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Keywords:

  • insomnia;
  • microarousal;
  • sleep fragmentation;
  • sleep onset latency

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

It is well established that insomniacs overestimate sleep-onset latency. Furthermore, there is evidence that brief arousals from sleep may occur more frequently in insomnia. This study examined the hypothesis that brief arousals from sleep influence the perception of sleep-onset latency. An average of four sleep onsets was obtained from each of 20 normal subjects on each of two nonconsecutive, counterbalanced, experimental nights. The experimental nights consisted of a control night (control condition) and a condition in which a moderate respiratory load was applied to increase the frequency of microarousals during sleep onset (mask condition). Subjective estimation of sleep-onset latency and indices of sleep quality were assessed by self-report inventory. Objective measures of sleep-onset latency and microarousals were assessed using polysomnography. Results showed that sleep-onset latency estimates were longer in the mask condition than in the control condition, an effect not reflected in objective sleep-stage scoring of sleep-onset latency. Furthermore, an increase in the frequency of brief arousals from sleep was detected in the mask condition, and this is a possible source for the sleep-onset latency increase perceived by the subjects. Findings are consistent with the concept of a physiological basis for sleep misperception in insomnia.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

People with insomnia frequently overestimate the time it takes them to fall asleep when compared with objective measures such as polysomnography (PSG). Good sleepers, however, tend to correctly estimate or slightly underestimate the amount of time taken to fall asleep ( Rechtschaffen and Monroe 1969; Bixler et al. 1973 ; Carskadon et al. 1976 ; Frankel et al. 1976 ; Lutz et al. 1977 ; Coates et al. 1983 ; Hauri and Olmstead 1983; Edinger and Fins 1995; Perlis et al. 1997 ). Frequent transient arousals, too brief to be recognized by conventional sleep-stage scoring or be subjectively experienced by sleepers ( Knab and Engel 1988; Perlis et al. 1997 ) have been associated with daytime symptoms of sleepiness and poor cognitive functioning ( Stepanski et al. 1984 ; Bonnet 1985; Guilleminault et al. 1993 ). In this study, the extent to which transient arousals influence the perception of sleep onset was assessed.

The frequency of brief awakenings, or microarousals, has been scored for clinical purposes to determine the extent of sleep fragmentation ( Mathur and Douglas 1995; Martin et al. 1997 ). A number of definitions for measuring sleep fragmentation have been proposed ( Atlas Task Force of the American Sleep Disorders Association 1992; Cheshire et al. 1992 ; Mathur and Douglas 1995). In general, such definitions regard a return to alpha frequencies in the electroencephalogram (EEG) or a conspicuous increase in submental electromyogram (EMG) tone as indicative of arousal. The current standard definition is that recommended by the American Sleep Disorders Association (ASDA), and requires a minimum duration of alpha activity of 3 s. A greater number of arousals can be identified with shorter duration criteria ( Martin et al. 1997 ). However, the most clinically useful definition for microarousals has yet to be determined as current definitions yield only a moderate relationship between arousal frequency and daytime sequalae.

Most investigations of sleep fragmentation have arisen from attempts to model sleep apnoea, and utilize estimates of daytime sleepiness or performance on tasks as outcome measures ( Stepanski et al. 1987 ; Roehrs et al. 1994 ; Chugh et al. 1996 ; Kimoff 1996). However, a recent review by Perlis et al. (1997 ) indicates that an increase in high-frequency EEG activity across the sleep-onset period may underlie the subjective underestimation of sleep duration in insomnia patients, suggesting that sleep fragmentation might have an impact on the self-perception of whether a person is asleep or not. However, to date, there have been no published studies that have adequately evaluated the effects of microarousals on either the subjective perception of sleep duration or on the perception that an individual is asleep.

