Sleep, 5:S82-S94
© 1982 Raven Press, New York
Circadian Variation of Sleep Tendency in
Elderly and Young Adult SUbjects
G. S. Richardson, M. A. Carskadon, E. J. Orav, and W. C. Dement
Sleep Research Center, Stanford University School of Medicine, Stanford, California
Since its introduction (1) and validation as an objective measure of sleepiness
(2-4), the Multiple Sleep Latency Test (MSLT) has been employed as a measure
of sleep tendency in over 200 normal adult subjects. From this large pool of data, a
consensus picture of the diurnal1 variation of sleepiness has emerged in which
sleep tendency exhibits a biphasic pattern, minimal both in the morning and in the
evening, and maximal in the early afternoon (5). Although it has long been realized
that similar diurnal patterns are seen in some measures of human performance (6),
this complex trend remains difficult to reconcile with existing theories regarding
the possible physiological components of sleepiness, alertness, and cognitive
function. In particular, circadian rhythms of physiological variables such as body
temperature (7) or subjectively assessed alertness (8) show no evidence of a midday decline paralleling the observed increase in sleep tendency.
The significance of the complex diurnal pattern of sleep tendency cannot be
completely assessed without the perspective of the complete 24-h pattern. However, determination of the entire 24-h pattern requires the measurement of sleep
tendency outside of normal waking hours, and this necessity presents obvious
methodological problems. One approach has been to use altered sleep-wake
schedules to allow measurement of sleep and waking variables throughout the
24-h day (9-12). Retrospective analysis of one such study using sleep latency
measurements at each sleep period as a measure analogous to the MSLT (3)
demonstrated.a circadian variation in sleep tendency without an obvious midday
peak. However, the cumulative sleep deprivation inherent in such manipulations
and also the altered schedule itself preclude generalization of these findings to
normal sleep-wake schedules.
Quantification of the entire 24-h pattern of sleep tendency under normal conditions would clearly be relevant to current models for a number of sleep-wake
disorders. In particular, age-related changes in sleep-wake distribution appear to
involve some alteration of underlying mechanisms controlling the expression of
sleep state. Otherwise normal elderly individuals show increased diurnal napping
(13,14) and increased nocturnal awakenings (15,16), a pattern described as a deAddress correspondence and reprint requests to G, S. Richardson, Sleep Research Center, Stanford
University School of Medicine, Stanford, California 94305.
1 Diurnal is used here to denote only the waking phase of the 24-h day. Nocturnal will be used to
refer to the sleep phase and 24-h will be expressly stated when the entire day is being described.
582
CIRCADIAN VARIATION IN SLEEP TENDENCY
S83
crease in the definition of the sleep-wake circadian rhythm (17). Carskadon et al.
(18) used the MSLT to show that normal elderly subjects have a significantly
elevated diurnal sleep tendency relative to younger adults, and that this change
cannot be completely attributed to occult disturbances of nocturnal sleep such as
sleep apnea or nocturnal myoclonus. One hypothesis is that the changes in
sleep-wake distribution seen in normal elderly subjects are the result of a fundamental change in the circadian rhythm of sleep tendency in which noturnal sleep
tendency declines and diurnal sleep tendency increases. Certain of the other insomnias, the disorders of initiating and maintaining sleep (DIMS) (19), may also
prove attributable to alterations in the 24-h profIle of sleep tendency.
The purpose of the study reported here was threefold: (a) to quantify the 24-h
pattern of sleep tendency under normal sleep-wake conditions; (b) to compare the
patterns obtained for young adults and elderly subjects as a means of evaluating
the hypothesis that changes in sleep tendency underlie age-related changes in
sleep, and (c) to use 24-h sleep tendency data to study the correlates of sleep
tendency throughout the circadian cycle.
METHODS
Subjects
Subjects were 10 (five women, five men) elderly volunteers, ages 60-83 years
(mean = 70), and 8 (four women, four men) young adults, ages 19-23 years (mean
= 21). Elderly subjects were recruited from local senior citizen recreation centers
or church groups; young adults were recruited from the university community. All
subjects were in good health and without sleep complaints.
