Showing papers on "Polysomnography published in 2019"

The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study.

Abstract: Aims Apnoea-hypopnoea index (AHI), the universal clinical metric of sleep apnoea severity, poorly predicts the adverse outcomes of sleep apnoea, potentially because the AHI, a frequency measure, does not adequately capture disease burden. Therefore, we sought to evaluate whether quantifying the severity of sleep apnoea by the 'hypoxic burden' would predict mortality among adults aged 40 and older. Methods and results The samples were derived from two cohort studies: The Outcomes of Sleep Disorders in Older Men (MrOS), which included 2743 men, age 76.3 ± 5.5 years; and the Sleep Heart Health Study (SHHS), which included 5111 middle-aged and older adults (52.8% women), age: 63.7 ± 10.9 years. The outcomes were all-cause and Cardiovascular disease (CVD)-related mortality. The hypoxic burden was determined by measuring the respiratory event-associated area under the desaturation curve from pre-event baseline. Cox models were used to calculate the adjusted hazard ratios for hypoxic burden. Unlike the AHI, the hypoxic burden strongly predicted CVD mortality and all-cause mortality (only in MrOS). Individuals in the MrOS study with hypoxic burden in the highest two quintiles had hazard ratios of 1.81 [95% confidence interval (CI) 1.25-2.62] and 2.73 (95% CI 1.71-4.36), respectively. Similarly, the group in the SHHS with hypoxic burden in the highest quintile had a hazard ratio of 1.96 (95% CI 1.11-3.43). Conclusion The 'hypoxic burden', an easily derived signal from overnight sleep study, predicts CVD mortality across populations. The findings suggest that not only the frequency but the depth and duration of sleep related upper airway obstructions, are important disease characterizing features.

Clinical Practice Guideline: Tonsillectomy in Children (Update):

Abstract: ObjectiveThis update of a 2011 guideline developed by the American Academy of Otolaryngology–Head and Neck Surgery Foundation provides evidence-based recommendations on the pre-, intra-, and postop...

Wearable Sleep Technology in Clinical and Research Settings.

Abstract: The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.

Clinical Practice Guideline: Tonsillectomy in Children (Update)—Executive Summary

Abstract: ObjectiveThis update of a 2011 guideline developed by the American Academy of Otolaryngology–Head and Neck Surgery Foundation provides evidence-based recommendations on the pre-, intra-, and postop...

Association of Unrecognized Obstructive Sleep Apnea With Postoperative Cardiovascular Events in Patients Undergoing Major Noncardiac Surgery.

Abstract: Importance Unrecognized obstructive sleep apnea increases cardiovascular risks in the general population, but whether obstructive sleep apnea poses a similar risk in the perioperative period remains uncertain. Objectives To determine the association between obstructive sleep apnea and 30-day risk of cardiovascular complications after major noncardiac surgery. Design, Setting, and Participants Prospective cohort study involving adult at-risk patients without prior diagnosis of sleep apnea and undergoing major noncardiac surgery from 8 hospitals in 5 countries between January 2012 and July 2017, with follow-up until August 2017. Postoperative monitoring included nocturnal pulse oximetry and measurement of cardiac troponin concentrations. Exposures Obstructive sleep apnea was classified as mild (respiratory event index [REI] 5-14.9 events/h), moderate (REI 15-30), and severe (REI >30), based on preoperative portable sleep monitoring. Main Outcomes and Measures The primary outcome was a composite of myocardial injury, cardiac death, heart failure, thromboembolism, atrial fibrillation, and stroke within 30 days of surgery. Proportional-hazards analysis was used to determine the association between obstructive sleep apnea and postoperative cardiovascular complications. Results Among a total of 1364 patients recruited for the study, 1218 patients (mean age, 67 [SD, 9] years; 40.2% women) were included in the analyses. At 30 days after surgery, rates of the primary outcome were 30.1% (41/136) for patients with severe OSA, 22.1% (52/235) for patients with moderate OSA, 19.0% (86/452) for patients with mild OSA, and 14.2% (56/395) for patients with no OSA. OSA was associated with higher risk for the primary outcome (adjusted hazard ratio [HR], 1.49 [95% CI, 1.19-2.01]; P = .01); however, the association was significant only among patients with severe OSA (adjusted HR, 2.23 [95% CI, 1.49-3.34]; P = .001) and not among those with moderate OSA (adjusted HR, 1.47 [95% CI, 0.98-2.09]; P = .07) or mild OSA (adjusted HR, 1.36 [95% CI, 0.97-1.91]; P = .08) ( P = .01 for interaction). The mean cumulative duration of oxyhemoglobin desaturation less than 80% during the first 3 postoperative nights in patients with cardiovascular complications (23.1 [95% CI, 15.5-27.7] minutes) was longer than in those without (10.2 [95% CI, 7.8-10.9] minutes) ( P Conclusions and Relevance Among at-risk adults undergoing major noncardiac surgery, unrecognized severe obstructive sleep apnea was significantly associated with increased risk of 30-day postoperative cardiovascular complications. Further research would be needed to assess whether interventions can modify this risk.

