Distracted driving in elderly and middle-aged drivers

Abstract

Automobile driving is a safety-critical real-world example of multitasking. A variety of roadway and in-vehicle distracter tasks create information processing loads that compete for the neural resources needed to drive safely. Drivers with mind and brain aging may be particularly susceptible to distraction due to waning cognitive resources and control over attention. This study examined distracted driving performance in an instrumented vehicle (IV) in 86 elderly (mean=72.5 years, SD=5.0 years) and 51 middle-aged drivers (mean=53.7 years, SD=9.3 year) under a concurrent auditory-verbal processing load created by the Paced Auditory Serial Addition Task (PASAT). Compared to baseline (no-task) driving performance, distraction was associated with reduced steering control in both groups, with middle-aged drivers showing a greater increase in steering variability. The elderly drove slower and showed decreased speed variability during distraction compared to middle-aged drivers. They also tended to "freeze up", spending significantly more time holding the gas pedal steady, another tactic that may mitigate time pressured integration and control of information, thereby freeing mental resources to maintain situation awareness. While 39% of elderly and 43% of middle-aged drivers committed significantly more driving safety errors during distraction, 28% and 18%, respectively, actually improved, compatible with allocation of attention resources to safety critical tasks under a cognitive load.

Figure

A single gas pedal-hold time (PHT) calculation is depicted for two participants. Dashed lines represent actual pedal position. The solid line shows the time interval over which the gas pedal position changed <0.01 compared to pedal position at a specific time (at the vertical time-reference bar). In these examples, PHT is approximately 2s for a middle-aged driver at baseline (top) and 11s for a multitasking elderly driver (bottom). The within-person summary of PHT used in our analyses was the 25th percentile of the PHT values calculated at every specific time, as the vertical reference bar slides across a driver s time series data. Note that the PHT is calculated at every time point. This means that as the time-reference bar slides across the flat regions corresponding to high PHT s, there are many high PHT values that are nearly identical. Hence, using the 25th percentile, rather than a central value such as the 50th percentile or the mean, helps remove the redundancy caused by these repeated values. This choice of the 25th percentile was made a priori, rather than being based on empirical sensitivity.