ArticleTobacco control2020
What factors reliably predict electronic cigarette nicotine delivery?
Article in Tobacco control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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Who cites it
13 citing papers in PubMed.
- Acute analgesic effect of nicotine vaping using three experimental pain induction tasks: a randomized, placebo-controlled laboratory study.Psychopharmacology · 2025Trial
- Pharmacokinetics and Pharmacodynamics of Inhaled Nicotine Salt and Free-Base Using an E-cigarette: A Randomized Crossover Study.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Trial
- Nicotine-containing products and the cardiovascular system: mechanisms and global health implications.Nature reviews. Cardiology · 2026Review
- Predicting nicotine emissions and plasma nicotine boost in E-cigarette users using machine learning.Scientific reports · 2026Article
- Method Development and Validation of an Aerosol Sampling Technique for the Analysis of Nicotine in Electronic Cigarette Aerosols.Molecules (Basel, Switzerland) · 2024Article
- Combining app-based behavioral therapy with electronic cigarettes for smoking cessation: a study protocol for a single-arm mixed-methods pilot trial.Addiction science & clinical practice · 2024Article
- Understanding the nicotine dose delivered by electronic nicotine delivery systems in a single puff: the importance of nicotine flux and puff duration.Tobacco control · 2024Article
- Using a Novel Connected Device for the Collection of Puffing Topography Data for the Vuse Solo Electronic Nicotine Delivery System in a Real-World Setting: Prospective Ambulatory Clinical Study.JMIR formative research · 2023Article
- Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device characteristics.Addictive behaviors · 2023Article
- Nicotine delivery and cigarette equivalents from vaping a JUULpod.Tobacco control · 2022Review
- Nicotine pharmacokinetics of electronic cigarettes: A pooled data analysis from the literature.Toxicology reports · 2021Article
- Variable Voltage, Tank-Style ENDS Do Not Always Deliver Nicotine.Tobacco regulatory science · 2020Article
- Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes.European heart journal · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe ability of an electronic cigarette (e-cigarette) to deliver nicotine effectively may be dependent on features of the device, the liquid and the user. Some of these features have been examined in previous work (eg, liquid nicotine concentration and puff topography), while others have not (eg, nicotine dependence and demographic characteristics). The purpose of this secondary analysis is to examine such features as predictors of e-cigarette nicotine delivery using a relatively large sample.
methodsFour studies were combined in which e-cigarette-experienced users (n=63; 89% men; 75% white) and e-cigarette-naïve cigarette smokers (n=67; 66% men; 54% white) took 10 puffs from an eGo-style e-cigarette (~7.3 watts) filled with liquid that had a nicotine concentration of 18, 25 or 36 mg/mL. Thus, held constant across all studies were device features of battery/cartomiser style and power level and the topography parameters of puff number and interpuff interval. Blood was sampled before and after use, and puff topography was measured. Three general linear models were conducted to predict plasma nicotine concentrations (pre-post increase) for: (1) e-cigarette users only, (2) smokers only and (3) both groups combined. Predictor variables included puff duration, puff volume, liquid nicotine concentration, presession plasma nicotine concentration, nicotine dependence score (smokers only), gender and race.
resultsIn all models tested, longer puff durations and higher liquid nicotine concentrations were associated significantly with increased nicotine delivery (p
conclusionsPuff duration and liquid nicotine concentration may be among the more important factors to consider as regulators attempt to balance e-cigarette safety with efficacy. These findings should be interpreted in the context of devices with relatively low power output, a variable not studied here but likely also directly relevant to product regulation.
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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.