Sleep-wake misperception in sleep apnea patients undergoing diagnostic versus titration polysomnography

A scoping review of sleep discrepancy methodology: What are we measuring and what does it mean?

STUDY OBJECTIVES

To examine how past studies have conceptualised sleep discrepancy and identify and evaluate the methods used for its measurement and analysis.

METHOD

We searched MEDLINE, Embase, PsycINFO, CINAHL Plus, PubMed, Scopus, and Web of Science in April 2022 for studies comparing self-report and objective measures of sleep. Methodological information was extracted from relevant studies and included measures of self-report and objective sleep, sleep variables (e.g., total sleep time), derived discrepancy indices (e.g., difference scores), handling of repeated measurements, and methods of measure comparison (e.g., Bland-Altman analyses).

RESULTS

Two hundred and forty-four relevant records were identified. Studies varied according to objective sleep measure; actigraphy algorithm, software, and rest interval; polysomnography setting and scoring criteria; sleep variables; self-report sleep measure; number of nights of objective recording; time frame of self-report measure; self-report sleep variable definition; sleep discrepancy derived index; presence and handling of repeated measurements; and statistical method for measure comparison.

CONCLUSIONS

Sleep discrepancy was predominantly conceived as discordance in sleep states or sleep time variables, and various forms of this discordance differed in their conceptual distance to sleep misperception. Furthermore, studies varied considerably in methodology with critical conceptual and practical implications that have received little attention to date. Substantive methodological issues were also identified relating to the use of derived indices for operationalising sleep discrepancy, defining objective sleep onset latency, calculating actigraphy rest intervals, measuring correlation and concordance, averaging sleep variables across nights, and defining sleep quality discrepancy. Solutions and recommendations for these issues are discussed.

Assessing sleep metrics in stroke survivors: a comparison between objective and subjective measures

INTRODUCTION

Stroke survivors are at risk of sleep disturbance, which can be reflected in discrepancies between objective and subjective sleep measures. Given there are limited studies on this phenomenon and using portable monitoring devices is more convenient for stroke survivors to monitor their sleep, this study aimed to compare objectively measured (Belun Ring) and subjectively reported (sleep diary) sleep metrics (total sleep time (TST) and wakefulness after sleep onset (WASO)) in stroke survivors.

METHODS

In this cross-sectional study, thirty-five participants wore a ring-shaped pulse oximeter (Belun Ring) and kept a sleep diary for three consecutive nights in one week. The effects of various factors on TST and WASO were analyzed by linear mixed models. Systematic bias between two measures was examined by the Bland-Altman analysis.

RESULTS

TST and WASO were significantly affected by measures (p <.001), but not night. TST was significantly lower and WASO was significantly higher in the Belun Ring than in the sleep diary (p <.05). Age was the only covariate that had a significant effect on WASO (p <.05). The Bland-Altman analysis demonstrated positive bias in TST (29.55%; 95% CI [16.57%, 42.53%]) and negative bias in WASO (-117.35%; 95% CI [-137.65%, -97.06%]). Proportional bias was exhibited in WASO only (r =.31, p <.05).

CONCLUSION

The findings revealed discrepancies between objective and subjective sleep measures in stroke survivors. It is recommended that objective measures be included when assessing and monitoring their sleep conditions.

Does providing feedback and guidance on sleep perceptions using sleep wearables improve insomnia? Findings from "Novel Insomnia Treatment Experiment": a randomized controlled trial

STUDY OBJECTIVES

Insomnia is a disorder diagnosed based on self-reported sleep complaints. Differences between self-reported and sensor-based sleep parameters (sleep-wake state discrepancy) are common but not well-understood in individuals with insomnia. This two-arm, parallel-group, single-blind, superiority randomized-controlled trial examined whether monitoring sleep using wearable devices and providing support for interpretation of sensor-based sleep data improved insomnia symptoms or impacted sleep-wake state discrepancy.

METHODS

A total of 113 (age M = 47.53; SD = 14.37, 64.9% female) individuals with significant insomnia symptoms (Insomnia Severity Index(ISI) ≥10) from the community were randomized 1:1 (permuted block randomization) to receive 5 weeks (1) Intervention (n = 57): feedback about sensor-based sleep (Fitbit and EEG headband) with guidance for data interpretation and ongoing monitoring, and (2) Control (n = 56): sleep education and hygiene. Both groups received one individual session and two check-in calls. The ISI (primary outcome), sleep disturbance (SDis), sleep-related impairment (SRI), depression, and anxiety were assessed at baseline and post-intervention.

RESULTS

In total, 103 (91.2%) participants completed the study. Intention-to-treat multiple regression with multiple imputations showed that after controlling for baseline values, compared to the Control group (n = 51), the Intervention group (n = 52) had lower ISI (p = .011, d = 0.51) and SDis (p = .036, d = 0.42) post-intervention, but differences in SRI, depression, anxiety, and sleep-wake state discrepancy parameters (total sleep time, sleep onset latency, and wake after sleep onset) were not meaningful (P-values >.40).

