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Updated: Jan 18, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
A digital, real-time, history-based sleep-management tool to enhance alertness
Yun Min Song1,2, Su Jung Choi3, Dongju Lim1,2
1Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea.
None:
In today's 24-h society, chronic sleep disruption and circadian misalignment have led to a "global sleep crisis," increasing the risk of cognitive impairment, workplace accidents, and long-term health consequences. Yet, most sleep management strategies rely on one-size-fits-all recommendations that overlook individual variability, resulting in suboptimal and/or impractical solutions. To address this, we previously developed a real-time, personalized sleep scheduling framework based on tracking of dynamic sleep pressure and circadian rhythms using a mathematical model. We recently implemented this framework in SleepWake, a mobile app designed for real-world applications. In a retrospective analysis of a 71-participant clinical study and a prospective trial with 19 shift workers, greater adherence to SleepWake's personalized recommendations led to significant improvements in alertness. These benefits stemmed from two key innovations that go beyond static sleep guidelines: real-time prescribed supplemental sleep to counteract prior deficits and personalized sleep phase alignment tailored to individual circadian patterns. This study provides the first direct evidence that continuously updated, individualized sleep schedules can optimize alertness and sleep health in real-world settings. By delivering sleep recommendations rooted in sleep physiology and evidence-based modeling, SleepWake has the potential to improve health, enhance safety, and elevate the overall quality of life in today's around-the-clock society. Statement of Significance The increasing demand for work outside daylight hours poses significant health and safety risks. To address these challenges, we previously developed a real-time, personalized sleep scheduling framework powered by a mathematical model. In this study, we implemented the framework through SleepWake, a mobile app that tailors sleep schedules based on real-time updates of individual sleep history. Unlike generic guidelines, SleepWake dynamically adjusts sleep schedules, offering additional rest when needed and aligning sleep with natural rhythms. Among 71 female and 19 male shift workers, those who followed the app's recommendations showed significant improvements in alertness. These findings underscore the benefits of continuously updated, personalized sleep schedules grounded in sleep physiology and evidence-based modeling for optimizing alertness.
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