Experimental protocol
Subjects lived in the laboratory for three consecutive nights and days in groups
of four or five; all but one of the four sessions included subjects of both age
groups. On each night, sleep was recorded from 2330 to 0800 using standard (20)
measures of EEG, EOG, and EMG. All sleep records were scored in 30-s epochs
according to standard criteria (20). On the 1st night, respiration (nasal thermistor
and abdominal or thoracic mercury-filled capillary strain gauges) and anterior
tibialis EMG were also recorded. No subject had evidence of sleep apnea or
nocturnal myoclonus syndromes. The first 2 nights of sleep were uninterrupted;
the 3rd night was disturbed as described below.
Daytime testing included the Multiple Sleep Latency Test (MSLT) (1), the Stanford Sleepiness Scale (SSS) (21), and a linear (visual analog) sleepiness rating
(LSR) (5). The MSLT was performed each day at 2-h intervals from 0930 until
2130 by standard research techniques (5). Each test was preceded by 15 min of inactivity and 5 minutes of standard calibration procedures. Subjects were recorded
in bed in a quiet, darkened room after being instructed to lie quietly, keep their
eyes closed, and try to fall asleep. The tests on the 1st day lasted 30 min; on the
2nd and 3rd days, tests were ended after sleep onset (three consecutive 30-s
epochs of sleep) or after 30 min if the sleep onset criterion was not met. Oral
temperature was taken with an IV AC electronic thermometer immediately follow-
Sleep, Vol. 5 (Suppl. 2), 1982
S84
G. S. RICHARDSON ET AL.
ing each test before subjects got out of bed. The last test was given at 1730 on the
3rd day.
On the 3rd night, sleep latency data were obtained at 2-h intervals by use of a
modification of the MSLT. When subjects went to bed at 2330, they were given
the same instructions as for the MSLT, but were permitted to sleep until 0115.
This procedure was repeated at 0130, 0330, 0530, and 0730, with each sleep latency
measure preceded by 15 min of wakefulness. During this IS-min interval, subjects stayed in bed to have temperature taken and talked quietly with a research
assistant until standard calibrations were performed. Subjects remained in bed for
the IS-min waking interval unless they needed to use the restroom. Subjects were
told that these nocturnal awakenings occurred at random times, and they were
given no time cues. Although the laboratory windows were darkened throughout,
some subjects were aware of audible time information (bird songs, which were
occasionally discernible during the 0730 test).
Subjective sleepiness was assessed at 30-min intervals throughout the days and
immediately before lights out at bedtime and for the nocturnal arousals. Two
measures were used. The SSS is a 7-point Likert rating scale ranging from" 1feeling active and vital; alert; wide awake" to "7-almost in reverie; sleep onset
soon; lost struggle to remain awake." The visual analog LSR is a IOO-mm horizontalline, labeled "very wide awake" on the left extreme and "very sleepy" on
the right extreme. Subjects were instructed to consider these extremes in terms of
their own previous experience and to draw a vertical mark through the line at the
point that corresponded to how they felt in comparison with these personal extremes. The scale was scored by measuring the distance (in mm) of the mark from
the left extreme. For the present study, sleepiness scale data were analyzed from
those scales completed immediately before each measurement of sleep latency.
Subjects were not permitted to ingest alcohol or caffeine during the study.
Meals were served at 0815, noon, and 1800. Snacks were provided at 2200. Three
I-h reading periods were given at 1000, 1400, and 2000. During these times subjects sat in their bedrooms, and EEG and EOG were monitored to insure constant
wakefulness.
Statistical analysis
For study of the correlates of sleep tendency as measured by the MSLT, it was
necessary to control for the dominant influence of time of day and inter-subject
variation. This was done by correcting the individual MSLT values by use of a
linear model to account for the contributions of these factors. Specifically, each
MSL T result was modeled according to the following formula:
MSLT (i, j) = J.L + a(i) + f3(j) + E(i, j)
where i = 1, ... I, with I the number of subjects, and j = 1, ... J, with J the
number of test times. The terms in the formula correspond to components of the
model as follows.
