Predicting sleep/wake behavior for model-based fatigue risk management.

SCIENCE PROVIDES A DEEPER UNDERSTANDING OF NATURE'S BASIC PRINCIPLES THROUGH AN INTERPLAY BETWEEN EXPERIMENTAL OBSERVATION AND theorization. This is exemplified by scientific progress in the area of sleep/wake behavior and its consequences for waking function. Key physiological mechanisms of sleep/wake regulation—dependence on prior time awake and asleep on the one hand, and dependence on time of day on the other hand—were already recognized in ancient Chinese literature.1 Yet, the far-reaching implications of the coexistence of these two regulatory mechanisms (respectively called the homeostatic process and the circadian process) were not always readily apparent. They became clear after the basic physiological principles had been cast in the form of the precisely formulated theory and mathematical abstractions of the seminal two-process model.2,3 This model stimulated new experimentation, which led to the recognition that the homeostatic and circadian processes are temporally aligned such that they promote consolidated wakefulness during the day and consolidated sleep during the night.4 The interaction of these two processes is now understood to give rise to a number of important features of sleep/wake physiology, including the existence of a “wake maintenance zone” which makes it difficult to fall asleep in the early evening,5,6 the emergence of sleepiness and neurobehavioral impairment when wakefulness is extended or placed during the night,3,4,7 and the phenomenon of “jet lag” following travel across multiple time zones.7,8 This understanding has motivated further theorization and development of physiology-based mathematical models for the prediction of sleep pressure and neurobehavioral impairment under conditions of sleep loss and circadian misalignment. However, sleep/wake physiology only partially explains the regulation of sleep/wake behavior and its consequences for waking neurobehavioral functioning. Occupational demands, lifestyle preferences, volitional decisions, and various other non-physiological factors co-determine sleep/wake and neurobehavioral performance patterns.9 Unfortunately, almost no experimental research has focused on these predominantly social/behavioral factors. The article by Darwent and colleagues10 in the present issue of SLEEP makes a welcome contribution to this area. The authors developed a model to estimate sleep probability distributions for long-haul commercial aviation pilots during layovers following international travel across multiple time zones. The model is based on the observation that layover sleep periods tend to cluster into multiple bouts anchored by the timing of night at the layover location, the timing of night in the pilots' domicile, and the times shortly after the end of the pre-layover duty period and before the beginning of the subsequent post-layover duty period,11 due in part to anticipatory sleep strategies that pilots use to balance work demands, social commitments, and sleep need.10 At the core of the model equations proposed by Darwent and colleagues is a sinusoidal curve (cosinor in Eq. 1),10 which represents the oscillatory variation of the probability to be asleep over the course of the 24-hour day.5 The phase position of this sleep probability curve (timing of its peak and trough) depends on the phase parameter P.10 An innovative aspect of the equation is the exponent parameter S, which compresses or expands the portion of the curve indicating high sleep probability, thereby reducing or increasing the expected amount of sleep per day.10 Together, P and S determine the expected average timing and duration of sleep represented by the curve. Estimation of baseline (i.e., domicile-based) values for P and S is straightforward, but challenges arise when dealing with the layover sleep probability curve. Darwent and colleagues postulate that a value for S during a layover can be estimated based on the assumption (as formalized in ancillary equation 2) that the probability of a pilot being asleep is inversely related to the amount of sleep predicted for the immediate past and also, because of anticipatory sleep strategies, to the amount of sleep predicted for the immediate future (as approximated on the basis of baseline sleep probabilities).10 Additionally, they make adjustments to P for layovers following travel across multiple time zones using a previously published equation for circadian phase drifting (given in ancillary equation 1).12 Finally, to reflect the observation that layover sleep is driven by both the timing of night at the layover location and the timing of night at home, a weighted average is taken over a layover sleep probability curve based on home time and one adjusted for circadian phase drifting, where weighting parameter W determines the relative contributions of these two curves to predicted layover sleep patterns (Eq. 2).10 Sleep diary and wrist actigraphy data sets from 306 long-haul pilots with multiple layovers were available for estimation of the model parameters (S, P, and W) and validation of model predictions.10 The model was calibrated with average sleep/wake times observed during international flight patterns from Australia (Sydney, Melbourne, or Brisbane) to London and Los Angeles, and validated against average sleep/wake times observed during another set of international flight patterns from Australia to London, Los Angeles, New York, and Johannesburg. Although the authors acknowledge that there is room for model refinement, goodness-of-fit to group-average sleep/wake data was generally high.10 Thus, the model is a useful prototype for the prediction of average sleep times during layovers after transmeridian travel, using only information obtainable or estimable from flight and duty schedules. The fact that the model by Darwent and coworkers is rooted in probabilities confers important advantages for future work. First, using Bayesian forecasting techniques,13 the model could be employed successfully to make predictions of future sleep/wake patterns tailored to a given individual scheduled to fly a specific route, based solely on data regarding the individual's sleep/wake patterns in similar or different schedules flown in the past. Second, having a tool for the prediction of probabilistic sleep distributions potentially allows for the development of models predicting levels of risk associated with a particular duty/rest schedule. One way this could be done is by making predictions for neurobehavioral impairment with a validated mathematical model of fatigue and performance14,15 for a full set of possible layover sleep/wake patterns, and weighing the results by the predicted probabilities of occurrence of these possible patterns. This would yield a quantitative estimate for intrinsic risk of impairment, on the basis of which comparisons may be made with alternative flight duty schedules that could improve operational safety—a cornerstone method of model-based fatigue risk management.16 It is noteworthy that such a valuable application of sleep/wake modeling is based on relative comparisons, and therefore puts less stringent requirements on absolute prediction accuracy than has hitherto been generally pursued. Absent an established theory about the influence of social/behavioral factors on sleep/wake behavior, the generalizability of the model of Darwent and colleagues10 to duty patterns flown elsewhere in the world is not a priori clear. Systematic effects from travel direction, geometric latitude of airport, and other, potentially unforeseeable factors may need to be accounted for explicitly. Even so, if continuing model refinement and validation efforts expose no fundamental problems with the underlying assumptions, then the model by Darwent and colleagues10 may be seen as a step toward a theoretical framework on the behavioral (versus physiological) principles of sleep/wake regulation. Building such a framework requires further productive interplay between experimental observation and theorization, for which large-scale, high-quality field data on sleep and performance in the operational environment are urgently needed.