What Does the Multiple Sleep Latency Test Measure in a Community ...

J "

Sleep, 18(10):827-835 © IYY5 American Sleep Disorders Association and Sleep Research Society

What Does the Multiple Sleep Latency Test Measure in a Community Sample? *Erkki Kronholm, *Markku T. Hyyppa, *Erkki Alanen, *lukka-Pekka Halonen and tMarkku Partinen *Psychosomatic Study Group, Research & Development, Social Insurance Institution, Turku, Finland; and tDepartment of Neurology, University of Helsinki, Helsinki, Finland

Summary: MUltiple sleep latency test (MSLT)-defined daytime sleepiness and its relationships with nocturnal and daytime psychophysiological activation were investigated in a random community sample of 77 subjects aged 3555 years. The correlation structure between all study variables was explained by a simple model of daytime sleepiness. The model suggested that indicators of psychophysiological arousal (psychological distress, nocturnal motor activity and serum thyrotropin level) and daytime reported tiredness, body mass index (BMI) and age were related significantly and independently to MSLT-defined daytime sleepiness, The arousal theory of insomnia and poor sleep in relation to MSLT behavior is discussed and the need of a multivariate approach is emphasized in MSLT studies. Key Words: Multiple sleep latency test-Daytime Sleepiness-Community sampling.

The multiple sleep latency test (MSLT) is considered to be a valid (1-6) and reliable method to detect abnormal daytime sleepiness in healthy subjects (7) and in patients (3,4,8). International standardization and guidelines for measurements have been reported (9-11). Several studies with correlation approaches have established that, in interaction with circadian rhythms, polysomnographically determined nocturnal sleep quantity (12-14), sleep structure and its disruption (8,13,15-18) have an influence on MSLT-defined physiological sleep need or propensity. However, sleep propensity is also modulated by internal and external activating factors (1,19,20). Consequently, the MSLT is interpreted as measuring an ability or a tendency to fall asleep at the time of measurement (19,21). Environmental activators are reduced to a minimum in laboratory circumstances (9-1 1) but the inner psychophysiological arousal of a subject cannot be controlled. Some MSLT studies have reported conflicting results in respect to the above-mentioned observations. They have failed to find associations of MSLT scores with sleep reduction in insomniacs (22,23), with night sleep measures in children (24), with sleep efficiency (25), with the severity of the respiratory disturbance (26), with periodic movements during sleep (27) or with the ability to maintain alertness under conditions Accepted for publication August 1995. Address correspondence and reprint requests to Erkki Kronholm, Ph.D., Social Insurance Institution, FIN-20720 Turku, Finland.

of extreme sleepiness (20). In addition, results concerning associations of MSLT scores with different subjective sleepiness scores are inconsistent. Both positive (2,12,28) and negative findings (18,23) with the same sleepiness scales have been published. Some studies have also failed to show a relation between MSLT scores and psychometric measures (23,29,30). Results concerning age are inconsistent. Positive (24,25,31) and negative (15,25,30) findings have been reported. Gender has been studied in children (24) and in the elderly (15,30) without association with the MSLT scores. Due to small sample sizes, conflicting results may be explained by chance. However, discrepancies may also be due to the influence of uncontrolled activating factors on sleep propensity. Studies on insomnia and poor sleep have suggested so-called 'arousal theory' (13,22,32-37), which offers an explanation to seemingly paradoxical results on MSLT scores of insomniacs (22,23). The arousal theory suggests that if psychophysiological arousal is constantly elevated (during day and night), it may interfere both with the sleep and with the daytime ability to fall asleep. The same explanation also seems suitable for inconsistent relationships between the subjective experience of one's sleepiness and MSLT-defined daytime sleepiness (2,12,18,23,28). The subjective experience of sleepiness depends not only on sleep need but also on situational psychophysiological arousal, reflected in different subjective stress experiences in the MSLT Subjective sleepiness scales may be dissimilar

827

E. KRONHOLM ET AL.

828

in their sensItIvIty to situational psychophysiological arousal. Also, direct evidence shows that daytime psychophysiological activation or arousal, i.e. autonomic (18) and central (38) nervous system activity, can have an influence on MSLT behavior. The rationale of this study is based on the 'arousal theory'. This approach has not been previously applied in MSLT studies. We selected several variables that we considered to be indicators of different aspects of psychophysiological arousal (i.e. nocturnal motor activity, heart rate, electrodermal activity, psychological distress, and serum cholesterol, thyrotropin and prolactin levels) (39-42). Their influence on MSLT behavior was studied together with clinically relevant variables [i.e. age; gender; body mass index (BMI); daytime tiredness; subjective sleep amount; health status, including medication; breathing disturbances]. This study is part of a larger community study, of which we have reported results on nocturnal motor activity (43). The purposes of the present study were 1) to test the applicability of the arousal theory to MSLT behavior and 2) to show the need for a multivariate approach in the interpretation of MSLT results.