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis.

Abstract: BACKGROUND Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. OBJECTIVE We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. METHODS In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. RESULTS The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. CONCLUSIONS Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Prevalence and association analysis of obstructive sleep apnea with gender and age differences – Results of SHIP‐Trend

Abstract: Identification of obstructive sleep apnea and risk factors is important for reduction in symptoms and cardiovascular risk, and for improvement of quality of life. The population-based Study of Health in Pomerania investigated risk factors and clinical diseases in a general population of northeast Germany. Additional polysomnography was applied to measure sleep and respiration with the objective of assessing prevalence and risk factors of obstructive sleep apnea in a German cohort. One-thousand, two-hundred and eight people between 20 and 81 years old (54% men, median age 54 years) underwent overnight polysomnography. The estimated obstructive sleep apnea prevalence was 46% (59% men, 33% women) for an apnea-hypopnea index ≥5%, and 21% (30% men, 13% women) for an apnea-hypopnea index ≥ 15. The estimated obstructive sleep apnea syndrome prevalence (apnea-hypopnea index ≥5; Epworth Sleepiness Scale >10) was 6%. The prevalence of obstructive sleep apnea continuously increased with age for men and women with, however, later onset for women. Gender, age, body mass index, waist-to-hip ratio, snoring, alcohol consumption (for women only) and self-reported cardiovascular diseases were significantly positively associated with obstructive sleep apnea, whereas daytime sleepiness was not. Diabetes, hypertension and metabolic syndrome were positively associated with severe obstructive sleep apnea. The associations became non-significant after adjustment for body mass. Women exhibited stronger associations than men. The prevalence of obstructive sleep apnea was high, with almost half the population presenting some kind of obstructive sleep apnea. The continuous increase of obstructive sleep apnea with age challenges the current theory that mortality due to obstructive sleep apnea and cardiovascular co-morbidities affect obstructive sleep apnea prevalence at an advanced age. Also, gender differences regarding obstructive sleep apnea and associations are significant for recognizing obstructive sleep apnea mechanisms and therapy responsiveness.

The Sleep of the Ring: Comparison of the ŌURA Sleep Tracker Against Polysomnography

Abstract: Objective/Background: To evaluate the performance of a multisensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants: Forty-one healthy adolescents and young adults (13 females; Age: 17.2 ± 2.4 years). Methods: Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results: Summary variables for sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p 7 hr, respectively. Conclusions: Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep-wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.

Sleep as a potential biomarker of tau and β-amyloid burden in the human brain

Abstract: Recent proposals suggest that sleep may be a factor associated with accumulation of two core pathological features of Alzheimer9s disease (AD): tau and β-amyloid (Aβ). Here we combined PET measures of Aβ and tau, electroencephalogram sleep recordings, and retrospective sleep evaluations to investigate the potential utility of sleep measures in predicting in vivo AD pathology in male and female older adults. Regression analyses revealed that the severity of impaired slow oscillation-sleep spindle coupling predicted greater medial temporal lobe tau burden. Aβ burden was not associated with coupling impairment but instead predicted the diminished amplitude of SIGNIFICANCE STATEMENT Several studies have linked sleep disruption to the progression of Alzheimer9s disease (AD). Tau and β-amyloid (Aβ), the primary pathological features of AD, are associated with both objective and subjective changes in sleep. However, it remains unknown whether late life tau and Aβ burden are associated with distinct impairments in sleep physiology or changes in sleep across the lifespan. Using polysomnography, retrospective questionnaires, and tau- and Aβ-specific PET, the present study reveals human sleep signatures that dissociably predict levels of brain tau and Aβ in older adults. These results suggest that a night of polysomnography may aid in evaluating tau and Aβ burden, and that treating sleep deficiencies within decade-specific time windows may serve in delaying AD progression.

