Sleep, 6(4):326-338
© 1983 Raven Press, New York
A Cluster Analysis of Insomnia
Peter J. Rauri
Dartmouth Medical School, Hanover, New Hampshire, US.A.
Summary: Data from three consecutive nights in the sleep laboratory and data
from psychological questionnaires and interviews were cluster-analyzed. A
total of 89 physician-referred insomniacs were included, together with 10 good
sleepers. The goal was to develop a purely empirical classification scheme of
insomnia and to compare it with the Association of Sleep Disorders Centers'
current nosology, which is based on clinical experience. The categories of
chronic psychophysiologic insomnia and of childhood-onset insomnia were
clearly identified in the cluster analysis. The insomnias associated with psychiatric disorders were split into five subgroups that made some intuitive
sense, but did not mesh exactly with DSM III (Diagnostic and Statistical
Manual, 3rd edition, American Psychiatric Association) categories. Key
Words: Cluster analysis- Nosology - Insomnia- Childhood-onset-Psychophysiology.
Over the last decade it has become clear that there are different kinds of insomnia
and that each of them might require a specific method of treatment (l-4). A comprehensive nosologic scheme of insomnia has recently been proposed by the Association
of Sleep Disorders Centers (ASDC) (5). This nine-point classification system of "Disorders of Initiating and Maintaining Sleep" (DIMS = Insomnia) is based on the best
clinical judgement available from a group of experts. However, there is little research
evidence supporting some of the ASDC categories.
For the current study, it was decided to investigate how far a purely mathematicalempirical approach might lead in the subclassification of insomnia, and how far the
results of such an approach might correspond with the clinical (ASDC) nosology. This
would be of interest, because one's faith in a classification scheme improves as conceptually very different diagnostic approaches converge on essentially similar categories. In addition, there is always the possibility that a purely mathematical-empirical
approach to a nosological problem might detect useful new associations between patients that may have been overlooked because of preconceived notions about clinical
reality.
Cluster analysis is a mathematical tool designed to sort objects (insomniacs) into
subgroups that make multivariate sense. This means that the procedure simultaneously
takes into consideration all the measured attributes of each patient when subgroup
Accepted for publication June 1983.
Address correspondence and reprint requests to Peter J. Hauri, Ph.D., Dartmouth Sleep Disorders Center,
Dartmouth Medical School, Remsen 703, Hanover, NH 03756, U.S.A.
326
A CLUSTER ANALYSIS OF INSOMNIA
327
assignment is made, rather than just considering a few characteristics. However, there
are no really correct or incorrect solutions with this procedure. Cluster analysis is a
hypothesis-generating procedure, not a hypothesis-testing one. Whether the groupings
suggested by this procedure become important later on depends on other factors, such
as the etiological or clinical usefulness of the new classification scheme.
Concerning the types of patients to be clustered, a priori it appears that certain
AS DC subcategories are relatively fixed, not really open for alternate classification
schemes. These more or less fixed categories include the clearly medical subtypes of
insomnia, such as those associated with drugs and alcohol, with sleep-induced respiratory impairment, and with sleep-related myoclonus. In addition, the well-researched
insomnia associated with major affective disease (6) is unlikely to be conceptualized
differently in future nosologic schemes. This left for analysis two kinds of insomnias
that might benefit from a cluster analysis. The first kind would be the insomnias associated with various psychiatric disorders (excluding the major affective disorders),
where DSM III (Diagnostic and Statistical Manual, 3rd edition, American Psychiatric
Association) categories had been suggested but other groupings might fit the insomnia
data better. The second kind would be the "free-standing" insomnias, i.e., those insomnia problems in which a complaint of poor sleep is associated with neither diagnosable medical nor psychiatric disease.
METHODS
Subject selection
Subjects for the study were 89 physician-referred insomniacs and 10 good sleepers
culled from our files. All insomniacs seen at the Dartmouth Sleep Disorders Center
between 1975 and 1980 were included in this study if they met the following criteria:
(a) They had been referred by their own physician for an evaluation of chronic and
serious insomnia.
(b) They had slept in the laboratory for three consecutive nights each. Besides the
Rechtschaffen and Kales (7) channels, each insomniac had the following monitored for
at least 1 night: air flow (nose thermistor), bilateral anterior tibialis muscles, and EKG.
Patients with abnormal recordings in any of these three channels were excluded.
