ArticleAddictive behaviors2021
Adults who use e-cigarettes have difficulty understanding nicotine concentrations presented as mg/ml and percent nicotine.
Article in Addictive behaviors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Investigating the Appeal of Nicotine Pouch Packaging, Flavour, and Nicotine Descriptors Among Adults in the UK: An Online Experiment.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Article
- Electronic Nicotine Delivery System Advertisement Trends After US Federal Policy Changes.JAMA network open · 2025Article
- Electronic Cigarette Device and Liquid Characteristics: Comparison of Self-Reports to User-Provided Pictures.Substance use & misuse · 2024Article
- Effects of Modified Tobacco Risk Products with Claims and Nicotine Features on Perceptions among Racial and Ethnic Groups.International journal of environmental research and public health · 2023Article
- Novel Nicotine Concentration Labels Improve Adolescents' and Young Adults' Understanding of the Nicotine Strength of Electronic Nicotine Delivery System Products.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022Article
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
introductionNicotine in e-cigarettes typically is labeled as mg/ml or percent nicotine, but these metrics may be difficult to understand. We examined whether adults who use e-cigarettes accurately can compare nicotine concentrations presented as mg/ml and percent and/or can identify the strength of concentrations presented using these metrics.
methods310 adults completed an online survey in 2020. Participants viewed nicotine concentration pairs (e.g., 24 mg/ml vs 3%) and indicated which concentration was stronger or if they were equal. Participants also categorized nicotine concentrations presented as mg/ml (0-60 mg/ml) and percent (0-6%) into strength categories (no nicotine, very low, low, medium, high, very high). "Correct" answers for the strength of each concentration (e.g., 30 mg/ml is "high" nicotine) were based on the subjective opinion of e-cigarette researchers.
resultsWhen making direct comparisons, adults correctly identified that one concentration was stronger or that both were equivalent about half of the time (8.30 [5.08] of 19). Adults answered correctly more often when the stronger concentration was presented in mg/ml (4.75 [2.74] of 8) than percent (2.54 [2.60] of 8), p < .001). Adults "correctly" identified the strengths of 8.90 (3.22) of 18 nicotine concentrations, with more "correct" responses for mg/ml (5.27 [2.15]) than percent (3.63 [1.71], p < .001). Adults classified concentrations presented as percent as weaker than equivalent concentrations presented as mg/ml.
conclusionsAdults had difficulty understanding nicotine concentrations labeled using the most common metrics, especially percent nicotine. A singular, easy-to-understand labeling system may increase public knowledge about the nicotine concentration/strength of vaping products.
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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.