Previous studies of sleep fragmentation have often used auditory evoked arousal or caffeine-induced hyperarousal methods to simulate disrupted sleep and produce a model of insomnia (e.g. Philip et al. 1994 ; Series et al. 1994 ; Alford et al. 1996 ; Bonnet and Arand 1996; Badr et al. 1997 ; Martin et al. 1997 ). A possible alternative method for producing an increase in microarousals is to exploit endogenous arousal mechanisms associated with respiratory control during sleep. Respiratory variables appear to be involved in initiating or maintaining arousals during sleep, particularly during the sleep-onset period ( Gleeson et al. 1989 ; Trinder et al. 1992 ; Kay et al. 1994 ; Mathur and Douglas 1995; Douglas and Martin 1996). This occurs because alternation in state during sleep onset produces fluctuations in ventilation and gas levels ( Trinder et al. 1992 ), which in turn result in arousals and even greater instability in the respiratory system ( Dunai et al. 1996 ). Consistent with this, increasing breathing difficulty by the addition of an inspiratory resistance, or by temporary occlusion of inspiration, leads to increased arousals ( Carskadon and Dement 1981). Increasing respiratory load therefore increases state instability, and provides a naturalistic method of increasing the frequency of microarousals during sleep onset, thus providing a model for investigating features of insomnia in normal individuals.

In this study, subjective indices of sleep parameters were collected in conjunction with PSG measures of sleep in a manipulation designed to increase state instability, and therefore the frequency of microarousals across the sleep-onset period. It was hypothesized that the subjects would be able to identify a period of state instability produced by respiratory load, and that the duration of their subjective estimates of sleep onset latency (SOL) would be increased by this perception. Furthermore, it was hypothesized that PSG measures of sleep onset would not be affected by an increase in microarousals.

METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

Subjects

Subjects were recruited from a university student population. Twenty subjects (18 females and two males) with a mean age of 22.9 ± 3.2 y, participated in the experimental nights. The subjects reported being nonsmokers (currently and in the past), free of respiratory problems (including asthma) and free of sleep problems. They were not on any medication, and were asked to refrain from alcohol and caffeine consumption for at least 12 h prior to their participation. Subjects were screened for psychopathology known to affect sleep using the Beck Anxiety Inventory ( Beck et al. 1988 ) and the Beck Depression Inventory ( Beck 1987). Habitual sleep was assessed for one week prior to testing using the Pittsburgh Sleep Diary ( Monk et al. 1994 ), to ensure that subjects had regular sleep/wake patterns.

Design

The experiment consisted of two within-subject variables (the recording condition and the method of measuring SOL). The recording condition involved two levels, a control condition and an experimental condition. In the control condition (Control), subjects slept with routine PSG recording equipment and with body position unrestricted. In the experimental recording condition (Mask), subjects wore respiratory apparatus with light resistance and the body position remained supine. The order of presentation of the conditions was counterbalanced across subjects. The two methods of measuring SOL were objective SOL estimates based on the PSG record, and the subjective SOL estimates of the subjects. The subjective measures comprised three interrelated estimates of SOL.

Procedure

Subjects reported to the sleep laboratory one week prior to testing for screening, briefing and distribution of sleep diaries. Subjects underwent an adaptation night in the laboratory prior to data collection to control for ‘first night’ phenomena ( Toussaint et al. 1995 ). Data collection nights were nonconsecutive.

Subjects in the Mask condition wore a Hans Rudolph silicon-rubber anaesthetic mask, which covered the nose and mouth and was held in place by a head strap. The mask was attached to a two-way nonrebreathing valve (Hans Rudolph series 2600). The inspiratory resistance of the mask and leaf valve assembly, measured at 0.25 L/s, was 1.25 cmH2O/L/s.

To maximize the number of sleep onsets recorded, a multiple sleep onset procedure was used. Subjects were woken after a variable period of uninterrupted Stage 2 sleep or deeper. This period was of three lengths: short (9–11 min), medium (14–16 min) and long (18–22 min). The times were presented in random order. Subjects were given the following instructions: ‘The lights will be turned off and you should go to sleep. After attaining some amount of sleep, you will be woken and asked to estimate the time spent asleep, and a number of other questions. This process will re-occur several times across the night.’

