The most widely consumed stimulant worldwide to counter the effects of sleep loss on neurobehavioral performance is caffeine, with some drinking caffeine all day long, while to others to be safe and effective it must be consumed in the right amount at the right time. Researchers proposed this study of an automated algorithm to optimize and identify safe and effective caffeine dosing strategies to maximize alertness under any sleep loss condition.
Researchers have found that using their algorithm to determine how much and when a subject should consume caffeine alertness was improved by up to 64%, a subject could reduce caffeine consumption by 65% and still achieve equivalent alertness improvements.
User provided information regarding sleep/wake schedules and maximum allowed caffeine inputs are used by the algorithm to provide a caffeine dosing strategy output. Algorithm was assessed by computing and comparing dosing strategies from 4 previous studies of sleep loss, each having two dosing strategies computed: one to enhance predicted PVT performance using same total amount of caffeine as in original studies, and the other which achieved equivalent level of performance using lower amounts of caffeine. Using a validated mathematical model to predict sleep loss effects and caffeine on psychomotor vigilance task performance combined with computationally efficient optimization the algorithm determines how much and when to consume caffeine to safely maximize alertness during sleep loss.
Compared to original dosing strategies the US Army’s algorithm identified strategies which enhanced neurobehavioral performance by as much as 64%, or reduced caffeine consumption by up to 65% according to the researchers, results suggest the algorithm can be tailored to timing and amount of caffeine to particular sleep/wake schedules of each study condition to maximize benefits, being the first quantitative tool to provide automated customized guidance for effective and safe caffeine dosing to maximize alertness when most needed during any sleep loss condition.
