Related Experiment Video
Updated: Aug 26, 2025

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies
Published on: August 25, 2018
Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device
Ashley E Douglas1, Nicholas J Felicione2, Margaret G Childers1
1Department of Psychology, West Virginia University, Morgantown, WV, United States.
Introduction:
ECIGs 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.
Methods:
Participants (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).
Results:
Longer 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.
Conclusions:
ECIG 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.
More Related Videos
10:44Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System
Published on: May 24, 2017
09:30A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol
Published on: January 18, 2018
Related Concept Videos
Stimulants
Cocaine can be administered via snorting, injection, or smoking. It primarily functions by blocking the reuptake of dopamine, resulting in a euphoric high characterized by an intense sensation of happiness and...
Drug Dependence
Statistical Methods for Analyzing Epidemiological Data
CNS Depressants: Alcohol and Nicotine
Drugs Acting on Autonomic Ganglia: Stimulants
Ganglionic stimulants activate NM nicotinic receptors in autonomic ganglia, falling into two categories: nicotine mimetics [e.g., lobeline, dimethylpiperazine, tetramethylammonium] and muscarinic receptor agonists [e.g., muscarine, methacholine]. The first category's action is rapid and blocked by nicotinic receptor antagonists, while the second category's action is delayed and blocked by atropine-like agents. Nicotine, an alkaloid, affects the heart rate by stimulating...
Stress Prevention and Stress Management Techniques IV