Summary: Researchers have developed a new algorithm that can determine the ideal time to consume, and correct dosage of caffeine that can maximize alertness under sleep loss conditions.

Source: AASM.

According to a recent study, a newly developed algorithm may be the key to optimizing alertness with caffeine.

Caffeine is the most widely consumed stimulant to counter the effects of sleep loss on neurobehavioral performance. However, to be safe and most effective, it must be consumed at the right time and in the right amount. This study proposed an automated optimization algorithm to identify safe and effective caffeine-dosing strategies that maximize alertness under any sleep-loss condition.

“We found that by using our algorithm, which determines when and how much caffeine a subject should consume, we can improve alertness by up to 64 percent, while consuming the same total amount of caffeine,” said principal investigator and senior author Jaques Reifman, PhD. “Alternatively, a subject can reduce caffeine consumption by up to 65 percent and still achieve equivalent improvements in alertness.”

Reifman is a senior research scientist and director of DoD Biotechnology High Performance Computing Software Applications Institute and the Telemedicine and Advanced Technology Research Center at the U.S. Army Medical Research and Materiel Command in Ft. Detrick, Maryland.

The study used a validated mathematical model, which predicts the effects of sleep loss and caffeine on psychomotor vigilance task (PVT) performance and combined it with a computationally efficient optimization algorithm to determine when and how much caffeine to consume to safely maximize alertness during sleep loss. The algorithm takes a user-provided sleep/wake schedule and maximum allowed caffeine as inputs and provides a caffeine-dosing strategy as the output.

The algorithm was assessed by computing and comparing dosing strategies for four previously published experimental studies of sleep loss. For each study, two dosing strategies were computed–one which enhanced the predicted PVT performance using the same total amount of caffeine as in the original studies, and another which achieved an equivalent level of performance as in the original studies using a lower amount of caffeine.

Compared to the original dosing strategies used in the studies, the U.S. Army’s algorithm identified strategies that enhanced neurobehavioral performance by up to 64 percent, or reduced caffeine consumption by up to 65 percent. According to the authors, these results suggest that the algorithm can tailor the timing and amount of caffeine to the particular sleep/wake schedule of each study condition to maximize its benefits.

“Our algorithm is the first quantitative tool that provides automated, customized guidance for safe and effective caffeine dosing to maximize alertness at the most needed times during any sleep-loss condition,” said Reifman.

About this neuroscience research article

Source: Corinne Lederhouse – AASM

Publisher: Organized by NeuroscienceNews.com.

Image Source: NeuroscienceNews.com image is in the public domain.

Original Research: The study “Caffeine Dosage Strategies that Efficiently Enhance Alertness during Sleep Loss” by Vital-Lopez F, Ramakrishnan S, Doty TJ, Balkin TJ, and Reifman J will be presented at SLEEP 2018.

Cite This NeuroscienceNews.com Article

[cbtabs][cbtab title=”MLA”]AASM “New Algorithm Determines Ideal Caffeine Dosage and Timing For Alertness.” NeuroscienceNews. NeuroscienceNews, 4 June 2018.

<https://neurosciencenews.com/ai-caffeine-alertness-9236/>.[/cbtab][cbtab title=”APA”]AASM (2018, June 4). New Algorithm Determines Ideal Caffeine Dosage and Timing For Alertness. NeuroscienceNews. Retrieved June 4, 2018 from https://neurosciencenews.com/ai-caffeine-alertness-9236/[/cbtab][cbtab title=”Chicago”]AASM “New Algorithm Determines Ideal Caffeine Dosage and Timing For Alertness.” https://neurosciencenews.com/ai-caffeine-alertness-9236/ (accessed June 4, 2018).[/cbtab][/cbtabs]

Abstract

Caffeine Dosage Strategies that Efficiently Enhance Alertness during Sleep Loss

Introduction: Caffeine is the most widely consumed stimulant to counter the effects of sleep loss on neurobehavioral performance. However, to be safe and most effective, it must be consumed at the right time and in the right amount. Caffeine-dosing recommendations offered by prior studies are not readily adaptable to any arbitrary sleeploss condition. Here, we propose an automated optimization algorithm to identify safe and effective caffeine-dosing strategies that maximize alertness under any sleep-loss condition.

Methods: We used our validated unified model of performance, which predicts the effects of sleep loss and caffeine on psychomotor vigilance task (PVT) performance, and combined it with a computationally efficient optimization algorithm to determine when and how much caffeine to consume to safely maximize alertness during sleep loss. The algorithm takes a user-provided sleep/wake schedule and maximum allowed caffeine as inputs, and provides a caffeine-dosing strategy as the output. We assessed the algorithm by computing and comparing dosing strategies for six previously published experimental studies of sleep loss. For each study, we computed two dosing strategies—one which enhanced the predicted PVT performance using the same total amount of caffeine as in the original studies, and another which achieved an equivalent level of performance as in the original studies using a lower amount of caffeine.

Results: Compared to the original dosing strategies used in the studies, our algorithm identified strategies that enhanced neurobehavioral performance by up to 64%, or reduced caffeine consumption by up to 65%. These results suggest that the algorithm can tailor the timing and amount of caffeine to the particular sleep/wake schedule of each study condition to maximize its benefits.

Conclusion: Our algorithm is the first quantitative tool that provides automated, customized guidance for safe and effective caffeine dosing to maximize alertness at the most needed times during any sleep-loss condition.

Support (If Any): This work was sponsored by the Military Operational Medicine Program Area Directorate of the U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD, and by the U.S. Department of Defense Medical Research and Development Program (Grant No. DMRDP_13200).

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