1. J.L is assumed to be a time- and subject-independent amplitude constant,
estimated by the overall mean MSLT; i.e.,
Sleep. Vol. 5 (Suppl. 2). 1982
CIRCADIAN VARIATION IN SLEEP TENDENCY
fl
MSLT
=
=
I
ij
S85
MSLT
IJ
2. a(i) represents the individual effect due to inter-subject variability for subject i
and is estimated as the difference between mean MSLT across all times for subject i, and the overall mean; i.e.,
a(i) =
I
MSLT(i, j) - MSLT
J
j
3. f3(j) represents the time of day effect at timej and is estimated as the difference between the mean MSLT across all subjects at time j and the overall
mean; i.e.,
f3(j) =
I
MSLT(i, j) - MSL T
i
I
4. E(i, j) represents the residual for subject i at timej and is assumed to reflect
the sum of random variation due to all sources, exclusive of time of day and
inter-subject effects. It is estimated according to the formula
E(i, j)
=
MSLT(i, j) - J.L - a(i) - f3(j)
=
MSLT(i, j) -
I
j
MSL T(i, j) J
I
i
MSLT(i, j) + MSLT
I
It is assumed that the residuals are independent across observations and possess a common, zero-mean distribution, it is hoped that the variation represents
fluctuations due only to the parameter of interest, but it may well be that the
variation is the sum of many sources of variation not accounted for in the model,
but for which the parameter value serves as proxy. Because of this ambiguity,
correlations between different parameters could actually represent correlations
between the embedded subsources of variation.
Oral temperature measurements and subjective sleepiness ratings (SSS and
LSR) were corrected in a similar fashion, and the corrected values for each parameter were then used to compute correlations between parameters. This approach was motivated by the fact that if the above model is a true representation
of the dat" and the true residuals (E(i, j)) are assumed to be independently and
identically distributed with mean zero, then the estimated residuals likewise have
mean zero, are identically distributed, and are independent of each other. Unless
the variables are adjusted in such a way as to have the same mean, correlation
coefficients could reflect trends due to time of day or inter-individual differences,
rather than underlying relationships between the parameters.
Nonparametric statistical methods (22,23) were used for all statistical testing
purposes except the analysis of nocturnal state percentages. The specific test used
is reported with the appropriate result. Where multiple tests were possible, the
most conservative result has been reported.
Sleep, Vol. 5 (Suppl. 2), 1982
G. S. RICHARDSON ET AL.
S86
TABLE 1. Comparison of nocturnal sleep on baseline
and experimental nights
TWT
B2
El
-
t;.
TST
MIN.
%TDT
MIN.
73.4
127.1
--53.7
p < .001
14.4
14.9
-0.5
N.S.
431.3
378.3
53.0
P < .001
SWS
REM
%TDT MIN.
%TST
MIN.
%TST
48.3
48.5
-0.2
N.S.
11.1
12.7
-1.6
p < .05
96.4
81.5
14.9
p < .05
22.3
21.5
-0.8
N.S.
84.6
84.1
0.5
N.S.
N = 18.
Statistical comparison was performed by matched-pairs t-test with significance threshold at .05.
RESULTS
Validation
Table I summarizes the relevant nocturnal sleep parameters for the second
baseline night and the experimental night. As expected, total wake time was
significantly greater on the interrupted night than on the prior, uninterrupted,
baseline night. Similarly, total sleep time and total REM time were significantly
reduced. Slow-wave sleep (stages 3 and 4) was unchanged. This disparity presumably reflects the tendency for slow-wave sleep to occur in the first few hours of the
night, in the interval preceding the first scheduled interruption.
The reduction in sleep time was not associated with any apparent disruption of
normal sleep configuration. Wake time was increased by only 57.7 min on average, less than the I h of enforced wakefulness. Consequently, the percentage of
total dark time constituted by wake and sleep was not significantly changed on the
experimental night. Similarly, the percentage of sleep time comprised by REM
was not significantly changed, and the percentage comprised by slow-wave sleep
was actually significantly increased.
