ArticleAddictive behaviors2023
Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device characteristics.
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.
What it found
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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.
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.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Sweet Flavors and the Addictive Potential of E-Cigarettes: A Systematic Narrative Review.European addiction research · 2026Pooled it
- The role of sweet/fruit-flavored disposable electronic cigarettes on early nicotine initiation - a systematic review.BMC public health · 2025Pooled it
- E-cigarette and cannabis use and co-use patterns and dependence trajectories among young adults in the United States.Addiction (Abingdon, England) · 2026Article
- What is the Addictive Potential from Vaping?Nordisk alkohol- & narkotikatidskrift : NAT · 2026Article
- Triple tank control: disposable vape devices with adjustable nicotine and flavour levels.Tobacco control · 2025Article
- Disposable e-cigarette use: Factors, frequency and cigarette smoking among United States high school students.Addiction (Abingdon, England) · 2025Article
- Electronic cigarette cue reactivity in exclusive electronic cigarette users.Drug and alcohol dependence · 2025Article
- Nicotine and cannabis routes of administration and dual use among U.S. young adults who identify as Hispanic, non-Hispanic Black, and non-Hispanic White.Preventive medicine reports · 2024Article
- Article
- A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era.Healthcare (Basel, Switzerland) · 2023Article
- 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 · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
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.