Age, gender, personality, burnout, job characteristics and job content as predictors of driver fatigue. Issue 4 (2nd October 2022) - Tàpia-Caballero, Patricia; Serrano-Fernández, María-José; Boada-Cuerva, Maria; Boada-Grau, Joan; Assens-Serra, Jordi; Robert-Sentís, Lluís
Abstract : Objectives. Several studies have shown that one of the most common causes of collision is driver fatigue since fatigue causes drowsiness while driving and this decreases the driver's ability to maneuver the vehicle and increases the probability of their nodding off and falling asleep at the wheel. This may be due to a variety of personal reasons and specific factors connected to working conditions. In the present work we therefore intend to develop a predictive model for fatigue in professional drivers using the following indicators: age, gender, personality, burnout, characteristics and job content. Method. The participants were 516 professional drivers from different transport sectors, obtained through non-probabilistic sampling. SPSS version 25.0 was used for data analysis. Results. The predictive capacity of a number of variables that affect drivers by causing fatigue is determined. Fatigue can be predicted through certain variables, with the best predictor being exhaustion (48.8%). Conclusions. This research contributes to a greater knowledge of the factors that produce fatigue in professional drivers. It highlights the importance of designing interventions to reduce the incidence of fatigue, resulting in greater well-being for the driver and a lower incidence of collisions.