Obstructive sleep apnea alters sleep stage transition dynamics

Obstructive sleep apnoea-hypopnoea syndrome

Obstructive sleep apnoea-hypopnoea syndrome (OSAHS) is a common disorder characterised by repetitive episodes of the complete or partial collapse of the pharyngeal airway during sleep. This results in cessation (apnoea) or reduction (hypopnoea) of airflow, leading to oxygen desaturation and sleep fragmentation. An individual's disposition to develop OSAHS depends on the collapsibility of a segment of the upper airway. The degree of collapsibility can be quantified by the balance between occluding or extraluminal pressures of the surrounding tissues. Patients can experience snoring, unrefreshing sleep, witnessed apnoeas, waking with a choking sensation and excessive daytime sleepiness. OSAHS has a broad range of consequences, including cardiovascular, metabolic, and neurocognitive sequelae. Treatment options include lifestyle measures, in particular weight loss, and strategies to maintain upper airway patency overnight, including continuous positive airway pressure, mandibular advancement devices and positional modifiers.

LPS and its relationship with subjective-objective discrepancies of sleep onset latency in patients with psychiatric disorders

Subjective-objective discrepancies in sleep onset latency (SOL), which is often observed among psychiatric patients, is attributed partly to the definition of sleep onset. Recently, instead of SOL, latency to persistent sleep (LPS), which is defined as the duration from turning out the light to the first consecutive minutes of non-wakefulness, has been utilized in pharmacological studies. This study aimed to determine the non-awake time in LPS that is most consistent with subjective sleep onset among patients with psychiatric disorders. We calculated the length of non-awake time in 30-s segments from lights-out to 0.5-60 min. The root mean square error was then calculated to determine the most appropriate length. The analysis of 149 patients with psychiatric disorders showed that the optimal non-awake time in LPS was 12 min. On the other hands, when comorbid with moderate or severe obstructive sleep apnea (OSA), the optimal length was 19.5 min. This study indicates that 12 min should be the best fit for the LPS non-awake time in patients with psychiatric disorders. When there is comorbidity with OSA, however, a longer duration should be considered. Measuring LPS minimizes discrepancies in SOL and provides important clinical information.

Sleep apnea syndrome associated with gonadal hormone imbalance (Review)

Patients with obstructive sleep apnea exhibit an increased risk of developing gonadal disorders. Because a notable number of people worldwide have sleep respiratory and reproductive disorders, it is essential to recognize the association between local upper airway dysfunction and its gonadal effects. Repeated breathing pauses cause sleep fragmentation, disorganization of sleep cycles and stages, sympathetic activation, intermittent hypoxemia and systemic inflammation. Nocturnal intermittent hypoxemia has a direct central effect on neurotransmitters, with disturbances in the normal production of hypothalamic-pituitary hormones. Awakenings and micro-awakenings at the end of apneic episodes produce a central stress responsible for hormonal changes and subsequent endocrine imbalances. The aim of the present study was to investigate the impact of obstructive sleep apnea syndrome (OSAS) on gonadal hormonal homeostasis and its consequences. Recognizing and understanding how local upper airway dysfunction causes gonadal imbalance may facilitate better care for patients with OSAS. Although there may be a direct relationship between sleep-disordered breathing and gonadal function mediated by hormones via the hypothalamic-pituitary-gonadal axis, to date, current therapies have not been effective.

Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch

STUDY OBJECTIVES

Although wrist-worn consumer wearables are widely used for home sleep monitoring, few have been validated. It is unclear whether consumer wearables could be an alternative to Actiwatch. This study aimed to establish and validate an automatic sleep staging system (ASSS) utilizing photoplethysmography and acceleration data collected from a wrist-worn wearable device.

METHODS

Seventy-five participants from a community population underwent overnight polysomnography (PSG) while wearing a smartwatch (MT2511) and Actiwatch Spectrum Plus (Philips Respironics, Inc; Murrysville, PA, USA). Photoplethysmography and acceleration data collected from the smartwatches were utilized to build a 4-stage (wake, light sleep, deep sleep, and rapid eye movement [REM] sleep) classifier, which was validated against PSG. The performance of the sleep/wake classifier was compared with Actiwatch. All analyses were conducted separately for participants with PSG sleep efficiency (SE) ≥ 80% and SE < 80%.

RESULTS

The 4-stage classifier and PSG showed fair overall epoch-by-epoch agreement (kappa, 0.55; 95% confidence interval, 0.52 to 0.57). The deep sleep and REM times were comparable between ASSS and PSG, while ASSS underestimated the wake time and overestimated the light sleep time among participants with SE < 80%. Moreover, ASSS underestimated sleep-onset latency and wake after sleep onset and overestimated total sleep time and SE among participants with SE < 80%, while all were comparable among participants with SE ≥ 80%. The biases were smaller for ASSS than for Actiwatch.

CONCLUSIONS

Our photoplethysmography- and acceleration-based ASSS was reliable for participants with SE ≥ 80% and had a smaller bias than Actiwatch among those with SE < 80%. Thus, ASSS may be a promising alternative to Actiwatch.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Validation of Sleep Healthcare System; URL: https://clinicaltrials.gov/study/NCT04252482; Identifier: NCT04252482.

CITATION

Liu P-K, Ting N, Chiu H-C, et al. Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch. J Clin Sleep Med. 2023;19(10):1797-1810.