METHODS Population and sample Every second subject in the random community sample of 199 persons was studied by MSLT. Complete data were obtained from 77 persons. The original sample was randomly selected from the Finnish population register of the city of Turku and its surroundings (200,000 inhabitants). The random selection process involved persons aged between 35 and 55. The only exclusion criterion was the institutionalized status of a subject. The original community sample has been described in detail elsewhere (43). The health status and demographic features of the study sample did not significantly differ from the original community sample (43). All subjects gave written consent to participate in the study, which had been approved by the Ethical Committee of the Rehabilitation Research Center of the Social Insurance Institution.

MSLT The mean MSLT score was used as the dependent variable referred to as 'daytime sleepiness' in the further analyses. The MSLT measurements were performed four times during the day, at 0900, 1100, 1300 and 1500 hours in a dim, sound-attenuated electroencephalographic (EEG) laboratory room. Sleep polysomnography was recorded on paper using a Mingograf EEG 21 (Siemens-Elema, Germany). Normal Sleep, Vol. 18, No. 10, 1995

measurement guidelines and requirements were followed (10) (e.g. alcohol, caffeine and nicotine were not allowed for 12 hours before or during the MSLT study), and the clinical form of the MSLT was used (11 ). Scoring of sleep stages was performed blindly according to standard criteria (44). Three experienced scorers (E.K., J-P.H. and M.P.) scored the sleep latencies and sleep stages of the first 12 subjects independently. The interscorer correlations varied from 0.89 to 0.91, and no significant differences among the three scorers were noticed in sleep latency scoring. Thereafter, E.K. alone scored the rest of the recordings.

Measurement of independent variables Nocturnal measures The subjects arrived at the Rehabilitation Research Center in the evening between 1800 and 2100 hours to sleep the night prior to MSLT in a single room of patient dormitorium. They were advised to go to bed at their usual bedtime, and all subjects were called at 0700 hours on in the following morning. Nocturnal body movement, ballistocardiac and respiratory activities during the night prior to MSLT were recorded using the validated static charge-sensitive bed method (Bio-Matt®, Turku, Finland) (43,45-48). Nocturnal respiration movements yielded three variables: breathing movement interruption ('apnea'), periodic breathing movement interruption ('periodic apnea') and periodic breathing. These three variables and reported information about snoring were then used to construct the breathing disturbance index described in detail elsewhere (43). Nocturnal motor activity was as described (43), with the exception that here the frequency of body movements during the time in bed, including subjective sleep latency, was used instead of sleep time. Daytime measures Serum total cholesterol, serum prolactin and serum thyrotropin (TSH) levels were measured as described elsewhere (43). The measurement procedure of electrodermal activity (EDA) has been described in detail elsewhere (43). The tonic skin conductance (SC) level, the stimulusunrelated rate of SC responses (SCR) during the first 7.5 minutes of the rest period (defined by the frequency of rapid SC level changes with an amplitude of at least 0.05 microsiemens) and the number of SCR during the stimulus trial were used for EDA measures. These were used as independent variables. The mean

-829

SLEEPINESS AND MSLT heart rate was calculated during the 'rest period' of EDA measurements_ The dichotomic health status index was based on drug use, classified into benzodiazepines, cardiovascular drugs, analgesics, antirheumatic nonsteroids, central nervous system drugs, antibiotics and nonprescription drugs. Medication was reported by the subjects in a daily 2-week sleep log, and additionally, the subjects were interviewed. Use of a single drug was classified as positive. In order to avoid circularity with psychological distress, self-reported medical diagnoses, although collected, were not used for health status index [for details, see (43)]. Self-reports

Psychological distress measures were the Beck depression inventory sum score (BDI) (49), the general severity index of brief symptom inventory (BS!) (50) and the psychic anxiety score of the Karolinska scales of personality (KSP) (51). Reported daytime tiredness was measured by three questions from the validated Sleep Habit Questionnaire from the Social Insurance Institution (36,52,53): "Do you feel tired during daytime?", "Do you feel that you often fall asleep compulsively?" and "Are you more tired than your workmates?". Each reply was scored from 0 to 2. The daytime tiredness index was the sum of these scores. Reported sleep time during the night prior to MSLT was used as an independent variable. Mean reported sleep time was calculated from the daily 2-week sleep log kept at home.

Statistical analyses The aim of the statistical approach was to analyze the correlation structure among study variables and thus to consider daytime sleepiness in its entirety. The matrix of Pearson correlation coefficients was computed. The matrix was analyzed in order to define a simple but acceptable model to explain the observed correlations. The theoretical background for the construction of this model lies within the analysis of covariance structures (54), and it belongs to the frames of LISREL models. The analyses were performed by the SAS® PROC CALIS procedure (55), which is analogous to LISREL programs. Modeling was started with a basic model-a regression model with the MSLT mean score as the dependent (outcome) variable. Accordingly, independent variables are further referred to as predictors. Two dichotomous variables (gender and health status) were among the predictors. If regression coefficients of the

