The distinction between sleep and wakefulness has important implications for the diagnosis, treatment and monitoring of sleep disorders. Patient perceptions of their sleep and wake durations may not accord with objective measures by current conventions. It is critical, therefore, to anticipate any potential disparities between subjective and objective measures of sleep or wakefulness. This study offers several observations regarding the mismatch between subjective and objective sleep–wake times, comparing these findings across insomnia or sleep apnea. We dispel common myths that might explain these differences and offer future directions to further explore this important topic.
Our study demonstrates five main findings about subjective–objective mismatch of the perception of sleep or wakefulness at night: (i) Substantial TST mismatch (>80 min underestimation compared with objective measurement) was common in patients who self-report insomnia; (ii) TST mismatch was less apparent in patients with OSA, whether or not they also had insomnia; (iii) TST mismatch was not explained by either shallow sleep (i.e. N1) or short sleep bouts being mistakenly perceived as wake; (iv) Subjective TST correlated modestly with objective TST, but not with other PSG metrics; (v) Perhaps the most surprising, wake time mismatch was context dependent. Specifically, people overestimated the time it took to initially fall asleep, while underestimating WASO. Our findings are consistent with a growing literature documenting subjective–objective mismatch in patients with insomnia. One study that showed an average of 1-h underestimation in chronic insomnia only found it among patients with objective sleep time >6 h (Fernandez-Mendoza et al., 2011). Some studies showed less mismatch than our study (Bonnet and Arand, 1997, 2003; Edinger and Fins, 1995; Martinez et al., 2010; Schneider-Helmert and Kumar, 1995; Vanable et al., 2000), some reports showed mismatch magnitudes similar to our findings (Mercer et al., 2002; Salin-Pascual et al., 1992; Tang and Harvey, 2006), while others showed greater magnitude of underestimation (>3 h; Manconi et al., 2010; Parrino et al., 2009). We do note that the populations studied in each of these studies differed from each other and from our study, which may account for some the variation. In summary, overall, our hypothesis that subjective–objective mismatch is driven by sleep fragmentation was not supported.
Estimation of waking times showed significant mismatch in all groups, and was notably context dependent: the wake time comprising sleep latency was overestimated, while the wake time within the sleep period (WASO) was underestimated. We find it tantalizing to consider the possibility that the process of emerging from sleep within the night alters either the perception of time or encoding of new memory of being awake. (Consider, for instance, anecdotes of people being awoken in the middle of the night by a telephone call, and having little or no memory for the content of that call, even though they were awake for the conversation.) This could be related, for example, to the phenomenon of sleep inertia, as patients transitioning from sleep to wake may not immediately achieve cognitive capacity typical of daytime wakefulness.
The notion that wakefulness immediately adjacent to episodes of sleep might disrupt memory would appear, on first inspection, to counter reports that demonstrate that sleep physiology tends to boost memory (Ellenbogen et al., 2006). However, those studies demonstrate an enhancement of existing memories from the previous day(s), as opposed to acquisition of brand new memories.
Sleep architecture correlates of subjective TST estimation
How people estimate the passage of time while asleep remains poorly understood (Harvey and Tang, 2012). In this study, significant mismatch between subjective and objective TST was only observed in the group reporting insomnia symptoms who did not also have OSA. In this group, subjective TST was positively correlated with objective TST, which was itself correlated with other sleep architecture metrics. Although we observed negative correlations between subjective TST and measures of wakefulness and N1 sleep, these markers of fragmentation were also correlated with objective TST. Multiple regression analysis suggests that if further variance in subjective experience is related to these features, beyond what is explained by TST itself, that the effects are either small or highly heterogeneous.