When woken, each subject was asked to complete subjective measures of SOL. Subjects read the statement ‘We often drift in and out of light sleep before going into continuous, or stable, sleep’, and were then asked three questions regarding subjective estimates of SOL.

Increased frequency of brief arousals from sleep in the Mask condition would be expected to result in longer latency estimates for question 2 than for question 1 (Table 1). In the Control condition, however, the latency estimates for both questions would be expected to be similar. A single question of the type used in previous studies (e.g. Puder et al. 1983 ; Van-Egeren et al. 1983 ; Scrima et al. 1989 ), such as ‘How long did it take you to fall asleep?’ does not discriminate between these forms of sleep onset. Question 3, the more usual question, was included to allow comparison with previous studies. Other aspects of the subject’s experience of the sleep onset were also assessed, primarily using visual analogue scales. This data will be reported separately.

Table 1.   Three subjective estimates of SOL Thumbnail image of

Subjects were allowed to return to sleep after completion of the subjective measures of SOL and the procedure repeated. The recording procedure provided an average of four sleep onsets for each subject on experimental nights. Recording continued until approximately 03.00 h at which time recording equipment was removed, and the subjects were allowed to sleep ad lib.

Measurement and scoring of sleep state

The recordings for the identification of sleep state consisted of a standard EEG montage, with central (C3–A2) and occipital (O1–A2) EEG sites. Gold cup surface electrodes were positioned according to the International 10–20 system. EOG (bipolar, with electrodes displaced at the outer canthus of each eye), and submental EMG were also used. All sleep variables were recorded using a 16-channel polygraph (Grass polygraph model 7D) onto paper charts with a paper speed of 10 mm/s. Occipital and central EEG were also recorded onto an IBM-compatible personal computer via a 16-bit analogue to digital converter sampling at 100 Hz for display and hard disk storage.

Scoring of sleep stages was performed by visual analysis according to standard criteria ( Rechtschaffen and Kales 1968) in 30-s epochs. Sleep spindles and K complexes were identified in the central EEG (indicative of Stage 2 sleep onset). These distinctions were made using standard methods by an experienced scorer blind to the recording condition.

Measurement and scoring of arousals from sleep

A level of intensity, ranging from 1 to 4 was assigned to each microarousal. The scoring criteria for arousal intensity are summarized in Table 2. Level 1 represents a subtle sign of cortical arousal, while level 4 represents an unambiguous return to EEG wakefulness together with gross body movements. The duration criteria of greater than 1.5 s are in accordance with Douglas and Martin (1996) and Cheshire et al. (1992 ). The termination of arousals was defined as the cessation of alpha and return of theta activity. However, in addition, a minimum of 10 continuous seconds of the indications of sleep was required before a new arousal could be scored. Rearousals within this period were regarded as continuous with the previous arousal.

Table 2.   Modified visual scoring criteria for assigning level of intensity to arousals Thumbnail image of

Arousal scoring was independent of Rechtschaffen and Kales (1968) epoch scoring. That is, an arousal could be scored in an epoch of recording that would be classified as sleep by these criteria. The criteria for intensity were based broadly on the ASDA criteria for scoring arousals ( ASDA 1992), however, arousals at intensity levels 1 and 2 would not be recognized as ‘arousal’ under the standard ASDA (1992) criteria.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

Onset position effects

Trials within subjects were initially analysed as a function of onset position (first, second… onset within a night) to assess order effects across the recording night. A comparison was made between mean SOL values at each onset position for SOL to Stage 2 and SOL estimated by question 1. The main effect of onset position was significant in each case (F5,35=3.8, P < 0.01, F5,35=4.1, P < 0.01). As a consequence, in order to control for effects of serial onset position, further comparisons between the two conditions were made only between data at the same serial onset position.