The experimental protocol utilized in this study was designed to simulate normal sleep-wake scheduling as much as possible, while still allowing the desired
experimental measurements. One significant disadvantage of the altered sleepwake schedules that have been used as methods of investigating 24-h sleep behavior is the significant sleep loss that accumulates with protracted exposure to
the schedule (24). Although a fixed quantity of sleep deprivation is inherent in our
protocol as well (1 h in four I5-min increments over the experimental night), it was
hoped that sleep deprivation would not accumulate rapidly enough to contaminate
the nocturnal sleep tendency measurements. This was retrospectively validated
by comparing daytime MSLT scores from the B2 day with results at the corresponding times on the recovery day following the nocturnal disruptions. We found
no evidence of a reduction in sleep latencies suggestive of significant sleep deprivation (Wilcoxon matched-pairs, signed-rank test). As no recovery sleep was
allowed between the nocturnal tests and those during the following day, it appears
Sleep, Vol. 5 (Suppl. 2), 1982
CIRCADIAN VARIATION IN SLEEP TENDENCY
S87
c
]
>
U
2
w
I<t
-I
Q..
W
w
-I
(I)
2
<t
w
2
0930
1330
1730
2130
0130
0530
0930
TIME
FIG. 1. Mean sleep latency in minutes for young (open circles, n = 8) and old (filled circles, n = 10)
subjects as a function of time of day. Shaded area demarks nocturnal sleep period.
reasonable to assume that the nocturnal sleep latency measurements were also
unaffected by sleep deprivation accumulating across 1 night of the experimental
protocol.
Twenty-four-hour sleep tendency
Figure 1 shows the 24-h curves for the sleep latency test results for the young
and elderly groups. The time-of-day effect is obviously significant (Friedman nonparametric analysis of variance, p < .001), and the biphasic contour is unambiguous in both curves.
In addition to the general shape, two specific features of the sleep latency
curves warrant mention. First, although sleep latency increased through the late
afternoon and early evening, when viewed in conjunction with points later in the
cycle a clear downward trend (increasing sleep tendency) in association with
nocturnal sleep onset is apparent. That this represents an underlying change in
sleep tendency and not an effect of the nocturnal sleep itself is evidenced by
similar changes in sleep latency test results seen in subjects kept awake beyond
habitual bedtime (2). Thus the sleep latency curve appears to be truly biphasic,
showing both morning and late afternoon maxima, in association with midday and
early morning minima. Significantly, the nocturnal and diurnal troughs of sleep
latency appear to be of roughly equivalent amplitude.
Second, the nocturnal portion of the sleep latency curve is a smoothly varying
function. It declines steadily over the first half of the night, and, at least in the
elderly subjects, rises smoothly over the second half. This implies that nocturnal
sleep tendency is not simply an on-off function, but rather varies consistently with
time of night, even well within the subject's habitual sleep period.
Sleep, Vol. 5 (Suppl. 2), 1982
G. S. RICHARDSON ET AL.
S88
ALERT 1
2
3
4
5
:SLEEPY
6
0930
1330
1730
2130
0130
0530
0930
TIME
FIG. 2. Mean Stanford Sleepiness scale (SSS) rating for young (open circles, n = 8) and old (filled
circles, n = 10) subjects as a function of time of day. Shaded area demarks nocturnal sleep period. A
significant age effect on SSS curve amplitude was seen.
Twenty-four-hour subjective sleepiness and oral temperature
The biphasic shape of the MSL T curve is in marked contrast to the relatively
simple diurnal peak and nocturnal nadir seen for the subjective sleepiness functions (Figs. 2 and 3). The obvious circadian variation in these functions results in a
clear time-of-day effect (Friedman, p < .001), but in neither curve is there any evidence of a significant midday decline. In this respect, these data are similar to
those of Czeisler, who used an analog sleepiness scale to measure subjective sleepiness in subjects living in a time-free environment (8). In subjects entrained to a
24-h period, the circadian pattern of this sleepiness measure showed no significant
midday trough. It should be noted that in both our study and in those of Czeisler,
individuals who habitually napped during the day were excluded from the subject
. populations.