Obstructive sleep apnea and epilepsy: understanding the pathophysiology of the comorbidity

Obstructive sleep apnea (OSA) is a sleep disorder of significant health concern with a high prevalence in the general population. It has been found to exhibit a high incidence of comorbidity with epilepsy, the exact underlying pathophysiology of which still remains poorly understood. OSA is characterized by apnea/hypopnea spells and arousals, leading to intermittent hypoxemia and sleep deprivation. Both sleep deprivation and hypoxemia adversely affect the cortical excitability and favor epileptogenesis and worsening of pre-existing epilepsy, if any. In patients with OSA, deprivation of rapid eye movement sleep (REMS) phase (known for its strong antiepileptic influence) is relatively more than that non rapid eye movement sleep phase leading to postulation of REMS deprivation as a significant factor in the development of epilepsy as a comorbidity in patients with OSA. Furthermore, OSA and epilepsy both have shown to exercise a bidirectional influence on one another and are also likely to exacerbate each other through a positive feedback mechanism. This is especially based on the reports of improved control of epilepsy upon treatment of comorbid OSA. This brief paper attempts to present an underlying pathophysiological basis of the comorbidity of OSA and epilepsy based upon sleep deprivation and hypoxemia that are characteristic features observed in patients with OSA.

Durative sleep fragmentation with or without hypertension suppress rapid eye movement sleep and generate cerebrovascular dysfunction

Either hypertension or chronic insomnia is the risk factor of developing vascular dementia. Durative hypertension can induce vascular remodeling and is used for modeling small vessel disease in rodents. It remains undetermined if the combination of hypertension and sleep disturbance exacerbates vascular dysfunction or pathologies. Previously, we found chronic sleep fragmentation (SF) dampened cognition in young mice without disease predispositions. In the current study, we superimposed SF with hypertension modeling in young mice. Angiotensin II (AngII)-releasing osmotic mini pumps were subcutaneously implanted to generate persistent hypertension, while sham surgeries were performed as controls. Sleep fragmentation with repetitive arousals (10 s every 2 min) during light-on 12 h for consecutive 30 days, while mice undergoing normal sleep (NS) processes were set as controls. Sleep architectures, whisker-stimulated cerebral blood flow (CBF) changes, vascular responsiveness as well as vascular pathologies were compared among normal sleep plus sham (NS + sham), SF plus sham (SF + sham), normal sleep plus AngII (NS + AngII), and SF plus AngII (SF + AngII) groups. SF and hypertension both alter sleep structures, particularly suppressing REM sleep. SF no matter if combined with hypertension strongly suppressed whisker-stimulated CBF increase, suggesting the tight association with cognitive decline. Hypertension modeling sensitizes vascular responsiveness toward a vasoactive agent, Acetylcholine (ACh, 5 mg/ml, 10 μl) delivered via cisterna magna infusion, while SF exhibits a similar but much milder effect. None of the modeling above was sufficient to induce arterial or arteriole vascular remodeling, but SF or SF plus hypertension increased vascular network density constructed by all categories of cerebral vessels. The current study would potentially help understand the pathogenesis of vascular dementia, and the interconnection between sleep and vascular health.

[Research progress on correlation between childhood obesity and obstructive sleep apnea]

Prevalence of childhood obesity is progressively increasing, reaching worldwide levels of 5.6% in girls and of 7.8% in boys. This also leads to a corresponding increase in the prevalence of obesity-associated morbidities particularly those involving obstructive sleep apnea（OSA）. Obesity is an independent risk factor and regulator of OSA in children. There is a bidirectional causal relationship between OSA and obesity in children. The factors involved in the association between OSA and obesity are systemic inflammation, oxidative stress, and gut microbiota etc. However, a causal link between obesity-related inflammatory state and OSA pathogenesis still needs to be properly confirmed. The present review aimed to investigate the links between childhood obesity and OSA.

Sleep Disturbances in Neurological Disease: A Target for Intervention

Sleep is a biological function required for neurological and general health, but a significant and under-recognized proportion of the population has disturbed sleep. Here, we briefly overview the biology of sleep, sleep requirements over the lifespan, and common sleep disorders. We then turn our attention to five neurological diseases that significantly contribute to global disease burden and neurology practice makeup: epilepsy, headache, ischemic stroke, Parkinson's disease, and Alzheimer's disease. For each disease, we review evidence that sleep disturbances contribute to disease risk and severity and discuss existing data that addressing sleep disturbances may have disease-modifying effects. We provide recommendations derived from the literature and existing clinical guidelines to facilitate the evaluation and management of sleep disturbances within the context of each neurological disease. Finally, we synthesize identified needs and commonalities into future directions for the field and practical sleep-related recommendations for physicians caring for patients at risk for or currently suffering from neurological disease.

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.

