The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging

A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments

To facilitate unobtrusive and continuous sleep monitoring and promote intelligent sleep quality assessment, we present a dataset that includes multiple nights of continuous ballistocardiogram (BCG) data collected using piezoelectric film sensors from 32 subjects in their regular sleep environments. Besides, the referenced heart rate and respiratory data are also recorded by reference sensors to validate the accuracy of the cardiac and respiratory components extracted from the BCG signals. The dataset serves as a foundation for research on unobtrusive vital sign monitoring based on BCG signals, offering data support for the evaluation and optimization of sleep quality.

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis

BACKGROUND

The field of neonatal sleep analysis is burgeoning with devices that purport to offer alternatives to polysomnography (PSG) for monitoring sleep patterns. However, the majority of these devices are limited in their capacity, typically only distinguishing between sleep and wakefulness. This study aims to assess the efficacy of a novel wearable electroencephalographic (EEG) device, the LANMAO Sleep Recorder, in capturing EEG data and analyzing sleep stages, and to compare its performance against the established PSG standard.

METHODS

The study involved concurrent sleep monitoring of 34 neonates using both PSG and the LANMAO device. Initially, the study verified the consistency of raw EEG signals captured by the LANMAO device, employing relative spectral power analysis and Pearson correlation coefficients (PCC) for validation. Subsequently, the LANMAO device's integrated automated sleep staging algorithm was evaluated by comparing its output with expert-generated sleep stage classifications.

RESULTS

Analysis revealed that the PCC between the relative spectral powers of various frequency bands during different sleep stages ranged from 0.28 to 0.48. Specifically, the correlation for delta waves was recorded at 0.28. The automated sleep staging algorithm of the LANMAO device demonstrated an overall accuracy of 79.60 %, Cohen kappa of 0.65, and F1 Score of 76.93 %. Individual accuracy for Wake at 87.20 %, NREM at 85.70 %, and REM Sleep at 81.30 %.

CONCLUSION

While the LANMAO Sleep Recorder's automated sleep staging algorithm necessitates further refinement, the device shows promise in accurately recording neonatal EEG during sleep. Its potential for minimal invasiveness makes it an appealing option for monitoring sleep conditions in newborns, suggesting a novel approach in the field of neonatal sleep analysis.

An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring

In-home automated scoring systems are in high demand; however, the current systems are not widely adopted in clinical settings. Problems with electrode contact and restriction on measurable signals often result in unstable and inaccurate scoring for clinical use. To address these issues, we propose a method based on ensemble of small sleep stage scoring models with different input signal sets. By excluding models that employ problematic signals from the voting process, our method can mitigate the effects of electrode contact failure. Comparative experiments demonstrated that our method could reduce the impact of contact problems and improve scoring accuracy for epochs with problematic signals by 8.3 points, while also decreasing the deterioration in scoring accuracy from 7.9 to 0.3 points compared to typical methods. Additionally, we confirmed that assigning different input sets to small models did not diminish the advantages of the ensemble but instead increased its efficacy. The proposed model can improve overall scoring accuracy and minimize the effect of problematic signals simultaneously, making in-home sleep stage scoring systems more suitable for clinical practice.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns.

Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.

Cognitive performance during exposure to moderate normobaric hypoxia after sleep restriction: Relationship to physiological and stress biomarkers

INTRODUCTION

Exposure to moderate levels of simulated hypoxia has subtle cognitive effects relative to ground level, in healthy individuals. However, there are few data on the cognitive consequences of the combination of hypoxia and partial sleep deprivation, which is a classic military or civilian operational context. In this study, we tested the hypothesis that exposure to moderate hypoxia while sleep-restricted impairs several domains of cognition, and we also assessed physiological parameters and salivary concentrations of cortisol and alpha-amylase.

METHOD

Seventeen healthy males completed two sessions of cognitive tests (sustained attention using the PVT psychomotor vigilance task and executive functions using the Go-NoGo inhibition task and N-Back working memory task) after 30 min (T + 30') and 4 h (T + 240') of exposure in a normobaric hypoxic tent (FIO2 = 13.6 %, ≃ 3,500 m) (HY). This was completed after one night of sleep restriction (3 a.m. to 6 a.m. bedtime, SRHY) and one night of habitual sleep (10 p.m. to 6 a.m. bedtime, HSHY) (with cross-over randomization). The two nights sleep architecture and physiological parameters (oxygen saturation (SpO2) and heart rate (HR) during T + 30' and T + 240'sessions were analyzed. Salivary cortisol and alpha-amylase (sAA) concentrations were analyzed before hypoxia, after the T + 30' and T + 240' cognitive sessions, and after leaving the hypoxic tent.

RESULTS

Sustained attention (RT and number of lapses in the PVT) and executive functions (Go-NoGo and 1-Back and 2-Back parameters, as inhibition and working memory signatures) were impaired in the SRHY condition compared to HSHY. SpO2 and HR were higher after 4 h compared with 30 min of hypoxia in the HSHY condition, while only HR was statistically higher in the SRHY condition. In SRHY, salivary AA concentration was lower and cortisol was higher than in HSHY. A significant increase in sAA concentration is observed after the cognitive session at 4 h of hypoxia exposure compared to that at 30 min, only in the SRHY condition. There are significant positive correlations between reaction time and the corresponding heart rate (a non-invasive marker of physiological stress) for the executive tasks in the two sleep conditions. This was not observed for salivary levels of sAA and cortisol, respective reliable indicators of the sympathoadrenomedullary system and the hypothalamic-pituitary adrenocortical system.

CONCLUSION

Exposure to moderate normobaric hypoxia (≃ 3500 m / ≃ 11,500 ft simulated) after a single night of 3-hour sleep impairs cognitive performance after 30 min and 4 h of exposure. The key determinants and/or mechanism(s) responsible for cognitive impairment when exposed to moderate hypoxia with sleep restriction, particularly on the executive function, have yet to be elucidated.