(c) They had an intensive 2-3-h interview with the author, a clinical psychologist
well versed in both the DSM III classification and the AS DC nosology. During this
interview, a detailed psychological, a medical, and a sleep history were obtained, and
extensive, often verbatim notes were taken.
(d) They had filled out the following questionnaires: Minnesota Multiphasic Personality Inventory (MMPI), Zung's Depression Inventory, Zuckerman and Lubin's Multiple Affect Adjective Check List (MAACL), Cattell and Scheier's Anxiety Scale, and
the Cornell Medical Inventory (CMI).
On the basis of all this information, the author then wrote a 2-3 page report for the
referring physician, giving test findings, diagnostic impressions, and suggestions for
treatment. However, for the early patients in this series, this report contained neither
a DSM III nor an ASDC diagnosis. In these cases such a diagnosis was assigned later,
on the basis of the data contained in our patient files. On rare occasions, when important data for making the appropriate diagnoses were missing, the patients were later
called back for additional information.
Sleep, Vol. 6, No, 4, 1983
328
P. J. HAURI
About half of the insomniacs were taking hypnotics or other medications when referred. With the consent of the referring physician such patients were then withdrawn
from their medications before their sleep evaluations. Such withdrawal had to be completed 3 weeks before the laboratory study began for long-acting drugs, such as flurazepam, and 1 week before the study began for short-acting drugs, such as chloral
hydrate. Patients who could not prudently be withdrawn from eNS-acting medications,
e.g., those taking phenytoin, were not included in this study. Although no blood or
urine samples were taken, compliance seemed to be very high, because patients knew
that their sleep evaluation would be unreliable unless they were drug-free. Having
chronically suffered from serious insomnia, most patients wanted this evaluation to be
as useful as possible.
Cluster analysis
The mathematical approach used in this paper is called K-means clustefing (8). Basically, one starts with K "seed points," in the case reported here, with nine different,
actual patients. Each remaining patient is then compared with each of these K seed
points on all variables simultaneously. The patient is then assigned to that seed point
which matches it most closely, leading to groups or clusters of patients. This task
accomplished, a "centroid" is computed; i.e., a hypothetical patient is created at the
exact middle of each cluster, and his scores on all variables are determined. These
scores of the idealized patient in each cluster (the centroids) then become the new
seed points. Because they are not identical with the original "seed" patients, some of
the insomniacs assigned to a given cluster might now better be assigned to another
one. This is done and new centroids are computed. The process goes through a number
of iterations until all centroids have become stable.
The original seed points either may be selected a priori by the researcher, or they
may be assigned randomly by the computer. If the objects to be sorted are distributed
in an essentially random fashion throughout space, the original selection of seed points
largely determines the cluster solution. If the objects cleave naturally into clusters that
are inherent in the data, the final cluster solutions will be nearly identical, no matter
what original seed points one started with. This was the case with the insomniac matrix
here. Over 90% of the patients usually found the same cluster, no matter what seed
points we started with. Less than 10% seemed to be "outliers," flipping from one
group to the next depending on differences in seed point selection. This statistical fact
gives some confidence to the solution presented here.
Unfortunately, there is no unique mathematical way to decide how many clusters
may be involved in a certain set of data. Indeed, the 89 insomniacs could all be assigned
to the same cluster, namely, "the insomniacs." On the other hand, one might argue
that no two insomniacs are truly alike. This would lead to a total of 89 different
"clusters." Although it is true that some algorithms have recently been developed to
determine the "natural" number of clusters in a matrix (9-11), these methods all rely
on arbitrarily selected constants. Depending on the numbers one assigns to these constants, the possible number of clusters again may vary from 1 to N.
For the current study, a more empirical method was used to determine the number
of clusters. With too few clusters, interpretation is difficult, because many, widely
differing patients are lumped together. With too many clusters, interpretation is also
difficult, because some of the differences between clusters become minute and esoteric.
Sleep, Vol. 6, No.4, 1983
A CLUSTER ANALYSIS OF INSOMNIA
329
Thus, the optimal number of clusters is the one that yields maximum clarity of interpretation.
For a relatively structured group of objects such as insomniacs, the empirical procedure described above is more easily performed than explained. One computes many
solutions, each containing one cluster more than the previous one. For nonrandom
objects, adding a new cluster rarely restructures the entire solution. Rather, the new
cluster is created by splitting an old one and siphoning a few patients from other
clusters. If this new cluster is well interpretable while also clarifying the older ones
from which it drew, the new solution is preferable to the old. In the case to be presented
here, this happened-up to nine clusters. However, when a 1O-cluster solution was
attempted, the new cluster seemed almost identical to a previous one, adding little new
insight. Thus, it was decided to use a nine-cluster solution.
