Measurement of Electronic Cigarette Frequency of Use Among Smokers Participating in a Randomized Controlled Trial.


Journal

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
ISSN: 1469-994X
Titre abrégé: Nicotine Tob Res
Pays: England
ID NLM: 9815751

Informations de publication

Date de publication:
21 04 2020
Historique:
received: 30 01 2018
accepted: 24 10 2018
pubmed: 27 10 2018
medline: 23 10 2020
entrez: 27 10 2018
Statut: ppublish

Résumé

The United States Food and Drug Administration has prioritized understanding the dependence potential of electronic cigarettes (e-cigs). Dependence is often estimated in part by examining frequency of use; however measures of e-cig use are not well developed because of varying product types. This study used an e-cig automatic puff counter to evaluate the value of self-reported e-cig use measures in predicting actual use (puffs). Data were collected from a two-site randomized placebo-controlled trial evaluating the effects of e-cigs on toxicant exposure in smokers attempting to reduce their cigarette consumption. Participants randomized to an e-cig condition self-reported their e-cig frequency of use (times per day-one "time" consists of around 15 puffs or lasts around 10 minutes) on the Penn State Electronic Cigarette Dependence Index (PSECDI) and kept daily diary records of the number of puffs per day from the e-cig automatic puff counter. A linear mixed-effects model was used to determine the predictive value of the times per day measure. Correlations were used to further investigate the relationship. A total of 259 participants with 1165 observations of e-cig use were analyzed. Self-reported e-cig use in times per day was a significant predictor of e-cig puffs per day (p < .01). The Spearman correlation between measures was r equal to .58. Examination of individual participant responses revealed some potential difficulties reporting and interpreting times per day because of the difference in use patterns between cigarettes and e-cigs. This study provides evidence that the self-reported PSECDI measure of times per day is a significant predictor of actual frequency of e-cig puffs taken. Self-reported measures of e-cig frequency of use are predictive of actual use, but quantifying e-cig use in patterns similar to cigarettes is problematic.

Sections du résumé

BACKGROUND
The United States Food and Drug Administration has prioritized understanding the dependence potential of electronic cigarettes (e-cigs). Dependence is often estimated in part by examining frequency of use; however measures of e-cig use are not well developed because of varying product types. This study used an e-cig automatic puff counter to evaluate the value of self-reported e-cig use measures in predicting actual use (puffs).
METHODS
Data were collected from a two-site randomized placebo-controlled trial evaluating the effects of e-cigs on toxicant exposure in smokers attempting to reduce their cigarette consumption. Participants randomized to an e-cig condition self-reported their e-cig frequency of use (times per day-one "time" consists of around 15 puffs or lasts around 10 minutes) on the Penn State Electronic Cigarette Dependence Index (PSECDI) and kept daily diary records of the number of puffs per day from the e-cig automatic puff counter. A linear mixed-effects model was used to determine the predictive value of the times per day measure. Correlations were used to further investigate the relationship.
RESULTS
A total of 259 participants with 1165 observations of e-cig use were analyzed. Self-reported e-cig use in times per day was a significant predictor of e-cig puffs per day (p < .01). The Spearman correlation between measures was r equal to .58. Examination of individual participant responses revealed some potential difficulties reporting and interpreting times per day because of the difference in use patterns between cigarettes and e-cigs.
CONCLUSION
This study provides evidence that the self-reported PSECDI measure of times per day is a significant predictor of actual frequency of e-cig puffs taken.
IMPLICATIONS
Self-reported measures of e-cig frequency of use are predictive of actual use, but quantifying e-cig use in patterns similar to cigarettes is problematic.

Identifiants

pubmed: 30365024
pii: 5145074
doi: 10.1093/ntr/nty233
pmc: PMC7171268
doi:

Types de publication

Journal Article Randomized Controlled Trial Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

699-704

Subventions

Organisme : NIDA NIH HHS
ID : P50 DA036105
Pays : United States
Organisme : NIDA NIH HHS
ID : U54 DA036105
Pays : United States
Organisme : NIDA NIH HHS
ID : P50 DA036107
Pays : United States

Informations de copyright

© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Références

Nicotine Tob Res. 2018 Sep 4;20(10):1283-1288
pubmed: 29059416
Int J Environ Res Public Health. 2014 Apr 22;11(4):4356-73
pubmed: 24758891
PLoS One. 2013 Jun 24;8(6):e66317
pubmed: 23826093
Malawi Med J. 2012 Sep;24(3):69-71
pubmed: 23638278
Open J Prev Med. 2014 Oct;4(10):789-800
pubmed: 25621193
Am J Prev Med. 2012 Nov;43(5 Suppl 3):S255-63
pubmed: 23079225
Br J Addict. 1991 Sep;86(9):1119-27
pubmed: 1932883
BMC Public Health. 2016 Mar 03;16:217
pubmed: 26941050
Br J Addict. 1989 Jul;84(7):791-9
pubmed: 2758152
Fed Regist. 2016 May 10;81(90):28973-9106
pubmed: 27192730
Drug Alcohol Rev. 2013 Mar;32(2):133-40
pubmed: 22994631
Addict Res Theory. 2016;24(1):80-88
pubmed: 29176939
Nicotine Tob Res. 2019 Sep 19;21(10):1408-1413
pubmed: 30107462
Drug Alcohol Depend. 2015 Feb 1;147:68-75
pubmed: 25561385
Nicotine Tob Res. 2015 Feb;17(2):193-200
pubmed: 25168035
Prev Chronic Dis. 2016 Jan 14;13:E07
pubmed: 26766848
Nicotine Tob Res. 2014 Jan;16(1):108-14
pubmed: 24154511
BMC Public Health. 2010 May 04;10:231
pubmed: 20441579
BMC Public Health. 2011 Oct 11;11:786
pubmed: 21989407
JMIR Res Protoc. 2017 May 29;6(5):e84
pubmed: 28554877
Nicotine Tob Res. 2015 Feb;17(2):186-92
pubmed: 25332459

Auteurs

Jessica Yingst (J)

Penn State Tobacco Center of Regulatory Science, Pennsylvania State University, Hershey, PA.

Jonathan Foulds (J)

Penn State Tobacco Center of Regulatory Science, Pennsylvania State University, Hershey, PA.

Susan Veldheer (S)

Penn State Tobacco Center of Regulatory Science, Pennsylvania State University, Hershey, PA.

Caroline O Cobb (CO)

Department of Psychology, Virginia Commonwealth University, Richmond, VA.
Center for the Study of Tobacco Products, Virginia Commonwealth University, Richmond, VA.

Miao-Shan Yen (MS)

Center for the Study of Tobacco Products, Virginia Commonwealth University, Richmond, VA.
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA.

Shari Hrabovsky (S)

Penn State Tobacco Center of Regulatory Science, Pennsylvania State University, Hershey, PA.

Sophia I Allen (SI)

Penn State Tobacco Center of Regulatory Science, Pennsylvania State University, Hershey, PA.

Christopher Bullen (C)

University of Auckland, Auckland, New Zealand.

Thomas Eissenberg (T)

Department of Psychology, Virginia Commonwealth University, Richmond, VA.
Center for the Study of Tobacco Products, Virginia Commonwealth University, Richmond, VA.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH