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U2 - 10.1093/ntr/ntw383

DO - 10.1093/ntr/ntw383

M3 - Article

VL - 19

SP - 1381

EP - 1385

JO - Nicotine and Tobacco Research

JF - Nicotine and Tobacco Research

SN - 1462-2203

IS - 11

M1 - ntw383

ER -