CONCLUSIONS

Providing feedback and guidance about sensor-based sleep parameters reduced insomnia severity and sleep disturbance but did not alter sleep-wake state discrepancy in individuals with insomnia more than sleep hygiene and education. The role of sleep wearable devices among individuals with insomnia requires further research.

CLINICAL TRIAL REGISTRATION

The Novel Insomnia Treatment Experiment (NITE): the effectiveness of incorporating appropriate guidance for sleep wearables in users with insomnia. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378452, Australia New Zealand Clinical Trials Registry: ACTRN12619001636145.

Effects of Continuous Positive Airway Pressure on Sleep State Misperception in Patients With Obstructive Sleep Apnea

BACKGROUND

Little is known regarding the effects of continuous positive airway pressure (CPAP) on sleep misperception in obstructive sleep apnea (OSA).

METHODS

Sleep state perception was measured by subtracting the objective total sleep time from the subjective sleep duration. Sleep underestimation and overestimation were defined as ± 60 minutes sleep perception. Insomnia and depressive symptoms were assessed using questionnaires. Finally, nonparametric statistical analyses were performed.

RESULTS

Of the 339 patients with OSA included in the study, 90 (26.5%) and 45 (13.3%) showed sleep underestimation and overestimation, respectively. Overall, a significant underestimation of sleep was noted during CPAP titration comparing to a diagnostic PSG (P < 0.001). OSA patients with insomnia or depressive symptoms did not show any changes in sleep perception between diagnostic and CPAP titration studies, whereas those without insomnia or depressed mood showed significantly underestimated sleep duration during CPAP titration. Patients with OSA and either underestimated or overestimated misperception showed perceptual improvements during CPAP titration regardless of the presence of insomnia or depressive symptoms. However, of 204 patients with normal sleep perception, 138 (67.6%) and 10 (4.9%) had underestimation and overestimation of sleep during CPAP titration.

CONCLUSION

CPAP titration may improve sleep perception with moderate to severe OSA who have sleep misperception. However, CPAP titration may result in sleep misperception especially underestimation of sleep in those who have normal sleep perception.

Migraine may disturb sleep perception during sleep onset: a retrospective data analysis

STUDY OBJECTIVES

As sleep latency is an important factor in the diagnosis of many disorders, it is important to know whether the patient's self-reported evaluation of sleep latency corresponds with an objectively measured evaluation. Some studies indicate that patients usually overestimate their sleep latency. We sought to determine how comorbidities affect the patient's ability to assess their sleep latency.

METHODS

This was a retrospective study of 240 patients who had a polysomnography recorded at our sleep unit or at home in 2017-2020. Data on comorbidities were collected from hospital records.

RESULTS

Mean objective sleep latency (29.5 minutes, standard deviation [SD] 35.5) was significantly lower than self-reported sleep (37.4 minutes, SD 41.6) (P < .001). The patients who overestimated their sleep latency had higher mean apnea-hypopnea index (18.8 events/h, SD 21.6, vs 13.4 events/h, SD 12.8; P = .04) and higher mean sleep efficiency (81.7%, SD 13.6%, vs 75.2%, SD 13.9%; P = .004) than those who underestimated their sleep latency. There were significantly more patients with migraine in the overestimation group than in the underestimation group (20/159 patients vs 3/81 patients; P = .035). This difference was not observed in patients with headache without migraine (P = 1.000).

CONCLUSIONS

We hypothesize that migraine is markedly associated with overestimation of sleep latency. This overestimation was not observed in patients with other headache types. Further studies are needed to explore the relation between migraine and sleep onset misperception.

CITATION

Rantanen O, Hollmen M, Bachour A. Migraine may disturb sleep perception during sleep onset: a retrospective data analysis. J Clin Sleep Med. 2022;18(9):2113-2117.

Association Between Subjective-Objective Discrepancy of Sleeping Time and Health-Related Quality of Life: A Community-Based Polysomnographic Study

OBJECTIVE

This study aimed to investigate the existence of a difference in quality of life (QOL) between individuals with and without significant subjective-objective discrepancy (SOD) in total sleep time (TST).

METHODS

From the Sleep Heart Health Study 2, 2540 individuals who had completed polysomnography, a morning sleep survey, and the 36-item Short-Form Health Survey (SF-36) were included in the analyses. The participants were classified as normoestimators (estimation of TST <±60 minutes), underestimators (underestimation of TST ≥60 minutes), or overestimators (overestimation of TST ≥60 minutes). The standardized SF-36 QOL scores were compared among the three groups. An adjusted partial correlation analysis was conducted between SOD and QOL.

RESULTS

Of the 2540 participants, 1617 (63.7%), 433 (17.0%), and 490 (19.3%) were assigned to the normoestimator, underestimator, and overestimator groups, respectively. The bodily pain and social functioning components of the SF-36 score were significantly lower in the underestimators than in the normoestimators, whereas the physical functioning component was significantly lower in the overestimators than in the normoestimators. The absolute value of SOD in the TST showed a significant negative correlation with the physical and mental components of the SF-36.

CONCLUSIONS

QOL was significantly better in the normoestimator than in the other groups and linearly correlated with the absolute value of SOD. This study suggests that a high prevalence of positive and negative sleep misperception in a community population can be a potential factor associated with poor QOL and potential comorbidities.

