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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jun 13, 2019
Date Accepted: Dec 19, 2019

The final, peer-reviewed published version of this preprint can be found here:

Distracted Driving on YouTube: Categorical and Quantitative Analyses of Messages Portrayed

Gjorgjievski M, Sprague S, Chaudhry H, Ginsberg L, Wang A, Bhandari M, Ristevski B

Distracted Driving on YouTube: Categorical and Quantitative Analyses of Messages Portrayed

JMIR Public Health Surveill 2020;6(1):e14995

DOI: 10.2196/14995

PMID: 32039816

PMCID: 7055766

Distracted Driving on YouTube™: A Qualitative and Quantitative Analysis of Messages Portrayed

  • Marko Gjorgjievski; 
  • Sheila Sprague; 
  • Harman Chaudhry; 
  • Lydia Ginsberg; 
  • Alick Wang; 
  • Mohit Bhandari; 
  • Bill Ristevski

ABSTRACT

Background:

Distracted driving is a global epidemic, injuring and killing thousands of people each year. Trauma from road traffic collisions ranks in the top 10 causes of disability, and motor vehicle crashes involving distracted driving, have become the number one killer of teenagers. Knowing that many people turn to the internet as their primary source of initial research on topics of interest, we conducted an assessment of popular distracted driving videos found on YouTube™.

Objective:

The purpose of this study was to assess YouTube™ videos on distracted driving to understand the content, context, popularity, and quality of the messages portrayed by these videos.

Methods:

YouTube™ videos on distracted driving with more than 3000 views were identified, collected, and screened for inclusion. Videos that mentioned or portrayed any aspect of distracted driving were included for analysis. Two independent reviewers screened all the videos for a variety of qualitative and quantitative variables. Discrepancies in data collection were resolved by consensus via the coding authors. Data collection included the number of videos uploaded each year, the type of distractions portrayed, consequences, orthopaedic injuries, and whether statistics were included and referenced in the video.

Results:

The study included a total of 788 videos for review, uploaded to YouTube™ from 2006 to 2018. The top three distractions depicted included: texting, talking on the phone, and eating/drinking. Motor vehicle crashes and death were present in 94.2% of the videos, while injury ranked third, depicted in 21.1%. Orthopaedic injuries were mentioned or portrayed in 11.4% of the videos. Popularity, as gauged by the number of videos uploaded each year with greater than 3000 views, has been relatively stable since 2011. All videos accumulated 223 million views, 46.5% of them being amongst the ten most viewed videos. Videos that contained statistics equalled 27.9%, but only 3.7% of videos referenced a peer-reviewed study.

Conclusions:

This study demonstrates that there is high interest in viewing videos focused on distracted driving, and the popularity of these videos appears to be relatively stable over time on a forum that fluxes based on the current opinions of its users. However, the overall quality of information presented in these videos can be regarded as poor, with a significant paucity of peer-reviewed data being presented. The videos mostly focused on phone related distractions, overlooking many other common forms of distracted driving. Additionally, death was portrayed greater than 1.7 times as much as an injury in terms of potential consequences of distracted driving. Data from the National Highway Traffic Safety Administration suggests that for every distracted driving fatality, there were approximately 113 injuries. Surprisingly, orthopaedic injuries, which lead to a massive source of long term disability, are vastly under-represented.


 Citation

Please cite as:

Gjorgjievski M, Sprague S, Chaudhry H, Ginsberg L, Wang A, Bhandari M, Ristevski B

Distracted Driving on YouTube: Categorical and Quantitative Analyses of Messages Portrayed

JMIR Public Health Surveill 2020;6(1):e14995

DOI: 10.2196/14995

PMID: 32039816

PMCID: 7055766

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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