Selection of variables for analysis
When patients are evaluated at a sleep disorders center, one customarily collects
information on excessive numbers of variables. Each night in the laboratory is traditionally scored on at least 25 different EEG-related variables, and the patients in this
study had slept in the laboratory for three consecutive nights. In addition, the patients
had also been interviewed, and they had answered six different questionnaires. One
of these questionnaires, the MMPI, could alone have been scored on over 100 scales.
On the other hand, cluster analysis requires that the number of variables be drastically
smaller than the number of objects (patients) to be clustered.
To reduce the extensive number of variables. a combination of empirical and clinical
methods was used. The entire data matrix (142 variables x 99 subjects) was first factoranalyzed to discover redundancies in the data. In addition, the literature was searched
to discover variables that had been thought important in the diagnosis of insomnia
[e.g., de la Pena's first-night effect, (12)]. The final selection of each variable then
was based on a combination of the results from the factor analysis, from the literature
search, and on clinical judgement. Each of the final 26 variables to be included in the
cluster analysis was then standardized (mean = 0, SD = 1), so that each had equal
input into the cluster analysis.
The selection of variables to be included in a cluster analysis is obviously of the
greatest importance. The analysis can only sort on the variables that are provided for
it. Furthermore, the K-means clustering method on standardized scores assigns equal
importance to each variable. Thus, if 10 different measures of depression had been
included in the analysis, but only one measure of sleep latency, the computer would
have found that depression scores are very important in determining the clusters of
insomnia, while the measure of sleep latency is not. Thus, it is crucial that a critical
balance of variables be maintained according to best clinical judgement.
Of the 26 variables that were selected for inclusion in this cluster analysis, 11 came
from psychological questionnaires and 9 from the sleep laboratory. In addition, 3 variables assessing the first-night effect in the laboratory were included, because de la
Pena (12) has suggested that a "reverse-first-night-effect" might be a crucial parameter
in the understanding of some types of insomnia. Finally, three interview questions
were included to illuminate childhood sleep functioning (13).
For the psychological assessment of each patient, the following variables were selected: MMPI (T-scores) of scales F, K, Hs, D, Hy, Pt, Ma, Welsh A, Welsh R; Zung
Sleep. Vol. 6, No, 4, 1983
330
P. J. HAURI
Depression Index (raw score) and Cornell Medical Index (total score). For the assessment of typicai sieep, the iaboratory means for nights 2 and 3 of the following variables
were selected: sleep latency (minutes), awake after first stage 2 (minutes), sleep efficiency (percent), REM sleep (percent), stage 1 (percent), delta sleep (percent), REM
latency (minutes), and number of body movements per 100 min. This latter measure
assesses each body movement that lasts at least 2.5 s and is seen not only in the chin
EMG but also in the EEG or EOG channels. Only movements initiated during sleep
are counted. Finally, a subjective evaluation of laboratory sleep was also included (1
= much worse than my average at home, 7 = average for me, 14 = the best in weeks).
To assess the insomniacs' first-night effect, differences between the means for nights
2 and 3 and the scores of night 1 were computed for sleep latency (minutes), sleep
efficacy (minutes), and REM latency (minutes). To evaluate the variables that were
found important for childhood-onset insomnia (13), three questions from the interview
were used: the age at which insomnia first became a real problem for the patient, a
rating of childhood sleep (0 = no problem, 1 = mild sleep problems, 2 = insomnia
serious enough to consult a physician), and a clinical impression of possible neurological involvement obtained during the interview (0 = no discernible neurological involvement, 1 = evidence of hyperkinesis, attention deficit disorder, head injury, etc.).
Neither age nor sex was entered as a parameter to be clustered. Rather, once the
clusters were statistically determined, age and sex ratio for each cluster was computed
later, to help with the interpretation.
In summary, this paper reports on a nine-cluster solution done on 26 variables obtained from 89 insomniacs and 10 normal sleepers.
RESULTS
Tables 1 and 2 give the centroids of the nine clusters; that is, they give the raw scores
that a hypothetical patient would have achieved if he or she were located exactly in
the middle of that cluster. Table 3 summarizes the most salient points of each cluster,
while Table 4 contrasts the mathematical cluster solution with the AS DC diagnosis of
these insomniacs.
