Evidence map›Paper›PMID 37101413›Full record

Trial reportNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2023

Young Adult Responses to Peer Crowd-Based Targeting in E-cigarette Advertisements: An Experimental Study.

Minji Kim, Torsten B Neilands, Steven E Gregorich, Jeffrey W Jordan, Pamela M Ling

Open access · bronzeAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 50% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

Minji KimDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.ORCID 0000-0002-7715-6826
Torsten B NeilandsDivision of Prevention Science, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0001-7936-9123
Steven E GregorichDivision of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Jeffrey W JordanRescue Agency, San Diego, CA, USA.ORCID 0000-0002-0794-2207
Pamela M LingDivision of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0001-6166-9347
University of California, San Francisco · USUniversity of South Carolina · US

Funding

Training and Education p489-500P50CA180890 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2013 to 2018
$20.5M
PROJECT 5: IMPACT OF CHANGING TOBACCO PRODUCT USE ON HEALTHCARE COSTS FOR GENERAL AND VULNERABLE POPULATIONSU54HL147127 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JACOB, PEYTON · 2018 to 2022
$20.4M
Understanding Smokeless Tobacco MarketingR01CA141661 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2009 to 2019
$5.4M
NCI NIH HHS P50 CA180890NCI NIH HHS R01 CA141661NHLBI NIH HHS U54 HL147127
6 · The paper itself

Abstract

aimsTo examine the effects of psychographically targeted e-cigarette advertisements on young adults.

methodsA total of 2100 young adults (18-29 years old) representing five peer crowds (groups with shared values, interests, and lifestyle: Mainstream, Young Professional, Hip Hop, Hipster, and Partier) were recruited from a nationwide opt-in online panel. Participants were randomly assigned to view e-cigarette advertisements featuring characters that either did or did not match their own peer crowd affiliation and evaluated advertising effectiveness using Likert-type and semantic differential scales. Participants also reported their attitudes toward using e-cigarettes.

resultsNo significant overall effect of peer crowd matching was observed. However, significant two-way interaction effects emerged where matching advertisements yielded higher evaluations than mismatching advertisements among those who currently do not use tobacco and nicotine products and among Mainstream participants. Advertisements featuring Mainstream characters were in general rated more highly than other advertisements. Additional analyses found significant effects of peer crowd matching among those who viewed advertisements featuring non-Mainstream characters.

conclusionsPeer crowd-based targeting can increase the effectiveness of e-cigarette advertisements which may impact initiation among current nonusers, requiring stricter marketing regulations. More research is needed to determine if anti-tobacco messaging tailored by peer crowds may effectively counteract targeted e-cigarette marketing. IMPLICATIONS: E-cigarette advertisements often use psychographic targeting strategies, using lifestyles, attitudes, and values. Low-risk young adults (eg, those who currently do not use tobacco and nicotine products) are susceptible to psychographically targeted e-cigarette advertisements. This may result in the initiation of e-cigarette use among young adults who would otherwise be less likely to use tobacco and nicotine products. Stricter marketing regulations for emerging tobacco and nicotine products are required to reduce marketing exposure.

Indexed as

Electronic Nicotine Delivery SystemsTobacco ProductsAdolescentAdultAdvertisingHumansNicotinePeer GroupSmokingSocial IdentificationYoung AdultNicotine

Identifiers

PMID37101413
PMCPMC10202626
OpenAlexW4367175241

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.