Anticipated stress predicts the cortisol awakening response: An intensive longitudinal pilot study

The cortisol awakening response (CAR) has been hypothesized to prepare the body for anticipated demands of the upcoming day. This pilot study investigates the influence of anticipated stress on the upcoming day on the CAR, using an intensive longitudinal design with ecological momentary assessments. Over a 30-day period, three healthy participants collected saliva samples each morning at three time points after awakening to measure cortisol levels and completed a questionnaire each evening on the anticipated stress for the following day. Additionally, they wore a smart headband to objectively determine the time point of awakening. There was high variability in the CAR magnitude within participants over time. A multi-level model was estimated to investigate the influence of anticipated stress on the CAR. Results indicated that anticipated stress is predictive of the CAR on the following morning, with higher anticipated stress being associated with increased cortisol levels at the post-awakening time points. These findings underscore the role of stress anticipation in modulating the CAR and highlight the importance of considering within-person variation and temporally lagged effects in biopsychological research.

Traumatic brain injury and sleep in military and veteran populations: A literature review

BACKGROUND

Traumatic brain injury (TBI) is a hallmark of wartime injury and is related to numerous sleep wake disorders (SWD), which persist long term in veterans. Current knowledge gaps in pathophysiology have hindered advances in diagnosis and treatment.

OBJECTIVE

We reviewed TBI SWD pathophysiology, comorbidities, diagnosis and treatment that have emerged over the past two decades.

METHODS

We conducted a literature review of English language publications evaluating sleep disorders (obstructive sleep apnea, insomnia, hypersomnia, parasomnias, restless legs syndrome and periodic limb movement disorder) and TBI published since 2000. We excluded studies that were not specifically evaluating TBI populations.

RESULTS

Highlighted areas of interest and knowledge gaps were identified in TBI pathophysiology and mechanisms of sleep disruption, a comparison of TBI SWD and post-traumatic stress disorder SWD. The role of TBI and glymphatic biomarkers and management strategies for TBI SWD will also be discussed.

CONCLUSION

Our understanding of the pathophysiologic underpinnings of TBI and sleep health, particularly at the basic science level, is limited. Developing an understanding of biomarkers, neuroimaging, and mixed-methods research in comorbid TBI SWD holds the greatest promise to advance our ability to diagnose and monitor response to therapy in this vulnerable population.

The utility of wearable headband electroencephalography and pulse photoplethysmography to assess cortical and physiological arousal in individuals with stress-related mental disorders

Several stress-related mental disorders are characterised by disturbed sleep, but objective sleep biomarkers are not routinely examined in psychiatric patients. We examined the use of wearable-based sleep biomarkers in a psychiatric sample with headband electroencephalography (EEG) including pulse photoplethysmography (PPG), with an additional focus on microstructural elements as especially the shift from low to high frequencies appears relevant for several stress-related mental disorders. We analysed 371 nights of sufficient quality from 83 healthy participants and those with a confirmed stress-related mental disorder (anxiety-affective spectrum). The median value of macrostructural, microstructural (spectral slope fitting), and heart rate variables was calculated across nights and analysed at the individual level (N = 83). The headbands were accepted well by patients and the data quality was sufficient for most nights. The macrostructural analyses revealed trends for significance regarding sleep continuity but not sleep depth variables. The spectral analyses yielded no between-group differences except for a group × age interaction, with the normal age-related decline in the low versus high frequency power ratio flattening in the patient group. The PPG analyses showed that the mean heart rate was higher in the patient group in pre-sleep epochs, a difference that reduced during sleep and dissipated at wakefulness. Wearable devices that record EEG and/or PPG could be used over multiple nights to assess sleep fragmentation, spectral balance, and sympathetic drive throughout the sleep-wake cycle in patients with stress-related mental disorders and healthy controls, although macrostructural and spectral markers did not differ between the two groups.

Cognitive Behavioral Therapy for Insomnia in Pain Management for Nonspecific Chronic Spinal Pain: A Randomized Clinical Trial

Importance

Insomnia is highly prevalent in patients with nonspecific chronic spinal pain (nCSP). Given the close interaction between insomnia and pain, targeting sleep problems during therapy could improve treatment outcomes.

Objective

To evaluate the effectiveness of cognitive behavioral therapy for insomnia (CBTi) integrated in best-evidence pain management (BEPM) vs BEPM only in patients with nCSP and insomnia.

Design, Setting, and Participants

A multicenter randomized clinical trial with 1-year follow-up was conducted between April 10, 2018, and April 30, 2022. Data and statistical analysis were performed between May 1, 2022, and April 24, 2023. Patients with nCSP and insomnia were evaluated using self-report and at-home polysomnography, to exclude underlying sleep pathologic factors. Participants were treated at the University Hospital Brussels or University Hospital Ghent, Belgium. Intention-to-treat analysis was performed.

Interventions

Participants were randomized to either CBTi-BEPM or BEPM only. Both groups received 18 treatment sessions over 14 weeks. The CBTi-BEPM treatment included 6 CBTi sessions and 12 BEPM sessions. The BEPM treatment included pain neuroscience education (3 sessions) and exercise therapy (9 sessions in the CBTi-BEPM group, 15 sessions in the BEPM-only group).

Main Outcomes and Measures

The primary outcome was change in mean pain intensity (assessed with Brief Pain Inventory [BPI]) at 12 months after the intervention. Exploratory secondary outcomes included several pain- and sleep-related outcomes. Blinded outcome assessment took place at baseline, posttreatment, and at 3-, 6-, and 12-month follow-up.

Results

A total of 123 patients (mean [SD] age, 40.2 [11.18] years; 84 women [68.3%]) were included in the trial. In 99 participants (80.5%) with 12-month BPI data, the mean pain intensity at 12 months decreased by 1.976 points (reduction of 40%) in the CBTi-BEPM group and 1.006 points (reduction of 24%) points in the BEPM-only group. At 12 months, there was no significant difference in pain intensity change between groups (mean group difference, 0.970 points; 95% CI, -0.051 to 1.992; Cohen d, 2.665). Treatment with CBTi-BEPM resulted in a response for BPI average pain with a number needed to treat (NNT) of 4 observed during 12 months. On a preliminary basis, CBTi-BEPM was, consistently over time and analyses, more effective than BEPM only for improving insomnia severity (Cohen d, 4.319-8.961; NNT for response ranging from 2 to 4, and NNT for remission ranging from 5 to 12), sleep quality (Cohen d, 3.654-6.066), beliefs about sleep (Cohen d, 5.324-6.657), depressive symptoms (Cohen d, 2.935-3.361), and physical fatigue (Cohen d, 2.818-3.770). No serious adverse effects were reported.