Association of short-term exposure to air pollution with depression in patients with sleep-related breathing disorders

Air pollution is associated with sleep-related breathing disorders; however, the effects of air pollution on depression in patients with SRBDs remain unclear. A cross-sectional study was conducted to collect polysomnographic (PSG) data and Beck Depression Inventory-IA (BDI-IA) responses from 568 subjects with SRDBs in a sleep center in 2015 to 2017. Exposure to air pollution, including particulate matter with an aerodynamic diameter of ≤10 μm (PM₁₀), particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5), nitrogen (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO) and ozone (O₃), in 1-month averages was collected. Associations of air pollution with the respiratory disturbance index (RDI), oxygen desaturation index (ODI), arousal index (ARI), sleep architecture, and BDI-IA were examined. We observed that interquartile range (IQR) increases in 1-month PM_2.5, PM₁₀, and NO₂ levels were respectively associated with 4.1/hour (h) (95% confidence interval (CI): 1.7/h to 6.4/h), 3.7/h (95% CI: 1.4/h to 6.0/h) and 1.9/h (95% CI: 0.1/h to 3.7/h) increases in the ARI. For sleep architecture, IQR increases in 1-month PM_2.5 and CO levels were respectively associated with a 6.2% (95% CI: 6.1% to 6.3%) increase in non-rapid eye movement sleep 1 (N1) and a 2.0% (95% CI: -3.8% to -0.1%) decrease in non-rapid eye movement sleep 2 (N2). For depression, an IQR change in the 1-month CO was associated a moderate/severe depressive status according to the BDI-IA (odds ratio, OR: 2.981, p < 0.05; 95% CI: 1.032 to 8.611). Short-term exposure to air pollution increased the risk of arousal and light sleep as well as depression in patients with SRBDs. The results suggest that SRBD patients could be a population at risk for depression due to short-term exposure to air pollution.

Sleep structure and electroencephalographic spectral power of middle-aged or older adults: Normative values by age and sex in the Korean population

The fine structure of sleep electrocortical activity reflects health and disease. The current study provides normative data for sleep structure and electroencephalography (EEG) spectral power measures derived from overnight polysomnography (PSG) and examines the effect of age and sex among Korean middle-aged and older adults with or without obstructive sleep apnea (OSA). We analysed home PSG data from 1,153 adult participants of an ongoing population-based cohort study, the Korean Genome and Epidemiology Study. Sleep stages were visually scored and spectral power was measured on a single-channel EEG (C4-A1). We computed spectral power for five frequency ranges. The EEG power was reported in relative (%) and log-transformed absolute values (µV² ). With ageing, the proportion of N1 sleep increased, whereas N3 decreased, which is more noticeable in men than in women. The amount of N3 was relatively low in this cohort. With ageing, relative delta power decreased and alpha and sigma power increased for the whole sleep period, which was more pronounced during REM sleep in non-OSA. For men compared with women, relative theta power was lower during REM and sigma and beta were higher during N1 sleep. The differences of relative powers by age and sex in OSA were comparable to those in non-OSA. In a community-based Korean population, we present normative data of sleep structure and spectral power for middle-aged or older adults of a non-Caucasian ethnicity. The values varied with age and sex and were not influenced by sleep apnea.

Sleep-wake regulation in preterm and term infants

Study Objectives

In adults, wakefulness can be markedly prolonged at the expense of sleep, e.g. to stay vigilant in the presence of a stressor. These extra-long wake bouts result in a heavy-tailed distribution (highly right-skewed) of wake but not sleep durations. In infants, the relative importance of wakefulness and sleep are reversed, as sleep is necessary for brain maturation. Here, we tested whether these developmental pressures are associated with the unique regulation of sleep–wake states.

Methods

In 175 infants of 28–40 weeks postmenstrual age (PMA), we monitored sleep–wake states using electroencephalography and behavior. We constructed survival models of sleep–wake bout durations and the effect of PMA and other factors, including stress (salivary cortisol), and examined whether sleep is resilient to nociceptive perturbations (a clinically necessary heel lance).

Results

Wake durations followed a heavy-tailed distribution as in adults and lengthened with PMA and stress. However, differently from adults, active sleep durations also had a heavy-tailed distribution, and with PMA, these shortened and became vulnerable to nociception-associated awakenings.

Conclusions

Sleep bouts are differently regulated in infants, with especially long active sleep durations that could consolidate this state’s maturational functions. Curtailment of sleep by stress and nociception may be disadvantageous, especially for preterm infants given the limited value of wakefulness at this age. This could be addressed by environmental interventions in the future.

Longitudinal sleep characteristics and hypertension status: results from the Wisconsin Sleep Cohort Study

AIMS

Sleep characteristics such as short sleep duration or sleep-disordered breathing are established predictors of hypertension. However, few studies have used in-lab polysomnography with a longitudinal design to measure how hypertension is associated with different sleep stages over time. The purpose of this study is to examine whether hypertension is associated with the longitudinal course of sleep quality over time.

METHODS

The current study evaluated data from the Wisconsin Sleep Cohort Study, which consists of 1525 adults in a community-based population of middle-aged to older adults followed for approximately 12-25 years. Sleep characteristics were objectively measured using polysomnography and subjectively assessed using a self-report questionnaire on insomnia complaints. We used linear mixed-effects regression models and cumulative logit models to assess whether the interaction of hypertension and time is associated with objective and subjective sleep.

RESULTS

We found people with hypertension exhibited a greater decline in total sleep time in rapid eye movement sleep (%) over time than those without hypertension (P < 0.05). Individuals with hypertension had less decline in % N3 sleep over time than those without hypertension (P < 0.05). Among the subjective insomnia complaints, our findings indicate hypertensive individuals have a higher probability of having higher levels of 'difficulties in falling asleep' compared with people without hypertension.