Factors associated with sleep state misperception in patients with obstructive sleep apnea

PURPOSE

Little is known regarding the prevalence of sleep state misperception and the factors related to this in patients with obstructive sleep apnea (OSA).

METHODS

This retrospective study included patients with OSA defined by an apnea-hypopnea index (AHI) of ≥ 5 and used the Insomnia Severity Index (ISI), the Epworth sleepiness scale, the Patient Health Questionnaire-9, and the Generalized Anxiety Disorder-7. Underestimation and overestimation of sleep state perception were defined as < 80% and > 120%, respectively, of the ratio between subjective and objective total sleep time. An ISI score > 14 indicated clinically significant insomnia and an AHI ≥ 30 indicated severe OSA. A multinomial logistic regression was conducted with the category of sleep state perception as an outcome variable.

RESULTS

Of the 707 patients with OSA, underestimation and overestimation of sleep state perception were noted in 22.5% and 10.6% of subjects, respectively. The median absolute differences (and percentages of the ratio) between subjective and objective total sleep time were 116 min (66.9%) and 87 min (127.3%) in the underestimated and overestimated perception groups, respectively. In the adjusted model, the underestimated group was more likely to have an ISI score > 14 (OR = 1.812, P = .006). The overestimated group was more likely to be older (OR = 1.025, P = .025) and has severe OSA (OR = 1.729, P = .035).

CONCLUSIONS

There are two patterns of sleep state misperception in patients with OSA: underestimation associated with comorbid insomnia symptoms and overestimation associated with severe OSA. These findings enhance understanding of the pathophysiology of sleep state misperception in patients with OSA.

A Study of Factors Causing Sleep State Misperception in Patients with Depression

PURPOSE

Sleep state misperception, which is the discrepancy between subjective and objective sleep, is often observed in patients with depression. This phenomenon may delay the remission of depression. Previous studies have focused on the total sleep time (TST) misperception, with many of these studies using actigraphy. Thus, our study investigated depressed patients with the exploratory aim of clarifying factors associated with the sleep state misperception including the wake after sleep onset (WASO) misperception, with their objective sleep additionally evaluated by polysomnography (PSG).

PATIENTS AND METHODS

We conducted a cross-sectional study. Before undergoing overnight PSG monitoring, 40 patients with depression completed questionnaires that included the Beck Depression Inventory (BDI), Epworth sleepiness scale, Temperament and Character Inventory, and the Pittsburgh sleep quality index. Patients were also asked to estimate their subjective sleep duration after they woke up in the morning. Based on this data, we calculated the misperception using the following formula: subjective sleep duration minus objective sleep duration. We compared each factor between negative and positive misperception groups and the multiple regression analysis was performed for TST and WASO misperception, respectively.

RESULTS

Although sleep architectures, age, severity of depression and obstructive sleep apnea (OSA) exhibited differences in underestimating or overestimating the WASO, only sex differences were associated with underestimating or overestimating their total sleep time (TST). Moreover, BDI, the severity of OSA, sleep architectures (N1% and N2%), and benzodiazepine (BZD) use were significantly correlated with WASO misperception, whereas only OSA severity was significantly correlated with TST misperception. A subsequent multiple regression analysis demonstrated the BDI was independently correlated with the WASO misperception (β=0.341, p=0.049).

CONCLUSION

In clinical practice, interventions especially for OSA, and the reduction of depressive symptoms are an important method for improving patient sleep perception. Moreover, current results suggest that BZD prescriptions should be avoided as well.

Sleep-wake misperception. A comprehensive analysis of a large sleep lab cohort

OBJECTIVES

Sleep-wake misperception has mainly been reported in insomnia patients. Conversely, the present study aimed to assess the prevalence and correlates of sleep-wake misperception in a large cohort of patients with various sleep-wake disorders, all diagnosed along the third version of the International Classification of Sleep Disorders.

METHODS

We retrospectively included 2738 patients examined by polysomnography, who in addition estimated upon awakening their total sleep time, sleep onset latency and Wake after sleep onset (WASO). We computed subjective-objective mismatch by the formula (subjective - objective value)/objective value ×100; negative and positive values indicated under- and overestimation, respectively.

RESULTS

In the entire sample, the magnitude of under- and overestimation of total sleep time was similar, but varied significantly between diagnostic groups, with insomnia and insufficient sleep syndrome showing the most pronounced underestimation and REM parasomnia and circadian rhythm disorders showing the most pronounced overestimation of total sleep time. In all diagnostic categories, a majority tended to overestimate their sleep onset latency and to underestimate the amount of WASO. Younger age was independently correlated with underestimation of total sleep time and WASO, and with overestimation of sleep onset latency. Overestimation of sleep onset latency independently correlated to an increased latency to N3 sleep stage on polysomnography.

CONCLUSIONS

While sleep-wake misperception is highly prevalent in all sleep-wake disorders, significant differences exist in magnitude of under- and overestimation between distinct diagnostic groups.

