Search

Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation \201TSD\202. However,during sleep restriction\201partial sleep loss\202performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction\201CSR\202rather than by episodic TSD,the practical utility of this class of models has been limited. To better quantify performance during both CSR and TSD,we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individual's capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models,and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore,this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking., Published in the Journal of Theoretical Biology, v331 p66-77, 24 Apr 2013. Prepared in collaboration with the Department of Behavioral Biology, Walter Reed Army Institute of Research, Silver Spring, MD. Sponsored in part by the US Defense Health Program award no. 13200.

A phenomenological model for circadian and sleep allostatic modulation of plasma cortisol concentration. Both circadian rhythmicity and sleep play significant roles in the regulation of plasma cortisol concentration by the hypothalamopituitary- adrenal (HPA) axis. Numerous studies have found links between sleep and changes in cortisol concentration, but the implications of these results have remained largely qualitative. In this article, we present a quantitative phenomenological model to describe the effects of different sleep durations on cortisol concentration. We constructed the proposed model by incorporating the circadian and sleep allostatic effects on cortisol concentration, the pulsatile nature of cortisol secretion, and cortisol s negative autoregulation of its own production and validated its performance on three study groups that experienced four distinct sleep durations. The model captured many disparate effects of sleep on cortisol dynamics, such as the inhibition of cortisol secretion after the wake-to-sleep transition and the rapid rise of cortisol concentration before morning awakening. Notably, the model reconciled the seemingly contradictory findings between studies that report an increase in cortisol concentration following total sleep deprivation and studies that report no change in concentration. This work provides a biomathematical approach to combine the results on the effects of sleep on cortisol concentration into a unified framework and predict the impact of varying sleep durations on the cortisol profile., Pub. in American Journal Physiology Endocrinology Metab., v303, pE1190-E1201, 2012.

In this work, we present a method for developing individualized biomathematical models for predicting cognitive performance impairment of individuals subjected to total sleep loss. The proposed method uses the two-process model of sleep regulation as the underlying parametric model, whose parameters are systematically customized for an individual by optimally combining the performance information obtained from the individual's performance measurements with a priori performance information using a Bayesian framework. As a result, the models incrementally account for an individual's uncertain initial state and unknown trait characteristics as each new performance measurement from the individual becomes available, yielding improved performance predictions. Additionally, the proposed method enables the analytical computation of statistically based measures of reliability of the model predictions in the form of prediction intervals. Results using data from subjects who participated in an 82-h total sleep loss laboratory study showed that the proposed method yielded individualized predictions that were up to 43% more accurate than group-average model predictions and better captured the circadian and homeostatic variations in the performance data., See also ADA562561. RTO-MP-HFM-181 Human Performance Enhancement for NATO Military Operations (Science, Technology and Ethics) (Amelioration des performances humaines dans les operations militaires de l'OTAN (Science, Technologie et Ethique)). RTO Human Factors and Medicine Panel (HFM) Symposium held in Sofia, Bulgaria, on 5-7 October 2009., The original document contains color images.

We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes available. This was achieved by optimally combining the performance information obtained from the individual's performance measurements with a priori performance information using a Bayesian framework, while retaining the strategy of transforming the nonlinear optimization problem of finding the optimal estimates of the two-process model parameters into a series of linear optimization problems. Second, by taking advantage of the linear representation of the two-process model, this new method enables the analytical computation of statistically based measures of reliability for the model predictions in the form of prediction intervals. Two distinct data sets were used to evaluate the proposed method. Results using simulated data with superimposed white Gaussian noise showed that the new method yielded 50% to 90% improvement in parameter-estimate accuracy over the previous method. Moreover, the accuracy of the analytically computed prediction intervals was validated through Monte Carlo simulations. Results for subjects representing three sleep-loss phenotypes who participated in a laboratory study (82 h of total sleep loss) indicated that the proposed method yielded individualized predictions that were up to 43% more accurate than group-average prediction models and, on average, 10% more accurate than individualized predictions based on our previous method., Published in Sleep, v32 n10 p1377-1392, 2009.

This study compares and contrasts the ability of three different mathematical modeling techniques to predict individual-specific body core temperature variations during physical activity. The techniques include a first-principles, physiology-based (SCENARIO) model, a purely data-driven model, and a hybrid model that combines first-principles and data-driven components to provide an early, short-term (20-30 min ahead) warning of an impending heat injury. Their performance is investigated using two distinct datasets, a Field study and a Laboratory study. The results indicate that, for up to a 30 min prediction horizon, the purely data-driven model is the most accurate technique, followed by the hybrid. For this prediction horizon, the first-principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques. Another important finding is that, if properly regularized and developed with representative data, data-driven and hybrid models can be made portable from individual to individual and across studies, thus significantly reducing the need for collecting developmental data and constructing and tuning individual-specific models., Published in IEEE Transactions on Biomedical Engineering, v55 n5 p1477-1487, 5 May, 2008. ISSN 0018-9294.

