Objective and subjective sleep in insomnia

Dr Han Hong Juan
Consultant Ear Nose Throat Surgeon & Medical Director
The ENT, Voice & Snoring Clinic


Insomnia is a common complaint that affects a large proportion of the population at any one time, However, the diagnosis of insomnia as a chronic sleep disorder is also prevalent. Ohayon in 2002 estimated that based on the DSM-IV classification, the prevalence of diagnosed insomnia is 6% (1). The prevalence figures are higher in elderly populations and in females. It is characterized by the inability to initiate or maintain sleep, leading to daytime symptoms of impaired function. These symptoms may include fatigability, reduced alertness, mood disturbances, poor concentration with impairment at work or school and reduced quality of life (2). Insomnia is also associated an increased risk of developing other comorbidities such as psychiatric illnesses and heart failure (3). There is a need to identify, diagnose and treat chronic insomnia. Unfortunately, insomnia therapy is a challenging issue. Current treatment strategies have limitations and best practice guidelines have been developed to aid in diagnosis and treatment of chronic insomnia (4,5).

It is a common observation for patients suffering from insomnia to report less sleep than what they are observed to have. The discrepancy between recorded objective sleep and reported subjective sleep, also known as sleep discrepancy, has long been observed in insomnia. In 1976, Carskadon et al compared the sleep laboratory recordings of 122 chronic insomniacs with their subjective sleep assessments and reported that most subjects underestimated their total sleep time and overestimated the sleep onset latency (6). These findings were consistently reported in subsequent studies against objective measures of polysomnography (7, 8) and even actigraphy (9). The common observation is an underestimation of total sleep time, an overestimation of sleep onset latency and an overestimation of wakening after sleep onset. Also known as “pseudo-insomnia” and “sleep state misperception”. Sleep discrepancy is a spectrum and can vary from an underestimation of sleep to an overestimation of sleep. Overestimation of objective sleep, meaning that patients were exhibiting worse sleep then they were reporting, was described by Trajanovic et al as positive sleep state misperception (10). This represents a small proportion of patients who had daytime symptoms despite reporting adequate sleep. On the other end of the spectrum is paradoxical insomnia. According to the ICSD-2, the hallmark of paradoxical insomnia is that the patient complains of severe and near total insomnia. However the objective measures of sleep do not correlate with the degree of daytime symptoms. This diagnosis is best made with concurrent polysomnographic and self-reported sleep scores (11). In the American Academy of Sleep Medicine clinical practice guideline for the pharmacologic treatment of chronic insomnia in adults published in 2017, they described sleep misperception to be characteristic of all insomnia disorders, varying only in extent.

So if this misperception is common to all patients suffering from insomnia, is there an explanation on why this happens? We will attempt in this essay to explore the reasons put forth in the literature that might help resolve this observation.


We will divide this section along the lines of 2 possible hypothesis. The first hypothesis is that sleep discrepancy in insomnia is a completely subjective phenomenon, a pure perception error and that there is no other basis for the observation. The second hypothesis is that sleep discrepancy has a physiological basis, but we have yet to elucidate the processes behind it. We will look at both lines of arguments and evaluate the evidence behind them.

Personality Traits/Psychological States

In 1997, Dorsey et al looked at 31 undergraduates in a 19 hour experimental protocol where each subject was classified into one of two groups, a sleep onset complaint group and a non-complaint group. This was based on an administered sleep questionnaire which looked at duration of reported sleep latency, frequency of prolonged sleep latency and chronicity of prolonged sleep latency. The subjects then participated in an experimental night (under polysomnographic monitoring) followed by an experimental day (multiple sleep latency testing). A morning questionnaire assessing subjective quality of sleep and an Eysenck’s Personality Inventory (EPI) was administered to these subjects as well. The sleep onset complaint group was then divided into a subjective insomnia group and an objective insomnia group, based on the ratio of self-reported sleep latency to objective latency to stage 2 sleep of the night in question. There were no differences in sleep parameters, performance and daytime sleepiness between the groups. What the results did find was that the subjective insomnia group scored higher for neuroticism than the non-complaining group and the objective insomnia group (12).