The oral temperature pattern (Fig. 4) falls somewhere between the two patterns
already described. A midday trough is evident, but its relative magnitude is not
nearly so great as that seen for the MSLT. However, it is a consistent feature of
both the young and old temperature curves, and it occurs at the same time (1530)
as the diurnal nadir in the MSLT function.
Age effects
Of the parameters measured, only oral temperature shows a significant difference between the elderly and young adult subjects. Figure 4 illustrates the consistently lower temperature seen in elderly subjects throughout the entire 24-h
cycle (Wilcoxon rank-sum test, p < .02). Although a tendency for elderly subjects
to have shorter diurnal sleep latencies is apparent in Fig. 1, this trend is much less
Sleep. Vol. 5 (Suppl. 2). 1982
CIRCADIAN VARIATION IN SLEEP TENDENCY
ALERT 0
S89
r-------------~
20
w
a::
o
40
~
a::
en
...J
Z
60
<I:
w
:E
80
SLE EPY 100
'---'-----'-------'-----'----""=
0930
1330
1730
2130
0130
0530
0930
TIME
FIG. 3. Mean linear sleepiness rating (LSR) for young (open circles, n = 8) and old (filled circles, n =
10) subjects as a function of time of day. Shaded area demarks nocturnal sleep period. A significant age
effect on LSR curve amplitude was seen.
..
apparent during the night, and no statistically significant difference was found
when all the latency measurements were taken into account.
The amplitudes for the 24-h rhythm of each variable were obtained for each
individual subject by simply computing the difference between the peak value and
the minimum value obtained over the entire cycle. To avoid prejudicial interpretation of the nature of the biphasic sleep tendency curves, we did not use the
phases at which maxima and minima occurred as a factor in their determination .
2
37.0
w
a::
:::l
I-
«
a::
36.8
UJ
0..
.0:
:E
UJ
I-
36.6
>Cl
0
ClJ
...J
36.4
«
a::
0
z
«
UJ
36.2
:;;;
36.0
0930
1330
1730
2130
0130
0530
0930
TIME
FIG. 4. Mean oral temperature in degrees Celsius for' young (open circles, n = 8) and old (filled
circles, n = 10) subjects as a function of time of day, Shaded area demarks nocturnal sleep period.
Sleep, Vol. 5 (Suppl, 2), 1982
,..
S90
G. S. RICHARDSON ET AL.
Once again, the apparently reduced amplitude of the elderly subjects' MSLT
and temperature curves seen in Figs. 1 and 4 is not statisticaiiy significant. However, significantly reduced amplitudes were seen in the elderly subjects' circadian
patterns for both measures of subjective sleepiness (SSS and LSR) (Wilcoxon
rank-sum test, p < .05).
Correlates of sleep tendency
Individual measurements of sleep latency, oral temperature, and subjective
sleepiness were corrected for time of day and subject effects (see Methods), and
correlations among the four parameters (MSLT, LSR, SSS, and temperature)
were computed using two nonparametric tests of correlation (Spearman rank correlation test and Kendall rank correlation test). If significant correlation was
proved with the nonparametric analysis, regression coefficients (slope and intercept) were computed using the Pearson correlation coefficient. The purpose of
this analysis was to ascertain whether significant relationships remained after the
two dominant sources of potentially common variability were removed.
As might be expected, the most strongly correlated variables are the two subjective measures of sleepiness (r = .63, p < .001; Kendall). Additionally, the
corrected SSS measurements also show significant negative correlation with corrected oral temperature (r = - .09, p < .02; Kendall). The correlation between the
LSR and temperature is in the same direction (increased sleepiness associated
with decreased temperature), but the relationship does not achieve statistical
significance.