CONCLUSION

These findings suggest that hypertension is associated with modified longitudinal changes of objective and subjective sleep characteristics.

Sleep Spindle Characteristics in Obstructive Sleep Apnea Syndrome (OSAS)

Background: We compared the density and duration of sleep spindles topographically in stage 2 and 3 of non-rapid eye movement sleep (N2 and N3) among adults diagnosed with Obstructive Sleep Apnea Syndrome (OSAS) and healthy controls.

Materials and Methods: Thirty-one individuals with OSAS (mean age: 48.50 years) and 23 healthy controls took part in the study. All participants underwent a whole night polysomnography. Additionally, those with OSAS were divided into mild, moderate and severe cases of OSAS.

Results: For N2, sleep spindle density did not significantly differ between participants with and without OSAS, or among those with mild, moderate and severe OSAS. For N3, post-hoc analyses revealed significantly higher spindle densities in healthy controls and individuals with mild OSAS than in those with moderate or severe OSAS. Last, in N2 a higher AHI was associated with a shorter sleep spindle duration.

Conclusion: OSAS is associated with a significantly lower spindle density in N3 and a shorter spindle duration in N2. Our results also revealed that, in contrast to moderate and severe OSAS, the sleep spindle characteristics of individuals with mild OSAS were very similar to those of healthy controls.

Drug-Resistant Epilepsy and Obstructive Sleep Apnea: Exploring a Link Between the Two

Drug-resistant epilepsy accounts for approximately one third of all epilepsy cases; yet its exact etiopathogenesis still remains under intense exploration. Several factors have been advocated for predicting drug resistance in patients with epilepsy. Obstructive sleep apnea (OSA) is a commonly prevalent sleep disorder that exhibits a bidirectional and strong comorbidity with epilepsy. The exact pathophysiology of this comorbidity is not yet clearly explained. This study analyzes the relationship between drug-resistant epilepsy and OSA, and the findings indicate a strong role of rapid eye movement sleep (REMS) in the pathogenesis of this relationship. It also emerges from the study that REMS reduction is a prominent feature of OSA, and drug resistance in patients with epilepsy and treatment of OSA has been shown to restore REMS in several studies with concomitant improvement in seizure control.

Is moderate depression associated with sleep stage architecture in adolescence? Testing the stage type associations using network and transition probability approaches

BACKGROUND

Depression even at the subclinical level is often accompanied by sleep disturbances, but little is known about the dynamics of the sleep stages in relation to depressive symptoms. We examined whether the amount, associations, and transition probabilities of various sleep stages were associated with depressive symptoms in a community sample of adolescents.

METHODS

The participants (N = 172, 59% girls, mean age 16.9 years) underwent overnight polysomnography and provided data on depressive symptoms (Beck Depression Inventory II). The association between depression status and total duration of each stage type was analyzed using ANOVA and survival analyses. The associations between the number of different sleep stage types were analyzed using graphical Gaussian models, mixed graphical models, and relative importance networks. A Markov chain algorithm was used to estimate the transition probabilities between each state and these probabilities were further compared between depression status groups.

RESULTS

The associations between N1 and N3 were significantly stronger in both directions of the association (p-values for interactions 0.012 and 0.006) in those with more depressive symptoms. Similarly, a stronger association was observed from N1 to wake stage in those with more depressive symptoms (p-value for interaction 0.002). In those with more depressive symptoms, it was more likely to transition from N2 to N3 and from REM to N2 compared to others.

CONCLUSIONS

These findings indicate that changes in sleep architecture are not limited to clinical depression and that the transitional dynamics of sleep stages are an important marker of subclinical depression.

Deep learning for automated sleep staging using instantaneous heart rate

Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

High sleep-related breathing disorders among HIV-infected patients with sleep complaints

BACKGROUND

Sleep-related breathing disorders (SRBD) not only adversely impact cardiovascular and pulmonary functions but also reduce the quality of life. This study attempted to investigate the severity and related factors of SRBD among human immunodeficiency virus (HIV)-infected people with sleep complaints.

METHODS

A cross-sectional study was conducted among HIV-infected people with Pittsburgh Sleep Quality Index (PSQI) values above 5 points. SRBDs were defined as Apnea-Hypopnea Index (AHI) over 5 events/hour by full channel home-based polysomnography (PSG). An AHI greater than 30 was categorized as moderate to severe apnea. All of the participants were asked to rate the severity of their snoring, complete a snore outcomes survey (SOS) and complete the PSQI. Multiple logistic regression analysis was conducted to examine the associated factors of severity of SRBD among HIV-infected people.

RESULTS

Of 54 male HIV-positive patients with sleep complaints, 34 subjects (62.96%) were diagnosed with SRBDs, and 29.63% were categorized as having moderate to severe SRBD. SOS score was significantly associated with HIV patients with moderate to severe SRBD (adjusted odds ratio = 0.93, 95% CI: 0.87-1.00, p = 0.045).

CONCLUSIONS

SRBDs is highly prevalent in male HIV-infected people with sleep complaints. Intensive snoring was one of the indicators associated with the occurrence of moderate to severe SRBDs among HIV-infected people. Healthcare professionals should include snoring screening in the regular course of clinical HIV care.