Comorbid Insomnia and Obstructive Sleep Apnea (COMISA): Current Concepts of Patient Management

Obstructive sleep apnea (OSA) and insomnia are the two most common sleep disorders among the general population, and they may often coexist in patients with sleep-disordered breathing (SDB). The higher prevalence of insomnia symptoms in patients with OSA (40–60%) compared to that observed in the general population has thus led researchers to identify a new disorder named comorbid insomnia and OSA (COMISA), whose true burden has been so far largely underestimated. The combined treatment of COMISA patients with positive-airway pressure ventilation (PAP) with cognitive behavioral therapy for insomnia (CBTi) has shown a better patient outcome compared to that obtained with a single treatment. Furthermore, recent evidence has shown that an innovative patient-centered approach taking into consideration patient characteristics, treatment preferences and accessibility to treatment is recommended to optimize clinical management of COMISA patients. However, in this complex mosaic, many other sleep disorders may overlap with COMISA, so there is an urgent need for further research to fully understand the impact of these therapies on outcomes for OSA patients with comorbidity. In light of this need, this review focuses on the major sleep disorders comorbid with OSA and the recent advances in the management of these insomniac patients.

The (mis)perception of sleep: factors influencing the discrepancy between self-reported and objective sleep parameters

STUDY OBJECTIVES

Self-reported perception of sleep often differs from objective sleep study measures, but factors predicting the discrepancy between self-reported and objective sleep parameters are controversial, and a comparison of laboratory vs ambulatory polysomnography (PSG) is lacking.

METHODS

We retrospectively analyzed PSGs conducted between 2012 and 2016. Linear regression was applied to predict the discrepancy between self-reported and objective sleep parameters (total sleep time, sleep efficiency, sleep latency, using age, sex, arousal index, type of sleep disorder, and PSG type [laboratory vs ambulatory] as regressors).

RESULTS

A total of 303 PSGs were analyzed (49% women, median age 48 years), comprising patients with insomnia (32%), sleep-related breathing disorders (27%), sleep-related movement disorders (15%), hypersomnia/narcolepsy (14%), and parasomnias (12%). Sleep disorder was the best predictor of discrepancy between self-reported and objective total sleep time, and patients with insomnia showed higher discrepancy values compared to all other patient groups (P < .001), independent of age and PSG type (P > .05). Contributory effects for higher discrepancy values were found for lower arousal index. Patients with insomnia underestimated both total sleep time (median discrepancy: 46 minutes, P < .001) and sleep efficiency (median discrepancy: 11%, P < .001). No significant predictor for discrepancy of sleep latency was found.

CONCLUSIONS

Misperception of sleep duration and efficiency is common in sleep lab patients, but most prominent in insomnia, independent of age, sex, or laboratory vs ambulatory recording setting. This underlines the role of PSG in patients with a clinical diagnosis of insomnia and its use in cognitive behavioral therapy.

High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains

STUDY OBJECTIVES

This study describes high-throughput phenotyping strategies for sleep and circadian behavior in mice, including examinations of robustness, reliability, and heritability among Diversity Outbred (DO) mice and their eight founder strains.

METHODS

We performed high-throughput sleep and circadian phenotyping in male mice from the DO population (n = 338) and their eight founder strains: A/J (n = 6), C57BL/6J (n = 14), 129S1/SvlmJ (n = 6), NOD/LtJ (n = 6), NZO/H1LtJ (n = 6), CAST/EiJ (n = 8), PWK/PhJ (n = 8), and WSB/EiJ (n = 6). Using infrared beam break systems, we defined sleep as at least 40 s of continuous inactivity and quantified sleep-wake amounts and bout characteristics. We developed assays to measure sleep latency in a new environment and during a modified Murine Multiple Sleep Latency Test, and estimated circadian period from wheel-running experiments. For each trait, broad-sense heritability (proportion of variability explained by all genetic factors) was derived in founder strains, while narrow-sense heritability (proportion of variability explained by additive genetic effects) was calculated in DO mice.

RESULTS

Phenotypes were robust to different inactivity durations to define sleep. Differences across founder strains and moderate/high broad-sense heritability were observed for most traits. There was large phenotypic variability among DO mice, and phenotypes were reliable, although estimates of heritability were lower than in founder mice. This likely reflects important nonadditive genetic effects.

CONCLUSIONS

A high-throughput phenotyping strategy in mice, based primarily on monitoring of activity patterns, provides reliable and heritable estimates of sleep and circadian traits. This approach is suitable for discovery analyses in DO mice, where genetic factors explain some proportion of phenotypic variation.

Analysis of Clinical Characteristics and Polysomnography Indicators of Obstructive Sleep Apnea-Hypopnea Syndrome Patients Based on Sleep Perception Types