The following is a description of the nine clusters. Obviously, while the numerical
data reported in Tables 1 and 2 are solid, their interpretation in Table 3 is open to
speCUlation. Similarly, the titles given to each cluster are meant to capture the most
outstanding features as understood by the author. These titles have no scientific value.
Indeed, there is even some question whether the clusters with very few members
(clusters 8 and 9) are interpretable at all.
Cluster 1.. Good Sleeper and Insomnia Complaint Without Objective Findings: The
typical person in cluster 1 sleeps well, falling asleep in about 15 min and waking up
for less than 30 min during the night. Sleep stages and REM latency are normal.
Surprisingly, this group of good sleepers shows the highest number of body movements
per 100 min asleep. This group also shows a normal, mild first-night effect: 7 min
longer to fall asleep and 3% less sleep efficiency on the first night, together with a 13min delay of the first REM period. Psychologically, this group shows a consistently
normal profile on all MMPI scales and on the Zung Depression Index, while the Cornell
Medical Index shows a very low number of medical complaints. In addition, this group
reports excellent sleep during childhood.
Sleep, Vol. 6, No.4, 1983
TABLE 1. Demographics and centroids a for sleep variables
Means for sleep in laboratoryb
First-night effect C
~
Group
no.
No.
included
Male/
female
Mean
age
Sleep
latency
(min)
Awake
after
sleep
(min)
I
2
3
4
5
6
7
8
9
23
12
10
14
7
18
8
4
3
13/10
2110
5/5
6/8
5/2
3/15
42
41
52
36
54
40
43
44
48
14.5
27.8
19.2
26.9
24.0
31.8
48.5
161.4
64.0
28.3
23.5
116.6
34.4
149.8
38.0
60.5
36.5
46.2
a
b
c
4/4
3/1
1/2
Sleep
efficiency
Stage I
sleep
Delta
sleep
REM
sleep
(%)
(%)
(%)
(%)
91.3
88.3
71.3
86.7
64.6
84.9
78.6
61.0
79.7
12.3
14.3
13.4
10.8
15.1
13.2
13.0
14.1
16.5
5.5
4.0
1.7
6.6
1.3
4.7
3.8
.6
2.8
21.8
20.5
17.0
19.9
19.4
22.0
18.8
18.3
15.2
REM
latency
(min)
No. of
movements
per 100
min
Subj.
eva!. of
sleepd
Sleep
latency
(min)
Sleep
efficiency
(%)
REM
latency
(min)
63.0
57.5
69.6
133.3
151.1
90.8
92.5
76.6
138.5
15.1
13.0
11.0
13.1
12.2
13.3
12.6
9.5
12.0
6.8
7.5
6.4
7.1
7.3
9.3
6.4
4.6
8.2
-7.4
-5.5
+.1
-2.8
-10.9
-11.1
-2.4
+104.9
-112.3
+3.0
+6.4
-2.7
+2.3
-.4
+5.4
+14.1
-15.2
+33.0
-12.9
-67.1
-43.4
+32.4
+40.6
-15.2
-141.7
-43.6
-22.2
Centroids indicate the raw values for the center point of that cluster.
Means of nights 2 and 3 in the laboratory.
Difference between the means for nights 2 and 3 and the scores obtained on night 1:
Nights 2 + 3
- - - - Night 1
2
~
~
A normal first-night effect involves a negative sleep latency, a positive sleep efficiency, and a negative REM latency.
d Subjective rating of sleep quality: "Compared with my home sleep, my lab sleep was":
I = much worse than my average home sleep.
7 = average for me.
14 = the best in weeks.
Numbers in boldface type were particularly important for the interpretation of the clusters.
p
~
~
::ti
~
~t"<
~
~
~
~
c."
a
~
s:
~
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~
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P. J. HAURI
332
TABLE 2. Centroids for the psychological variables
MMPI
Group
no.
F
K
Hs
D
Hy
Pt
Ma
Welsh
A
Welsh
R
1
2
3
4
5
6
7
8
9
55
55
59
61
59
59
57
60
60
60
56
56
54
59
53
56
63
50
56
56
56
59
75
64
61
62
71
59
63
79
69
73
60
59
66
63
58
62
68
68
71
46
51
55
54
51
59
49
52
62
68
66
70
66
72
53
57
53
53
48
54
56
56
56
70
76
89
71
77
71
66
68
67
64
71
73
77
69
72
78
69
Zung
Depr.