When the corrected sleep latency measurements are correlated with the other
three variables, only one, the linear sleepiness rating, shows a significant correlation (r = -.10, P < .005; Spearman). The weak negative slope suggests that increases in SUbjective sleepiness on this scale are associated with small declines in
sleep latency (increased sleep tendency). The SSS ratings were correlated with
sleep latency in a similar fashion, but the relationship was not found to be significant.
The final parameter examined as a potential correlate of sleepiness was prior
sleep state. The four SLT measurements preceded by sleep on the experimental
night (0130, 0330, 0530, and 0730) were corrected for time-of-day and subject
effects and then were sorted according to the sleep-wake state that dominated the
10-min period immediately prior to awakening (wake, stages 1 and 2, stages 3 and
4, or REM). The results ofthis analysis showed that no stage was associated with
a significant effect on sleep latency (Friedman analysis of variance).
DISCUSSION
Sleep latency, as measured by the Multiple Sleep Latency Test (MSLT), is
consistently maximal immediately after arising in the morning, falls to a midday
minimum, and then rises again in the evening to levels approximating those of the
morning measurements. This pattern is consistent across subjects and across days
and persists despite limited sleep loss or sleep extension (5). Two aspects of this
diurnal pattern are somewhat paradoxical in that they are inconsistent with simple
Sleep, Vol. 5 (Suppl. 2), 1982
:.
CIRCADIAN VARIATION IN SLEEP TENDENCY
S91
models of the determinants of daytime sleepiness. First, the afternoon and evening decline in sleep tendency (increasing latency) without intervening sleep is
evidence that sleepiness is clearly not a simple linear function of time awake.
Second, the declining slope of sleep tendency at habitual sleep time argues that
sleep tendency does not play a significant role in the timing of nocturnal sleep
onset under normal entrained conditions.
Czeisler et al. (25) have shown that the timing and composition of nocturnal
sleep are principally determined by the underlying circadian rhythms. However,
the unambiguously bimodal pattern of diurnal sleepiness is not a simple mirror of
any known physiological circadian variable. Body temperature, as measured in
this study, does indeed show a midday decline at the same time as that seen in the
MSLT function; however, this decline is much smaller in relative magnitude than
that of sleep tendency. Further, when data from other sources are compared, a
midday decline is, at most, an inconsistent feature of temperature rhythms measured in subjects who are not put in bed at regular intervals as are those on the
MSLT protocol (8,28). More important, free-running temperature rhythms,
theoretically a more direct index of the underlying circadian mechanism, show no
evidence of a midday trough in normal, non-napping subjects (8,25,28). It seems
probable that the observed decline in body temperature represents a "masking
effect" (28), resulting indirectly from the increase in sleep tendency, rather than a
true decline in the underlying circadian temperature function.
It is possible that the midday peak in sleep tendency is an artifactual consequence of either the measure employed (i.e. the MSLT itself) or the setting in
which we have used it. However, neither hypothesis provides an adequate explanation for the observations. Other researchers, using various measures of
human performance and cognitive function, have obtained diurnal patterns with
midday troughs (26,27) corresponding temporally to the observed peak in sleep
tendency. It thus appears that there is a real midday decline in certain aspects of
neuropsychological function which may not have an observable analogue in general physiological variables. The hypothesis that this variation represents a postprandial effect is also clearly inadequate in that sleep tendency is seen to be rising
(sleep latency declines) before the noon meal, and this trend continues for two
measurements after the meal. Further, no similar effect is seen in association with
either the morning or evening meals.
An adequate explanation for the complex shape of the sleep tendency rhythm
therefore appears to require the inclusion of a combination of factors. Conceivably, the morning and evening peaks in alertness might be the result of two distinct
physiological or neurophysiological circadian rhythms that have as their common
consequence a decrease in sleep tendency. Thus, the rhythm in physiological
function, indexed by the body temperature rhythm, might be distinct from the
rhythm in neurological or cerebral function, although both result in decreased
tendency for sleep. A phase-angle difference between the two rhythms would
result in a midday trough, during which sleep tendency could increase to nocturnal
levels. The thesis that neurological and physiological function may peak at different times is supported by evidence that diurnal patterns in performance vary
significantly in their time course, depending on the nature ofthe performance task
being measured (6).