Purpose: The aim of this study was to explore the clinical characteristics of different sleep perception types of obstructive sleep apnea-hypopnea syndrome (OSAHS) patients and to analyze the correlation between sleep perception and polysomnography (PSG) indicators in OSAHS patients. Methods: We retrospectively analyzed 355 patients diagnosed with OSAHS by PSG at the Sleep Medicine Center of Shengjing Hospital of China Medical University. Patients saw a doctor due to snoring and daytime sleepiness from March 2017 to March 2018. We excluded the patients who are <18 years old, had a history of OSAHS treatment, had other sleep and psychiatric disorders, and could not provide complete data. According to the patients' explanation, medical history, PSG indicators, and morning questionnaire after PSG, the patients were divided into normal sleep perception (NSP), positive sleep perception abnormality (PSPA), and negative sleep perception abnormality (NSPA). We analyze the demographic characteristics and PSG of the three groups with ANOVA and non-parametric tests. In addition, we conducted correlation analysis between sleep perception and PSG indicators. Results: Of OSAHS patient, 55.5% had sleep perception abnormalities, of which 35.5% were positive-perception abnormalities and 20% were negative-perception abnormalities. From the analysis of PSG indicators, the sleep perception abnormality was related to the frequency of spontaneous arousal of the patient (P = 0.003) and was not related to the slight arousal caused by respiratory events, oxygen desaturations, and limb movement events. OSAHS patients with PSPA had a higher oxygen desaturation index (P = 0.046) but no significant difference in post hoc test. PSPA group had significantly lower rapid eye movement (REM) latency and sleep efficiency and more wake after sleep onset (WASO) than had the other sleep perception groups. Multivariate linear regression analyses after adjusting for age and sex revealed that sleep perception was related to lowest oxygen saturation (LSaO2), TS90%, sleep efficiency, and WASO. Conclusion: Sleep perception abnormality is common in OSAHS patients. OSAHS patients with different sleep perception types have different PSG profiles. The OSAHS patients with PSPA have more severe hypoxia levels at night that require timely personalized treatment.

Perception of sleep duration in adult patients with suspected obstructive sleep apnea

PURPOSE

Discrepancies between subjective and objective measures of total sleep time (TST) are frequent among insomnia patients, but this issue remains scarcely investigated in obstructive sleep apnea (OSA). We aimed to evaluate if sleep perception is affected by the severity of OSA.

METHODS

We performed a 3-month cross-sectional study of Brazilian adults undergoing overnight polysomnography (PSG). TST was objectively assessed from PSG and by a self-reported questionnaire (subjective measurement). Sleep perception index (SPI) was defined by the ratio of subjective and objective values. Diagnosis of OSA was based on an apnea/hypopnea index (AHI) ≥ 5.0/h, being its severity classified according to AHI thresholds: 5.0-14.9/h (mild OSA), 15.0-29.9/h (moderate OSA), and ≥ 30.0/h (severe OSA).

RESULTS

Overall, 727 patients were included (58.0% males). A significant difference was found in SPI between non-OSA and OSA groups (p = 0.014). Mean SPI values significantly decreased as the OSA severity increased: without OSA (100.1 ± 40.9%), mild OSA (95.1 ± 24.6%), moderate OSA (93.5 ± 25.2%), and severe OSA (90.6 ± 28.2%), p = 0.036. Using logistic regression, increasing SPI was associated with a reduction in the likelihood of presenting any OSA (p = 0.018), moderate/severe OSA (p = 0.019), and severe OSA (p = 0.028). However, insomnia was not considered as an independent variable for the presence of any OSA, moderate/severe OSA, and severe OSA (all p-values > 0.05).

CONCLUSION

In a clinical referral cohort, SPI significantly decreases with increasing OSA severity, but is not modified by the presence of insomnia symptoms.

Rationale, design, and development of SleepWell24: A smartphone application to promote adherence to positive airway pressure therapy among patients with obstructive sleep apnea

BACKGROUND

Positive airway pressure (PAP) therapy is the gold standard treatment for obstructive sleep apnea (OSA), a chronic disorder that affects 6-13% of the adult population. However, adherence to PAP therapy is challenging, and current approaches to improve adherence have limited efficacy and scalability.

METHODS/DESIGN

To promote PAP adherence, we developed SleepWell24, a multicomponent, evidence-based smartphone application that delivers objective biofeedback concerning PAP use and sleep/physical activity patterns via cloud-based PAP machine and wearable sensor data, and behavior change strategies and troubleshooting of PAP therapy interface use. This randomized controlled trial will evaluate the feasibility, acceptability, and initial efficacy of SleepWell24 compared to a usual care control condition during the first 60 days of PAP therapy among patients newly diagnosed with OSA.

DISCUSSION

SleepWell24 is an innovative, multi-component behavior change intervention, designed as a self-management approach to addressing the psychosocial determinants of adherence to PAP therapy among new users. The results will guide lengthier future trials that assess numerous patient-centered and clinical outcomes.

The paradox of paradoxical insomnia: A theoretical review towards a unifying evidence-based definition