From a cognitive standpoint, we assessed the extent to which subjects might be guessing at these estimates, which would introduce confounding variance, by requiring a measure of certainty for all estimates. If guessing was random, this would lead to regression to the mean (i.e. no correlation between self-reported certainty and the associated error). However, it is possible that some patients employ a heuristic for estimation that translates their uncertainty into underestimates of sleep time (i.e. ‘I didn't sleep well, so I'm guessing I didn't sleep much’). Although there was some suggestion of pessimism in the estimation of sleep latency in the insomnia group, there was no correlation of the TST error or the WASO error with the certainty of each of these estimates (data not shown).
As mentioned by others (Edinger and Krystal, 2003; Manconi et al., 2010; Vanable et al., 2000), heterogeneity is seen in the distribution of TST mismatch errors: in our study, half of the subjects with insomnia alone underestimated by at least 80 min, but only a small fraction overestimated TST by that amount. It is also worth noting that the absolute magnitude of mismatch may have context-dependent implications. For example, underestimating sleep by 80 min may not be as concerning for the patient already sleeping more than 8 h compared with another patient only sleeping, for example, 4 h. In this regard, Manconi et al. (2010) proposed a metric of mismatch that places the absolute error in the context of the TST value. This index has the added benefit of centralizing the mismatch between 0 and 1 for those who underestimate their TST. In their study, this index correlated highly with the absolute mismatch value (R = 0.95), which was used in the current study. The method of characterizing mismatch could impact phenotyping efforts, as the calculation methods may impact the resulting distributions. If treatment strategies are to be tested and eventually targeted based on phenotyping, the methods of phenotyping must be carefully considered.
It is possible that clustering or similar methods of objectively phenotyping subpopulations may lead to further insights into insomnia mechanisms. Clearly, objective sleep measurements in patients with insomnia are characterized by variability (Edinger et al., 1991; Means et al., 2003), which supports the need for ongoing phenotyping efforts. Patients with insomnia also differ by non-sleep clinical characteristics, such as psychiatric and personality scales, which may also be useful for phenotyping (Fernandez-Mendoza et al., 2011). Patients with insomnia may also differ by subjective reports of sleep-related topics: subgrouping has been reported in terms of magnitude of TST underestimation according to self-reported periodic limb movements in sleep, perceived causes of their insomnia, and the presence of both onset and maintenance difficulties (Edinger and Fins, 1995).
Formal cluster analysis using a large dataset of insomnia and normal sleepers revealed 14 subgroups (based on factor analysis of 38 chosen clinical variables), and interestingly these empirically derived clusters did not correlate well with clinical diagnostic phenotyping according to ICSD and psychiatric criteria (Edinger et al., 1996). These findings lend support to the idea that routine clinical phenotyping may be augmented by more formal (i.e. statistical) methods of phenotyping. Another report indicated four clusters of sleep perception patterns in patients with insomnia, where most subjects fell into the accurate group or the stable mild underestimation group, while one smaller subgroup showed marked underestimation, and the final subgroup showed overestimation (Means et al., 2003). More recent use of clustering approaches has revealed patterns of self-reported sleep in subjects with insomnia, in which three groups were evident: an unpredictable pattern; a high probability of insomnia; and a low probability of insomnia (Vallieres et al., 2011). One can imagine considering multiple factors including temporal predictability of subjective and objective sleep, and thus also the degree of mismatch, as well as evidence of homeostatic response after 1–2 ‘bad nights’. Such empiric phenotyping may offer a complementary view of insomnia relative to the current diagnostic subclassifications based on clinical history alone. This type of analysis may benefit from larger populations to ensure adequate sampling of potential subpopulations, as well as repeated measures to determine whether state or trait factors may influence the mismatch. It will be particularly important to include patients with insomnia with a spectrum of objective sleep difficulty (based on metrics such as TST, sleep efficiency); patients with severe objective sleep disturbance were not well represented in the current cohort.
