Article Text

Original research
Effects of brief exposure to misinformation about e-cigarette harms on twitter: a randomised controlled experiment
  1. Caroline Wright1,
  2. Philippa Williams1,
  3. Olga Elizarova2,
  4. Jennifer Dahne3,4,
  5. Jiang Bian5,
  6. Yunpeng Zhao5,
  7. Andy S L Tan6,7
  1. 1 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
  2. 2 Play Collaborate Change, Boston, Massachusetts, USA
  3. 3 Psychiatry and Behavioral Sciences, Medical University of South Carolina College of Medicine, Charleston, South Carolina, USA
  4. 4 Hollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina, USA
  5. 5 Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
  6. 6 Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  7. 7 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  1. Correspondence to Dr Caroline Wright; caroline.wright{at}bristol.ac.uk

Abstract

Objectives To assess the effect of exposure to misinformation about e-cigarette harms found on Twitter on adult current smokers’ intention to quit smoking cigarettes, intention to purchase e-cigarettes and perceived relative harm of e-cigarettes compared with regular cigarettes.

Setting An online randomised controlled experiment conducted in November 2019 among USA and UK current smokers.

Participants 2400 adult current smokers aged ≥18 years who were not current e-cigarette users recruited from an online panel. Participants’ were randomised in a 1:1:1:1 ratio using a least-fill randomiser function.

Interventions Viewing 4 tweets in random order within one of four conditions: (1) e-cigarettes are just as or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) e-cigarette harms are uncertain, and (4) a control condition of tweets about physical activity.

Primary outcomes measures Self-reported post-test intention to quit smoking cigarettes, intention to purchase e-cigarettes, and perceived relative harm of e-cigarettes compared with smoking.

Results Among US and UK participants, after controlling for baseline measures of the outcome, exposure to tweets that e-cigarettes are as or more harmful than smoking versus control was associated with lower post-test intention to purchase e-cigarettes (β=−0.339, 95% CI −0.487 to –0.191, p<0.001) and increased post-test perceived relative harm of e-cigarettes (β=0.341, 95% CI 0.273 to 0.410, p<0.001). Among US smokers, exposure to tweets that e-cigarettes are completely harmless was associated with higher post-test intention to purchase e-cigarettes (β=0.229, 95% CI 0.002 to 0.456, p=0.048) and lower post-test perceived relative harm of e-cigarettes (β=−0.154, 95% CI −0.258 to –0.050, p=0.004).

Conclusions US and UK adult current smokers may be deterred from considering using e-cigarettes after brief exposure to tweets that e-cigarettes were just as or more harmful than smoking. Conversely, US adult current smokers may be encouraged to use e-cigarettes after exposure to tweets that e-cigarettes are completely harmless. These findings suggest that misinformation about e-cigarette harms may influence some adult smokers’ decisions to consider using e-cigarettes.

Trial registration number ISRCTN16082420.

  • preventive medicine
  • public health
  • social medicine
  • epidemiology

Data availability statement

Data are available upon reasonable request. The data are deidentified participant data as outlined at http://www.isrctn.com/ISRCTN16082420. The data will be made available upon reasonable requests. We also plan to publish the data from the study once all research outlined in our research proposal has been submitted for publication.

https://creativecommons.org/licenses/by/4.0/

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Data availability statement

Data are available upon reasonable request. The data are deidentified participant data as outlined at http://www.isrctn.com/ISRCTN16082420. The data will be made available upon reasonable requests. We also plan to publish the data from the study once all research outlined in our research proposal has been submitted for publication.

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Footnotes

  • CW and PW are joint first authors.

  • Contributors CW, ASLT, OE, JD and JB contributed to the original research idea. JB and YZ did the machine learning and JB and PW annotated the tweets. All authors contributed to the design of the survey instrument. CW and PW did the statistical analysis with input from all the authors. All authors contributed to the drafting and editing of the paper.

  • Funding Research reported in this publication was supported by a Cancer Policy Research Centre Innovation grant (C60153/A28664). CW is funded by a Cancer Research UK Population Research Postdoctoral Fellowship (C60153/A23895). JD is supported by the National Institute on Drug Abuse, K23 DA045766. We would also like to thank the National Cancer Institute (NCI) for their support during the sandpit event where this research idea came about.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.