Paradoxical insomnia is one of the most intriguing yet challenging subtypes of insomnia. Despite being recognized for a long time by the international community, it is still unclear whether this entity really exists, which are its features and boundaries. Much of the debate is fuelled by the lack of a consensus on its precise definition. To help filling some of the existing gaps, a systematic review of the literature was conducted, through which 19 different quantitative definitions were obtained. These definitions were then applied to two distinct datasets. The first consisted of 200 chronic primary insomnia patients, diagnosed according to the DSM-IV-TR criteria. The second consisted of 200 age- and sex-matched healthy persons without insomnia. For each dataset, available data from the objective sleep parameters and their subjective estimation were imported and analysed in MATLAB. Depending on the definition used, the prevalence of paradoxical insomnia ranged from 8 to 66%, while agreement between different definitions ranged from -0.19 to 0.9 (using Cohen's kappa coefficient). Based on the results garnered, necessary features for a quantitative definition of paradoxical insomnia were identified. Several open questions remain, such as whether there is a minimum number of hours a patient should sleep to fulfill the criteria for a diagnosis of paradoxical insomnia, and whether sleep latency can be used in the definition along with total sleep time. We conclude by advocating continued study of paradoxical insomnia and sleep state misperception and by providing specific directions for future research. STATEMENT OF SIGNIFICANCE: The current understanding of paradoxical insomnia and, more broadly, of sleep state misperception, is greatly hampered by the lack of agreement on a quantitative and evidence-base measure of the discrepancy between subjective and objective sleep evaluation. The current study provides a critical analysis about the strength and the limitations of the available definitions, using both a data-driven and a theory-driven approach. The overarching goal is to motivate a rigorous discussion involving the main experts of the field, to build a consensus, and develop an evidence-based measure of sleep state misperception and/or of paradoxical insomnia.

Is subjective sleep evaluation a good predictor for obstructive sleep apnea?

OBJECTIVE

To compare subjective sleep evaluation obtained using four questionnaires with polysomnography results for individuals with and without obstructive sleep apnea.

METHODS

Observational and analytical study in which individuals underwent polysomnography were studied retrospectively to investigate sleep disorders. We compared subjective data from a research database used to predict obstructive sleep apnea based on the STOP-BANG questionnaire, evaluation of excessive daytime sleepiness (Epworth Sleepiness Scale), sleep quality questionnaire (Mini Sleep Questionnaire) and Post-Sleep Data Collection Instrument with the self-reported total sleep time and sleep-onset latency for subjects with and without obstructive sleep apnea.

RESULTS

The STOP-BANG questionnaire was a good predictor for the diagnosis of obstructive sleep apnea. However, the other instruments did not show a significant difference between healthy and sick individuals. Patients' perceptions of their sleep onset time were significantly lower than the polysomnographic data, but this difference remained for both subjects with and without obstructive sleep apnea. No difference was found between the subjective duration of sleep and the total sleep time assessed by polysomnography in either the healthy subjects or the patients.

CONCLUSION

Except for the STOP-BANG questionnaire, subjective evaluation of sleepiness, sleep quality, perception of onset, and total sleep time are not important parameters for the diagnosis of obstructive sleep apnea, which reinforces the need for an active search for better management of these patients.

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

BACKGROUND

Normal sleep is associated with typical physiological changes in both the central and autonomic nervous systems. In particular, nocturnal blood pressure dipping has emerged as a strong marker of normal sleep physiology, whereas the absence of dipping or reverse dipping has been associated with cardiovascular risk. However, nocturnal blood pressure is not measured commonly in clinical practice. Heart rate (HR) dipping in sleep may be a similar important marker and is measured routinely in at-home and in-laboratory sleep testing.

METHODS

We performed a retrospective cross-sectional analysis of diagnostic polysomnography in a clinically heterogeneous cohort of n=1047 adults without sleep apnea.

RESULTS

We found that almost half of the cohort showed an increased HR in stable nonrapid eye movement sleep (NREM) compared to wake, while only 13.5% showed a reduced NREM HR of at least 10% relative to wake. The strongest correlates of HR dipping were younger age and male sex, whereas the periodic limb movement index (PLMI), sleep quality, and Epworth Sleepiness Scale (ESS) scores were not correlated with HR dipping. PLMI was however significantly correlated with metrics of impaired HR variability (HRV): increased low-frequency power and reduced high-frequency power. HRV metrics were unrelated to sleep quality or the ESS value. Following the work of Vgontzas et al, we also analyzed the sub-cohort with insomnia symptoms and short objective sleep duration. Interestingly, the sleep-wake stage-specific HR values depended upon insomnia symptoms more than sleep duration.

CONCLUSION

While our work demonstrates heterogeneity in cardiac metrics (HR and HRV), the population analysis suggests that pathological signatures of HR (nondipping and elevation) are common even in this cohort selected for the absence of sleep apnea. Future prospective work in clinical populations will further inform risk stratification and set the stage for testing interventions.

Cardiopulmonary coupling spectrogram as an ambulatory clinical biomarker of sleep stability and quality in health, sleep apnea, and insomnia

STUDY OBJECTIVES

Ambulatory tracking of sleep and sleep pathology is rapidly increasing with the introduction of wearable devices. The objective of this study was to evaluate a wearable device which used novel computational analysis of the electrocardiogram (ECG), collected over multiple nights, as a method to track the dynamics of sleep quality in health and disease.

METHODS

This study used the ECG as a primary signal, a wearable device, the M1, and an analysis of cardiopulmonary coupling to estimate sleep quality. The M1 measures trunk movements, the ECG, body position, and snoring vibrations. Data from three groups of patients were analyzed: healthy participants and people with sleep apnea and insomnia, obtained from multiple nights of recording. Analysis focused on summary measures and night-to-night variability, specifically the intraclass coefficient.