Unique sleep-stage transitions determined by obstructive sleep apnea severity, age and gender

In obstructive sleep apnea, patients' sleep is fragmented leading to excessive daytime sleepiness and co-morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two-step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two-step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep-states for power-laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake-state durations followed a power-law distribution, while sleep-state durations were characterized by an exponential distribution. Sleep-stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea-related clinical outcomes like arterial hypertension and daytime sleepiness.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

BACKGROUND

It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown.

OBJECTIVE

This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy.

METHODS

A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors.

RESULTS

Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.

CONCLUSIONS

Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Associations between sleep disturbances, mental health outcomes and burnout in firefighters, and the mediating role of sleep during overnight work: A cross-sectional study

This study investigated whether sleep disorder risk and mental health outcomes in firefighters were associated with burnout, particularly emotional exhaustion, and examined the mediating role of sleep at work in these relationships. A secondary aim was to investigate associations between habitual sleep characteristics and burnout. North American firefighters (n = 6,307) completed the Maslach Burnout Inventory (emotional exhaustion, depersonalisation, personal accomplishment), and were screened for sleep disorders and self-reported current mental health conditions and sleep characteristics. Multiple logistic regression analyses examined associations between sleep, mental health outcomes and burnout. Firefighters screening positive for a sleep disorder, particularly insomnia, had increased risk of emotional exhaustion (adjusted odds ratio 3.78, 95% confidence interval 2.97-4.79). Firefighters self-reporting a current mental health condition were at greater risk of emotional exhaustion (adjusted odds ratio 3.45, 95% confidence interval 2.79-4.27). Sleep during overnight work mediated the impact of having a sleep disorder and mental health condition on high burnout. Sleepiness and sleep deficit (difference between required and actual sleep), even in firefighters without sleep disorder risk, were associated with depersonalisation (adjusted odds ratio 1.65, 95% confidence interval 1.34-2.03 and adjusted odds ratio 1.29, 95% confidence interval 1.06-1.57, respectively) and low personal accomplishment (adjusted odds ratio 1.25, 95% confidence interval 1.07-1.47 and adjusted odds ratio 1.17, 95% confidence interval 1.01-1.35, respectively). Sleep and mental health problems were associated with increased risk of burnout in firefighters, and sleep during overnight work mediated these relationships. The results suggest the need to examine the effectiveness of occupational interventions that improve the opportunity for sleep, together with screening for and treating sleep disorders, to reduce burnout risk.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

Markov modeling of sleep stage transitions and ultradian REM sleep rhythm

OBJECTIVE

One of the highly characteristic features of sleep is the cyclic occurrence of non-rapid eye movement (NREM) and REM sleep, which is referred to as the ultradian rhythm of sleep. Even though REM sleep was discovered over half a century ago, surprisingly, the mechanism of the ultradian REM sleep rhythm has not yet been fully elucidated. In the present study, we aim to provide a mechanistic insight into the generation of the ultradian REM sleep rhythm. Approach and Main results: By simulating hypnograms with the dynamic features of sleep stage transitions, i.e. stage transition probabilities and stage-specific survival time functions, we show that the second-order Markov transition probabilities and the stage-specific survival time functions can reproduce the central position (∼90 min) of the REM-onset intervals (ROIs), but with a larger variance in distribution. In addition, we demonstrate the direct effect of the increased probability of the transitions from light to deep sleep within NREM sleep on the prolongation of the ROIs in a dose-response manner.

SIGNIFICANCE

These results suggest that dynamic sleep stage transitions constitute the basis of the formation of the ultradian rhythm of sleep; however, further elaboration of the model would be required to reduce the variability in rhythmicity.

Sleep devices: wearables and nearables, informational and interventional, consumer and clinical

The field of sleep is in many ways ideally positioned to take full advantage of advancements in technology and analytics that is fueling the mobile health movement. Combining hardware and software advances with increasingly available big datasets that contain scored data obtained under gold standard sleep laboratory conditions completes the trifecta of this perfect storm. This review highlights recent developments in consumer and clinical devices for sleep, emphasizing the need for validation at multiple levels, with the ultimate goal of using personalized data and advanced algorithms to provide actionable information that will improve sleep health.

A comparison of probabilistic classifiers for sleep stage classification

OBJECTIVE

To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients.

APPROACH

The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1  +  N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA).

MAIN RESULTS

CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62.8% and 0.44 for five classes, 68.8% and 0.47 for four classes, and 77.6% and 0.55 for three classes. An advantage was found in training classifiers, specifically for 'regular' and 'OSA' participants, achieving an improvement in classification performance for these groups. For 'regular' participants, CRFt achieved a median accuracy and Cohen's κ of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 59.9% and 0.40, 69.7% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively.

SIGNIFICANCE

The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification-the standard for sleep scoring used in clinical PSG. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Study Objectives

Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters.

Methods

Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests.

Results

People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters.

Conclusions

Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter.

Sleeping oxygen saturation, rapid eye movement sleep, and the adaptation of postprandial metabolic function in insulin sensitive and resistant individuals without diabetes

AIMS

Sleeping oxygen saturation (SaO₂) and sleep stage duration have been linked with prediabetic alterations but the pathogenic pathways are not well understood. This study of insulin sensitive and resistant adults examined the effect on postprandial metabolic regulation of repeated mixed-meal challenges of different carbohydrate loading. The aim was to examine whether the relationship between lower sleeping oxygen saturation (SaO₂) and poorer fasting and postprandial metabolic function may be linked with reduced slow wave sleep (SWS) and rapid eye movement (REM) duration, independent of age, sex and total adiposity.