In 2000, Vanable et al published their findings in a study that looked at factors that characterise the subjects who reportedly misperceive total sleep time and sleep latency in a cohort of sleep disordered patients (n=104). This included patients with insomnia, periodic limb movement disorders and sleep disordered breathing.  Patients completed a sleep history questionnaire and a Minnesota Multiphasic Personality Inventory (MMPI). The MMPI is a psychological test that assesses personality traits and psychopathology. They then underwent an overnight polysomnographic study and were asked to estimate their total sleep time and sleep latency the following morning. Vanable et al found that patients diagnosed with sleep state misperception (n=8) were more likely to underestimate total sleep time but there was no differences in subjective sleep latency scoring in this group as compared to the other groups. These patients were more likely to score higher on the psychasthenia subscale as compared to the other subjects. Psychasthenia scores relate to neurotic conditions such as phobias, obsessions, compulsions and anxiety (13). They hypothesised that “cognitive rumination, physiological symptoms of tension/anxiety and catastrophic expectations about the impact of poor sleep” may be underlying the discrepancy between subjective and objective sleep.

If personality traits and the resultant psychological states lead to misperceptions of sleep and sleep discrepancy, then would it mean that inducing these states in normal individual might yield similar results? Tang et al in 2004 performed a study to determine if pre-sleep cognitive arousal had any impact on sleep perception. In a cohort of 97 subjects with no sleep complaints, they were randomly divided into 3 groups. The first group was called the anxious pre-sleep arousal group. The subjects in this group were told that that they were required to give a talk to 3 psychologists and a video camera after a 90 minute nap, just before the nap. The second group was called the neutral pre-sleep arousal group. This group was told that they were required to write an essay after waking from their nap. The topic of the talk and essay was similar, foot and mouth disease. The last group was the no-manipulation group and they were simply instructed to nap. The subjects in the first 2 groups were asked to rate their task anxiety levels on a scale of between 1-10. The subsequent sleep of all the subjects were then monitored with actigraphy. They then completed a self-reported sleep questionnaire upon waking. What was discovered was that in the anxious pre-sleep arousal group, subjects reported a higher anxiety level compared to the other 2 groups, reflecting a psychological state of anxiety and cognitive arousal. Also,  subjects in both manipulated groups overestimated their sleep onset latency. The anxious pre-sleep arousal group underestimated the total sleep time compared to the no- manipulation group. Finally, the anxious pre-sleep arousal group had a greater level of sleep discrepancy compared to the neutral pre-sleep arousal group, although this was not statistically significant. Both manipulated groups therefore had sleep discrepancies between their subjectively perceived sleep and actigraphy defined sleep, while the control group did not (13). This interventional study did then show that psychological states that lead to cognitive arousals do distort the perception of sleep.

Deficit in Time Estimation

Total sleep time and sleep onset latency may be under or overestimated if the ability to estimate time was affected. Do insomniacs then also have a perceptual distortion with regards to estimating length of time? Or is this situational and only applicable to sleep? In 2006, Rioux et al examined if subjects with insomnia also demonstrated an impairment in time estimation. They recruited 11 subjects with chronic insomnia and 11 subjects with no sleep complaints. They were all asked to complete a time estimation task. This task was a finger tapping task in 2 phases. The first phase was to synchronise finger tapping on a keyboard to a tone that was generated by a computer at a fixed frequency. The second phase was to continue the same rhythm and timing even after the tone was discontinued. The tone frequency was changed and several series of tones were tested.  The task lasted 20 to 25 minutes. The results showed that there was no difference in the time estimation measures between the 2 groups. The conclusion was that there was no impairment in time estimation demonstrated in this task (14). In an earlier study by Tang et al (2005), similar findings were demonstrated. 20 subjects with chronic insomnia and 20 subjects who were normal sleepers were asked to perform 2 time estimation tasks. The test was done in 2 contexts. The first was done during the day and in the setting of a laboratory and the second was done at night in the subjects’ own bedroom. They were then asked to estimate the length of time between 2 sounds, a “dong’ and a “beep-beep”. The intervals between the 2 sounds were varied at 5 seconds, 15 seconds, 35 seconds, 1 minute and 15 minutes. They found that the was no difference in time estimation between the 2 groups and between the 2 contexts (15). These findings imply that there does not seem to be an inherent deficit in time estimation in insomnia subjects.