Conclusions and Relevance

In this randomized clinical trial, adding CBTi to BEPM did not further improve pain intensity reduction for patients with nCSP and comorbid insomnia more than BEPM alone. Yet, as CBTi-BEPM led to significant and clinically important changes in insomnia severity and sleep quality, CBTi integrated in BEPM should be considered in the treatment of patients with nCSP and comorbid insomnia. Further research can investigate the patient characteristics that moderate the response to CBTi-BEPM in terms of pain-related outcomes, as understanding of these moderators may be of utmost clinical importance.

Trial Registration

Clinical Trials.gov Identifier: NCT03482856.

Nutritional interventions in treating menopause-related sleep disturbances: a systematic review

CONTEXT

Sleep disturbances are a core symptom of menopause, which refers to the permanent cessation of menstrual periods. Nutritional interventions may alleviate menopause-related sleep disturbances, as studies have shown that certain interventions (eg, tart cherry juice, or tryptophan-rich foods) can improve relevant aspects of sleep.

OBJECTIVE

The aim of this systematic review was to examine the effect of nutritional interventions for menopause-related sleep disturbances, in order to inform the subsequent development of specific interventional trials and assess their potential as a treatment for menopause-related sleep disturbances.

DATA SOURCES

Published studies in English were located by searching PubMed and PsycArticles databases (until September 15, 2022).

DATA EXTRACTION

Following full-text review, a final total of 59 articles were included. The search protocol was performed in accordance with PRISMA guidelines.

DATA ANALYSIS

A total of 37 studies reported that a nutritional intervention improved some aspect of sleep, and 22 studies observed no benefit. Most (n = 24) studies recruited postmenopausal women, 18 recruited menopausal women, 3 recruited perimenopausal women, and 14 recruited women from multiple groups. The majority of the studies were of low methodological quality. Due to the heterogeneity of the studies, a narrative synthesis without meta-analysis is reported.

CONCLUSION

Despite the large heterogeneity in the studies and choice of intervention, the majority of the identified studies reported that a nutritional intervention did benefit sleep, and that it is mainly subjective sleep that is improved. More high-quality, adequately powered, randomized controlled trials of the identified nutritional interventions are necessary.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no. CRD42021262367.

Biomarkers of sleep-wake disturbance as predictors of cognitive decline and accelerated disease progression

INTRODUCTION

In older adults, where sleep disturbances and cognitive impairment are common, mounting evidence suggests a potential connection between sleep and cognitive function, highlighting the significance of utilizing sleep as a biomarker for early detection of cognitive impairment to improve clinical outcomes in a noninvasive, cost-effective manner.

AREAS COVERED

This review describes the relationship between sleep and cognitive function in older adults, encompassing both subjective and objective measures of sleep quality, duration, architecture, and sleep-disordered breathing. The authors consider the directionality of the associations observed in prospective and cross-sectional studies, exploring whether sleep disturbances precede cognitive decline or vice versa. Furthermore, they discuss the potential bidirectional relationships between sleep and Alzheimer's disease (AD) risks in older adults while also examining the neurodegenerative pathways of this relationship.

EXPERT OPINION

Routine sleep monitoring in primary care settings has the potential to bolster early detection and treatment of sleep disturbance, and by extension, reduce the risk of dementia. Improving sleep assessment tools, such as wearables, provide scalable alternatives to traditional methods like polysomnography, potentially enabling widespread monitoring of sleep characteristics. Standardized measurement and inclusive participant recruitment are needed to enhance generalizability, while longitudinal studies are essential to understand the interaction between sleep and AD pathology.

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

Breathwork-induced psychedelic experiences modulate neural dynamics

Breathwork is an understudied school of practices involving intentional respiratory modulation to induce an altered state of consciousness (ASC). We simultaneously investigate the phenomenological and neural dynamics of breathwork by combining Temporal Experience Tracing, a quantitative methodology that preserves the temporal dynamics of subjective experience, with low-density portable EEG devices. Fourteen novice participants completed a course of up to 28 breathwork sessions-of 20, 40, or 60 min-in 28 days, yielding a neurophenomenological dataset of 301 breathwork sessions. Using hypothesis-driven and data-driven approaches, we found that "psychedelic-like" subjective experiences were associated with increased neural Lempel-Ziv complexity during breathwork. Exploratory analyses showed that the aperiodic exponent of the power spectral density-but not oscillatory alpha power-yielded similar neurophenomenological associations. Non-linear neural features, like complexity and the aperiodic exponent, neurally map both a multidimensional data-driven composite of positive experiences, and hypothesis-driven aspects of psychedelic-like experience states such as high bliss.

Twenty four-hour passive heat and cold exposures did not modify energy intake and appetite but strongly modify food reward

Effects of acute thermal exposures on appetite appear hypothetical in reason of very heterogeneous methodologies. The aim of this study was therefore to clearly define the effects of passive 24-h cold (16°C) and heat (32°C) exposures on appetitive responses compared with a thermoneutral condition (24°C). Twenty-three healthy, young and active male participants realised three sessions (from 13.00) in a laboratory conceived like an apartment dressed with the same outfit (Clo = 1). Three meals composed of three or four cold or warm dishes were served ad libitum to assess energy intake (EI). Leeds Food Preference Questionnaires were used before each meal to assess food reward. Subjective appetite was regularly assessed, and levels of appetitive hormones (acylated ghrelin, glucagon-like peptite-1, leptin and peptide YY) were assessed before and after the last meal (lunch). Contrary to the literature, total EI was not modified by cold or heat exposure (P = 0·120). Accordingly, hunger scores (P = 0·554) were not altered. Levels of acylated ghrelin and leptin were marginally higher during the 16 (P = 0·032) and 32°C (P < 0·023) sessions, respectively. Interestingly, implicit wanting for cold and low-fat foods at 32°C and for warm and high-fat foods at 16°C were increased during the whole exposure (P < 0·024). Moreover, cold entrées were more consumed at 32°C (P < 0·062) and warm main dishes more consumed at 16°C (P < 0·025). Thus, passive cold and hot exposures had limited effects on appetite, and it seems that offering some choice based on food temperature may help individuals to express their specific food preferences and maintain EI.