METHODS

The 24 men and women, aged 25-54 years, had no diabetes or other diagnosed conditions, were evaluated with polysomnography to derive indices of SaO₂ and sleep architecture. In addition, an OGTT and two 14-h serial mixed-meal tests were administered over 3 successive in-patient days. The carbohydrate content of the mixed-meals was manipulated to compare a standard-load day with a double-load day (300 vs. 600 kcal/meal). Quantitative modeling was applied to derive β-cell glucose sensitivity (β-GS), early insulin secretion rate sensitivity (ESRS), and total postprandial insulinemia (AUC_INS).

RESULTS

Analyses showed that, for the 14-h tests, the SaO₂ relationship with metabolic outcomes was associated significantly with percent time spent in REM but not SWS, independent of age, sex and total adiposity. Specifically, indirect pathways indicated that lower SaO₂ was related to shorter REM duration, and shorter REM was respectively associated with higher β-GS, ESRS, and AUC_INS for the 300- and 600-load days (300 kcal/meal: β = -8.68, p < .03, β = -8.54, p < .002, and β = -10.06, p < .008; 600 kcal/meal: β = -11.45, p < .003, β = -11.44, p < .001, and β = -11.00, p < .03).

CONCLUSION

Sleeping oxygen desaturation and diminished REM duration are associated with a metabolic pattern that reflects a compensatory adaptation of postprandial insulin metabolism accompanying preclinical diabetic risk.

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients

The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixed-effect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at ¼ and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. NEW & NOTEWORTHY We present a new methodology for deriving, from polysomnography data, the individual conditional probability for the duration of the bouts of wake, N1, N2, N3, and REM sleep. We evaluated the variability of this probability within and between primary insomnia patients and along sleep time. The multiexponential shapes of the probability distributions within the individuals revealed memory-free mechanisms and sleep subcomponents with consistent features in the studied population.

Long-term adherence to continuous positive airway pressure in patients with rapid eye movement-only obstructive sleep apnea: a prospective cohort study

Background

Obstructive sleep apnea (OSA) during rapid eye movement (REM) sleep is a special form of OSA that occurs predominantly during REM sleep. No study has assessed the long-term adherence to continuous positive airway pressure (CPAP) among patients with REM-only OSA. To objectively assess the long-term adherence to CPAP therapy among patients with REM-predominant (REM-only) OSA.

Methods

This was a prospective observational cohort study that included consecutive patients who were diagnosed with OSA. Patients were divided into the following two groups: (I) REM-only OSA [an apnea hypopnea index (AHI) of ≥5, with a REM-AHI/NREM-AHI of >2, an NREM-AHI of <15, and a minimum of 15 min of REM-sleep duration], and (II) non-stage specific OSA. Follow-up was performed at 1, 6 and 12 months after the initiation of CPAP-therapy.

Results

The study included 175 patients; 30 met the criteria for REM-only OSA, and the remaining patients constituted the non-stage specific OSA group. The number of hours of CPAP use per day was significantly lower in the REM-only OSA group at 1, 6 and 12 months compared to the non-stage specific OSA group. At 12 months, the number of hours used per day was 3.8±1.8 and 5.1±2.1 hours in the REM-only and non-stage specific OSA groups, respectively (P=0.003). Approximately 80% of the enrolled participants in both groups were still using CPAP at the end of the study; 23.3% of REM-only OSA patients met the criteria for good adherence (>4 hours/day), and 56.7% met the criteria for partial adherence.

Conclusions

CPAP adherence is lower among patients with REM-only OSA compared to patients with non-stage specific OSA.

Transient knock-down of kcna2 reduces sleep in larval zebrafish

In the current study we set out to determine the effects of morpholino oligonucleotide (MO) knock-down of kcna2 on sleep-wake cycles in zebrafish. The results were compared to a non-overlapping MO injection, Dec2, who's mutant is also linked with a short sleep phenotype. Four groups of fish were used in the experiment: naïve fish, and fish injected with either control, kcna2, or Dec2 MO. All groups underwent 24-h behavioral monitoring of sleep-wake cycles at four and seven days-post-fertilization (dpf). First, we established an immobility dependent, sleep related, increase in arousal thresholds at both 4 and 7 dpf. Secondly, we show that kcna2 MO injected fish exhibit significantly less sleep behavior than controls and naïve fish, whereas Dec2 MO injections had similar but less severe effects. Finally, using kcna2 MO injected fish only, we turn to local field recordings at the level of the telencephalon and tectum opticum and rule out that the knock-down resulted in a non-specific increase in neural excitability that would mask sleep behavior.

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.

Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets

Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.

Statistics for Sleep and Biological Rhythms Research

The Journal of Biological Rhythms will be publishing articles exploring analysis and statistical topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. By using case examples and highlighting the pearls and pitfalls of statistical inference, the authors will identify and explain ways in which experimental scientists can avoid common analytical and statistical mistakes and use appropriate analytical and statistical methods in their research. In this first article, we address the first steps in analysis of data: understanding the underlying statistical distribution of the data and establishing associative versus causal relationships. These ideas are then applied to sample size, power calculations, correlation testing, differences between description and prediction, and the narrative fallacy.