Index
(Raw)
Cornell
Medical
Invent.
(Total)
30
33
42
35
40
47
40
37
50
18
25
31
29
28
47
26
32
45
Childhood sleep
Age at
onsetO
Childhood
sleepb
Neurological
_d
0.2
0.3
0.5
0.6
0.6
0.6
0.8
1.5
0.7
0.2
0.4
0.3
0.1
0.1
0.3
0.6
0.5
0
26
24
17
28
21
16
16
31
signs C
At what age were you first aware that you had real sleep problems?
Type of sleep during childhood: 0 = No problem
1 = Mild sleep problems
2 = Insomnia serious enough to consult physician
, Did the interview reveal any suggestions of neurological involvement? 0 = No; I = Yes (e.g., hyperkinesis, attention
deficit disorders, head injury)
d No data, because normal sleepers could not give a date of onset for their insomnia.
The figures for the MMPI are T -scores.
a
b
Comparing cluster 1 with the ASDC nosology (Table 4), we find most good sleepers
classified here, as well as most of the 13 patients whom ASDC had classified as having
insomnia complaints without objective findings. It seems important to note that the
cluster analysis was unable to separate these two groups. This does not eliminate the
possibility that the two groups might be different on other parameters, such as beta or
alpha frequencies intruding into the EEG of sleep, or that these patients may be conditioned insomniacs, sleeping poorly only at home. It does, however, suggest that
patients who have a complaint of insomnia without objective finding are not necessarily
neurotic or otherwise abnormal according to psychological questionnaires. Rather, on
the MMPI they score well within the normal range. This replicates the findings of
Stepanski et al. (14).
Cluster 2; Mild Hypomania: Typical persons from this cluster show a somewhat
longer sleep latency, but then sleep as well as those in cluster 1. On the 1st night in
the laboratory, the first REM period in these patients is delayed for over 1 h; for nights
2 and 3, REM latency is normal. Psychologically, persons in this group score well
within the normal range on all tests. They do, however, show the highest score of any
clusters on the MMPI Ma scale, a scale that appears to measure the available psychological energy level of the respondent. This constellation of MMPI variables suggests
an expansive and outgoing temperament, and an energetic, mildly hypomanic mode of
living.
Table 4 indicates that patients grouped into cluster 2 came from many different ASDC
categories. Indeed, in the literature there seems to have been no suggestion that some
mild insomnias may stem simply from chronically excessive energy levels. Thus, this
is a category newly created by the cluster analysis. Future experience will indicate
whether it makes sense to sort out such a group of mildly hypomanic insomniacs.
Possibly, one might try to help them sleep better simply by having them "slow down"
and relax in the evening.
Cluster 3; Psychophysiologic Insomnia: Patients in this cluster are slightly older than
others, and they do sleep poorly in the laboratory. They usually have little problem
Sleep, Vol. 6, No.4, 1983
TABLE 3. Summary of the insomnia clusters
No.
~
:-
:?'
Childhood
sleep
Normal, mild
High energy ( t Ma)
Good (efficiency 88%)
REM reI. early (57 min)
Normal; but REM
delayed I h
Good
Psychophysiologic
insomnia
Normal, although
reI. dissatisfied
and tense
Poor (efficiency 71%)
and fragmented
Mild reverse
first· night effect
Good
4
Insomnia associated
with unconventional
lifestyle
Dissatisfied,
sullen, rebellious
Mild insomnia with
2 h REM latency
Normal, mild
Good
5
Insomnia in
depleted neurotics
High MMPI
neuroticism plus
low energy (Ma t
Serious insomnia (65%)
with 21/2 h REM latency
Normal, mild
Good
Insomnia associated
with dysthymia
Unhappy, depressed,
many medical
complaints
Mild insomnia (85%),
sleep better in
laboratory than home
Normal, mild
Good
7
Childhood insomnia,
moderate
Relatively normal
Moderate insomnia
(efficiency 79%)
Normal, but REM
delayed 21/2 h
Poor
8
Childhood insomnia,
severe
Denial and repression
of emotional material
Severe insomnia (61%)
with few movements
Strong reverse
first-night effect
Very poor
9
Hyperreactive to
stress
Passive, dependent,
helpless
Long sleep latency,
moderate insomnia
Severe firstnight effect
Poor
Normal
2
Mild hypomania
3
C/o
'"
First·night effect
(Mean of 2 + 3)
Good
(efficiency 91%)
Good sleeper, or
complaint without
objective findings
6
;;;'3"
Subtype name
Average sleep
in laboratory
(Nights 2 + 3)
Psychological
assessment
)
Good
~
p
~
t;l
~
~
~
t-<
~
(;;
a"'1j
~
V}
a
s:~
(80%)
~
!~
e;
lJv
lJv
lJv
P. J. HAURI
334
TABLE 4. Relationship between cluster analysis and ASDC nosology
...'"
