ArticleBMJ open2024
Identifying attributes of effective cigar warnings: a choice-based conjoint experiment in an online survey of US adults who smoke cigars.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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
3 citing papers in PubMed.
- Little Cigar and Cigarillo Graphic Health Warnings and Quitting Behaviors: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Differential Enhancement of Cigar Tobacco Leaf Aroma by Single-Strain Inoculation withJournal of microbiology and biotechnology · 2026Article
- Perceptions of cannabis warnings and recommendations for improvement: a qualitative study with people who use cannabis from the United States.BMC public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveLittle evidence exists on which cigar warning statement attributes may impact cigar warning effectiveness; research is needed to identify the most effective cigar warning topics and text. This study was designed to inform the development of improved cigar warnings.
designWe conducted a choice-based conjoint experiment. The experiment systematically manipulated cigar warning statement attributes, including cancer health effect (mention of colon cancer and/or oral cancer), non-cancer health effect (mention of heart disease and/or blood clots), causal language, warning marker word, verb use and tobacco type. Participants evaluated eight choice sets, each containing three cigar warnings with contrasting attributes, and chose the warnings that most and least encouraged them to quit smoking cigars. Using a Bayesian mixed logit model, we estimated the relative importance of each attribute and the attribute part-worth utility.
settingAn online survey of adults in the USA.
participantsWe enrolled 959 US adults who used little cigars, cigarillos, or large cigars in the past 30 days using an online survey from October to December 2020. PRIMARY OUTCOME MEASURES: The primary outcomes for the experiment are relative attribute importance and attribute part-worth utility.
resultsThe most important attributes to participant selection of warnings were the non-cancer and the cancer health effects (29.3%; 95%CI 28.6% to 30.0% and 29.0%; 95% CI 28.4% to 29.6%, respectively), followed by causal language (16.3%; 95% CI 15.7% to 16.8%), marker word (10.3%; 95% CI 9.9% to 10.7%), verb use (8.8%; 95% CI 8.5% to 9.2%) and tobacco type (6.3%, 95% CI 5.9% to 6.6%).
conclusionsOur findings indicate that health effects are the most important attributes when designing cigar warning statements, but other attributes, like causal terminology, also influence perceived warning effectiveness. Based on our findings, 'DANGER: Tobacco causes heart disease and blood clots' is an example of a highly effective warning statement for cigars.
Indexed as
Identifiers
What OpenQuestion holds
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.