Caffeine may disrupt the impact of real-time drowsiness on cognitive performance: a double-blind, placebo-controlled small-sample study.

Caffeine is widely used to promote alertness and cognitive performance under challenging conditions, such as sleep loss. Non-digestive modes of delivery typically reduce variability of its effect. In a placebo-controlled, 50-h total sleep deprivation (TSD) protocol we administered four 200 mg doses of caffeine-infused chewing-gum during night-time circadian trough and monitored participants' drowsiness during task performance with infra-red oculography. In addition to the expected reduction of sleepiness, caffeine was found to disrupt its degrading impact on performance errors in tasks ranging from standard cognitive tests to simulated driving. Real-time drowsiness data showed that caffeine produced only a modest reduction in sleepiness (compared to our placebo group) but substantial performance gains in vigilance and procedural decisions, that were largely independent of the actual alertness dynamics achieved. The magnitude of this disrupting effect was greater for more complex cognitive tasks.

Synchronized drowsiness monitoring and simulated driving performance data under 50-hr sleep deprivation: A double-blind placebo-controlled caffeine intervention.

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18-28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle frame-mounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-wake and time-on-task performance dynamics and associated drowsiness levels. It also enables direct examination of the relationships between drowsiness and task performance measures. The question of how these relationships might change under various intervention conditions (caffeine in our case) seems worth further investigation.