commentary
Predicting Sleep/Wake Behavior for Model-Based Fatigue Risk Management
Commentary on Darwent et al. Prediction of probabilistic sleep distributions following travel across multiple time zones.
SLEEP 2010;33:185-195.
Hans P. A. Van Dongen, PhD
Sleep and Performance Research Center, Washington State University, Spokane, WA
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., domicilebased) 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 longhaul 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
Submitted for publication January, 2010
Accepted for publication January, 2010
Address correspondence to: Hans P.A. Van Dongen, PhD, Sleep and Performance Research Center, Washington State University Spokane, P.O.
Box 1495, Spokane, WA 99210-1495; Tel: (509) 358-7755; Fax: (509)
358-7810; E-mail: [email protected]
SLEEP, Vol. 33, No. 2, 2010
144
Commentary—Van Dongen
Disclosure Statement
Dr. Van Dongen has received research support from The Boeing
Company and Continental Airlines; has participated in a speaking
engagement for Clayton Sleep Institute, St. Louis, MO; and has
consulted for FedEx Corporation.
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.
SLEEP, Vol. 33, No. 2, 2010
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