Key Words driver, occupational health, exercise, health promotion, stress management ■ Abstract Strategies to achieve ambitious targets for reducing road accidents (34) have largely focused on engineering and technological advancements, the modification of occupational demands, and, to a lesser extent, human factors. These factors include stress and psychological states; sleep, fatigue, and alertness; and health status. Physical activity appears to influence all these human factors but has not previously been systematically considered as a direct or indirect risk factor for driver accidents. This chapter provides an overview, within an evidence-based framework, of the impact each of these human factors has on driver performance and risk of at-work road traffic accidents and then examines how physical (in)activity may moderate and mediate these relationships. Finally, we consider practical implications for work site interventions. The review aims to offer an evidence base for the deployment of resources to promote physical activity, manage stress, facilitate sleep, reduce fatigue, and enhance alertness to improve physical and psychological health among professional drivers.

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Effects of stress on driving performance can depend on the nature of driver's stress reactions and on the traffic environment. In an experimental study, we assessed multiple dimensions of vulnerability to driver stress by a questionnaire that was validated in previous field studies and related those dimensions to performance on a driving simulator. Results were broadly consistent with prediction. A dimension of habitual dislike of driving was associated with reduced control skills, greater caution, and disturbance of moods. A measure of aggressive driving predicted more frequent and more error-prone overtaking, which are effects attributed to the use of confrontive coping strategies in interaction with other vehicles. An alertness measure predicted speed of reaction to pedestrian hazards. This research has practical applications for system design, automated monitoring of driver performance, selection and assessment of drivers, and training.

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Many estimates of risk of crash involvement may have been wrong. Given the potential for erroneous criteria, a number of studies may make invalid assumptions from their data.