Evidence map›Paper›PMID 36194979›Full record

ArticleAddictive behaviors2023

Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device characteristics.

Ashley E Douglas, Nicholas J Felicione, Margaret G Childers, Eric K Soule, Melissa D Blank

Open access · greenAbstract read
In one paragraph

Article in Addictive behaviors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. What is the Addictive Potential from Vaping?Nordisk alkohol- & narkotikatidskrift : NAT · 2026
    Article
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  11. Evaluation of the Psychometric Properties of Dependence Measures for Exclusive Electronic Cigarette Users.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Ashley E DouglasDepartment of Psychology, West Virginia University, Morgantown, WV, United States. Electronic address: aed0034@mix.wvu.edu.
Nicholas J FelicioneDepartment of Health Behavior, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States. Electronic address: nicholas.felicione@roswellpark.org.
Margaret G ChildersDepartment of Psychology, West Virginia University, Morgantown, WV, United States. Electronic address: mgc0002@mix.wvu.edu.
Eric K SouleHealth Education and Promotion, East Carolina University, Greenville, NC, United States. Electronic address: soulee18@ecu.edu.
Melissa D BlankDepartment of Psychology, West Virginia University, Morgantown, WV, United States. Electronic address: mdblank@mail.wvu.edu.
West Virginia University · USEast Carolina University · USRoswell Park Comprehensive Cancer Center · US

Funding

Research Training Program in Behavioral and Biomedical SciencesT32GM132494 · NIGMS · WEST VIRGINIA UNIVERSITY · PI Anne Courtney DeVries, Kevin T Larkin · 2019 to 2026
$2.8M
NIGMS NIH HHS T32 GM132494
6 · The paper itself

Abstract

introductionECIGs differ in their ability to deliver nicotine to the user and, consequently, they may differ in their ability to produce dependence. This study examined individual device characteristics, device type, and user behaviors as predictors of ECIG dependence in a sample of never-smoking ECIG users.

methodsParticipants (N = 134) completed an online survey that assessed demographics, ECIG use behavior, and ECIG dependence as measured via the Penn State Electronic Nicotine Dependence Index (PSECDI) and E-cigarette Dependence Scale (EDS-4). Participants uploaded a picture of their personal ECIG device/liquid, which was coded by raters to identify product features. Multivariable linear regressions examined device characteristics (e.g., adjustable power, nicotine concentration) and device type (e.g., vape pen, mod, pod, modern disposable) as predictors of dependence controlling for demographics and user behaviors (e.g., ECIG use duration and frequency, other tobacco use).

resultsLonger durations of ECIG use and more use days/week were associated significantly with higher PSECDI (β's = 0.91 and 1.90, respectively; p's < 0.01) and EDS-4 scores (β's = 0.16 and 0.28, respectively; p's < 0.01). Higher nicotine concentrations were associated with higher PSECDI scores only (β = 0.07, p =.011). Dependence scores did not differ as a function of ECIG device types after controlling for covariates.

conclusionsECIG dependence was observed among the never-smoking ECIG users in this sample, regardless of their ECIG device/liquid features. Findings suggest that regulatory efforts aimed at reducing the dependence potential of ECIGs in never smokers should focus on overall nicotine emissions rather than product features.

Indexed as

Electronic Nicotine Delivery SystemsVapingHumansNicotineSmokersSurveys and QuestionnairesNicotineBehaviorDependenceDeviceElectronic cigaretteLiquid

Identifiers

PMID36194979
PMCPMC10873757
OpenAlexW4296887838

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.