Effects of Aerobic Exercise on Brain Age and Health in Middle-Aged and Older Adults: A Single-Arm Pilot Clinical Trial

BACKGROUNDS

Sleep disturbances are prevalent among elderly individuals. While polysomnography (PSG) serves as the gold standard for sleep monitoring, its extensive setup and data analysis procedures impose significant costs and time constraints, thereby restricting the long-term application within the general public. Our laboratory introduced an innovative biomarker, utilizing artificial intelligence algorithms applied to PSG data to estimate brain age (BA), a metric validated in cohorts with cognitive impairments. Nevertheless, the potential of exercise, which has been a recognized means of enhancing sleep quality in middle-aged and older adults to reduce BA, remains undetermined.

METHODS

We conducted an exploratory study to evaluate whether 12 weeks of moderate-intensity exercise can improve cognitive function, sleep quality, and the brain age index (BAI), a biomarker computed from overnight sleep electroencephalogram (EEG), in physically inactive middle-aged and older adults. Home wearable devices were used to monitor heart rate and overnight sleep EEG over this period. The NIH Toolbox Cognition Battery, in-lab overnight polysomnography, cardiopulmonary exercise testing, and a multiplex cytokines assay were employed to compare pre- and post-exercise brain health, exercise capacity, and plasma proteins.

RESULTS

In total, 26 participants completed the initial assessment and exercise program, and 24 completed all procedures. Data are presented as mean [lower 95% CI of mean, upper 95% CI of mean]. Participants significantly increased maximal oxygen consumption (Pre: 21.11 [18.98, 23.23], Post 22.39 [20.09, 24.68], mL/kg/min; effect size: -0.33) and decreased resting heart rate (Pre: 66.66 [63.62, 67.38], Post: 65.13 [64.25, 66.93], bpm; effect size: -0.02) and sleeping heart rate (Pre: 64.55 [61.87, 667.23], Post: 62.93 [60.78, 65.09], bpm; effect size: -0.15). Total cognitive performance (Pre: 111.1 [107.6, 114.6], Post: 115.2 [111.9, 118.5]; effect size: 0.49) was significantly improved. No significant differences were seen in BAI or measures of sleep macro- and micro-architecture. Plasma IL-4 (Pre: 0.24 [0.18, 0.3], Post: 0.33 [0.24, 0.42], pg/mL; effect size: 0.49) was elevated, while IL-8 (Pre: 5.5 [4.45, 6.55], Post: 4.3 [3.66, 5], pg/mL; effect size: -0.57) was reduced.

CONCLUSIONS

Cognitive function was improved by a 12-week moderate-intensity exercise program in physically inactive middle-aged and older adults, as were aerobic fitness (VO2max) and plasma cytokine profiles. However, we found no measurable effects on sleep architecture or BAI. It remains to be seen whether a study with a larger sample size and more intensive or more prolonged exercise exposure can demonstrate a beneficial effect on sleep quality and brain age.

Acoustic Stimulation to Improve Slow-Wave Sleep in Alzheimer's Disease: A Multiple Night At-Home Intervention

OBJECTIVES

To investigate the efficacy of closed-loop acoustic stimulation (CLAS) during slow-wave sleep (SWS) to enhance slow-wave activity (SWA) and SWS in patients with Alzheimer's disease (AD) across multiple nights and to explore associations between stimulation, participant characteristics, and individuals' SWS response.

DESIGN

A 2-week, open-label at-home intervention study utilizing the DREEM2 headband to record sleep data and administer CLAS during SWS.

SETTING AND PARTICIPANTS

Fifteen older patients with AD (6 women, mean age: 76.27 [SD = 6.06], mean MOCA-score: 16.07 [SD = 6.94]), living at home with their partner, completed the trial.

INTERVENTION

Patients first wore the device for two baseline nights, followed by 14 nights during which the device was programmed to randomly either deliver acoustic stimulations of 50 ms pink noise (± 40 dB) targeted to the slow-wave up-phase during SWS or only mark the wave (sham).

RESULTS

On a group level, stimulation significantly enhanced SWA and SWS with consistent SWS enhancement throughout the intervention. However, substantial variability existed in individual responses to stimulation. Individuals received more stimulations on nights with increased SWS compared to baseline than on nights with no change or a decrease. In individuals, having lower baseline SWS correlated with receiving fewer stimulations on average during the intervention.

CONCLUSION

CLAS during SWS is a promising nonpharmacological method to enhance SWA and SWS in AD. However, patients with lower baseline SWS received fewer stimulations during the intervention, possibly resulting in less SWS enhancement. Individual variability in response to stimulation underscores the need to address personalized stimulation parameters in future research and therapy development.

Chronic sleep deficiency and its impact on pain perception in healthy females

Acute sleep deprivation in experimental studies has been shown to induce pain hypersensitivity in females. However, the impact of natural sleep deficiency and fluctuations across the week on pain perception remains unclear. A sleep-monitoring headband and self-reports were utilized to assess objective and subjective sleep in longer (> 6 hr) and short sleepers (< 6 hr). Pain sensitivity measures including heat, cold, pressure pain thresholds, pain inhibition (conditioned pain modulation) and facilitation (tonic pain summation) were assessed on Mondays and Fridays. Forty-one healthy young (23.9 ± 0.74 years) women participated. Short sleepers slept on average 2 hr less than longer sleepers (297.9 ± 8.2 min versus 418.5 ± 10.9 min) and experienced impaired pain inhibitory response (mean = -21.14 ± 7.9°C versus mean = 15.39 ± 9.5°C; p = 0.005). However, no effect was observed in pain thresholds and pain summation (p > 0.05). Furthermore, pain modulatory responses differed between Mondays and Fridays. Chronic sleep deficiency (< 6 hr) compromises pain responses, notably on Mondays. Maintaining a consistent sleep pattern with sufficient sleep (> 6 hr) throughout the week may protect against pain sensitization and the development of chronic pain in females. Further research is needed, especially in patients with chronic pain.