latter variables differed from zero, they were interpreted as a level difference in the MSLT mean score between groups defined by dichotomous variables. The basic model is saturated, i.e. it yields exactly the observed correlations. In order to gain a deeper understanding of daytime sleepiness, a simplified and more interpretable model was needed. The number of predictor variables was reduced and defined as to which of them were independently (directly) associated with the MSLT mean score. The associations of other predictors with MSLT were interpreted to be present through the independent predictors. Reduction of the number of predictors was based on strong mutual correlation. Correlated variables were interpreted to be indicators of the same latent (nonobservable) variable. Two groups of these indicators were found: BDI, general severity index and the psychic anxiety score of KSP were interpreted as indicators of a latent variable named 'psychological distress', and SC, SCR and the number of SCR during the stimulus trial were interpreted as indicators of a latent variable named 'sympathetic activity'. It was possible to replace six indicator variables by two latent variables and thus reduce the number of predictors from 19 to 15 by constructing two single-factor models of the correlation structure among indicator variables. The justification for the procedure was analyzed by testing the model against the observed correlation matrix. At this level of simplification, the model was referred to as 'the secondary model'. Finally, all associations (~ coefficients) among the predictors and the MSLT mean score were fixed at zero, if so doing did not markedly impair the fitness of the model for observed data. This model was referred to as 'the final model'. Estimation of the parameters of the model was done by the maximum likelihood estimation method. Every variable, including the MSLT mean score, showed a non-normal distribution. However, the maximum likelihood estimation method is justified if the residuals of the variables are normally distributed. Diagnostical examination of the model showed that this requirement was fulfilled. Fitness of the model for observed data was tested by the asymptotic X2 test. We emphasize that our aim was to explain the correlation structure between all studied variables. Therefore, the process of constructing our model must not be mistaken for backward stepwise regression analysis, for example. We did not exclude any variable from the model.

RESULTS Dependent variable The mean MSLT scores varied from 6.2 to 20 minutes [mean = 15.1 minutes, standard deviation (SD) Sleep. Vol. 18. No. 10. 1995

E. KRONHOLM ET AL.

830 TABLE 1.

The raw MSLT results (mean ± SD) in minutes by age and gender Age

Men Women All

35-41 years

42-48 years

49-55 years

16.4±3.1 (n = 21) 14.8 ± 4.6 (n = 16) 15.7 ± 3.9 (n = 37)

15.5 ± 3.9 (n = 8) 12.9±3.7 (n = IS) 13.8 ± 3.9 (n = 23)

18.6 ± 1.1 (n = 6) 14.1 ± 4.3 (n = II) 15.7 ± 4.1 (n = 17)

All 16.5 ± 3.2 (n = 35) 13.9 ± 4.2 (n = 42)

= 4.0 minutes]. The raw MSLT scores are displayed by age and gender groups (Table 1).

Predictor variables The breathing disturbance index varied from 0 to 7 (mean = 2.4, SO = 1.6). Seventy-one (71.3)% of the subjects had an index value in the range of 0 to 3. Only two subjects had an index value of 26. Nocturnal motor activity (movements/minute) during time in bed (time from lights off to getting up from bed) varied from 0.091 to 1.051 (mean = 0.325, SO = 0.210). The tonic SC level, the stimulus-unrelated rate of SCR and the number of SCR during the stimulus trial intercorrelated (Table 2). They were considered to measure the same latent variable, which was named 'sympathetic activity'. This was statistically tested in TABLE 2.

Pearson correlation coefficients between study variables 2

I Gender 2 Daytime tiredness 3 Mean subjective sleep time at home 4 Nocturnal motor activity 5 Tonic skin conductance level 6 Spontaneous electrodermal activity 7 Electrodermal reactivity 8 Beck depression inventory 9 General severity index of brief symptom inventory 10 Psychic anxiety score of Karolinska scales of personality II Cholesterol 12 Health status index 13 Age 14 Subjective sleep time during recording night 15 BMI 16 Heart rate 17 Thyrotropin 18 Prolactin 19 Breathing disturbance index 20 MSLT mean score

3

4

5

6

7

0.11 0.04 -0.21 -0.26

0.10 0.14 -0.26

0.25 0.01

0.08

0.01 -0.27 -0.10

-0.18 -0.18 0.29

-0.09 -0.06 0.19

0.19 -0.04 0.41

0.26 0.42 -0.16

0.43 0.02

-0.14

0.02

0.37

0.25

0.46

-0.07

0.20

-0.11

0.07 -0.18 0.15 0.14

0.25 -0.16 0.03 0.10

0.08 -0.02 0.25 0.02

0.25 0.13 0.30 0.25

-0.05 -0.04 -0.07 -0.26

0.01 0.21 -0.06 0.12

-0.10 0.11 -0.16 -0.29

0.15 0.07 0.07 0.23 0.39 -0.18 -0.33

-0.06 -0.05 -0.05 0.03 -0.10 -0.03 -0.26

0.06 0.12 0.21 0.02 -0.13 0.20 0.26

-0.20 0.33 0.19 0.31 -0.07 0.34 0.29

-0.03 0.20 0.09 -0.09 0.01 -0.02 0.18

0.02 0.15 0.28 0.28 0.01 0.04 0.03

0.00 -0.07 0.13 0.10 0.01 -0.05 0.10

BMI, body mass index; MSLT, multiple sleep latency test. Sleep, Vol. 18, No. 10, 1995