A Review of Obstructive Sleep Apnea Detection Approaches

Abstract: Sleep disorders are a common health condition that can affect numerous aspects of life. Obstructive sleep apnea is one of the most common disorders and is characterized by a reduction or cessation of airflow during sleep. In many countries, this disorder is usually diagnosed in sleep laboratories, by polysomnography, which is an expensive procedure involving much effort for the patient. Multiple systems have been proposed to address this situation, including performing the examination and analysis in the patient's home, using sensors to detect physiological signals that are automatically analyzed by algorithms. However, the precision of these devices is usually not enough to provide clinical diagnosis. Therefore, the objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends. The performance of different algorithms and methods for apnea detection through the use of different sensors (pulse oximetry, electrocardiogram, respiration, sound, and combined approaches) has been evaluated. 84 original research articles published from 2003 to 2017 with the potential to be promising diagnostic tools have been selected to cover multiple solutions. This paper could provide valuable information for those researchers who want to carry out a hardware implementation of potential signal processing algorithms.

Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device.

Abstract: Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Comparison of Lemborexant With Placebo and Zolpidem Tartrate Extended Release for the Treatment of Older Adults With Insomnia Disorder: A Phase 3 Randomized Clinical Trial

Abstract: Importance Insomnia disorder is prevalent and associated with health risks in older adults; however, efficacy and safety issues with existing treatments create significant unmet needs in this patient population. Objective To compare treatment with the orexin receptor antagonist lemborexant with placebo and zolpidem tartrate extended release in participants with insomnia disorder. Design, Setting, and Participants The Study of the Efficacy and Safety of Lemborexant in Subjects 55 Years and Older With Insomnia Disorder (SUNRISE 1) clinical trial was a global randomized double-blind parallel-group placebo-controlled active-comparator phase 3 study conducted at 67 sites in North America and Europe from May 31, 2016, to January 30, 2018. Data analyses were conducted from January 31, 2018, to September 10, 2018. Participants were 55 years and older with insomnia disorder characterized by reported sleep maintenance difficulties and confirmed by sleep history, sleep diary, and polysomnography. Participants could have also had sleep onset difficulties. Interventions Participants received placebo, zolpidem tartrate extended release (6.25 mg), or lemborexant (5 mg or 10 mg) for 1 month at bedtime. Main Outcomes and Measures Paired polysomnograms were collected at baseline, the first 2 nights, and the last 2 nights of treatment. The primary end point was the change from baseline in latency to persistent sleep for lemborexant therapy vs placebo. Key secondary end points were changes from baseline in sleep efficiency and wake-after-sleep onset compared with placebo, and wake-after-sleep onset in the second half of the night compared with zolpidem therapy. Results Among 1006 participants randomized (placebo, n = 208; zolpidem, n = 263; lemborexant 5 mg, n = 266; and lemborexant 10 mg, n = 269), 869 (86.4%) were women and the median age was 63 years (range, 55-88 years). Both doses of lemborexant therapy demonstrated statistically significant greater changes from baseline on objective sleep onset as assessed by latency to persistent sleep (log transformed) that was measured using polysomnography at the end of 1 month of treatment (nights 29 and 30) compared with placebo (primary end point for least squares geometric means treatment ratio vs placebo: for lemborexant 5 mg, 0.77; 95% CI, 0.67-0.89;P Conclusions and Relevance In this randomized clinical trial, lemborexant therapy significantly improved both sleep onset and sleep maintenance, including in the second half of the night, compared with both placebo and zolpidem measured objectively using polysomnography. Lemborexant therapy was well tolerated. Trial Registrations ClinicalTrials.gov identifier:NCT02783729; EudraCT identifier:2015-004347-39

Prevalence of sleep-disordered breathing after stroke and TIA: A meta-analysis.