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.

Relationship of symptoms with sleep-stage abnormalities in obstructive sleep apnea-hypopnea syndrome

BACKGROUND

Patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) present with a variety of sleep-related symptoms. In polysomnography, sleep architecture is almost always abnormal, but it is not known which of the sleep-stage abnormalities are related to symptoms. Finding key sleep-stage abnormality that cause symptoms may be of therapeutic importance to alleviate symptoms. So far the mainstay of treatment is continuous positive airway pressure (CPAP)/bi-level positive airway pressure (BIPAP) therapy, but many patients are non-compliant to it. Correcting the sleep-stage abnormality that cause symptoms by pharmacotherapy may become an important adjunct to CPAP/BIPAP therapy.

METHODS

A cross-sectional study. Adult subjects who attended a sleep laboratory for diagnostic polysomnography for a period of 1 month were recruited consecutively. OSAHS was diagnosed using American Academy of Sleep Medicine criteria. Subjects filled a questionnaire for symptoms prior to polysomnography.

RESULTS

Thirty subjects, of whom 83.3% were obese, met diagnostic criteria, with males constituting 46.7% and females constituting 53%. Mean age was 53.40±11.60 years. Sleep architecture comprised N1 19.50±19.00%, N2 53.93±13.39%, N3 3.90±19.50%, and rapid eye movement 8.92±6.21%. Excessive fatigue or sleepiness, waking up tired, falling asleep during the day, trouble paying attention, snoring and insomnia were significantly related to decreased N3 sleep.

CONCLUSIONS

Most of the symptoms in OSAHS in adults are related to decreased stage N3 sleep. If confirmed by larger controlled studies, correcting N3 sleep deficiency by pharmacotherapy may become an important adjunct to CPAP/BIPAP therapy to alleviate symptoms.

Significance of the zero sum principle for circadian, homeostatic and allostatic regulation of sleep-wake state in the rat

Sleep-wake behavior exhibits diurnal rhythmicity, rebound responses to acute total sleep deprivation (TSD), and attenuated rebounds following chronic sleep restriction (CSR). We investigated how these long-term patterns of behavior emerge from stochastic short-term dynamics of state transition. Male Sprague-Dawley rats were subjected to TSD (1day×24h, N=9), or CSR (10days×18h TSD, N=7) using a rodent walking-wheel apparatus. One baseline day and one recovery day following TSD and CSR were analyzed. The implications of the zero sum principle were evaluated using a Markov model of sleep-wake state transition. Wake bout duration (a combined function of the probability of wake maintenance and proportional representations of brief and long wake) was a key variable mediating the baseline diurnal rhythms and post-TSD responses of all three states, and the attenuation of the post-CSR rebounds. Post-NREM state transition trajectory was an important factor in REM rebounds. The zero sum constraint ensures that a change in any transition probability always affects bout frequency and cumulative time of at least two, and usually all three, of wakefulness, NREM and REM. Neural mechanisms controlling wake maintenance may play a pivotal role in regulation and dysregulation of all three states.

Sleep disturbance in patients taking opioid medication for chronic back pain

Poor sleep is an increasingly recognised problem with chronic pain and further increases the effect on daily function. To identify the relationship between chronic pain, opioid analgesia and sleep quality, this study investigated activity and sleep patterns in patients taking opioid and non‐opioid analgesia for chronic back pain. Thirty‐one participants (10 healthy controls, 21 patients with chronic pain: 6 on non‐opioid medication; 15 on opioid medication) were assessed using actigraphy, polysomnography and questionnaires. Patients with chronic pain subjectively reported significant sleep and wake disturbances as shown by decreased overall sleep quality (Pittsburgh Sleep Quality Index, p < 0.001), increased symptoms of insomnia (Insomnia Severity Index, p < 0.001) and increased fatigue (Fatigue Severity Scale, p = 0.002). They also spent increased time in bed (p = 0.016), took longer to get to sleep (p = 0.005) and had high interindividual variability in other measures of activity but no overall irregular rest‐activity pattern. Patients on high doses of opioids (> 100 mg morphine‐equivalent/day) demonstrated distinctly abnormal brain activity during sleep suggesting that polysomnography is necessary to detect sleep disturbance in this population in the absence of irregular rest‐activity behaviour. Night‐time sleep disturbance is common in individuals suffering from chronic pain and may be further exacerbated by opioid treatment. Considerations must be made regarding the appropriate use of combined actigraphy and miniaturised polysomnography for future population‐based studies.

☛ CPD available at http://www.learnataagbi.org

Nocturnal Hot Flashes: Relationship to Objective Awakenings and Sleep Stage Transitions

STUDY OBJECTIVES

While women report sleep interruption secondary to nighttime hot flashes, the sleep disrupting impact of nocturnal hot flashes (HF) is not well characterized. We utilized a model of induced HF to investigate the relationship of nighttime HF to sleep architecture and sleep-stage transitions.

METHODS

Twenty-eight healthy, premenopausal volunteers received the depot gonadotropin-releasing hormone agonist (GnRHa) leuprolide to rapidly induce menopause, manifesting with HF. Sleep disruption was measured on 2 polysomnograms conducted before and after 4-5 weeks on leuprolide, when HF had developed.