the single-factor measurement model. The sympathetic activity factor explained 31 % of the variance of the tonic SC level, 27% of the variance of the stimulusunrelated rate of SCR and 60% of the variance of the number of SCR during the stimulus trial. The sympathetic activity factor was used as a single latent predictor variable to reduce the number of predictors in the secondary and final models. Mean heart rate (beats/minute) varied from 50 to 107 (mean = 72, SO = II). Descriptive statistics for chemical variables were as follows: serum total cholesterol (mmolll) mean = 5.8, range = 3.3-8.9, SO = 1.1; prolactin (mUll) mean = 378, range = 95-1,713, SO = 291, and thyrotropin (mUll) mean = 2.1, range = 0.1-8.8, SD = 1.4. Five subjects had serum thyrotropin values over 4.5 mUll, which is the upper limit of normality. The indicators of psychological symptom level, i.e. the BDI sum score, the general severity index of BSI and the psychic anxiety scale of KSp, intercorrelated (Table 2). They were considered to measure the same latent variable, named 'psychological distress'. It was used as a single latent predictor variable to reduce the number of predictors in the secondary and final models. Psychological distress explained 69% of the variance of BDI sum score, 67% of the variance of the general severity index of BSI and 37% of the variance of the psychic anxiety scale of KSP. Of the 77 subjects, 45.5% were scored as drug users. Medication consisted of analgesics and antirheumatic

"

.j

,"

831

SLEEPINESS AND MSLT nonsteroids (18.2%), cardiovascular drugs (16.9%), nonprescription drugs (7.8%), benzodiazepines (7.8%), central nervous system drugs (i.e. anti epileptics, antiparkinsonian drugs, psycholeptics, psychoanaleptics) (5.2%) and antibiotics (2.6%). The three questions measuring the experience of daytime tiredness correlated with each other. Their intercorrelation coefficients varied from 0.35 to 0.54. The use of the sum of the question scores as the daytime tiredness index was considered to be justified. The index range was 0-4 (mean = 0.7, SD = 1.1). Descriptive statistics for the rest of the predictor variables were as follows: age (years) mean = 43.4, range = 35-55, SD = 5.9; body mass index (BMI; kg/ m 2) mean = 25.9, range = 19.5-36.5, SD = 3.9; mean subjective sleep time at home (from sleep log) (minutes) mean = 452, range = 366-617, SD = 47; subjective sleep time during the recording night (minutes) mean = 429, range = 233-564, SD = 57. Subjective sleep time during the recording night was on the average 23 minutes shorter than the corresponding average value from sleep logs. The wake-up time after the recording night (0700 hours) was on the average 22 minutes later than the average awakening of the subjects during workdays, as defined in their sleep logs. Pearson correlations among all variables are shown in Table 2. This matrix served as an observed correlation structure that was further explained by simplified models. TABLE 2. 9

10

II

12

0.50 0.14 0.24 0.39

0.52 0.14 0.27 0.35

0.16 0.25 0.32

0.23 0.35

0.41

-0.32 0.30 0.06 0.13 -0.34 0.40 0.17

-0.11 0.32 0.23 0.37 -0.19 0.27 0.05

-0.13 0.35 0.04 0.11 -0.10 0.24 -0.05

-0.06 0.10 0.22 0.11 -0.19 0.18 0.14

-0.04 0.33 0.11 -0.02 -0.02 0.10 0.04

8

Model of daytime sleepiness The basic model (a regression model), which yields exactly the same correlation structure as the observed data, explained 46.6% of the variance of MSLT-defined sleepiness. The basic model was simplified by reducing the number of predictors. The product of the simplification was called the secondary model (Table 3).

In the secondary model, the residuals of the indicators of two latent predictors were not allowed to correlate, either mutually or with other predictors except for the residual of the general severity index of BSI. The latter was allowed to correlate with subjective sleep time during the recording night and with the serum thyrotropin level. The secondary model yielded a correlation structure with an acceptable fitness for the observed data. The asymptotic X2 test gave X2 = 74.5, with df = 62. The secondary model explained 46.5% of the variance of the mean MSLT sleep latency. Further simplification of the secondary model was achieved by fixing at zero the 13 coefficients of the predictors, which could be performed without markedly reducing the fitness of the model (Table 3). It must be emphasized that the predictor with a fixed zero 13 value is not dropped out of the model. At this stage, the model is called the 'final model'. The final model consisted of six significant and independent predictor variables (13 coefficients of nine predictors were fixed at zero) (Table 3). The fitness of Extended.

