Evidence map›Paper›PMID 35749387›Full record

Trial reportPloS one2022

Developing health and environmental warning messages about red meat: An online experiment.

Lindsey Smith Taillie, Carmen E Prestemon, Marissa G Hall, Anna H Grummon, Annamaria Vesely, Lindsay M Jaacks

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Trial
  2. Effects of red meat taxes and warning labels on food groups selected in a randomized controlled trial.The international journal of behavioral nutrition and physical activity · 2024
    Trial
  3. Trial
  4. Trial
  5. Testing front-of-package warnings to discourage red meat consumption: a randomized experiment with US meat consumers.The international journal of behavioral nutrition and physical activity · 2021
    Trial
  6. Article
  7. Article
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  10. Review
  11. 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 at 3 institutions in 2 countries.

Lindsey Smith TaillieCarolina Population Center, University of North Carolina, Chapel Hill, NC, United States of America.ORCID 0000-0002-4555-2525
Carmen E PrestemonCarolina Population Center, University of North Carolina, Chapel Hill, NC, United States of America.ORCID 0000-0002-5978-8061
Marissa G HallCarolina Population Center, University of North Carolina, Chapel Hill, NC, United States of America.
Anna H GrummonCenter for Population and Development Studies, Harvard TH Chan School of Public Health, Cambridge, MA, United States of America.ORCID 0000-0002-8705-038X
Annamaria VeselyDepartment of Nutrition, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, United States of America.
Lindsay M JaacksGlobal Academy of Agriculture and Food Security, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-7791-5167
University of North Carolina at Chapel Hill · USHarvard University · USUniversity of Edinburgh · GB

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Venkata Saroja Voruganti · 1999 to 2026
$31.6M
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
Designing and Evaluating High-Impact Pictorial Health Warnings for Food ProductsK01HL147713 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HALL, MARISSA · 2019 to 2023
$738k
NHLBI NIH HHS K01 HL147713NICHD NIH HHS P2C HD050924NIDDK NIH HHS P30 DK056350Wellcome TrustWellcome Trust 216042/Z/19/Z
6 · The paper itself

Abstract

introductionThe United States has among the highest per capita red meat consumption in the world. Reducing red meat consumption is crucial for minimizing the environmental impact of diets and improving health outcomes. Warning messages are effective for reducing purchases of products like sugary beverages but have not been developed for red meat. This study developed health and environmental warning messages about red meat and explored participants' reactions to these messages.

methodsA national convenience sample of US red meat consumers (n = 1,199; mean age 45 years) completed an online survey in 2020 for this exploratory study. Participants were randomized to view a series of either health or environmental warning messages (between-subjects factor) about the risks associated with eating red meat. Messages were presented in random order (within-subjects factor; 8 health messages or 10 environmental messages). Participants rated each warning message on a validated 3-item scale measuring perceived message effectiveness (PME), ranging from 1 (low) to 5 (high). Participants then rated their intentions to reduce their red meat consumption in the next 7 days.

resultsHealth warning messages elicited higher PME ratings than environmental messages (mean 2.66 vs. 2.26, p<0.001). Health warning messages also led to stronger intentions to reduce red meat consumption compared to environmental messages (mean 2.45 vs. 2.19, p<0.001). Within category (health and environmental), most pairwise comparisons of harms were not statistically significant.

conclusionsHealth warning messages were perceived to be more effective than environmental warning messages. Future studies should measure the impact of these messages on behavioral outcomes.

Indexed as

IntentionRed MeatConsumer BehaviorDietHumansMiddle AgedSurveys and QuestionnairesUnited States

Identifiers

PMID35749387
PMCPMC9231779
OpenAlexW4283462349

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.