RESULTS

Data were collected from 10 healthy participants, 18 people with positive pressure-treated sleep apnea, and 20 people with insomnia, 128, 65, and 121 nights, respectively. In any participant, all nights were consecutive. High-frequency coupling (HFC), the signal biomarker of stable breathing and stable sleep, showed high intraclass coefficients (ICCs) in healthy participants and people with sleep apnea (0.83, 0.89), but only 0.66 in people with insomnia. The only statistically significant difference between weekday and weekend in healthy subjects was HFC duration: 242.8 ± 53.8 vs. 275.8 ± 57.1 minutes (89 vs. 39 total nights), F(1,126) = 9.86, p = .002.

CONCLUSIONS

The M1 and similar wearable devices provide new opportunities to measure sleep in dynamic ways not possible before. These measurements can yield new biological insights and aid clinical management.

Dexmedetomidine promotes biomimetic non-rapid eye movement stage 3 sleep in humans: A pilot study

OBJECTIVES

Sleep, which comprises of rapid eye movement (REM) and non-REM stages 1-3 (N1-N3), is a natural occurring state of decreased arousal that is crucial for normal cardiovascular, immune and cognitive function. The principal sedative drugs produce electroencephalogram beta oscillations, which have been associated with neurocognitive dysfunction. Pharmacological induction of altered arousal states that neurophysiologically approximate natural sleep, termed biomimetic sleep, may eliminate drug-induced neurocognitive dysfunction.

METHODS

We performed a prospective, single-site, three-arm, randomized-controlled, crossover polysomnography pilot study (n = 10) comparing natural, intravenous dexmedetomidine- (1-μg/kg over 10 min [n = 7] or 0.5-μg/kg over 10 min [n = 3]), and zolpidem-induced sleep in healthy volunteers. Sleep quality and psychomotor performance were assessed with polysomnography and the psychomotor vigilance test, respectively. Sleep quality questionnaires were also administered.

RESULTS

We found that dexmedetomidine promoted N3 sleep in a dose dependent manner, and did not impair performance on the psychomotor vigilance test. In contrast, zolpidem extended release was associated with decreased theta (∼5-8 Hz; N2 and N3) and increased beta oscillations (∼13-25 Hz; N2 and REM). Zolpidem extended release was also associated with increased lapses on the psychomotor vigilance test. No serious adverse events occurred.

CONCLUSIONS

Pharmacological induction of biomimetic N3 sleep with psychomotor sparing benefits is feasible.

SIGNIFICANCE

These results suggest that α2a adrenergic agonists may be developed as a new class of sleep enhancing medications with neurocognitive sparing benefits.

Potential Underestimation of Sleep Apnea Severity by At-Home Kits: Rescoring In-Laboratory Polysomnography Without Sleep Staging

STUDY OBJECTIVES

Home sleep apnea testing (HSAT) is increasingly available for diagnosing obstructive sleep apnea (OSA). One key limitation of most HSAT involves the lack of sleep staging, such that the respiratory event index is calculated using the total recording time (TRT) rather than total sleep time (TST).

METHODS

We performed a retrospective analysis of n = 838 diagnostic polysomnography (PSG) nights from our center; n = 444 with OSA (4% rule, apneahypopnea index (AHI) ≥ 5), and n = 394 with AHI < 5. We recalculated the AHI using time in bed (TIB) instead of TST, to assess the predicted underestimation risk of OSA severity.

RESULTS

Of all the patients with OSA, 26.4% would be reclassified as having less severe or no OSA after recalculating the AHI using TIB rather than TST. Of the n = 275 with mild OSA, 18.5% would be reclassified as not having OSA. The risk of underestimation was higher in those with moderate or severe OSA. Of the n = 119 moderate OSA cases, 40.3% would be reclassified as mild, and of the n = 50 severe OSA cases, 36.0% would be reclassified as moderate. Age strongly correlated with the degree of underestimation of the AHI, because age was significantly correlated with time awake during PSG.

CONCLUSIONS

The risk of sleep apnea underestimation is predicted to be substantial in a tertiary sleep center population. Phenotyping errors included risk of falsely negative results (from mild to normal), as well as category errors: moderate or severe moving to mild or moderate severity, respectively. Clinicians should recognize this underestimation limitation, which directly affects diagnostic phenotyping and thus therapeutic decisions.

COMMENTARY

A commentary on this article appears in this issue on page 531.

Sleep Misperception in Chronic Insomnia Patients with Obstructive Sleep Apnea Syndrome: Implications for Clinical Assessment

STUDY OBJECTIVES

To investigate whether sleep perception (SP), defined by the ratio of subjective and objective total sleep time, and habitual sleep time in various sleep disorders may be based on comorbid insomnia status.

METHODS

We enrolled 420 patients (age 20-79 y) who underwent polysomnography (PSG). They were divided into three groups based on chief complaints: chronic insomnia (CI, n = 69), patients with both obstructive sleep apnea and insomnia (OSA-I, n = 49) or OSA only (OSA, n = 149). Healthy volunteers were also recruited (normal controls [NC], n = 80). We compared differences in PSG parameters and habitual sleep duration and investigated the discrepancy between objective and subjective total sleep time (TST) and sleep latency among four groups. Subjective TST was defined as sleep time perceived by participants the next morning of PSG.

