Sleep-wake time perception varies by direct or indirect query

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.

Quantile regression to explore association of sleep duration with depression among adults in NHANES and KNHANES

BACKGROUND

Sleep duration has been associated with depression. However, mean regression, such as linear regression or logistic regression, may not capture relationships that occur mainly in the tails of outcome distribution. This study aimed to evaluate the associations between sleep duration and depression along the entire distribution of depression using quantile regression approach.

METHODS

This study included 55,954 adults aged 18 to 80 years from the National Health and Nutrition Examination Survey (N = 34,156) and the Korea National Health and Nutrition Examination Survey (N = 21,798). The coefficients corresponding to cross-group differences in PHQ-9 scores were estimated when comparing short or long sleep duration with normal sleep duration on deciles of PHQ-9 score distribution.

RESULTS

At lower quantiles, either short or long sleep duration was not associated with depression. At higher quantiles, the association of both short and long sleep duration with depression became much more pronounced. Compared with normal sleep duration, short and long sleep duration were associated with increases of 1.34 (95 % CI: 1.16, 1.51) and 0.28 (95 % CI: 0.04, 0.52) in PHQ-9 scores at the 50th quantile, while the corresponding increases were 3.27 (95 % CI: 2.83, 3.72) and 1.65 (95 % CI: 0.86, 2.45) at the 90th quantile, respectively. We also found that the magnitude of association between short sleep duration and depression was stronger among females and individuals with chronic diseases.

CONCLUSIONS

The beneficial effect of sufficient sleep in decreasing depression severity may be more evident among individuals with severe depression. Further studies could explore whether these heterogeneous associations can be generalized to populations with different characteristics.

Sleep discrepancy is associated with alterations in the salience network in patients with insomnia disorder: An EEG-fMRI study

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Simultaneous EEG-fMRI was used to clarify the association between the brain functional connectivity and sleep discrepancy between self-report and polysomnography in patients with insomnia disorder.

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An altered anterior insula-based connectivity across wakefulness and all NREM stages.

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Sleep discrepancy was significantly associated with anterior insula–putamen/thalamus connectivity during wakefulness.

Background

Positron emission tomography – computed tomography (PET-CT) research has shown that sleep discrepancy recorded by self-report and polysomnography (PSG) may be related to the altered metabolic rate of the anterior insula (aINS) during non-rapid eye movement (NREM) sleep in patients with insomnia disorder. We aim to explore the functional connectivity of aINS across wake and NREM sleep in the patients and to reveal the association between aINS connectivity and sleep discrepancy.

Methods

Patients with insomnia disorder (n = 33) and healthy controls (n = 31) underwent simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) during nighttime sleep, and aINS-based connectivity was calculated across wake and NREM sleep. A linear mixed-effects model was used to assess the main effect of group and group-by-stage (wake, NREM stages 1–3) interaction effect on aINS connectivity. Similar mixed models were used to assess the potential correlation between aINS connectivity and the sleep misperception index (MI).

Results

A significant group-by-stage interaction effect on aINS-based connectivity was observed in the bilateral frontal gyrus, right inferior temporal gyrus, bilateral middle occipital gyrus and right postcentral gyrus (p < 0.05, corrected). There was also a significant group-by-MI interaction effect on aINS connectivity with the putamen and thalamus during wakefulness (p < 0.05 corrected); MI was significantly associated with aINS–putamen/thalamus connectivity in the control group, whereas the association was weak or even nonsignificant in the patient group. There was no significant main effect of group.

Conclusion

The waking activity of a neural pathway containing the aINS, putamen, and thalamus may underlie sleep perception, potentially providing important perspectives to reveal complex mechanisms of sleep discrepancy between self-report and PSG.

Self-reported sleep duration and timing: A methodological review of event definitions, context, and timeframe of related questions

Study Objectives

Clinical and population health recommendations are derived from studies that include self-report. Differences in question wording and response scales may significantly affect responses. We conducted a methodological review assessing variation in event definition(s), context (i.e., work- versus free-day), and timeframe (e.g., "in the past 4 weeks") of sleep timing/duration questions.

Methods

We queried databases of sleep, medicine, epidemiology, and psychology for survey-based studies and/or publications with sleep duration/timing questions. The text of these questions was thematically analyzed.

Results

We identified 53 surveys with sample sizes ranging from 93 to 1,185,106. For sleep duration, participants reported nocturnal sleep (24/44), sleep in the past 24-hours (14/44), their major sleep episode (3/44), or answered unaided (3/44). For bedtime, participants reported time into bed (19/47), first attempt to sleep (16/40), or fall-asleep time (12/47). For wake-time, participants reported wake-up time (30/43), the time they "get up" (7/43), or their out-of-bed time (6/43). Context guidance appeared in 18/44 major sleep duration, 35/47 bedtime, and 34/43 wake-time questions. Timeframe was provided in 8/44 major sleep episode duration, 16/47 bedtime, and 10/43 wake-time questions. One question queried the method of awakening (e.g., by alarm clock), 18 questions assessed sleep latency, and 12 measured napping.

Conclusion

There is variability in the event definition(s), context, and timeframe of questions relating to sleep. This work informs efforts at data harmonization for meta-analyses, provides options for question wording, and identifies questions for future surveys.

Objective sleep duration is associated with cognitive deficits in primary insomnia: BDNF may play a role

Study Objectives

Objective sleep duration has been linked to insomnia severity. However, cognitive functions of people with insomnia with different sleep durations have been seldom addressed. Brain-derived neurotrophic factor (BDNF) has an important role in cognitive function and has been linked to clinical insomnia recently. The present study aimed to evaluate the comprehensive cognitive functions in people with primary insomnia with different objective sleep durations, and further examine the involvement of peripheral BDNF.

Methods

Fifty-seven people with insomnia were subdivided into short sleep duration (SSD, sleep time < 6 hr) group and normal sleep duration (NSD, sleep time ≥ 6 hr) group based on polysomnography data. Twenty-nine healthy controls (HC) were matched on age, gender, and education. Cognitive function was assessed using a comprehensive and sensitive neuropsychological test battery. Both objective and subjective insomnia statuses were estimated. Serum BDNF level was measured using enzyme-linked immune sorbent assay.

Results

Compared with HC, the SSD group showed impaired neuropsychological performances in spatial span, brief visuospatial memory test, fluency, managing emotions, and continuous performance tests. In contrast, NSD had bad performance only in brief visuospatial memory test and continuous performance tests, and relatively better than SSD group in the latter test. People with SSD insomnia but not NSD had decreased BDNF levels compared with HC, and neuropsychological performance was positively correlated with BDNF levels only in SSD group.

Conclusions

Primary insomnia was associated with impaired neuropsychological performance, and the impairment might be related to decreased objective sleep duration. In addition, decreased peripheral BDNF might mediate the impaired cognitive functions of people with insomnia with SSD.

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.

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.

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

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.