13

14

15

-0.10 0.23 -0.08 0.04 -0.21 0.27 -0.08

0.13 0.09 -0.13 -0.05 0.04 -0.21

0.17 0.17 -0.23 0.22 -0.12

16

17

18

0.15 0.14 -0.15

-0.13 -0.12

19

0.67

0.11 0.12 0.04 0.21

0.21

Sleep, Vol. 18. No. 10, 1995

832

E. KRONHOLM ET AL. TABLE 3.

Secondary and final models of MSLT-defined daytime sleepiness Secondary model"

Predictors Gender Daytime tiredness Mean subjective sleep time at home Nocturnal motor activity 'Psychological distress' 'Sympathetic activity' Cholesterol Health status index Age Subjective sleep time during recording night BMI Heart rate Thyrotropin Prolactin Breathing disturbance index

Final model"

13

SE

t value

13

SE

t value

-0.02 -0.39

0.13 0.11

-0.138 -3.507

0 -0.50

0.11

-4.748

0.16 0.32 0.21 0.02 0.04 -0.01 -0.20

0.10 0.12 0.19 0.13 0.11 0.11 0.12

1.640 2.686 1.126 0.122 00413 -0.090 -1.612

0 0.36 0.50 0 0 0 -0.26

0.11 0.15

3.209 3.376

0.11

-2.450

-0.13 -0.32 0.13 -0.24 -0.13 0.10

0.12 0.11 0.10 0.10 0.11 0.11

-1.135 -2.831 1.320 -2.344 -1.100 0.906

0 -0.37 0 -0.26 0 0

0.11

-3.472

0.10

-2.62

p values for 13 coefficients are significant if 13 is at least two times greater than SE, which means that t values are 2:2. R2 = 46.5%. b R2 = 42.9%. a

the final model for the observed correlations was acceptable. The asymptotic X2 test gave X2 = 81.9, with df = 71. The final model accounted for 42.S% of the variance of the MSLT mean score. Chronic daytime tiredness, nocturnal motor activity, psychological distress, age, BMI, and the serum TSH level were independently and significantly related to the MSLT-defined daytime sleepiness. The information concerning medication, alcohol consumption, etc. was included into the model through different operationalizations or controlled in the study design. The success of this procedure can, to some degree, be checked by analyzing how the MSLT mean score and the explanatory power of the final model will change if subjects carrying this information are excluded from the analyses. There were eight subjects using benzodiazepines and/or central nervous system drugs. When they were excluded from the analyses, the MSLT mean score was IS.O minutes (SD = 3.9 minutes) and R2 = 42.4%. When the six subjects using alcohol at a rate of more than S days during a 2-week period were excluded from the analyses, the corresponding values were IS.O minutes (SD = 4.0 minutes) and R2 = 4S.2%. When 12 subjects smoking more than 10 cigarettes per day were excluded from the analyses, the corresponding values were IS.1 minutes (SD = 3.8 minutes) and R2 = 36.6%.

DISCUSSION This study suggests that daytime sleepiness can be better interpreted by using comprehensive multivariate methods than by simple bivariate correlational approaches. We studied the associations of MSLT with Sleep, Vol. 18, No. 10, 1995

19 variables describing different aspects of psychophysiological arousal or with other clinically relevant factors. In the basic regression model, intercorrelated predictors accounted for 46.6% of the variance of daytime sleepiness in the study sample. However, the interpretation of such a highly complicated model would be almost impossible. We greatly simplified the basic model so that in the final model only six independent and significant predictors remained, but the model still fits at an acceptable level for the observed data. It accounted for 42.S% of the variance of daytime sleepiness. The observed correlation structure can now be explained in an interpretable way. The arousal theory of insomnia or poor sleep seems to be useful in explaining the MSLT-defined daytime sleepiness. The final model suggests that, in a decreasing order of impact, chronic daytime tiredness, BMI, psychological distress, nocturnal motor activity, the serum TSH level, and age significantly and independently affect the MSLT-defined ability to fall asleep (or sleep propensity) during the daytime. Health status, gender, heart rate, serum cholesterol and prolactin levels, mean subjective sleep time at home, subjective sleep time during the previous night, breathing disturbances or sympathetic activity did not significantly contribute to the MSLT-defined daytime sleepiness. However, the latter variables were associated with, at least, one of the significant predictor variables and cannot be ignored when interpreting the model. Persistent experience of daytime tiredness was the most powerful predictor of the mean MSLT score. Subjective sleepiness scales addressing the moment of measurement or tiredness at a particular time are not associated with MSLT-defined daytime sleepiness