Abstract: Objective To perform a systematic review and meta-analysis on the prevalence of sleep-disordered breathing (SDB) after stroke. Methods We searched PubMed, Embase (Ovid), the Cochrane Library, and CINAHL (from their commencements to April 7, 2017) for clinical studies reporting prevalence and/or severity of SDB after stroke or TIA. Only sleep apnea tests performed with full polysomnography and diagnostic devices of the American Academy of Sleep Medicine categories I–IV were included. We conducted random-effects meta-analysis. PROSPERO registration number: CRD42017072339. Results The initial search identified 5,211 publications. Eighty-nine studies (including 7,096 patients) met inclusion criteria. Fifty-four studies were performed in the acute phase after stroke (after less than 1 month), 23 studies in the subacute phase (after 1–3 months), and 12 studies in the chronic phase (after more than 3 months). Mean apnea-hypopnea index was 26.0/h (SD 21.7–31.2). Prevalence of SDB with apnea-hypopnea index greater than 5/h and greater than 30/h was found in 71% (95% confidence interval 66.6%–74.8%) and 30% (95% confidence interval 24.4%–35.5%) of patients, respectively. Severity and prevalence of SDB were similar in all examined phases after stroke, irrespective of the type of sleep apnea test performed. Heterogeneity between studies (I2) was mostly high. Conclusion The high prevalence of SDB after stroke and TIA, which persists over time, is important in light of recent studies reporting the (1) feasibility and (2) efficacy of SDB treatment in this clinical setting.

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Abstract: Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

Neural signatures of sleep in zebrafish

Abstract: Slow-wave sleep and rapid eye movement (or paradoxical) sleep have been found in mammals, birds and lizards, but it is unclear whether these neuronal signatures are found in non-amniotic vertebrates Here we develop non-invasive fluorescence-based polysomnography for zebrafish, and show—using unbiased, brain-wide activity recording coupled with assessment of eye movement, muscle dynamics and heart rate—that there are at least two major sleep signatures in zebrafish These signatures, which we term slow bursting sleep and propagating wave sleep, share commonalities with those of slow-wave sleep and paradoxical or rapid eye movement sleep, respectively Further, we find that melanin-concentrating hormone signalling (which is involved in mammalian sleep) also regulates propagating wave sleep signatures and the overall amount of sleep in zebrafish, probably via activation of ependymal cells These observations suggest that common neural signatures of sleep may have emerged in the vertebrate brain over 450 million years ago Fluorescence-based polysomnography in zebrafish reveals two major sleep signatures that share features with those of amniotes, which suggests that common neural sleep signatures emerged in the vertebrate brain over 450 million years ago

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes

Abstract: Excessive daytime sleepiness (EDS) affects 10–20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing. A main symptom of chronic insufficient sleep is excessive daytime sleepiness. Here, Wang et al. report 42 genome-wide significant loci for self-reported daytime sleepiness in 452,071 individuals from the UK Biobank that cluster into two biological subtypes of either sleep propensity or sleep fragmentation.

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Abstract: Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this paper, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state of the art. Furthermore, when these predictions are mapped to the apnea–hypopnea index, a considerable improvement in per-patient scoring is achieved over conventional methods. This paper presents a powerful aid for trained staff to quickly diagnose sleep apnea.

Sleep Duration and Sleep Quality in Caregivers of Patients With Dementia: A Systematic Review and Meta-analysis

Abstract: Importance In the United States, 16 million family caregivers provide long-term care for patients with dementia. Although one’s physical, mental, and cognitive health depends on sleep, many caregivers experience chronic stress, and stress is typically associated with worse sleep quantity and quality. Objective To quantify the extent, nature, and treatability of sleep problems in dementia caregivers. Data Sources PubMed and Scopus databases were systematically searched for articles published through June 2018 using the following keywords:caregiverorspouseorcaretakerANDsleeporcircadianANDdementiaorAlzheimer. Backward citation tracking was performed, and corresponding authors were contacted for additional data to conduct meta-analyses and pooled analyses. Study Selection Two reviewers independently screened 805 studies to identify those that reported sleep duration or sleep quality in caregivers of patients with dementia. Data Extraction and Synthesis Following the PRISMA guidelines, 2 reviewers independently extracted data from all studies and conducted National Heart, Lung, and Blood Institute study quality assessments. Meta-analyses with random-effects models were performed to evaluate sleep duration, sleep quality, and sleep interventions in dementia caregivers. Main Outcomes and Measures Sleep quality and total sleep time were measured by polysomnography, actigraphy, and self-report. Results Thirty-five studies were analyzed with data from 3268 caregivers (pooled mean age [SD of sample means], 63.48 [5.99] years; 76.7% female) were analyzed. Relative to age-matched control noncaregiver adults, caregivers had lower sleep durations akin to losing 2.42 to 3.50 hours each week (Hedgesg = −0.29; 95% CI, −0.48 to −0.09;P = .01). Sleep quality was significantly lower in caregivers (Hedgesg = −0.66; 95% CI, −0.89 to −0.42;P Conclusions and Relevance Sleep debt is known to have cumulative associations with physical, mental, and cognitive health; therefore, poor sleep quality in dementia caregivers should be recognized and addressed. Although the caregiving role is stressful and cognitively demanding by its nature, better sleep quality was observed in caregivers who received low-cost behavioral interventions.