RESULTS

165 HF episodes were recorded objectively during 48 sleep studies (mean 3.4 HF/night). After standardizing to sleep-stage time distribution, the majority of HF were recorded during wake (51.0%) and stage N1 (18.8%). Sixty-six percent of HF occurred within 5 minutes of an awakening, with 80% occurring just before or during the awakening. Objective HF were not associated with sleep disruption as measured by increased transitions to wake or N1, but self-reported nocturnal HF correlated with an increase from pre- to post-leuprolide in the rate of transitions to wake (p = 0.01), and to N1 (p = 0.008).

CONCLUSIONS

By isolating the effect of HF on sleep in women without the confound of age-related sleep changes associated with natural menopause, this experimental model shows that HF arise most commonly during N1 and wake, typically preceding or occurring simultaneously with wake episodes. Perception of HF, but not objective HF, is linked to increased sleep-stage transitions, suggesting that sleep disruption increases awareness of and memory for nighttime HF events.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01116401.

Relationships Between Questionnaire Ratings of Sleep Quality and Polysomnography in Healthy Adults

This study aimed to examine the association between polysomnographic sleep and subjective habitual sleep quality and restoration from sleep. Thirty-one normal sleepers completed the Karolinska Sleep Questionnaire and multiple home polysomnography recordings (n = 2-5). Using linear regression, sleep quality and restoration were separately analyzed as functions of standard polysomnography parameters: sleep efficiency, total sleep time, sleep latency, stage 1 and 2 sleep, slow-wave sleep, rapid eye movement sleep, wake time after sleep onset, and awakenings (n), averaged across recordings. Stage 2 and slow-wave sleep predicted worse and better sleep quality, respectively. Also, slow-wave sleep predicted less subjective restoration, although adjustment for age attenuated this relation. Our findings lend some physiological validity to ratings of habitual sleep quality in normal sleepers. Data were less supportive of a physiological correlate of ratings of restoration from sleep.

Clinical and polysomnographic predictors of severe obstructive sleep apnea in the South Indian population

Background:

With the emergence of lifestyle diseases in epidemic proportions, obstructive sleep apnea (OSA) is being increasingly recognized in less developed countries as well.

Aim:

We sought to study the demographic, clinical, and polysomnographic (PSG) predictors of OSA severity in a cohort of South Indian patients.

Materials and Methods:

Consecutive patients with PSG proven OSA [apnea hypopnea index (AHI) ≥5/h] were prospectively recruited. The study period was from January 2012 to December 2012. Demographic data, history of vascular risk factors, substance abuse, sleep quality, snoring, and witnessed apneas were collected using a structured pro forma. In addition, PSG variables such as AHI, sleep latency and efficiency, duration of slow wave and rapid eye movement (REM) sleep, and other parameters were collected. Correlations between AHI severity and clinical and PSG parameters were done.

Results:

There were 152 (119 males and 33 females) subjects with a mean age of 53.8 years and body mass index (BMI) of 29.31. Mean AHI was 36.2/h (range: 5.1-110) and 66 subjects had severe OSA. Around 12% had the presenting complaint as insomnia, mainly of sleep maintenance. Of the subjects, 35% had witnessed apneas and 67% had excessive daytime sleepiness (EDS); 40% of patients had ≥2 risk factors. PSG parameters showed short sleep onset latency with a high arousal index. Mean apnea duration was 24.92 s. We found that age >55 years, BMI >25 kg/m², witnessed apneas, EDS, hypertension, dyslipidemia, reduced slow wave sleep duration, mean apnea duration >20 s, and desaturation index >10/h correlated well with OSA severity while the arousal index, sleep latency and efficiency, and exposure to smoking and alcohol showed no association.

Conclusions:

Older subjects with witnessed apneas are likely to have more severe OSA. Even though overall sleep architecture was similar between the groups, severe OSA had shorter slow wave sleep, longer apneas, and higher nocturnal hypoxemia.

Detection of Nocturnal Slow Wave Sleep Based on Cardiorespiratory Activity in Healthy Adults

Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This study aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features, and thus, enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large dataset of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and tenfold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min, which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.

Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions

OBJECTIVES

For the first time ever, as emphasized by inclusion in the Healthy People 2020 goals, sleep health is an emphasis of national health aims. The National Healthy Sleep Awareness Project (NHSAP) was tasked to propose questions for inclusion in the next Behavioral Risk Factor Surveillance System (BRFSS), a survey that includes a number of questions that target behaviors thought to impact health, as a means to measure community sleep health. The total number of questions could not exceed five, and had to include an assessment of the risk for obstructive sleep apnea (OSA).

METHODS

An appointed workgroup met via teleconference and face-to-face venues to develop an inventory of published survey questions being used to identify sleep health, to develop a framework on which to analyze the strengths and weaknesses of current survey questions concerning sleep, and to develop recommendations for sleep health and disease surveillance questions going forward.

RESULTS

The recommendation was to focus on certain existing BRFSS questions pertaining to sleep duration, quality, satisfaction, daytime alertness, and to add to these other BRFSS existing questions to make a modified STOP-BANG questionnaire (minus the N for neck circumference) to assess for risk of OSA.

CONCLUSIONS

Sleep health is an important dimension of health that has previously received less attention in national health surveys. We believe that 5 questions recommended for the upcoming BRFSS question banks will assist as important measures of sleep health, and may help to evaluate the effectiveness of interventions to improve sleep health in our nation.