RESULTS

SP for TST was highest in the OSA group (median 92.9%), and lowest in the CI group (80.3%). SP of the NC group (91.4%) was higher than the CI, but there was no difference between OSA-I and OSA groups. OSA-I had higher depressive mood compared to the OSA group (p < 0.001). SP was positively associated with the presence of OSA and habitual sleep duration and negatively related to the presence of insomnia and arousal index of PSG. Insomnia patients with (OSA-I) or without OSA (CI) reported the smallest discrepancy between habitual sleep duration and objective TST.

CONCLUSIONS

Patients with OSA with or without insomnia have different PSG profiles, which suggests that objective measures of sleep are an important consideration for differentiating subtypes of insomnia and tailoring proper treatment.

COMMENTARY

A commentary on this articles appears in this issue on page 1437.

Sleep Fragmentation Does Not Explain Misperception of Latency or Total Sleep Time

STUDY OBJECTIVES

Perception of sleep-wake times may differ from objective measures, although the mechanisms remain elusive. Quantifying the misperception phenotype involves two operational challenges: defining objective sleep latency and treating sleep latency and total sleep time as independent factors. We evaluated a novel approach to address these challenges and test the hypothesis that sleep fragmentation underlies misperception.

METHODS

We performed a retrospective analysis on patients with or without obstructive sleep apnea during overnight diagnostic polysomnography in our laboratory (n = 391; n = 252). We compared subjective and objective sleep-wake durations to characterize misperception. We introduce a new metric, sleep during subjective latency (SDSL), which captures latency misperception without defining objective sleep latency and allows correction for latency misperception when assessing total sleep time (TST) misperception.

RESULTS

The stage content of SDSL is related to latency misperception, but in the opposite manner as our hypothesis: those with > 20 minutes of SDSL had less N1%, more N3%, and lower transition frequency. After adjusting for misperceived sleep during subjective sleep latency, TST misperception was greater in those with longer bouts of REM and N2 stages (OSA patients) as well as N3 (non-OSA patients), which also did not support our hypothesis.

CONCLUSIONS

Despite the advantages of SDSL as a phenotyping tool to overcome operational issues with quantifying misperception, our results argue against the hypothesis that light or fragmented sleep underlies misperception. Further investigation of sleep physiology utilizing alternative methods than that captured by conventional stages may yield additional mechanistic insights into misperception.

COMMENTARY

A commentary on this article appears in this issue on page 1211.

Sleep apnea in patients reporting insomnia or restless legs symptoms

OBJECTIVE

Insomnia and restless legs syndrome (RLS) are defined by self-reported symptoms, and polysomnography (PSG) is not routinely indicated. Occult obstructive sleep apnea (OSA), common even in asymptomatic adults, may complicate management of patients presenting with insomnia or restless legs. To this end, we investigated objective sleep apnea metrics in a large retrospective cohort according to self-reported symptom profiles.

METHODS

We compared sleep apnea findings in patients referred to our center according to self-reported symptoms associated with insomnia, sleep apnea, and restless legs. The cohort included over 1900 adults who underwent diagnostic (n = 1418) or split-night (n = 504) PSGs and completed a symptom and medical history questionnaire.

RESULTS

More than 30% of patients who did not endorse any OSA symptoms, but did endorse insomnia or restless legs symptoms, were found to have OSA based on apnea-hypopnea index (AHI) >5 during overnight laboratory testing. Regression models of the full cohort showed that the risk of OSA was related, as expected, to older age, male sex, elevated body mass index, and presence of OSA symptoms. The presence of insomnia symptoms did not alter the risk of OSA. The presence of restless legs symptoms showed a small odds ratio for lowered OSA risk.

CONCLUSIONS

Objective evidence of OSA occurs similarly in those with insomnia or restless legs symptoms, even among those without self-reported OSA symptoms. Providers should be aware of the potential for occult OSA in populations with insomnia and restless legs, which may complicate their management in addition to presenting an independent medical risk itself.

Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

INTRODUCTION

Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge.

METHODS

We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis.

RESULTS

Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy.

CONCLUSION

Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors.

Beliefs about the 'shape' and continuity of healthy sleep as a function of age

OBJECTIVE

Treating insomnia includes challenging unrealistic beliefs that may contribute to anxieties and wakefulness. This study explored beliefs about the shape and continuity of healthy adult sleep at different adult ages.

METHODS

Younger (n=113, M=21.4 (2.4)) and older adults (n=110, M=72.3 (7.7)) depicted their concept of the normal sleep of both a healthy 18 and 65 year olds. Plots were drawn to show sleep depth and awakenings across the night.

RESULTS

Seventy percent conceptualized healthy sleep as an unbroken U shape and 18 year olds were depicted with deeper sleep than 65 year olds. About 95% of younger adults and three quarters of older adults showed both healthy 18 and 65 year olds sleep without awakenings.

CONCLUSION

Unrealistic sleep beliefs are widespread, with healthy sleep usually being mistakenly conceptualized as unbroken, even in older adults. Education that awakenings are part of normal sleep may have preventative health implications and reduce sleep anxieties.