Sleep, Vol. 5 (Suppl. 2), 1982
S92
G. S. RICHARDSON ET AL.
Alternatively, the sleep tendency rhythm might represent the sum of circadian
and noncircadian functions. Specifically, the rising phase of the physiological
rhythm indexed by body temperature might reverse the nonrhythmic trend towards increasing sleep tendency as a function of time awake. The phase position
of the body temperature curve would thus result in a midday trough in alertness
with morning and evening peaks.
Regardless of its underlying mechanism, quantification of the 24-h pattern of
sleep tendency allows a more thorough description of the relationship between
nocturnal sleep and daytime sleepiness. Although sleep tendency declines through
the late afternoon and early evening, the extended measurement in this study
discloses the reversal of the trend in the later evening, with a rapid increase in
sleep tendency corresponding to the time of habitual sleep onset. The opposite
pattern is seen at the end of the night, with a decline in sleep tendency anticipating
the end of the sleep period. The absence of any effect of prior sleep state on
subsequent sleep latency suggests that the nocturnal curve of sleep tendency is an
intrinsic circadian function and not merely an artifact of circadian changes in sleep
state distribution.
The demonstration that nocturnal sleep tendency is a continuously variable
function may have a significant potential application in the diagnosis and characterization of sleep pathology. The recent nosology of sleep disorders (19) classifies
the heterogeneous group of insomnias as "disorders of initiating and maintaining
sleep (DIMS)," reflecting the intuitive importance of each of these distinct functions to the expression of normal sleep. The extent to which initiation and maintenance of sleep are significant to a specific disorder may prove discernible by use of
measurements of nocturnal sleep tendency, just as the MSLT currently allows
quantification of excessive daytime sleepiness.
The failure to demonstrate a significantly reduced amplitude in the sleep tendency curves of healthy elderly subjects could be explained in either of two ways:
(a) the change in sleep-wake distribution in the elderly is due to a change in the
capacity to maintain sleep rather than a change in the capacity for sleep onset; or
(b) our selection of healthy, non-napping elderly subjects biased our subject
population away from the typical aged sleep-wake patterns. This question cannot
be answered until napping and non-napping elderly subjects are compared.
Nonetheless, the concept that circadian amplitude may generally decrease with
age (17) is supported by the clear trend in that direction seen in three of the four
variables we measured (Figs. 2-4).
A small but significant correlation between subjective indices of sleepiness and
the MSLT remains after the dominant time-of-day and subject effects are removed. It is assumed that this correlation arises from a common sensitivity to
changes in sleep tendency. However, the correlation between the MSLT and the
subjective measures is not nearly so strong as is that between the two subjective
measures themselves, and the most striking finding is the marked independence of
the two types of sleepiness indices.
The failure of the subjective measures of sleepiness to parallel the significant
midday increase in sleep tendency underscores the potential disparity between the
two types of measures. While it is possible that subjectively perceived sleepiness
and objective sleep tendency are actually two different quantities, it seems more
. Sleep, Vol. 5 (Suppl. 2), 1982
CIRCADIAN VARIATION IN SLEEP TENDENCY
S93
likely that the discrepancy is the result of the susceptibility of the subjective
measures to a variety of influences that do not affect the MSLT. It is intuitively
clear that factors such as the subject's motivation, self-perception, and capacity
for introspection can have a significant effect on self-ratings of sleepiness. The
inadequacy of subjective sleepiness indices as measures of sleep tendency in
young children and individuals with chronic excessive daytime sleepiness (EDS)
has already been described (5,4). It now seems clear that, even in an optimal
subject population, subjective sleepiness indices may also fail to reflect adequately the circadian variation in sleep tendency.
Acknowledgment: We thank Drs. Richard Kronauer and Charles Czeisler for their
helpful comments, and Ms. Susana Segat for her invaluable help in the preparation of the
manuscript. This study was supported by NIA Grant AG01435. WCD is supported by
NIMH Award MH05804.
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."
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