Objective measures of sleep stage

The number of epochs spent in each stage of sleep was compared between conditions. The analysis consisted of a 2 × 6 factorial design with repeated measures on each factor. The factors were condition (Control vs. Mask) and sleep stage (Wake, Stages 1, 2, 3, 4 and REM). Data were averaged across all pairs of onsets for the two conditions. The main effect of condition (F17,1=0.23, P > 0.05) was not significant. The main effect of sleep stage (F17,1=24.95, P < 0.001) was significant. The interaction between the factors of condition and sleep stage was not significant (F17,1=0.01, P > 0.05). Thus, the use of the respiratory apparatus and supine sleeping position did not change the proportion of time spent in each stage of sleep as defined by Rechtschaffen and Kales (1968) criteria.

Objective measures of SOL

A comparison between objectively measured SOL, represented as the first epoch of Stage 2 sleep, averaged across all pairs of onsets for the two conditions is shown in Fig. 1. Mean SOL in the Control condition was 8.1 min (SD=11.2) while in the Mask condition it was 8.0 min (SD=9.9). A repeated measures t-test indicated no difference in objective SOL between the two conditions to Stage 2 sleep (t=0.03, P > 0.05). Thus, the use of the respiratory apparatus and supine sleeping position did not change objective latency to the onset of Stage 2 sleep as defined by Rechtschaffen and Kales (1968) criteria.

image

Figure 1. Estimates of sleep-onset latency (SOL) using subjective and objective criteria. Three subjective SOL estimates are shown based on responses to Q1. , Q2 and Q3 (dotted line). SOL estimates based on objective criteria are also shown (solid line).

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Subjective measures of SOL

Two primary analyses were conducted on the subjective measures of SOL. The first assessed the effect of recording condition on inventory questions 1 and 2, and the relationship between these questions. The second assessed the effect of recording condition on question 3.

The first analysis consisted of a 2 × 2 factorial design with repeated measures on each factor. The factors were conditions (Control vs. Mask) and questions (question 1 vs. question 2). Mean values for these factors are represented in Fig. 1. The main effects of both condition (F89,1=15.13, P < 0.001) and question (F89,1=28.49, P < 0.001) were significant, while the interaction between the factors of condition and question approached significance (F89,1=3.84, P=0.053). As shown in Fig. 1, mean values for both questions were higher in the mask condition, and higher for question 2 than for question 1 in both conditions.

The second analysis consisted of a repeated measures t-test across the two conditions for question 3. The difference between the mean response, in minutes, in the Control condition (M=8.5, SD=9.7) and Mask conditions (M=12.9, SD=11.5) was also significant for question 3 (t (89)=–3.57, P < 0.001).

Objective vs. subjective estimates of SOL

To examine objective vs. subjective estimates of SOL, comparisons were made between the three primary questions (questions 1, 2 and 3) and SOL to Stage 2 sleep onset for the two conditions. These comparisons utilized a 2 × 2 factorial design with repeated measures on each factor. The factors were condition (Control vs. Mask) and SOL estimates (Stage 2 sleep vs. questions 1, 2 or 3). The effect of interest is the interaction of condition and SOL estimate. The interactions were significant in the case of question 1 (F89,1=10.6, P < 0.01), question 2 (F89,1=13.5, P < 0.001) and question 3 (F89,1=11.3, P=0.001). These interactions indicate that SOL increased as a function of the mask condition for the subjective measures but not for the objective measures.