<l)
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ASDC
nosology
~
...
<l)
0.
<l)
<l)
"iii
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analysis
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12
insomnia complaint
2. Mild hypomania
3
I
10
3. Psychophysiologic
insomnia
10
4. Unconventional
lifestyle
2
7
2
5. Depleted neurotic
2
3
2
6. Insomnia associated
with dysthymia
I
3
13
7. Childhood insomnia,
moderate
2
I
Totals
10
22
17
7
18
I
4
I
8
4
4
2
I
3
19
14
8. Childhood insomnia,
severe
9. Hyperreactivity to
stress
14
3
4
\3
99
falling asleep, but then show almost 2 h of wake time later on. Psychologically, these
patients appear to be somewhat dissatisfied and tense (elevated MMPI D and Pt), but
otherwise quite normal. Further, this is one of only two groups that show a reverse
first-night effect.
All 10 patients in cluster 3 carry an ASDC label of persistent psychophysiological
insomnia. Both the psychological and the first-night pattern of cluster 3 would seem
to fit such a diagnosis. However, the cluster-defined pattern adds to the AS DC-defined
parameters the characteristics of troubled sleep in older patients: no problem falling
asleep, but difficulties maintaining sleep later on during the night. Younger patients of
our sample who also carried the ASDC diagnosis of persistent psychophysiological
insomnia were assigned to many other clusters. This finding may be an artifact of
selecting variables for clustering without including pertinent aspects of the sleep his-
Sleep, Vol. 6, No.4, 1983
A CLUSTER ANALYSIS OF INSOMNIA
335
tory. For the ASDC diagnosis, the interviewer probed carefully for a history of conditioning, loss of sleep disturbance when away from the bedroom, or when attempting
to remain awake, and response to behavioral treatment. These facts from the history
could not easily be quantified into one or two parameters; therefore, this information
was not included for the cluster analysis. On the other hand, this makes it all the more
surprising that at least a subgroup of psychophysiological insomniacs was identified
mathematically, purely on the basis of their relatively normal MMPI personality pattern, combined with poor sleep and a mild reverse-first-night effect.
Cluster 4; Insomnia Associated with Unconventional Lifestyle: The typical patient
in this cluster is younger than patients in the other groups of insomnia. Sleep seems
only mildly disturbed, if at all. Nevertheless, all of these patients had been referred to
the laboratory for a chronic and persistent complaint of insomnia. Remarkable for this
group is the very long REM latency: over 2 h on nights 2 and 3. Psychologically, these
patients show an elevated F scale. Together with the rest of their relatively normal
MMPI pattern, the elevated F scale has often been interpreted as indicating unusual
or unconventional thinking, possibly associated with sullen, somewhat rebellious personalities.
According to Table 4, half of these patients had been classified as having insomnia
associated with personality disorders: four carried a DMS III, axis 2 diagnosis of
passive-aggressive personality disorder, two of compulsive, and one antisocial personality disorder. Of the other seven patients clustered here, five showed similar personality patterns, but not severe enough to be diagnosable on axis 2 of DSM III.
Cluster 5; Insomnia in "Depleted" Neurotic Patients: Patients in cluster 5 are older
than the average insomniac. Their sleep is very poor throughout the night. Also remarkable in this group is a REM latency of about 21/2 h on nights 2 and 3. Psychologically, patients classified here show elevations on most MMPI scales that indicate
neurosis (especially Hs, D, Hy, and Welsh R). These patients also have a low score
on the MMPI Ma scale, indicating low energy levels. Going through the interview
notes on the seven patients classified here, one gains an impression of extreme fatigue,
lack of energy, depletion of psychological resources, which these patients ascribe to
their inability to sleep.