NREM Slow-Wave Activity in Adolescents Is Differentially Associated With ADHD Levels and Normalized by Pharmacological Treatment

BACKGROUND

A compelling hypothesis about attention-deficit/hyperactivity disorder (ADHD) etiopathogenesis is that the ADHD phenotype reflects a delay in cortical maturation. Slow-wave activity (SWA) of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is an electrophysiological index of sleep intensity reflecting cortical maturation. Available data on ADHD and SWA are conflicting, and developmental differences, or the effect of pharmacological treatment, are relatively unknown.

METHODS

We examined, in samples (Mage = 16.4, SD = 1.2), of ever-medicated adolescents at risk for ADHD (n = 18; 72% boys), medication-naïve adolescents at risk for ADHD (n = 15, 67% boys), and adolescents not at risk for ADHD (n = 31, 61% boys) matched for chronological age and controlling for non-ADHD pharmacotherapy, whether ADHD pharmacotherapy modulates the association between NREM SWA and ADHD risk in home sleep.

RESULTS

Findings indicated medication-naïve adolescents at risk for ADHD exhibited greater first sleep cycle and entire night NREM SWA than both ever-medicated adolescents at risk for ADHD and adolescents not at risk for ADHD and no difference between ever-medicated, at-risk adolescents, and not at-risk adolescents.

CONCLUSIONS

Results support atypical cortical maturation in medication-naïve adolescents at risk for ADHD that appears to be normalized by ADHD pharmacotherapy in ever-medicated adolescents at risk for ADHD. Greater NREM SWA may reflect a compensatory mechanism in middle-later adolescents at risk for ADHD that normalizes an earlier occurring developmental delay.

Case report: Nocturnal low-frequency stimulation of the centromedian thalamic nucleus improves sleep quality and seizure control

Sleep disturbances and drug-resistant seizures significantly impact people with idiopathic generalized epilepsy (IGE). Thalamic deep brain stimulation (DBS) offers potential treatment, but its effect on sleep and seizure control needs clarification. In this study, we combined wearable sleep monitoring with electroencephalogram (EEG) confirmation to investigate the impact of nocturnal centromedian nucleus (CM) DBS parameters in a patient with drug-resistant IGE. We found that high-frequency (125 Hz) CM stimulation during sleep severely disrupted sleep macro architecture and exacerbated seizures. Conversely, switching to low-frequency (10 Hz) stimulation enhanced both sleep quality and seizure control. This study underscores the critical need to personalize DBS settings, tailoring them to individual patients' sleep patterns to maximize therapeutic benefits. While larger-scale trials are needed, our findings pave the way for patient-centric approaches to thalamic neuromodulation, offering a transformative path to improve treatment outcomes and quality of life for those with refractory epilepsy.

Daytime dexmedetomidine sedation with closed-loop acoustic stimulation alters slow wave sleep homeostasis in healthy adults

Background

The alpha-2 adrenergic agonist dexmedetomidine induces EEG patterns resembling those of non-rapid eye movement (NREM) sleep. Fulfilment of slow wave sleep (SWS) homeostatic needs would address the assumption that dexmedetomidine induces functional biomimetic sleep states.

Methods

In-home sleep EEG recordings were obtained from 13 healthy participants before and after dexmedetomidine sedation. Dexmedetomidine target-controlled infusions and closed-loop acoustic stimulation were implemented to induce and enhance EEG slow waves, respectively. EEG recordings during sedation and sleep were staged using modified American Academy of Sleep Medicine criteria. Slow wave activity (EEG power from 0.5 to 4 Hz) was computed for NREM stage 2 (N2) and NREM stage 3 (N3/SWS) epochs, with the aggregate partitioned into quintiles by time. The first slow wave activity quintile served as a surrogate for slow wave pressure, and the difference between the first and fifth quintiles as a measure of slow wave pressure dissipation.

Results

Compared with pre-sedation sleep, post-sedation sleep showed reduced N3 duration (mean difference of -17.1 min, 95% confidence interval -30.0 to -8.2, P=0.015). Dissipation of slow wave pressure was reduced (P=0.02). Changes in combined durations of N2 and N3 between pre- and post-sedation sleep correlated with total dexmedetomidine dose, (r=-0.61, P=0.03).

Conclusions

Daytime dexmedetomidine sedation and closed-loop acoustic stimulation targeting EEG slow waves reduced N3/SWS duration and measures of slow wave pressure dissipation on the post-sedation night in healthy young adults. Thus, the paired intervention induces sleep-like states that fulfil certain homeostatic NREM sleep needs in healthy young adults.

Clinical trial registration

ClinicalTrials.gov NCT04206059.

The impact of sleep on factual memory retention over 24 hr

Although a period of sleep seems to benefit the retention of declarative memories, recent studies have challenged both the size of this effect and its active influence on memory consolidation. This study aimed to further investigate the effect of sleep and its time dependency on the consolidation of factual information. In a within-subjects design, 48 participants (Mage = 24.37 ± 4.18 years, 31F) were asked to learn several facts in a multi-sensory "flashcard-like" memory task at 21:00 hours (sleep first condition) or at 09:00 hours (wake first condition). Then, in each condition, participants performed an immediate recall test (T0), and two delayed tests 12 hr (T1) and 24 hr (T2) later. Participants' sleep was recorded at their homes with a portable device. Results revealed that memory retention was better after a night of sleep compared with wakefulness, regardless of the delay from encoding (a few hr versus 12+ hr), but the sleep effect was modest. The decline in memory during the wake period following sleep was smaller compared with the decline observed during the 12 hr of wakefulness after encoding. However, after 24 hr from the encoding, when all participants experienced a period of both sleep and wakefulness, memory performance in the two conditions was similar. Overall, our data suggest that sleep exerts a small, yet beneficial, influence on memory retention by likely reducing interference and actively stabilizing memory traces.