Arousals

Arousals from sleep were scored by intensity level (level 1–4) using criteria described previously. Comparisons were made between the number of arousals at each level across the two conditions after the onset of sleep (taken as the first epoch of Stage 2 sleep). These comparisons are illustrated in Fig. 2. The analysis consisted of a 2 × 4 factorial design with repeated measures on each level. The factors were condition (Control vs. Mask) and frequency of arousals at each arousal intensity (level). After Huynh-Feldt correction (ɛ(condition)=1.0, ɛ(level)= 0.76), the main effects of condition (F1,86=4.1, P < 0.05) and level (F2.28, 258=17.32, P < 0.01) were significant, while the interaction between the factors of condition and level was not significant (F2.63, 258=1.7, P > 0.05). While there appears to be a difference in frequency of arousals between the conditions, Fig. 2 suggests that the distribution of arousals across the four levels is similar in both conditions. That is, the frequency of arousals was higher at all levels of arousal intensity in the Mask condition.

image

Figure 2. .  Mean values for arousals at each level across all included onsets for both the Control and Mask conditions.

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Subjective estimates of arousal frequency were elicited in response to the question ‘How many times did you wake up?’. The relationship between subjective estimates of arousal frequency and objective estimates of arousal frequency was examined using a 2 × 2 factorial design with repeated measures on each factor. The factors were condition (Control vs. Mask) and arousal frequency estimate (subjective vs. objective summed across intensity levels). In the Control condition, mean subjective and objective estimates of arousal frequency were 2.7/h and 8.7/h, respectively. In the Mask condition these estimates were 3.5/h and 12.8/h, respectively. The main effects of both condition (F1,88=6.32, P < 0.05) and arousal frequency estimate (F1,88=47.45, P < 0.001) were significant, indicating that the mask increased both subjective and objective arousals and that subjects underestimated the frequency of arousals from sleep as defined by objective criteria. The interaction of condition and arousal frequency estimate was not significant (F1,88=3.27, P > 0.05).

There was no significant correlation between the total number of objectively defined arousals (sum of levels 1–4) and subjective estimates of arousal frequency in either the Control condition (r=0.163, P > 0.05) or the Mask condition (r=−0.108, P > 0.05). This analysis indicates the absence of a relationship between subjective perception of arousal frequency and objectively defined arousals.

The relationship between subjective perception of sleep disruption and objective estimates of arousal frequency was further investigated by three questions requiring a response on a visual analogue scale (100 mm). On this scale, a higher score represents a rating of more disturbed sleep. A significant increase in rated sleep disturbance was found in the Mask condition than in the baseline condition in response to the questions; ‘Was your sleep deep or shallow?’ (M=57.9 ± 22.1, M=46.6 ± 25.1, t=3.49, P < 0.001), ‘Did you have difficulty falling asleep?’ (M=43.6 ± 26.4, M=34.9 ± 25.5, t=–2.73, P < 0.05) and ‘Was you sleep disturbed?’ (M=42.5 ± 24.7, M=30.2 ± 22.1, t=–4.43, P < 0.001). This finding indicates that while subjects were not able to directly perceive an increase in arousals, they were able to report disturbed sleep in more general terms.

Sleep duration

The relationship between subjective estimates of sleep duration and actual sleep time (from the onset of Stage 2 sleep until awakening by experimenter) was examined further using a 2 × 2 factorial design with repeated measures on each factor. The factors were condition (Control vs. Mask) and sleep duration estimate (subjective vs. objective). Subjective duration estimates, represented as a percentage of actual sleep time, were 111.5% in the Control condition and 92.9% in the Mask condition. However, the main effects of both condition (F1,87=0.59, P > 0.05) and duration estimate (F1,87=0.76, P > 0.05) were not significant, and the interaction between these factors (F1,87=1.09, P > 0.05) was also not significant.