Cluster 6; Insomnia Associated with Dysthymia: Typical laboratory sleep in these
patients is only mildly disturbed. They have a relatively high REM percentage. Patients
classified here show a normal first night effect, and they report sleeping better in the
laboratory on nights 2 and 3 than they usually do at home. Psychologically, these
patients are unhappy and depressed (Zung Depression Inventory, MMPI D Scale).
They also report a large number of medical complaints (Cornell Medical Inventory).
Considering the ASDC nosology (Table 4), the large majority of these patients had been
classified as insomniacs associated with affective disorders. According to DSM III, 12
of them had been classified as having dysthymic disorder (depressive neurosis).
In evaluating this cluster, it is important to remember that all patients with major
affective disorders had been specifically excluded from this analysis. In addition, all
insomniacs in this study were physician-referred. This referral was usually done as a
matter of last resort, after drug trials both with hypnotics and with antidepressants had
failed. Furthermore, physicians usually refer patients to psychiatry, not to the sleep
disorders center, if they suspect depression. Thus, cluster 6 appears to include a large
number of hard-to-diagnose "character spectrum disorders" as described by Akiskal
et al. (15). According to these authors, this is a group that is relatively resistant to
tricyclic antidepressants, contains more women, and shows normal REM latencies,
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rather than the short REM latencies described by Reynolds et al. (16) for the more
typical forms of depression.
Cluster 7; Childhood Insomnia, Moderate: Patients in this cluster sleep poorly: they
show a sleep latency of 1 h and a sleep efficiency of 79%. Remarkable is the fact that
on their 1st night in the laboratory the first REM period did not occur until almost 4
h of NREM sleep had accumulated (141.7 min plus 92.5 min). Psychologically, this
group of eight insomniacs seems surprisingly normal in view of their severe sleep
disturbances. On the other hand, five of the eight patients in this group had carried a
childhood diagnosis of dyslexia, hyperkinesis, or "minimal brain damage," a finding
commensurate with the study of Hauri and Olmstead (13).
Cluster 8; Childhood Insomnia, Severe: The typical patient in this group sleeps
extremely poorly, with a sleep latency of over 21/2 h and a sleep efficiency of 61%.
There is practically no delta sleep in this group. In addition, these patients move very
little during sleep. After a remarkably good first night (very strong reversed first night
effect), patients in this cluster report sleeping worse in the laboratory than at home on
nights 2 and 3. Psychologically, they are marked by high MMPI K and Welsh R scales,
suggesting a pattern of denial and repression. Childhood sleep was poor in all subjects
of this group; in two of the four patients it was so poor that a physician had been
consulted. Three of the four patients in this group had been diagnosed as having either
dyslexia or "minimal brain damage" as children.
Overall, one might interpret the findings in cluster 8 as stemming from a small group
of patients who suffer from very serious childhood-onset insomnia, but have learned
to deal with their problem mainly by a rigid pattern of repression and denial of any
emotional problems. However, on the first night in the laboratory, these patients may
have been so relieved that something was finally done about their insomnia that they
slept very well (strong reverse-first-night effect).
Although the cluster analysis separated the two types of childhood-onset insomnias,
one would not clinically distinguish this subgroup from cluster 7, especially because
cluster 7 contains only eight patients and cluster 8 only four. Rather, there seems to
be a continuity between these two clusters. Patients in cluster 7 show moderate childhood-onset insomnia with few psychological problems. When childhood insomnia is
more severe (cluster 8), the psychological defenses of repression and denial may have
to be marshalled to cope with the situation.
Cluster 9; Hyperreactivity to Stress: It seems debatable whether this cluster of only
three patients is even interpretable, or whether these are just three individual "outliers," who do not fit elsewhere. It may be worth noting that the three patients in this
group had difficulties falling asleep, followed by excessive stage 1 sleep and a lack of
REM. They showed an extreme first-night effect, but reported sleeping better in the
laboratory than at home on nights 2 and 3. They scored in a highly disturbed range on
most psychological scales. They were clearly unhappy (Zung Depression Index and
MMPI D) and they had many medical complaints (Cornell Medical Index). Their high
elevations on the MMPI D, Hy, and Pt scales suggest a passive, dependent, helpless
attitude in the face of marked insomnia. This insomnia appears to be seriously aggravated by even slight stress, such as sleeping the first night in the laboratory.