Deciphering the Link: Correlating REM Sleep Patterns with Depressive Symptoms via Consumer Wearable Technology

This study investigates the correlation between REM sleep patterns, as measured by the Apple Watch, and depressive symptoms in an undiagnosed population. Employing the Apple Watch for data collection, REM sleep duration and frequency were monitored over a specified period. Concurrently, participants' depressive symptoms were evaluated using standardized questionnaires. The analysis, primarily using Spearman's correlation, revealed noteworthy findings. A significant correlation was observed between an increased REM sleep proportion and higher depressive symptom scores, with a correlation coefficient of 0.702, suggesting a robust relationship. These results highlight the potential of using wearable technology, such as the Apple Watch, in early detection and intervention for depressive symptoms, suggesting that alterations in REM sleep could serve as preliminary indicators of depressive tendencies. This approach offers a non-invasive and accessible means to monitor and potentially preempt the progression of depressive disorders. This study's implications extend to the broader context of mental health, emphasizing the importance of sleep assessment in routine health evaluations, particularly for individuals exhibiting early signs of depressive symptoms.

The Future of Telemedicine for Obstructive Sleep Apnea Treatment: A Narrative Review

Obstructive sleep apnea is a common type of sleep-disordered breathing associated with multiple comorbidities. Nearly a billion people are estimated to have obstructive sleep apnea, which carries a substantial economic burden, but under-diagnosis is still a problem. Continuous positive airway pressure (CPAP) is the first-line treatment for OSAS. Telemedicine-based interventions (TM) have been evaluated to improve access to diagnosis, increase CPAP adherence, and contribute to easing the follow-up process, allowing healthcare facilities to provide patient-centered care. This narrative review summarizes the evidence available regarding the potential future of telemedicine in the management pathway of OSA. The potential of home sleep studies to improve OSA diagnosis and the importance of remote monitoring for tracking treatment adherence and failure and to contribute to developing patient engagement tools will be presented. Further studies are needed to explore the impact of shifting from teleconsultations to collaborative care models where patients are placed at the center of their care.

Temporal dynamics of awakenings from slow-wave sleep in non-rapid eye movement parasomnia

Non-rapid eye movement parasomnia disorders, also called disorders of arousal, are characterized by abnormal nocturnal behaviours, such as confusional arousals or sleep walking. Their pathophysiology is not yet fully understood, and objective diagnostic criteria are lacking. It is known, however, that behavioural episodes occur mostly in the beginning of the night, after an increase in slow-wave activity during slow-wave sleep. A better understanding of the prospect of such episodes may lead to new insights in the underlying mechanisms and eventually facilitate objective diagnosis. We investigated temporal dynamics of transitions from slow-wave sleep of 52 patients and 79 controls. Within the patient group, behavioural and non-behavioural N3 awakenings were distinguished. Patients showed a higher probability to wake up after an N3 bout ended than controls, and this probability increased with N3 bout duration. Bouts longer than 15 min resulted in an awakening in 73% and 34% of the time in patients and controls, respectively. Behavioural episodes reduced over sleep cycles due to a reduction in N3 sleep and a reducing ratio between behavioural and non-behavioural awakenings. In the first two cycles, N3 bouts prior to non-behavioural awakenings were significantly shorter than N3 bouts advancing behavioural awakenings in patients, and N3 awakenings in controls. Our findings provide insights in the timing and prospect of both behavioural and non-behavioural awakenings from N3, which may result in prediction and potentially prevention of behavioural episodes. This work, moreover, leads to a more complete characterization of a prototypical hypnogram of parasomnias, which could facilitate diagnosis.

Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an F1 score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels.

Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin-electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%-43.9%, p < 0.001) and less absolute power (at 1-40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3-1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.

Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults

Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

Deciphering Post-Stroke Sleep Disorders: Unveiling Neurological Mechanisms in the Realm of Brain Science

Sleep disorders are the most widespread mental disorders after stroke and hurt survivors' functional prognosis, response to restoration, and quality of life. This review will address an overview of the progress of research on the biological mechanisms associated with stroke-complicating sleep disorders. Extensive research has investigated the negative impact of stroke on sleep. However, a bidirectional association between sleep disorders and stroke exists; while stroke elevates the risk of sleep disorders, these disorders also independently contribute as a risk factor for stroke. This review aims to elucidate the mechanisms of stroke-induced sleep disorders. Possible influences were examined, including functional changes in brain regions, cerebrovascular hemodynamics, neurological deficits, sleep ion regulation, neurotransmitters, and inflammation. The results provide valuable insights into the mechanisms of stroke complicating sleep disorders.

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review

BACKGROUND

Discomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort.

METHODS

A systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted.

RESULTS

Of the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase.

CONCLUSIONS

Noninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.

Objective sleep quality predicts subjective sleep ratings

In both clinical and observational studies, sleep quality is usually assessed by subjective self-report. The literature is mixed about how accurately these self-reports track objectively (e.g. via polysomnography) assessed sleep quality, with frequent reports of little to no association. However, previous research on this question focused on between-subject designs, which may be confounded by trait-level variables. In the current study, we used the novel Budapest Sleep, Experiences and Traits Study (BSETS) dataset to investigate if within-subject differences in subjectively reported sleep quality are related to sleep macrostructure and quantitative EEG variables assessed using a mobile EEG headband. We found clear evidence that self-reported sleep quality in the morning is influenced by within-subject variations in sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. These effects were replicated if detailed sleep composition metrics (percentage and latency of specific vigilance states) or two alternative measures of subjective sleep quality were used instead. We found no effect of the number of awakenings or relative EEG delta and sigma power. Between-subject effects (relationships between individual mean values of sleep metrics and subjective sleep quality) were also found, highlighting that analyses focusing only on these may be erroneous. Our findings show that while previous investigations of this issue may have been confounded by between-subject effects, objective sleep quality is indeed reflected in subjective sleep ratings.

Mobile sleep EEG suggests delayed brain maturation in adolescents with ADHD: A focus on oscillatory spindle frequency

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder. Although data show ADHD is associated with sleep problems, approaches to analyze the association between ADHD and sleep electrophysiology are limited to a few methods with circumscribed foci.

