ArticleSleep2016
A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.
Article in Sleep, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05588934 (A Head-to-Head Comparison of the 2B-Alert Caffeine Optimization Algorithm Versus Standard Caffeine Dosing on Performance During Sleep Deprivation), which is not on this map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Head-to-Head Comparison of the 2B-Alert Caffeine Optimization Algorithm Versus Standard Caffeine Dosing on Performance During Sleep Deprivation (2B-2)
Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- Randomized, double-blind, placebo-controlled, crossover study of the effects of repeated-dose caffeine on neurobehavioral performance during 48 h of total sleep deprivation.Psychopharmacology · 2019Trial
- Aircrew rostering workload patterns and associated fatigue and sleepiness scores in short and medium haul flights in Brazil.Scientific reports · 2025Article
- Factors associated with caffeine intake among undergraduates: a cross-sectional study from Palestine.Journal of health, population, and nutrition · 2025Article
- Predicting neurobehavioral performance of resident physicians in a Randomized Order Safety Trial Evaluating Resident-Physician Schedules (ROSTERS).Sleep health · 2024Article
- Coffee Consumption Behavior in Young Adults: Exploring Motivations, Frequencies, and Reporting Adverse Effects and Withdrawal Symptoms.Psychology research and behavior management · 2023Article
- 2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.Journal of medical Internet research · 2022Article
- Efficient embedded sleep wake classification for open-source actigraphy.Scientific reports · 2021Article
- Recent advances in modeling sleep: from the clinic to society and disease.Current opinion in physiology · 2020Article
- Motivations Influencing Caffeine Consumption Behaviors among College Students in Korea: Associations with Sleep Quality.Nutrients · 2020Article
- 2B-Alert App: A mobile application for real-time individualized prediction of alertness.Journal of sleep research · 2019Article
- Effects of Sleep Deprivation on Surgeons Dexterity.Frontiers in neurology · 2019Review
- Effects of caffeine on sleep quality and daytime functioning.Risk management and healthcare policy · 2018Review
- Article
- Excessive day time sleepiness, poor sleep quality, and their association to caffeine consumption among young Informational Technology professionals.Industrial psychiatry journalArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesExisting mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies.
methodsWe hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg).
resultsThe UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%.
conclusionsThe UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.