DISCUSSION
The cluster analysis described here shows some remarkable similarities to the ASDC
nosology. Persistent psychophysiologic insomnia and childhood-onset insomnia were
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337
both recognized by mathematical analysis as distinct subgroups of insomnia, although
the cluster analysis drew the demarcations around persistent psychophysiologic insomnia more narrowly than the ASDC nosology did. The cluster analysis also suggested that there may be two levels of severity for childhood-onset insomnia: a moderate form of insomnia associated with relatively normal psychological functioning,
but an extreme delay of REM sleep on the first night in the laboratory; and a more
serious form of childhood-onset insomnia associated with patients who use denial and
repression as their main psychological defenses to cope with the stress. This latter
form of insomnia also seems to show a strong reverse first-night effect.
It is gratifying that this study provided objective, mathematical evidence for a few
associations in insomnia that had been clinically suspected but never documented so
far. Among them are the associations between psychophysiologic insomnia and the
mild reverse first-night effect demanded by learning theory (4), and the association
between childhood-onset insomnia, relatively normal psychological functioning, and
at least a clinical suspicion of neurological impairment (13).
For the insomnias associated with psychiatric disorders, the ASDC nosology committee had decided a priori to follow standard DSM III categories. The cluster analysis
suggests some other grouping (e.g. clusters 2, 4, and 5) for psychiatric problems associated with insomnia. Only in time will it become clear whether these new groupings
are preferable to DSM III classifications for the understanding and treatment of insomniacs. For the present, it appears that these groups make at least intuitive clinical
sense. It seems that most sleep clinicians would recognize from their practice the mildly
hypomanic insomniac, the unconventional, mildly rebellious personality disorders
sometimes seen in younger insomniacs, and the "washed-out," depleted, and highly
neurotic picture presented by some older insomniacs.
With all its mathematical sensitivity, the cluster analysis could not separate good
sleepers from those who present with a DIMS complaint without objective findings.
This suggests that those who have a complaint of insomnia without objective findings
do not show any personality deviations that could be assessed with the questionnaire
tests used here. In particular, they do not seem to suffer from malingering, hypochondriasis, or somatoform disorders, because these patterns could have been assessed on
the MMPI. In summary, whatever separates the good sleepers from those who have
an insomnia complaint without objective findings cannot be found in the 26 sleep and
personality variables that were used for this analysis.
The cluster analysis suggests that attention to the first night effect might be a useful
tool when trying to understand certain subtypes of insomnia. Whether the first night
in the laboratory causes a further deterioration in sleep or an improvement compared
with later nights seems to yield important diagnostic information about the sleeper.
Impressive, but currently unexplained, seem the extreme differences in REM latency
among the groups, both on the first night in the laboratory and on the means of nights
2 and 3. REM latency, in this paper, involves only time spent in NREM sleep. Intermittent wakefulness is excluded from this computation. It might be useful to pay more
attention to this variable in future attempts to understand subtypes of insomnia, as
Kupfer's group has done for major affective disease (6).
The observed relationship between sleep and body movements seemed surprising.
The good sleepers moved most often, the worst sleepers least. One might speculate
that there is an optimal number of movements during sleep, of the order of one move-
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P. J. HAURI
ment about every 6 min of sleep. Moving too little may be directly related to insomnia.
Might it be that those who do not move enough in their sleep are forced to wake up?
Before the ASDC nosology appeared, most clinicians distinguished sleep onset problems from sleep maintenance problems. That distinction seems not to have contributed
much to the cluster analysis, except, possibly, for clusters 3, 5, and 8. Even in these
three clusters, the distinction does not seem to help much with an understanding of
the insomnia.
Obviously, a cluster analysis can only be based on the variables that were provided.
As discussed in Methods, it remains for clinical judgement to provide the pertinent
variables and to select among the possible cluster solutions the one that makes sense.
Nevertheless, the fact that a mathematical solution, based on 26 variables in 99 people,
could come quite close to some groupings of the ASDC nosology and make a number
of relatively sensitive distinctions among insomniacs was impressive. This fact
strengthens the belief in some of the ASDC insomnia categories, such as persistent
psychophysiological and childhood-onset insomnia.
Acknowledgments: I thank E. Olmstead, L. Percy, B. Hayes, and M. Sateia for their help
with this manuscript. Special thanks to Victor E. McGee who taught me multivariate methods,
including cluster analysis. This study was supported by grant MH24268 from the National Institute of Mental Health.
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