Evidence map›Paper›PMID 38989961›Full record

ArticleNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2024

Recognition of Online E-cigarette Marketing and E-cigarette-Related Attitudes and Behaviors Among Young Adults.

Scott I Donaldson, Trista A Beard, Julia C Chen-Sankey, Ollie Ganz, Olivia A Wackowski, Jon-Patrick Allem

Abstract read
In one paragraph

Article in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. 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

6 authors.

Scott I DonaldsonInstitute for Nicotine and Tobacco Studies, Rutgers Biomedical and Health Sciences, Newark, NJ, USA.ORCID 0000-0001-8145-0860
Trista A BeardDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0001-5543-4795
Julia C Chen-SankeyInstitute for Nicotine and Tobacco Studies, Rutgers Biomedical and Health Sciences, Newark, NJ, USA.ORCID 0000-0002-1797-5248
Ollie GanzInstitute for Nicotine and Tobacco Studies, Rutgers Biomedical and Health Sciences, Newark, NJ, USA.
Olivia A WackowskiInstitute for Nicotine and Tobacco Studies, Rutgers Biomedical and Health Sciences, Newark, NJ, USA.
Jon-Patrick AllemInstitute for Nicotine and Tobacco Studies, Rutgers Biomedical and Health Sciences, Newark, NJ, USA.ORCID 0000-0001-9135-8689

Funding

Understanding the Influence of E-cigarette Advertisement FeaturesR00CA242589 · NCI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI CHEN-SANKEY, JULIA CEN · 2021 to 2024
$842k
California Tobacco Prevention BranchNCI NIH HHS R00 CA242589Tobacco Industry Monitoring EvaluationU.S. Food & Drug Administration
6 · The paper itself

Abstract

introductionPast research examining the relationship between exposure to online e-cigarette marketing and e-cigarette-related attitudes and behaviors has relied on unaided recall measures that may suffer from self-report bias. To date, few studies have presented participants with e-cigarette marketing stimuli and assessed recognition. This study examined the associations between recognition of online e-cigarette marketing stimuli and e-cigarette-related attitudes and behaviors among young adults in California. AIMS AND

methodsA non-probability representative sample of young adults (ages 18-24; N = 1500) living in California completed an online survey assessing their recognition of online e-cigarette marketing stimuli, including image-based (ie, Instagram and email) and audiovisual (ie, YouTube and TikTok) promotions, and positive e-cigarette-related attitudes (eg, appeal of e-cigarettes) and behaviors (eg, e-cigarette use). Adjusted and weighted logistic regression analyses were used.

resultsA total of 79.0% (n = 1185) of young adults, including 78.1% (n = 310/397) of participants under 21 years old, recognized online e-cigarette marketing. Participants who reported recognition of stimuli, compared with those who did not, had greater odds of reporting appeal of e-cigarettes (AOR = 2.26, 95% CI = 1.65 to 3.09) and e-cigarette purchase intentions (AOR = 1.66, 95% CI = 1.13 to 2.43) among all participants, and susceptibility to use e-cigarettes among never users (AOR = 2.29, 95% CI = 1.59 to 3.29).

conclusionsYoung adults in California recognized audiovisual and image-based online e-cigarette marketing. Such recognition may lead to positive e-cigarette-related attitudes and behavioral intentions, especially among never users. Future research should examine the causal relationships between the associations found in this study. Findings may inform the development and evaluation of psychometrically valid measures of online e-cigarette marketing exposures. IMPLICATIONS: Recognition of online e-cigarette marketing stimuli was associated with greater odds of reporting the appeal and benefits of e-cigarettes, purchase intentions, and lifetime e-cigarette use among all participants, and susceptibility to use e-cigarettes among never users. These findings may motivate the development and evaluation of psychometrically valid measures of online e-cigarette marketing exposures.

Indexed as

Electronic Nicotine Delivery SystemsMarketingVapingAdolescentAdultCaliforniaFemaleHealth Knowledge, Attitudes, PracticeHumansInternetMaleSurveys and QuestionnairesYoung Adult

Identifiers

PMID38989961
PMCPMC11663800

What OpenQuestion holds

Textmetadata
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.