Simulator performance, microsleep episodes, and subjective sleepiness: normative data using convergent methodologies...

Area of Science:

• Neuroscience
• Sleep Medicine
• Human Factors Engineering

Background:

• Drowsiness significantly impairs driving performance.
• Current methods for assessing drowsiness-related impairment may be insufficient.
• A standardized methodology is needed to accurately detect driving deficits due to sleepiness.

Purpose of the Study:

• To evaluate a novel, standardized assessment methodology for detecting drowsiness-induced driving impairment in a healthy population.
• To establish normative data for driving performance under varying levels of alertness.

Main Methods:

• Thirty-one healthy subjects participated in a driving simulation paradigm.
• Simulations were conducted at two-hour intervals throughout the day (1000-1600h).
• Data collected included subjective sleepiness ratings, electroencephalogram-verified microsleep (MS) episodes, and driving simulator performance metrics (speed, lane tracking, reaction time (RT), crashes).

Main Results:

• Objective measures of RT, velocity, tracking, and MS events showed significant diurnal fluctuations, peaking in the afternoon.
• Subjective ratings of sleepiness and alertness did not exhibit significant circadian variation.
• A high correlation was found between the incidence of MS episodes and crash risk (r = .748).

Conclusions:

• Multiple, convergent measures are essential for a comprehensive assessment of driving impairment.
• Healthy individuals may underestimate their neurophysiologically mediated performance deficits.
• The findings provide normative data and suggest this methodology may offer more accurate crash risk prediction than traditional daytime sleepiness tests.