A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation

Rajdev, Pooja; Thorsley, David; Rajaraman, Srinivasan; Rupp, Tracy L.; Wesensten, Nancy J.; Balkin, Thomas J.; Reifman, Jaques

doi:10.1016/j.jtbi.2013.04.013

Cited by 52 publications

(60 citation statements)

References 19 publications

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“…User entries of sleep durations < 8 h per day degrade performance, and sleep durations > 8 h per day improve performance. Consistent with our previously published "fading memory" concept, 6 the more recent the sleep/wake period, the greater its influence on predicted performance.…”

Section: Initial Conditions and Model Assumptionssupporting

confidence: 88%

2B-AlertWeb: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance

Reifman

Kumar

Wesensten

et al. 2016

Sleep

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Study Objectives: Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. Methods: We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. Results:The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. Conclusions: The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a group of individuals. This evidencebased tool can be used as a decision aid to design effective work schedules, guide the design of future sleep restriction and caffeine studies, and increase public awareness of the effects of sleep amounts, time of day, and caffeine on alertness. INTRODUCTIONOver the past decade, computational tools have been increasingly used across a range of public and commercial sectors (e.g., aviation, mining, and nuclear power plant operations) as components of fatigue risk management systems aimed at optimizing dutytime alertness and thereby minimizing fatigue-related errors and accidents.1,2 These tools are based on biomathematical models that predict daily variations in neurobehavioral performance as a function of sleep/wake amounts and time of day. Such tools are particularly beneficial in industries that are engaged in 24-h operations and require shift-work schedules. However, to date, there are no open-access tools available, and none of the commercially available tools contain...

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Section: Initial Conditions and Model Assumptionssupporting

confidence: 88%

2B-AlertWeb: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance

Reifman

Kumar

Wesensten

et al. 2016

Sleep

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“…Here, we used the original UMP to obtain P0(t) for the durations of wake and sleep. 2,7 To obtain the caffeine-effect factor gPD(t,c), we used our previously developed PD model of caffeine, which relates the PK of caffeine to the PD effects via the Hill equation.…”

Section: Extension Of the Ump To Account For Caffeine Effectsmentioning

confidence: 99%

“…Accordingly, it is not clear how well this model predicts objective measures pii: sp-00051- 16 http://dx.doi.org/10.5665/sleep.6164 of neurobehavioral performance under more typical operational conditions-characterized by milder sleep loss and repeated caffeine dosing at levels ranging from ~100 mg to ~200 mg. We recently developed the unified model of performance (UMP), a more parsimonious ("caffeine-free") model, and showed that the UMP provides accurate predictions of objective measures of human performance across numerous studies spanning the continuum of sleep loss-from chronic sleep restriction (CSR) to TSD-at a group-average level. 2,7 Separately, we developed a model that considers the neurobehavioral effects of caffeine, and validated it using three TSD studies. 4,5 Here, we combined the original UMP with our caffeine model into a single, unified modeling framework (henceforth termed UMP), and validated its predictions across a wide range of sleep/wake schedules and caffeine doses.…”

Section: Introductionmentioning

confidence: 99%

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine

Ramakrishnan

Wesensten

Kamimori

et al. 2016

Sleep

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Study Objectives: Existing 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. Methods: We 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). Results: The 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%. Conclusions:The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Keywords: biomathematical model, caffeine model, chronic sleep restriction, PVT, total sleep deprivation Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827-1841. INTRODUCTIONMathematical models that accurately predict the effects of sleep/wake schedules on human neurobehavioral performance are valuable tools for effective management of operational alertness and fatigue. However, to be of practical use, they must also be able to predict the alertness-and performance-enhancing effects of caffeine, the most widely used stimulant compound. Caffeine is available in a wide range of concentrations in myriad foods and beverages, including coffee, tea, soft drinks, and energy drinks, providing a spectrum of performance-improving effects. Nevertheless, the most thoroughly validated predictive models of performance do not account for the effects of caffeine, 1,2 and the few that do have limitations. For example, the models proposed by Benitez et al. 3 and by Ramakrishnan et al.

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“…Early efforts included the three process model of alertness/performance (TPM), developed to predict group performance and alertness throughout a day (14). The more recent unified model of performance expands on the TPM and more closely models individual psychomotor performance variance (15–18). In order to use the previous models to predict individual cognitive performance using supervised learning techniques, actual cognitive performance data, usually provided by the psychomotor vigilance test (PVT), is required to update the model–parameter estimates (19).…”

Section: Introductionmentioning

confidence: 99%

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

2017

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Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

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A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation

Cited by 52 publications

References 19 publications

2B-AlertWeb: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance

2B-AlertWeb: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

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