U33: Impact of Distraction and Health on Commercial Driving Performance Final Report

Fine, Philip Russ; Stavrinos, Despina; Hanowski, Richard J.; McGwin, Gerald; Heaton, Karen; Foster, P. Jeffrey; Franklin, Crystal A.; Vance, David · 2012 · ROSA P / United States. Dept. of Transportation. Research and Innovative Technology Administration

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Summary

This study investigated the interaction between cognitive distraction, technological secondary tasks, and physical health factors on the driving performance of commercial truck drivers. Motivated by the U.S. Department of Transportation’s priority to combat distracted driving and concerns regarding driver wellness, the research aimed to determine how health conditions, sleep, medication use, and age interact with distraction to impact safety. The study specifically sought to evaluate the relative impact of visual and cognitive distractions, assess health effects on cognitive function, and examine operational efficiency under varying mental workload conditions. The methodology involved 55 participants (50 included in final analyses) recruited from Alabama-based trucking companies. Participants, who had a mean age of 40.5 years and an average of 8.6 years of commercial driving experience, provided baseline health, demographic, and anthropometric data. They completed standardized cognitive testing, including the Useful Field of View (UFOV®) and Psychomotor Vigilance Test (PVT). Subsequently, participants drove an 88-mile simulated trip in an L-3 Communications TranSim™ truck driving simulator under four conditions: no secondary task, cell phone conversation, text messaging, and email exchange. Statistical analyses, including Generalized Estimating Equation (GEE) models, were used to assess associations between distraction, health indicators, and driving violations. Results indicated that emailing and texting significantly impaired driving performance compared to the no-task condition. These conditions were associated with a nearly two-fold increase in overall violations, specifically a 5.5-fold increase in collision rates and a three-fold increase in lane deviations during emailing. Texting also increased lane deviations and eye glances off the road. In contrast, the cell phone condition resulted in a 42% reduction in eye glances off the road but increased instances of riding the clutch. Regarding health factors, increased sleep time was associated with a 34% decrease in collision rates and a 24% reduction in speeding violations exceeding 15 mph over the limit. Conversely, increased mean reaction time was linked to a slight increase in collision rates. Driver characteristics generally did not correlate with driving violations, though higher Epworth Sleepiness Scale scores were associated with decreased cognitive performance. The findings suggest that while all secondary tasks impact performance, visual-manual distractions like texting and emailing are significantly more detrimental than voice-only tasks. The study highlights the critical role of sleep in mitigating risky driving behaviors, supporting the development of fatigue management programs and informing hours-of-service regulations. Additionally, the results imply that current bans on handheld cell phones may be insufficient, suggesting a need for broader restrictions on on-board communication devices. These insights contribute to future intervention studies and policy development aimed at enhancing commercial driver safety and operational efficiency.

Key finding

Emailing and texting conditions caused significantly more collisions and lane deviations than no secondary tasks, whereas cell phone use reduced eye glances off the road but increased clutch riding.

Methodology

simulator

Sample size: 55

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• truck driver fatigue
• visual
• manual
• external distraction
• temporal
• distraction detection algorithms

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence, behavioral performance data, physiological data