From a diagnostic standpoint, when restricting case definitions to the specific circumstance of a patient with insomnia complaints who has normal sleep architecture, it has been estimated that so-called paradoxical insomnia has a low prevalence of <5% (ICSD 2005). However, discrepancies between subjective experience and objectively measured sleep may be more common and may overlap with other causes of insomnia (Bonnet and Arand, 1997; Edinger and Fins, 1995; Edinger and Krystal, 2003; Edinger et al., 2000; Fernandez-Mendoza et al., 2011; Frankel et al., 1976; Manconi et al., 2010; Means et al., 2003). Our results are in line with the findings reported in these studies, with respect to underestimation of TST. However, some studies (Manconi et al., 2010) have shown a bimodal distribution of mismatch, with a population with extreme mismatch distinct from those with accurate perception or less severe mismatch. Mismatch may of course be part of the presentation of insomnia in general, most particularly evident in the paradoxical subtype where it is a hallmark and perhaps categorical rather than dimensional feature. However, it is also important to recognize that a continuum of misperception may also present in other forms of insomnia, including the most common subtype of psychophysiological insomnia. Recognition of mismatch has important implications from a diagnostic and therapeutic standpoint. Assessing a patient's subjective experience of sleep in the laboratory setting may provide useful information for the patient and treating providers, by informing the extent to which mismatch might be relevant to that individual.
It is worth noting that subjective reporting of sleep duration may vary with time frame of recollection. One study showed that immediate report (morning diary) for seven consecutive days of objective sleep monitoring differed from a retrospective self-assessment completed at the end of the study in which subjects gave global estimates of the prior week (Fichten et al., 2005). Specifically, the retrospective gestalt estimate showed an exaggeration compared with the average of the underestimations provided each morning for the prior week. One could speculate that this exaggeration reflects a trait of some patients with insomnia. Considering that clinic patients reporting insomnia symptoms are often asked to recollect average or trends in sleep–wake times over weeks or months or longer, such findings as reported by Fichten et al. (2005) serve to emphasize the uncertainty inherent in the clinical assessment of insomnia. When evaluating subjective sleep complaints, sleep diaries may provide some insight into this issue of immediate versus long-term retrospective estimations. Diaries do not, however, directly address the question of mismatch, with the possible exception that non-physiological reports (for example, several days or weeks of little or no sleep with no homeostatic rebound) are more likely to represent mismatch.
From a symptomatic standpoint, there are mixed data regarding the daytime consequences of insomnia (Riedel and Lichstein, 2000). In one interesting study, in which normal subjects were ‘yoked’ to the patterns of fragmentation seen in patients with insomnia, although objective sleepiness and mood disturbance increased, personality scales and subjective sleep perception remained intact (Bonnet and Arand, 1996). Interestingly, it has been shown that poor memory performance and other functional/mood assessments may be independently linked to subjective impression of sleep as well as objective EEG findings (Rosa and Bonnet, 2000). This suggests that identifying and addressing misperception might be of therapeutic benefit.
The extent of mismatch may provide practical information that could direct treatment. A simplified framework could consider whether or not objective sleep abnormalities are present, and within each of these two binary categories, whether subjective estimates were accurate or not. Although each of these axes is likely a continuum, the dichotomous construct may be useful to consider four possible combinations, but even this simplified framework could not be employed without objective measurement. Repeated measures might prove useful to distinguish state versus trait pathophysiology of mismatch.
Comparing the PSG sleep time with the subjective estimates, ideally over multiple nights, could be used to stratify patients with insomnia according to who might respond positively to feedback regarding their objective sleep durations. Perception of sleep may be malleable by non-pharmacological means, as has been recently demonstrated (Tang and Harvey, 2006). Over 40 years ago, it was suggested that one could be trained to more accurately guess the stage of sleep from which one was awoken (Antrobus, 1967), suggesting that feedback about sleep physiology can be quite powerful. In fact, feedback-driven training has been effective in one study of insomniacs (Downey and Bonnet, 1992). It is intriguing that even ‘random’ feedback (unrelated to actual sleep measurements) can shape waking function (Semler and Harvey, 2005), again emphasizing the potential impact of feedback in non-pharmacological insomnia management. A recent small case series (n = 4) suggested that providing feedback related to the PSG findings improved mismatch only in the two subjects with fairly normal sleep (Geyer et al., 2011).