Evidence in Sleep Microstructure

So far, we have looked at personality traits and time estimation leading to sleep misperception. What if there was no misperception? What if there was actual disturbances in the quality of sleep in insomnia subjects that we have been unable to identify and these disturbances can only be reflected “subjectively” at the moment. There is a possibility that there may exist some abnormalities in the sleep that are too subtle to be detected by the standard polysomnographic recording methods. We will look at some evidence in the microstructure of sleep which may explain why sleep discrepancy exists, as a limitation of conventional methodology in sleep measurements. 2 common explanations for sleep discrepancy are that of hyperarousal and impaired inhibition, currently not measurable by conventional scoring methods.

We know that the low subjective sleep quality that is commonly seen in insomnia is unable to be verified with standard polysomnography. The limitations of traditional polysomnography methods include the “majority rule” standard for deciding sleep stages. In a situation of a sleep stage transition, the stage assigned is the one which occupies the largest proportion of the epoch. Only 1 sleep stage can only be scored per 30 second epoch. Sleep stages changes are only scored when there are at least 3 consecutive minutes of the new stage observed. Transient events are often ignored relative to the macro view of the current scoring process (16). There might be a role to analyse sleep in greater detail using methods such as spectral analysis, micro-arousal analysis, cyclic alternating patterns (CAP) and event-related potentials (ERPs) (17).

Spectral analysis is a statistical technique used to characterise and analyse sequenced data. A series of signals can be represented as a sum of harmonic waves. With EEG data, spectral analysis is capable of separating the slower and faster parts of the obtained signals. Slow delta waves can then be assessed independent from faster waveforms. Faster waveforms such as beta and gamma waves can also be assessed. These faster waveforms are seen more commonly in wake, stage 1 and REM stage compared to the other sleep stages, implying greater cortical arousals associated with these waveforms (18). Perlis et al in 2001 looked at these fast waves in 3 groups of subjects: primary Insomnia, insomnia secondary to major depression, and good sleeper controls. Each group had 9 matched subjects. They wanted to observe if these high frequency wave activity was associated with sleep discrepancies. They found that subjects in the primary insomnia group had more beta and gamma activity in NREM compared to the other 2 groups. The NREM beta activity was also negatively correlated with the observed sleep discrepancy in total sleep time and sleep latency, meaning that the greater amount of beta fast wave frequencies detected, the greater the degree of sleep discrepancy (19). In 2002, Krystal et al also looked at the spectral analysis of sleep in 3 groups of subjects: subjective insomnia, objective insomnia and normal controls. The subjective insomnia subjects had a relative underestimation of sleep time compared with total sleep time recorded on PSG. There were 20 matched subjects in each group. The authors found that that there was lower delta and higher alpha, sigma and beta EEG activity in NREM sleep in subjects with subjective insomnia as compared to the normal group. There were no reported differences seen in the spectral indexes in REM sleep. Lower delta NREM EEG activity was observed to predict for greater differences in sleep discrepancy. Based on these observations, the authors concluded that there was a link between the findings of reduced delta and increased higher frequency NREM wave activity with the differences found in sleep discrepancy (20). These suggest that increased NREM high frequency EEG activity is characteristic of patients with insomnia. This may reflect greater cortical arousals, perhaps leading to a hyperarousal state not detectable on routine polysomnography. This may explain why patients with insomnia have sleep discrepancies when compared to their PSG data. However, not all studies report similar findings. Buysse et al in 2008 performed EEG spectral analysis between 48 primary insomnia subjects and 25 good sleepers. They found that the men in the 2 groups showed no difference in EEG power but women in the primary insomnia group showed increased high and low frequency EEG activity in NREM sleep compared to the good sleeper group. There was also no association seen between high-frequency EEG activities and subjective sleep ratings in the primary insomnia group (21).

How else can we look for hyperarousal states? Hyperarousal states may also be estimated with micro-arousals. Micro-arousals are abrupt shifts in EEG frequency that last minimally 3 seconds and are scored in REM sleep with a simultaneous increase in submental EMG amplitude and without in NREM sleep. Feige at al in 2008 and their corrigendum in 2012 looked at the sleep ratings and polysomnographic data from 100 subjects with primary insomnia and 100 good sleepers. Insomnia patients had an increased frequency of micro-arousals in both REM and NREM sleep compared with the control group. REM sleep was more affected by a factor of two to three times. The insomnia patients also displayed a greater degree of sleep discrepancy compared to their controls (22).