.,

,>

'.1

SLEEPINESS AND MSLT (18,23,56). However, MSLT-defined daytime sleepiness was associated with a self-reported stable (traitlike) tendency to doze in a variety of everyday situations (28). The latter resembles our daytime tiredness index, which is constructed of constant tiredness, an irresistible tendency to fall asleep, and feeling more tired than workmates or friends. It seems that persistent MSLT-defined daytime sleepiness can be introspectively self-recognized but cannot be measured by assessment methods that are dependent on the preceeding activity levels (12). The final model suggested that BMI may be a significant and independent modulator of the MSLT-defined daytime sleepiness. The inverse correlation between BMI and sleep latency in MSLT has not previously been reported, but BMI had not been controlled in the earlier MSLT studies, either. Controlling is important because this study suggests that the possible influences on daytime sleepiness of health status, subclinical breathing disturbances and the heart rate level are mediated via BMI. We emphasize that neither pathologically increased sleep need nor clinically significant breathing disturbances were noticed among the studied subjects. Consequently, breathing disturbances (although associated with the MSLT mean score) do not add significant information to the model of daytime sleepiness. Our results suggest that the more a subject experiences psychological distress, the longer the mean sleep latency in MSLT is. This fits well in the 'arousal hypothesis' of insomnia or poor sleep (22,33,57). Psychological distress has been studied only in some earlier MSLT studies. Psychometric findings were found either not to be associated (23,29,30) or to be associated (58) with MSLT scores. We interpret psychological distress as an indicator of chronic inner psychophysiological arousal, which can prolong sleep latency in the MSLT test situation. The higher the nocturnal motor activity is, the longer the sleep latency in MSLT. We assume that high general activation or arousal causes heightened motor activity in sleep and prevents falling asleep during the daytime. Studies on insomniacs (13,22,23,37) and on community samples (43,57) support this assumption. This interpretation is in accordance with the suggestion (by the model) that nocturnal motor activity partially mediates the effects of gender, subjective sleep amount, health status, subclinical breathing disturbances and heart rate on daytime sleepiness. It is reasonable to suppose that no physiologically significant fragmentation of sleep has been caused by the recording. Nocturnal motor activity shows a considerable intrasubject variability across consecutive nights (59,60), but the static-charge sensitive bed re-

833

cording does not cause any first-night effect in nocturnal motor activity (59). A high level of serum TSH in the morning relates to short sleep latency in MSLT. Only five subjects had serum TSH values over 4.5 mUll, which is the upper limit of normality. Correlation between the serum TSH level and MSLT did not markedly change when they were excluded from the analysis. Hence, thyroid pathology does not explain this observation. The older subjects tended to show shorter sleep latencies in MSLT than the younger subjects. This is in accordance with the previous report that retired persons are sleepier than young adults (15). However, an age factor was not observed among subjects between 30 and 80 years old (25) or between 55 and 77 years old (29). In our study, age alone without other predictor variables accounts for only 0.6% of the variance in the mean MSLT sleep latency, but in the final model it turns out to be an independent predictor. This observation urges multivariate approaches to understand the role of age in MSLT behavior. The study sample was randomly drawn from a larger community population (43). It did not significantly differ from the original community population. However, single men aged 35-39 years were slightly underrepresented. To our knowledge, random population samples of middle-aged adults had not previously been investigated by MSLT. The mean MSLT score in this sample was 15.1 minutes, ranging from 6.2 to 20 minutes. It agrees with earlier findings on healthy control subjects (within approximately the same age range), with the mean MSLT sleep latency ranging from 12.2 to 15.8 minutes (3,7,22,25). Among demographic variables, age and BMI are directly and significantly related to the MSLT mean score, but the influence of gender is mediated via nocturnal motor activity, age and the serum TSH level. We conclude that demographic variables should be more carefully controlled in MSLT studies than they have been previously. The critical point for the validity of the results stands in the correlation matrix, which was the starting point of statistical modeling. We carefully scrutinized and did not find technical outlier artifacts or uninterpretable values in the distribution of variables. The possibility of chance correlations, however, always exists, but we do not argue about the statistical significance concerning single correlation coefficients in the matrix. Our approach is to explain the correlation structure in its entirety. The validity of results also depends on the selection of predictors. If fundamentally important predictors are lacking, the validity of the model will be impaired. We did not have polysomnographic data on the night prior to MSLT, but we have estimators of sleep structure as Sleep, Vol. 18. No. 10, 1995

