2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study

Area of Science:

• Sleep science and chronobiology
• Human performance optimization
• Pharmacology of stimulants

Background:

• Sleep loss affects one-third of the US population, impairing cognitive function, productivity, and safety.
• Computer-based fatigue management systems can predict sleep effects and guide caffeine use.
• The 2B-Alert Web tool was previously developed to predict alertness based on sleep and caffeine intake.

Purpose of the Study:

• To enhance the 2B-Alert Web tool with automatic recommendations for safe and effective caffeine interventions.
• To enable the tool to optimize alertness levels at user-specified times under various sleep-loss conditions.

Main Methods:

• Integrated a caffeine-optimization algorithm into the 2B-Alert Web predictive models.
• Simulated four distinct sleep-deprivation scenarios to test the algorithm's recommendations.
• Compared algorithm-based interventions against US Army caffeine-countermeasure guidelines.
• Implemented a drag-and-drop graphical interface for improved usability.

Main Results:

• 2B-Alert Web interventions increased mean alertness by 36% to 94% across simulated conditions.
• Peak alertness impairment was reduced by 31% to 71% with the enhanced tool.
• Recommended caffeine doses were equivalent to or smaller than those in US Army guidelines.

Conclusions:

• The enhanced 2B-Alert Web tool provides evidence-based, automated caffeine recommendations for mitigating sleep loss effects.
• The tool's improved capability increases efficiency for designing research and work/rest schedules.
• Public availability of this tool benefits diverse communities in managing fatigue and optimizing performance.