This talk illustrates two approaches by which biomathematical models can be developed to construct individualized, i.e. subject specific, physiologic predictive algorithms. We describe, in layman's terms, the pros and cons of first-principles physiology-based algorithms and data-driven, autoregressive algorithms. We also discuss how these algorithms may be customized to predict the physiologic state of specific individuals some time into the future. We illustrate the predictive power of these approaches in the prediction of: (i) performance impairment due to total sleep, (ii) body core temperature during physical activity, and (iii) glucose levels of type 1 and type 2 diabetes patients. In conjunction with real-time physiologic monitoring devices, such predictive algorithms may be used to optimize the timing and dosing of fatigue countermeasures, e.g. naps and caffeine, so that performance peaks and is maintained during desired times of day, minimize the occurrence of heat-related injuries, such as heat strokes, and allow for proactive glucose regulatory interventions be for glycemic levels drift from the desired range. [TATRC website, 15 Dec 2008], Presented at the Army Telemedicine Partnerships Series 2008: Personal Health Monitoring, Annual Meeting (8th), Seattle, WA, 5 Apr 2008; held in conjunction with the American Telemedicine Association's (ATA) Annual Meeting and Conference (13th), 6-8 Apr 2008. The original document contains color images.

Mathematical models of neurobehavioral function are useful both for understanding the underlying physiology and for predicting the effects of rest-activity-work schedules and interventions on neurobehavioral function. In a symposium titled Modeling Human Neurobehavioral Performance I: Uncovering Physiologic Mechanisms at the 2006 Society for Industrial and Applied Mathematics/Society for Mathematical Biology (SIAM/SMB) Conference on the Life Sciences, different approaches to modeling the physiology of human circadian rhythms, sleep, and neurobehavioral performance and their usefulness in understanding the underlying physiology were examined. The topics included key elements of the physiology that should be included in mathematical models, a computational model developed within a cognitive architecture that has begun to include the effects of extended wake on information-processing mechanisms that influence neurobehavioral function, how to deal with interindividual differences in the prediction of neurobehavioral function, the applications of systems biology and control theory to the study of circadian rhythms, and comparisons of these methods in approaching the overarching questions of the underlying physiology and mathematical models of circadian rhythms and neurobehavioral function. A unifying theme was that it is important to have strong collaborative ties between experimental investigators and mathematical modelers, both for the design and conduct of experiments and for continued development of the models., Sponsored in part by Air Force Office of Scientific Research (AFOSR) Grants nos. FA9550-05-1-0086 and FA9550-06-0080; by Air Force Research Laboratory Grant no. 04HE02COR; National Institutes of Health (NIH) Grants nos. K02-HD045459 and GM078993; and National Space Biomedical Research Institute (NSBRI) Grant no. HPF00405. Prepared in collaboration with the Division of Applied Sciences, Harvard University, Cambridge, MA; Air Force Research Laboratory, Mesa, AZ; Sleep and Performance Research Center, Washington State University, Spokane, WA; and Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA. Published in the Journal of Biological Rhythms, v22 n3 p233-245, Jun 2007.

The Departments of Transportation and Treasury and Independent Agencies Appropriations Bill (House Rpt. 108-671) included a directive to the Federal Aviation Administration to conduct a study of flight attendant fatigue. The NASA Ames Research Center Fatigue Countermeasures Group (FCG) was contracted by CAMI to conduct the study. To meet the goals of the study, this report contains a literature review on fatigue as potentially experienced by flight attendants, an evaluation of currently used (actual vs. scheduled) flight attendant duty schedules, and a comparison of these schedules to the current CFRs. The report additionally reviews fatigue-related incident/accident information from the Aviation Safety Reporting System (ASRS) and the NTSB database. One report section describes the application of three different performance and fatigue models to assess how flight attendant duty schedules contribute to increased levels of fatigue and predicted changes in performance. The report concludes with 6 recommendations concerning issues that require further evaluation, including: (1) Survey of Field Operations. To assess the frequency with which fatigue is experienced, the situations in which it appears, and the consequences that follow; (2) Focused Study of Incident Reports. To better understand details of the incidents; (3) Field Research on the Effects of Fatigue. To explore physiological and neuropsychological effects of fatigue, sleepiness, circadian factors, and rest schedules on flight attendants; (4) Validation of Models for Assessing FA Fatigue. An important step to understanding whether and how models could be used in conjunction with field operations; (5) International Carrier Policies and Practices Review. To learn how other countries address these issues and with what results; and (6) Training. FAs could benefit from information on fatigue., This report was integrated by the Civil Aerospace Medical Institute from reports prepared by the NASA Ames Fatigue Countermeasures Group, Moffett Field, CA.