E. KRONHOLM ET AL.

834

reflected by objectively measured nocturnal motor activity and time in bed. We also used subjective reports on sleep latency and sleep episodes during the night prior to MSLT. We collected subjective information about the subjects' sleep habits at home via a diary. Thus, we feel that our model includes all of the important information for the conclusions about the relevance of arousal theory in partially explaining MSLT behavior in a general population. The model also demonstrates that the need for a multivariate approach in MSLT studies deserves further attention. REFERENCES I. Carskadon MA, Dement We. Daytime sleepiness: quantification of a behavioral state. Neurosci Biobehav Rev 1987;11:307-17. 2. Carskadon MA, Dement We. Effects of total sleep loss on sleep tendency. Percept Mot Skills 1979;48:495-506. 3. Richardson GS, Carskadon MA, Flagg W, van den Hoed J, Dement WC, Mitler MM. Excessive daytime sleepiness in man: multiple sleep latency measurement in narcoleptic and control subjects. Electroencephalogr Clin Neurophysiol 1978;45:621-7. 4. Zorick F, Roehrs T, Koshorek G, et al. Patterns of sleepiness in various disorders of excessive daytime somnolence. Sleep 1982;5:S165-74. 5. Roehrs T, Zorick F, Sicklesteel J, Wittig R, Roth T. Excessive daytime sleepiness associated with insufficient sleep. Sleep 1983;6:319-25. 6. Lumley M, Roehrs T, Zorick F, Lamphere J, Roth T. The alerting effects of naps in sleep-deprived subjects. Psychophysiology 1986;23:403-8. 7. Zwyghuizen-Doorenbos A, Roehrs T, Schaefer M, Roth T. Testretest reliability of the MSLT. Sleep 1988; II :562-5. 8. van den Hoed J, Kraemer H, Guilleminault C, et al. Disorders of excessive daytime somnolence: polygraphic and clinical data for 100 patients. Sleep 198 I ;4:23-37. 9. Carskadon MA, Dement WC, Mitler MM, Roth T, Westbrook PR, Keenan S. Guidelines for the multiple sleep latency test (MSLT): a standard measure of sleepiness. Sleep 1986;9:51924. 10. Roehrs T, Roth T. Multiple sleep latency test: technical aspects and normal values. J Clin Neurophysiol 1992;9:63-7. II. The Standards of Practice Committee of the American Sleep Disorders Association, Thorpy MJ, chairman. The clinical use of the multiple sleep latency test. Sleep 1992;15:268-76. 12. Carskadon MA, Harvey K, Dement We. Sleep loss in young adolescents. Sleep 1981 ;4:299-312. 13. Stepanski E, Lamphere J, Badia P, Zorick F, Roth T. Sleep fragmentation and daytime sleepiness. Sleep 1984;7: 18-26. 14. Rosenthal L, Roehrs TA, Rosen A, Roth T. Level of sleepiness and total sleep time following various time in bed conditions. Sleep 1993; 16:226-32. 15. Carskadon MA, Brown ED, Dement WC. Sleep fragmentation in the elderly: relationship to daytime sleep tendency. Neurobiol Aging 1982;3:321-7. 16. Levine B, Roehrs T, Stepanski E, Zorick F, Roth T. Fragmenting sleep diminishes its recuperative value. Sleep 1987;10:590-9. 17. Philip P, Stoohs R, Guilleminault e. Sleep fragmentation in normals: a model for sleepiness associated with upper airway resistance syndrome. Sleep 1994;17:242-7. 18. Pressman MR, Fry JM. Relationship of autonomic nervous system activity to daytime sleepiness and prior sleep. Sleep 1989; 12:239-45. 19. Carskadon MA, Dement WC. The multiple sleep latency test: what does it measure? Sleep 1982;5:S67-72. 20. Sugerman JL, Walsh JK. Physiological sleep tendency and ability to maintain alertness at night. Sleep 1989; 12: 106-12. Sleep, Vol. 18, No. 10, 1995

21. Carskadon MA, Dement We. Sleepiness and sleep state on a 90-min schedule. Psychophysiology 1977; 14: 127-33. 22. Stepanski E, Zorick F, Roehrs T, Young D, Roth T. Daytime alertness in patients with chronic insomnia compared with asymptomatic control subjects. Sleep 1988; 11 :54-60. 23. Seidel WF, Ball S, Cohen S, Patterson N, Yost D, Dement We. Daytime alertness in relation to mood, performance, and nocturnal sleep in chronic insomniacs and noncomplaining sleepers. Sleep 1984;7:230-8. 24. Palm L, Persson E, Elmqvist D, Blennow G. Sleep and wakefulness in normal preadolescent children. Sleep 1989; 12:299308. 25. Levine B, Roehrs T, Zorick F, Roth T. Daytime sleepiness in young adults. Sleep 1988; 11 :39-46. 26. Valencia-Flores M, Campos RM, Mendez J, et al. Multiple sleep latency test (MSLT) and sleep apnea in aged women. Sleep 1993;16:114-7. 27. Mahowald MW, Ettinger MG. Things that go bump in the night: the parasomnias revisited. J Clin Neurophysiol 1990;7:119-43. 28. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991; 14:540-5. 29. Bliwise DL, Carskadon MA, Seidel WF, Nekich JC, Dement WC. MSLT-defined sleepiness and neuropsychological test performance do not correlate in the elderly. Neurobiol Aging 1991; 12:463-8. 30. Hoch CC, Reynolds CF III, Jennings JR, et al. Daytime sleepiness and performance among healthy 80 and 20 year olds. Neurobiol Aging 1992; 13:353-6. 31. Brendel DH, Reynolds CF III, Jennings JR, et al. Sleep state physiology, mood, and vigilance responses to total sleep deprivation in healthy 80-year-olds and 20-year-olds. Psychophysiology 1990;27:677-85. 32. Monroe LJ. Psychological and physiological differences between good and poor sleepers. J Abnorm Psychol 1967 ;72:25564. 33. Kales A, Caldwell AB, Soldatos CR, Bixler EO, Kales JD. Biopsychobehavioral correlates of insomnia. II. Pattern specificity and consistency with the Minnesota multiphasic personality inventory. Psychosom Med 1983;45:341-56. 34. Johns MW, Gay TJA, Masterton Jp' Bruce DW. Relationship between sleep habits, adrenocortical activity and personality. Psychosom Med 1971 ;33:499-508. 35. Adam K, Tomeny M, Oswald I. Physiological and psychological differences between good and poor sleepers. J Psychosom Res 1986;20:301-16. 36. Hyyppa MT, Kronholm E. Sleep movements and poor sleep in patients with non-specific somatic complaints-II. Affective disorders and sleep quality. J Psychosom Res 1987;31:631-7. 37. Broman J-E, Hetta J. Electrodermal activity in patients with persistent insomnia. J Sleep Res 1994;3:165-70. 38. Broughton R, Aguirre M, Dunham W. A comparison of multiple and single sleep latency and cerebral evoked potential (P300) measures in the assessment of excessive daytime sleepiness in narcolepsy-cataplexy. Sleep 1988; II :537-45. 39. Francis KT. Psychological correlates of serum indicators of stress in man: a longitudinal study. Psychosom Med 1979;41: 617-28. 40. Baum A, Davidson LM, Singer JE, Street SW. Stress as a psychophysiological process. In: Baum A, Singer JE, eds. Handbook of psychology and health, vol. V. Stress. Hillsdale, NJ: Lawrence Erlbaum Associates 1987: 1-24. 41. Meyerhoff JL, Oleshansky MA, Mougey EH. Psychologic stress increases plasma levels of prolactin, cortisol, and POMC-derivated peptides in man. Psychosom Med 1988;50:295-303. 42. Cacioppo JT. Social neuroscience: autonomic, neuroendocrine, and immune responses to stress. Psychophysiology 1994;31: 113-28. 43. Kronholm E, Alanen E, Hyyppa M. Nocturnal motor activity in a community sample. Sleep 1993:16:565-71. 44. Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Bethesda, MD: U.S. Department of Health, Education and Welfare Public Series, 1968.