A of sleep instability beyond looking at individual micro-arousal events is the use of cycling alternating pattern (CAP). Characterized as alternating periodic episodes of aroused EEG activity (phase A) followed by a period of quiet sleep (phase B) recurring in 20-40s periods, CAP has been described as a macro measure of arousal and sleep instability (23). In the context of several different sleep disorders, CAP has been demonstrated to reflect quality of sleep. The higher the CAP, the poorer the quality of sleep. This was seen in patients with insomnia (24) and patients with obstructive sleep apnea (25). Parrino et al in 2009 looked at the role of CAP in sleep discrepancy. They studied 20 subjects with the diagnosis of paradoxical insomnia and 20 matched controls with no sleep complaints. All subjects underwent 2 nights of PSG recording and additional scoring measures of CAP and EEG arousals. Those in the paradoxical insomnia group had lowered amount of slow wave sleep, increased number of awakenings, higher arousal index and CAP rate. There was a significantly higher CAP rate in S1 and S2 sleep in this group. The total CAP rate was 58.1% compared to 35.5% in the controls. The CAP rate was also higher in the period between objective and subjective sleep onset, 64.4% in the paradoxical insomnia group compared to 45.1% in controls. Another interesting finding was that the paradoxical insomnia group reported subjectively less awakenings than recorded, 4 out of 11. The authors hypothesized that due to the increased CAP activity between consecutive awakenings, subjects developed a perception that these individual events were a single prolonged awakening (26). Besides the volume of activity, recent work by Chouvarda et al has analysed the dynamic structure and content of CAP to qualify differences that might be meaningful in characterising micro-structure of sleep and sleep disorders (27,28). This would require more validation studies.

A rather more complex micro-structure marker of hyperarousal states is event-related potentials (ERPs). Event related potentials are EEG measured electrical responses of the brain to either external stimuli like sound or internal mental activities. It is a measure of information processing. It is measured by the small amplitude changes in EEG after application of a stimulus. This is seen as an initial positive wave deflection (P1) followed by a negative deflection (N2). During sleep there must be an inhibition of information processing in order to transition from consciousness to unconsciousness and this is reflected in changes to the ERPs. A possible explanation for insomnia is the inability to inhibit information processing during the onset of sleep and during sleep itself. If this were true, then the differences in ERPs between insomniacs and good sleepers will evident in ERPs. Yang et al in 2007 studied the ERPs in response to an auditory stimulus in 15 insomnia subjects and 15 good sleepers in an overnight study. They found that insomnia subjects had ERPs with larger N1 and smaller P2 during the first 5 minutes of stage 2 sleep compared to good sleepers. There were no differences in ERP waveforms between the 2 groups when averaged over the entire night. They concluded that these observations represented a decrease in the inhibition of information processing that normally occurs during sleep onset, supporting a state of hyperarousal (29). Turcotte et al in 2011 looked at ERPs changes in subjects with paradoxical insomnia and psychophysiological insomnia and compared them with matched controls of good sleepers. There were 26 subjects in each group and the ERPs were measured in wakefulness in the evening, at sleep onset and early stage 2 sleep for 4 consecutive nights in a sleep laboratory. Auditory stimuli was administered and the subjects were asked to ignore the stimuli. The results showed that during wakefulness and sleep onset, N1 was smaller for the psychophysiological insomnia group and larger for the paradoxical insomnia group. At sleep onset, P2 was smaller for the psychophysiological insomnia group. In wakefulness and stage 2 sleep, P2 was larger for the paradoxical insomnia group. They concluded that the psychophysiological insomnia group had a decrease in the inhibition of information processing at sleep onset while the paradoxical insomnia group had an increase in information processing across all three measurement times. The distinct differences in ERPs seen in the paradoxical insomnia subjects might account for sleep discrepancy (30).