AIMS

Sleep EEG was analyzed by a mixed-radix FFT routine and power spectrum parametrization in adolescents with ADHD and adolescents not at-risk for ADHD. Spectral components of sleep EEG were analyzed employing a novel, model-based approach of EEG power spectra.

METHODS AND PROCEDURES

The DREEM mobile polysomnography headband was used to record home sleep EEG from 19 medication-free adolescents with ADHD and 29 adolescents not at-risk for ADHD (overall: N = 56, age range 14-19 years) and groups were compared on characteristics of NREM sleep.

OUTCOMES AND RESULTS

Adolescents with ADHD exhibited lower frequency of spectral peaks indicating sleep spindle oscillations whereas adolescents not at-risk for ADHD showed lower spectral power in the slow sleep spindle and beta frequency ranges.

CONCLUSIONS AND IMPLICATIONS

The observed between-groups difference might indicate delayed brain maturity unraveled during sleep in ADHD.

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease

Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.

Design of an injectable, self-adhesive, and highly stable hydrogel electrode for sleep recording

High-quality and continuous electroencephalogram (EEG) monitoring is desirable for sleep research, sleep monitoring, and the evaluation and treatment of sleep disorders. Existing continuous EEG monitoring technologies suffer from fragile connections, long-term stability, and complex preparation for electrodes under real-life conditions. Here, we report an injectable and spontaneously cross-linked hydrogel electrode for long-term EEG applications. Specifically, our electrodes have a long-term low impedance on hairy scalp regions of 17.53 kΩ for more than 8 h of recording, high adhesiveness on the skin of 0.92 N cm-1 with repeated attachment capability, and long-term wearability during daily activities and overnight sleep. In addition, our electrodes demonstrate a superior signal-to-noise-ratio of 23.97 decibels (dB) in comparison with commercial wet electrodes of 17.98 dB and share a high agreement of sleep stage classification with commercial wet electrodes during multichannel recording. These results exhibit the potential of our on-site-formed electrodes for high-quality, prolonged EEG monitoring in various scenarios.

Sleep and motor learning in stroke (SMiLES): a longitudinal study investigating sleep-dependent consolidation of motor sequence learning in the context of recovery after stroke

INTRODUCTION

There is growing evidence that sleep is disrupted after stroke, with worse sleep relating to poorer motor outcomes. It is also widely acknowledged that consolidation of motor learning, a critical component of poststroke recovery, is sleep-dependent. However, whether the relationship between disrupted sleep and poor outcomes after stroke is related to direct interference of sleep-dependent motor consolidation processes, is currently unknown. Therefore, the aim of the present study is to understand whether measures of motor consolidation mediate the relationship between sleep and clinical motor outcomes post stroke.

METHODS AND ANALYSIS

We will conduct a longitudinal observational study of up to 150 participants diagnosed with stroke affecting the upper limb. Participants will be recruited and assessed within 7 days of their stroke and followed up at approximately 1 and 6 months. The primary objective of the study is to determine whether sleep in the subacute phase of recovery explains the variability in upper limb motor outcomes after stroke (over and above predicted recovery potential from the Predict Recovery Potential algorithm) and whether this relationship is dependent on consolidation of motor learning. We will also test whether motor consolidation mediates the relationship between sleep and whole-body clinical motor outcomes, whether motor consolidation is associated with specific electrophysiological sleep signals and sleep alterations during subacute recovery.

ETHICS AND DISSEMINATION

This trial has received both Health Research Authority, Health and Care Research Wales and National Research Ethics Service approval (IRAS: 304135; REC: 22/LO/0353). The results of this trial will help to enhance our understanding of the role of sleep in recovery of motor function after stroke and will be disseminated via presentations at scientific conferences, peer-reviewed publication, public engagement events, stakeholder organisations and other forms of media where appropriate.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov: NCT05746260, registered on 27 February 2023.

Urban environment influences on stress, autonomic reactivity and circadian rhythm: protocol for an ambulatory study of mental health and sleep

Introduction

Converging evidence suggests that urban living is associated with an increased likelihood of developing mental health and sleep problems. Although these aspects have been investigated in separate streams of research, stress, autonomic reactivity and circadian misalignment can be hypothesized to play a prominent role in the causal pathways underlining the complex relationship between the urban environment and these two health dimensions. This study aims at quantifying the momentary impact of environmental stressors on increased autonomic reactivity and circadian rhythm, and thereby on mood and anxiety symptoms and sleep quality in the context of everyday urban living.

Method

The present article reports the protocol for a feasibility study that aims at assessing the daily environmental and mobility exposures of 40 participants from the urban area of Jerusalem over 7 days. Every participant will carry a set of wearable sensors while being tracked through space and time with GPS receivers. Skin conductance and heart rate variability will be tracked to monitor participants' stress responses and autonomic reactivity, whereas electroencephalographic signal will be used for sleep quality tracking. Light exposure, actigraphy and skin temperature will be used for ambulatory circadian monitoring. Geographically explicit ecological momentary assessment (GEMA) will be used to assess participants' perception of the environment, mood and anxiety symptoms, sleep quality and vitality. For each outcome variable (sleep quality and mental health), hierarchical mixed models including random effects at the individual level will be used. In a separate analysis, to control for potential unobserved individual-level confounders, a fixed effect at the individual level will be specified for case-crossover analyses (comparing each participant to oneself).

Conclusion

Recent developments in wearable sensing methods, as employed in our study or with even more advanced methods reviewed in the Discussion, make it possible to gather information on the functioning of neuro-endocrine and circadian systems in a real-world context as a way to investigate the complex interactions between environmental exposures, behavior and health. Our work aims to provide evidence on the health effects of urban stressors and circadian disruptors to inspire potential interventions, municipal policies and urban planning schemes aimed at addressing those factors.

