Evidence map›Paper›PMID 33197793›Full record

ArticleAccident; analysis and prevention2021

Texting while driving: A discrete choice experiment.

Anne M Foreman, Jonathan E Friedel, Yusuke Hayashi, Oliver Wirth

Abstract read
In one paragraph

Article in Accident; analysis and prevention, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
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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

4 authors.

Anne M ForemanNational Institute for Occupational Safety and Health, United States. Electronic address: amforeman@cdc.gov.
Jonathan E FriedelNational Institute for Occupational Safety and Health, United States.
Yusuke HayashiPennsylvania State University, Hazelton, United States of America.
Oliver WirthNational Institute for Occupational Safety and Health, United States.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

Texting while driving is one of the most dangerous types of distracted driving and contributes to a large number of transportation incidents and fatalities each year. Drivers text while driving despite being aware of the risks. Although some factors related to the decision to text while driving have been elucidated, more remains to be investigated in order to better predict and prevent texting while driving. To study decision making involved in reading a text message while driving, we conducted a discrete choice experiment with 345 adult participants recruited from Amazon's Mechanical Turk. Participants were presented with multiple choice sets, each involving two different scenarios, and asked to choose the scenario in which they would be more likely to text while driving. The attributes of the scenarios were the relationship to the text-message sender, the road conditions, and the importance of the message. The attributes varied systematically across the choice sets. Participants were more likely to read a text message while driving if the sender of the message was a significant other, the message was perceived to be very important, and the participant was driving on rural roads. Discrete choice experiments offer a promising approach to studying decision making in drivers and other populations because they allow for an analysis of multiple factors simultaneously and the trade-offs among different choices.

Indexed as

Automobile DrivingDangerous BehaviorDistracted DrivingText MessagingAccidents, TrafficAdultHumansDecision makingDiscrete choice experimentDistracted drivingTexting while drivingTransportation incidents

Identifiers

PMID33197793
PMCPMC8190565

What OpenQuestion holds

Textmetadata
LicenceTDM
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.