The above studies show that there are quantifiable differences seen in sleep micro-structure that might account for the subjective differences in sleep perception seen in insomnia. They demonstrate a possible hyperarousal state that exists not only during sleep onset, between the periods of objective and subjective sleep onset but also during sleep. However, what is an accurate measure of hyperarousal states? Are these accurate measures? Are these measures looking the same thing? More data is required.

REM Sleep Instability

In 2008, Feige et al performed a study on 100 subjects with primary insomnia and 100 subjects with no sleep complaints. They measured subjective estimates of sleep and PSG variables in both groups. They found that there was an increased in arousal time in the insomnia group. This was attributable to a disproportionate increase within the REM component of total sleep time. They also found that the size of the discrepancy between objective and subjective sleep was proportional to the amount of REM sleep in the insomnia group. They hypothesized that neuronal activity during REM sleep contributed towards subjective wake times and disturbed sleep (31). The same group led by Riemann in 2012 proposed a “REM sleep instability” hypothesis as a possible mechanism for explaining sleep discrepancy. They theorised that in insomnia patients, the highly aroused brain activity state of REM sleep would be predisposed to cognitive intrusions with pre-sleep concerns. These concerns include inability to fall asleep and maintain sleep. These pre-sleep concerns then intrude into and dominate dreams leading to misperception of sleep/wake and lead to discrepancy of subjective sleep (32). A study done in 2013 by St-Jean et al seems to support this hypothesis. They used spectral analysis to examine the EEG activities of 3 groups of subjects, 26 psychophysiological insomnia subjects, 20 paradoxical insomnia subjects and 21 good sleepers over 3 nights. They found that in the paradoxical insomnia group, that there was reduced cortical activity in NREM sleep and increased cortical activity in REM sleep. They postulated that this may contribute to the sleep discrepancy seen in the paradoxical insomnia group (33). In a review article published in 2009, Schredl looked at sleep disorders studied in relation to dreams. He described insomnia being associated with enhanced dream recall and dreams reflecting real life stressors (34). It would be hard to prove, but more directed studies are needed  to prove that REM instability has a direct relationship with sleep discrepancy.

Neurophysiological Basis

We have looked at some form of increased cortical activity as a basis for sleep discrepancy. Is there a physiological model that supports this explanation?

In 2017, Kay et al hypothesised that sleep onset discrepancy was associated with increased glucose metabolism in the areas of the brain responsible for conscious awareness. They performed fluorodeoxyglucose positron emission tomography (FDG-PET) scans on 32 primary insomnia and 30 good sleep subjects during NREM sleep. The relative regional cerebral metabolic rate for glucose was measured. These subjects also completed subjective sleep questionnaires. In the insomnia group, the greater the discrepancy between subjective and objective SOL, the higher the relative glucose metabolism during NREM sleep in the right anterior insula and middle/posterior cingulate cortex. Interestingly, in the good sleepers group, the same findings of metabolism were also observed if there were higher positive discrepancy in SOL. These findings are in keeping with the proposed hypothesis, which is that altered activity during NREM sleep, in the brain areas responsible for awareness, could account for sleep discrepancy. Also positive and negative sleep discrepancies may involve the  activity of different set of neurons within the same brain areas (35).

In 2018, Hsiao et al performed a simultaneous EEG and functional MRI study on 36 healthy

Subjects while they were attempting to fall asleep. They were then awakened after either entering into stable N1 or N2 sleep or after 90 minutes of not falling asleep. They were then asked to recall the moment just before awakening. In those exhibiting sleep discrepancy that there was increase in fMRI activity seen in the fronto-parietal pathways governing executive control. This was hypothesised to be explained by hyperarousal of the brain in these regions (36).


I propose that the above explanations can be amalgamated. Herbet et al in 2017 has demonstrated that patients with higher pre-sleep cognitive activity and greater sleep effort had more sleep discrepancy (37). This is likely secondary to a psychophysiological factor leading to a an increase in cortical activity. This increase in cortical activity can be measured with multiple means analysing electrophysiological sleep microstructure. These measures are unfortunately not detected by current accepted scoring methodology, hence the discrepancy. It also has a demonstrable metabolic physiological basis. The broad pieces seem to be in place. However this challenges the electrophysiological definition of sleep, beyond our current consensus. As always, more studies and data will be required.


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