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Effects of morning and evening physical exercise on subjective and objective sleep quality: an ecological study

To optimise the relationship between exercise and sleep quality, the intensity of exercise and its proximity to sleep are key factors to manage. Although low-to-moderate exercises promote sleep quality, late-evening vigorous exercise instead of morning should still be avoided. It potentially impacts the objective and subjective markers of sleep quality. In the present study, we investigated the effects of vigorous morning and evening exercise on objective and subjective sleep features in an ecological context. A total of 13 recreational runners (mean [SD] age 27.7 [7.2] years, four females) performed a 45-60 min run (70% maximal aerobic velocity) either in the MORNING (30 min to 2 h after waking-up) or in the EVENING (2 h to 30 min before sleep). The two exercise conditions were separated by a REST day. After each condition, sleep was objectively assessed using an electroencephalographic headband and subjectively using the Spiegel Sleep Inventory. Compared with REST, both MORNING and EVENING exercise increased the time spent in non-rapid eye movement (NREM, +24.9 min and +22.7 min; p = 0.01, η2  = 0.11, respectively). Longer NREM duration was mainly due to sleep stage 2 extension after both MORNING (+20.8 min) and EVENING (+22.8 min) exercise relative to REST (p = 0.02, η2  = 0.12). No other effect of exercise on either objective or subjective sleep could be observed. Exercise, independently of the time at which it takes place, leads to extended NREM sleep without other effects on sleep quality. Considering the crucial role of exercise in achieving good health, sleep hygiene guidelines should be updated to promote exercise at any time of the day.

Performance of the Dreem 2 EEG headband, relative to polysomnography, for assessing sleep in Parkinson's disease

GOAL AND AIMS

To pilot the feasibility and evaluate the performance of an EEG wearable for measuring sleep in individuals with Parkinson's disease.

FOCUS TECHNOLOGY

Dreem Headband, Version 2.

REFERENCE TECHNOLOGY

Polysomnography.

SAMPLE

Ten individuals with Parkinson's disease.

DESIGN

Individuals wore Dreem Headband during a single night of polysomnography.

CORE ANALYTICS

Comparison of summary metrics, bias, and epoch-by-epoch analysis.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Correlation of summary metrics with demographic and Parkinson's disease characteristics.

CORE OUTCOMES

Summary statistics showed Dreem Headband overestimated several sleep metrics, including total sleep, efficiency, deep sleep, and rapid eye movement sleep, with an exception in light sleep. Epoch-by-epoch analysis showed greater specificity than sensitivity, with adequate accuracy across sleep stages (0.55-0.82).

IMPORTANT SUPPLEMENTAL OUTCOMES

Greater Parkinson's disease duration and rapid eye movement behavior were associated with more wakefulness, and worse Parkinson's disease motor symptoms were associated with less deep sleep.

CORE CONCLUSION

The Dreem Headband performs similarly in Parkinson's disease as it did in non-Parkinson's disease samples and shows promise for improving access to sleep assessment in people with Parkinson's disease.

Reduced REM and N2 sleep, and lower dream intensity predict increased mind-wandering

Mind-wandering is a mental state in which attention shifts from the present environment or current task to internally driven, self-referent mental content. Homeostatic sleep pressure seems to facilitate mind-wandering as indicated by studies observing links between increased mind-wandering and impaired sleep. Nevertheless, previous studies mostly relied on cross-sectional measurements and self-reports. We aimed to combine the accuracy of objective sleep measures with the use of self-reports in a naturalistic setting in order to examine if objective sleep parameters predict the tendency for increased mind-wandering on the following day. We used mobile sleep electroencephalographic (EEG) headbands and self-report scales over 7 consecutive nights in a group of 67 healthy participants yielding ~400 analyzable nights. Nights with more wakefulness and shorter REM and slow wave sleep were associated with poorer subjective sleep quality at the intraindividual level. Reduced REM and N2 sleep, as well as less intense dream experiences, predicted more mind-wandering the following day. Our micro-longitudinal study indicates that intraindividual fluctuations in the duration of specific sleep stages predict the perception of sleep quality as assessed in the morning, as well as the intensity of daytime mind-wandering the following hours. The combined application of sleep EEG assessments and self-reports over repeated assessments provides new insights into the subtle intraindividual, night-to-day associations between nighttime sleep and the next day's subjective experiences.

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N = 94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N = 60), patients with obstructive sleep apnea (OSA, N = 60), insomnia (N = 60), and REM sleep behavioral disorder (RBD, N = 22), combined with two EEG channels, F3-M2 and F3-F4. Results: No differences in performance between the training strategies was observed in the age-matched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. Discussion: We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training.

Which Are the Most Reliable Sleep Parameters that Predict Cognitive Decline and Alzheimer's Disease?

Sleep disorders can represent an independent risk factor for cognitive decline and Alzheimer's disease (AD). It remains to be clarified if specific sleep parameters could be considered biomarkers of AD-related neurodegeneration. Several studies solely investigated the results of cross-sectional research, without providing conclusive evidence. Few longitudinal studies showed some inconsistencies in macrostructural and microstructural sleep findings. Methodological heterogeneity among studies can explain the discrepancies in the results. Moreover, the polysomnographic findings are usually related to only one-night recording. The combination of actigraphic recordings with sleep EEG monitoring for some consecutive days should be considered in future research.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Artificial intelligence in sleep medicine: Present and future

Artificial intelligence (AI) has impacted many areas of healthcare. AI in healthcare uses machine learning, deep learning, and natural language processing to analyze copious amounts of healthcare data and yield valuable outcomes. In the sleep medicine field, a large amount of physiological data is gathered compared to other branches of medicine. This field is primed for innovations with the help of AI. A good quality of sleep is crucial for optimal health. About one billion people are estimated to have obstructive sleep apnea worldwide, but it is difficult to diagnose and treat all the people with limited resources. Sleep apnea is one of the major contributors to poor health. Most of the sleep apnea patients remain undiagnosed. Those diagnosed with sleep apnea have difficulty getting it optimally treated due to several factors, and AI can help in this situation. AI can also help in the diagnosis and management of other sleep disorders such as insomnia, hypersomnia, parasomnia, narcolepsy, shift work sleep disorders, periodic leg movement disorders, etc. In this manuscript, we aim to address three critical issues about the use of AI in sleep medicine: (1) How can AI help in diagnosing and treating sleep disorders? (2) How can AI fill the gap in the care of sleep disorders? and (3) What are the ethical and legal considerations of using AI in sleep medicine?

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.