Objectives: This qualitative investigation sought to identify the motivational factors that contribute to drowsy driving in college students and to discover important messaging strategies that may help prevent or reduce this behavior in this population.
Design: Four focus groups of college students.
Setting: A large university in the Washington, DC, metropolitan area during the Fall 2016 term.
Participants: Twenty-six undergraduate students between the ages of 18 and 25 years.
Measurements: Notes and transcripts from the focus group sessions were analyzed to identify recurring themes regarding attitudes, motivations, experiences, influences, and potential preventive messaging strategies related to drowsy driving.
Results: Although most participants had heard of drowsy driving and were concerned about it, they did not associate it with legal risks and were more concerned about alcohol-impaired and distracted driving as crash risks. Participants viewed drowsy driving as a normal and unavoidable part of their lives over which they had little control. For potential anti-drowsy driving messaging strategies, participants preferred messages delivered via audiovisual or social media that featured graphic and emotional portrayals of crashes and their consequences. Participants also voiced strong support for preventive messaging strategies equating various degrees of sleep deprivation to known impairing levels of alcohol, as well as messages providing cues to action to actual drowsy drivers on roadways.
Conclusions: Increased enforcement, education, and public messaging campaigns are needed to increase knowledge and influence attitudes and opinions among young drivers about the dangers and social unacceptability of drowsy driving.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.sleh.2017.10.007 | DOI Listing |
J Sleep Res
January 2025
Flinders Health and Medical Research Institute: Sleep Health, Flinders University, Adelaide, South Australia, Australia.
Sleepiness-related errors are a leading cause of driving accidents, requiring drivers to effectively monitor sleepiness levels. However, there are inter-individual differences in driving performance after sleep loss, with some showing poor driving performance while others show minimal impairment. This research explored if there are differences in self-reported sleepiness and driving performance in healthy drivers who exhibited vulnerability or resistance to objective driving impairment following extended wakefulness.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.
View Article and Find Full Text PDFCureus
November 2024
Department of Emergency Medicine, University of British Columbia, Vancouver, CAN.
Resident physicians often work extended-duration work shifts (EDWSs) exceeding 16 hours. EDWSs are associated with fatigue, workplace errors, mental health problems, and motor vehicle incidents. A 2019 systematic review reported that resident physicians had an increased risk of motor vehicle collisions (MVCs) and of falling asleep at the wheel after EDWSs.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea.
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers' facial expressions and detect fatigue indicators such as yawning and eye states.
View Article and Find Full Text PDFSleep
December 2024
Sleep Medicine Center, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!