Subjective estimates of having been asleep or awake

At the end of each sleep-onset trial, subjects were asked whether they had been awake or asleep just before the experimenter ‘woke’ them (by turning on the light and entering the room). A sleep perception ratio was derived from the number of ‘asleep’ reports divided by total arousals. Only cases in which the subject was asleep as indicated by PSG immediately prior to being woken were included. A higher ratio, therefore, represents a more accurate perception of having been asleep when asleep as defined by objective measures. The mean sleep perception ratio across all Control onsets was 0.81, and across all Mask onsets was 0.58. The difference between condition means was significant (t (89)=2.0, P < 0.05). This suggests that accurate perception of sleep was more common in the control condition than in the mask condition. Overall accuracy in subjective awareness of sleep was only moderate.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

The intervention imposed in the Mask condition was associated with a significant increase in subjective SOL reported by the subjects, compared with their estimates in the Control condition. It appeared that subjects could identify an increased period of interrupted sleep before they achieved stable sleep. For example, they were able to discriminate light or disturbed sleep from ‘stable’ sleep and could perceive greater difficulty in falling asleep. In addition, subjects were less likely to report having been asleep in the Mask condition, even after 15–25 min of conventionally defined Stage 2 sleep. These findings are consistent with previous reports of sleep perception in insomnia.

Importantly, this study suggested that significant differences in subjective estimates of SOL can be induced in normal sleepers in the absence of a change in the Rechtschaffen and Kales (1968) defined sleep-onset criterion. Overestimation of SOL can therefore occur without the influence of personality traits or other psycholophysiological factors. Instead, overestimation of SOL may be a normal response to poor sleep. If similar phenomena occur in a clinical environment, the perception of disturbed sleep might be categorized as ‘overestimation’ of SOL, when in fact the conventional sleep scoring underestimates the degree of sleep disturbance.

As expected, the respiratory apparatus used in the Mask condition also caused an increase in objectively scored arousals. However, it is not clear that increased frequency of arousal was the primary cause of increased subjective SOL or that the mechanism acted primarily on the respiratory system. First, there was not a significant relationship between the number of objectively scored and subjectively reported arousals. Second, the subjects did not report any specific increase in arousals in the Mask condition, yet reported a perception of reduced sleep quality and duration. It is possible that other aspects of the Mask condition may have lead directly to this perception and that an increase in arousals simply co-occurred. For example, the apparatus may have decreased the subjects’ arousal thresholds via discomfort, or may have acted as a salient environmental feature when the subject awoke. In contrast, global measures of sleep disturbance may provide a better indice than specific questions regarding arousal frequency as the duration of wake necessary to define arousals may be too brief to be experienced, or recalled, as time spent awake.

It is also possible that the mask and valve assembly acts as a placebo or anxiety-provoking stimulus rather than as an intervention acting on respiratory mechanisms. However, specific attempts to provoke state anxiety in normal subjects have met with little success ( Bonnet and Webb 1976; Trinder et al. 1994 ). Respiratory load could be a useful alternative to auditory or drug-mediated arousal manipulations in studies of sleep fragmentation. However, further investigation of the properties of this stimulus is needed.

The findings are consistent with a physiological model of insomnia and a ‘neurocognitive’ model for misperception of sleep in insomnia. That is, increased wake induced by the respiratory apparatus led to an increase in cognitive arousal. The increased frequency of arousals may then have been perceived by the subjects as an increased duration of wake, or less time asleep, and therefore influenced their retrospective judgements of sleep onset latency. This study differs from most investigations of subjective SOL in that the period from sleep onset to report of sleep estimates was relatively short (15–25 min), compared with the full night of sleep experienced by subjects in most previous studies. It is less likely in this case that subjects based their SOL estimates on subjective feelings of restedness or other daytime indicators of sleep quality.

These findings suggest that brief arousals from sleep can occur relatively frequently without major change to conventional sleep scoring parameters. In addition, they can occur without being directly perceived by the sleeper. These arousals can nevertheless significantly influence the perception of sleep, even in normal sleepers. Martin et al. (1997 ) concluded that as there was little relationship between microarousal frequency and daytime sleepiness, scoring of microarousals might not be useful in clinical studies. However, the relationship between microarousal frequency and perception of sleep quality suggested by the current study suggests that microarousal scoring may be useful in cases of insomnia.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References

This work was supported by a grant from the Australian Research Council to Professor John Trinder, University of Melbourne, and by a Melbourne Research Award to Simon Smith.

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  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. References
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