One potential reason this feedback strategy has not been widely employed is that multiple nights of PSG are inconvenient and costly. From a management standpoint, it could be that the extent of mismatch influences the types of hypnotic (or even non-pharmacological) treatments that are undertaken. The correlations between subjective TST and sleep architecture are interesting in this context. Our results raise the possibility that competing influences of medications on time spent in these stages may account for some of the variance in clinical responses. For example, benzodiazepine hypnotics may decrease N1 and WASO, and increase N2 while decreasing REM and slow-wave sleep. However, benzodiazepines and possibly other sleep aids may alter either the cognitive estimation of time or memory of the passage of time, and teasing these factors apart is deserving of further study. It has been shown that the subjective response to benzodiazepine hypnotics may exceed objective improvement of sleep time (Holbrook et al., 2000); a portion of the efficacy of benzodiazepines may be related to the amnestic properties. A greater subjective than objective benefit has also been reported, however, for non-pharmacological interventions (Epstein et al., 2012). The basis for disproportionate subjective benefit in cognitive behavioral therapy versus pharmacological treatments remains uncertain. For patients with prominent mismatch, who may be achieving a reasonable number of hours of sleep per night, one would question whether the risks of long-term hypnotic therapy outweighed the arguably non-medical benefit of improving perception alone. From a non-pharmacological standpoint, perhaps with the growing availability of home sleep monitors, feedback-driven management of insomnia may become more commonplace.
Another important treatment consideration relates to longitudinal management of insomnia. It has been suggested that mismatch is a perpetuating factor for those with chronic insomnia (Mercer et al., 2002). This finding raises the important but poorly understood issue of whether mismatch is a ‘trait’ that is difficult to reverse, or whether it is a ‘state’ linked to behaviors or other variables contributing to sleep fragmentation. One could imagine that patients with a trait type of mismatch might find it challenging to wean from hypnotic use; conversely, it is possible that those with a state type of mismatch might be more amenable to behavioral interventions and less likely to need chronic hypnotic treatment.
Finally, the spectrum of mismatch is relevant for interpretation of large epidemiological studies on sleep duration (Bliwise and Young, 2007; Kessler et al., 2011; Phillips and Mannino, 2005). These studies are often, by practical necessity, limited to self-reporting of sleep duration (although, see Vgontzas et al., 2010). Among the concerns regarding interpretation of such studies, the heterogeneity may arise from the confounds of mismatch as well as altered retrospective (compared with immediate) self-reporting (Fichten et al., 2005).
Limitations and future directions
We recognize several limitations that could be addressed in future studies. First, self-reported insomnia complaints were documented by intake form as a routine part of clinical PSG, and thus we lack formal diagnostic characterization. Careful clinical phenotyping as described above (temporal patterns of subjective and objective sleep) may shed light on possible correlations of mismatch with clinically defined insomnia subtypes. Second, because these studies were performed for clinical purposes, we are limited to a single night of sleep in each patient. The question of state versus trait mismatch thus cannot be addressed without repeated measures; further work using multiple nights of laboratory study, or with home sleep monitoring, are needed to address this question. Night-to-night variability in sleep quality and quantity has been suggested in the literature, and the variability itself (or lack thereof) may be an important feature of improved and objective insomnia phenotyping. Third, because our database consists of PSGs performed for clinical purposes, we lack a normal control group against which to compare the sleep architecture values.
Future studies are needed to better understand the subjective perception of time during sleep and wakefulness, and to better quantify objective elements of sleep physiology. Current standards for objective assessment of sleep contain conventions that might not capture those aspects most salient to patients' subjective experience. Advanced signal-processing techniques might enhance this process, providing a more refined, objective appraisal of sleep. Similarly, a better understanding of the psychology of time perception, in both health and disease, might better refine the subjective metric.