SLEEPINESS AND MSLT 45. Alihanka 1, Vaahtoranta K. A static charge sensitive bed: a new method for recording body movements during sleep. Electroencephalogr Clin Neurophysiol 1979;46:731-4. 46. Alihanka 1, Vaahtoranta K, Saarikivi I. A new method for longterm monitoring of the ballistocardiogram, heart rate, and respiration. Am J Physiol 1981 ;240:R384-92. 47. Salmi T, Telakivi T, Partinen M. Evaluation of automatic analysis of SCSB, airflow and oxygen saturation signals in patients with sleep apneas. Chest 1989;96:255-61. 48. Polo 0, Brissaud L, Sales B, Besset A, Billiard M. The validity of the static charge sensitive bed in detecting obstructive apnoeas. Eur Respir J 1988; I :330-6. 49. Beck AT, Ward CH, Mendelson M, Mock 1, Erbaugh 1. An inventory for measuring depression. Arch Cen Psychiatry 1961;4:561-71. 50. Derogatis LR, Melisaratos N. The brief symptom inventory: an introductory report. Psychol Med 1983;13:595-605. 51. Schalling D, Cronholm B, Asberg M. Components of state and trait anxiety as related to personality and arousal. In: Levi L, ed. Emotions~their parameters and measurement. New York: Raven Press, 1975:603-17. 52. Hyyppa MT, Kronholm E. Quality of sleep and chronic illnesses. J Clin Epidemiol 1989;7:633-8. 53. Hyyppa MT, Kronholm E, Mattlar C-E. Mental well-being of

54. 55. 56.

57.

58. 59.

60.

835

good sleepers in a random popUlation sample. Br J Med Psychol 1991 ;64:25-34. 1breskog KG. A general method for analysis of covariance structures. Biometrika 1970;57 :239-51. SAS Institute Inc. SAS/STA"J® Users guide, version 6, 4th edition, vol J. Cary, NC: SAS Institute, 1989. 10hnson LC, Freeman CR, Spinweber CL, Gomez SA. Subjective and objective measures of sleepiness: effect of benzodiazepine and caffeine on their relationship. Psychophysiology 1991 ;28:65-71. Hyyppa MT, Lindholm T, Kronholm E, Lehtinen V. Functional insomnia in relation to alexithymic features and cortisol hypersecretion in a community sample. Stress Med 1990;6:27783. Roehrs T, Timms V, Zwyghuizen-Doorenbos A, Buzenski R, Roth T. Polysomnographic, performance, and personality differences of sleepy and alert normals. Sleep 1990;13:395-402. Kronholm E, Alanen E, Hyyppa MT. Sleep movements and poor sleep in patients with non-specific somatic complaints-I. No first-night effect in poor and good sleepers. J Psychosom Res 1987;31 :623-9. van Hilten 11, Braat EAM, van der Velde EA, Middelkoop HAM, Kerkhof GA, Kamphuisen HAC. Ambulatory activity monitoring during sleep: an evaluation of internight and intrasubject variability in healthy persons aged 50-98 years. Sleep 1993; 16: 146-150.

Sleep, Vol. 18, No. 10, 1995