Cognitive behavioral treatments for insomnia and pain in adults with comorbid chronic insomnia and fibromyalgia: clinical outcomes from the SPIN randomized controlled trial

Abstract: Study objectives To examine the effects of cognitive behavioral treatments for insomnia (CBT-I) and pain (CBT-P) in patients with comorbid fibromyalgia and insomnia. Methods One hundred thirteen patients (Mage = 53, SD = 10.9) were randomized to eight sessions of CBT-I (n = 39), CBT-P (n = 37), or a waitlist control (WLC, n = 37). Primary (self-reported sleep onset latency [SOL], wake after sleep onset [WASO], sleep efficiency [SE], sleep quality [SQ], and pain ratings) and secondary outcomes (dysfunctional beliefs and attitudes about sleep [DBAS]; actigraphy and polysomnography SOL, WASO, and SE; McGill Pain Questionnaire; Pain Disability Index; depression; and anxiety) were examined at posttreatment and 6 months. Results Mixed effects analyses revealed that both treatments improved self-reported WASO, SE, and SQ relative to control at posttreatment and follow-up, with generally larger effect sizes for CBT-I. DBAS improved in CBT-I only. Pain and mood improvements did not differ by group. Clinical significance analyses revealed the proportion of participants no longer reporting difficulties initiating and maintaining sleep was higher for CBT-I posttreatment and for both treatments at 6 months relative to control. Few participants achieved >50% pain reductions. Proportion achieving pain reductions of >30% (~1/3) was higher for both treatments posttreatment and for CBT-I at 6 months relative to control. Conclusions CBT-I and CBT-P improved self-reported insomnia symptoms. CBT-I prompted improvements of larger magnitude that were maintained. Neither treatment improved pain or mood. However, both prompted clinically meaningful, immediate pain reductions in one third of patients. Improvements persisted for CBT-I, suggesting that CBT-I may provide better long-term pain reduction than CBT-P. Research identifying which patients benefit and mechanisms driving intervention effects is needed. Clinical trial Sleep and Pain Interventions in Fibromyalgia (SPIN), clinicaltrials.gov, NCT02001077.

Accurate whole-night sleep monitoring with dry-contact ear-EEG

Abstract: Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen’s kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.

Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations.

Abstract: Study Objectives:To compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents.Methods:Fifty-eig...

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Abstract: Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low‐cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex‐printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self‐applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (“random forests”) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter‐individual variation in sleep parameters. The results demonstrate that machine‐learning‐based scoring of around‐the‐ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine‐learning‐based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine‐learning‐based scoring holds promise for large‐scale sleep studies.

Developmental changes of sleep spindles and their impact on sleep-dependent memory consolidation and general cognitive abilities: A longitudinal approach.

Abstract: Sleep spindles are related to sleep-dependent memory consolidation and general cognitive abilities However, they undergo drastic maturational changes during adolescence Here we used a longitudinal approach (across 7 years) to explore whether developmental changes in sleep spindle density can explain individual differences in sleep-dependent memory consolidation and general cognitive abilities Ambulatory polysomnography was recorded during four nights in 34 healthy subjects (24 female) with two nights (baseline and experimental) at initial recording (age range 8-11 years) and two nights at follow-up recording (age range 14-18 years) For declarative learning, participants encoded word pairs with a subsequent recall before and after sleep General cognitive abilities were measured by the Wechsler Intelligence Scale Higher slow (11-13 Hz) than fast (13-15 Hz) spindle density at frontal, central, and parietal sites during initial recordings, followed by a shift to higher fast than slow spindle density at central and parietal sites during follow-up recordings, suggest that mature spindle topography develops throughout adolescence Fast spindle density increases from baseline to experimental night were positively related to sleep-dependent memory consolidation In addition, we found that the development of fast spindles predicted the improvement in memory consolidation across the two longitudinal measurements, a finding that underlines a crucial role for mature fast spindles for sleep-dependent memory consolidation Furthermore, slow spindle changes across adolescence were related to general cognitive abilities, a relationship that could indicate the maturation of frontal networks relevant for efficient cognitive processing A video abstract of this article can be viewed at: https://wwwyoutubecom/watch?v=7NXJzm8HbIw and https://wwwyoutubecom/watch?v=iuMQY1OIJ0s