Sleep Medicine

RationaleObstructive sleep apnea (OSA) is a common, treatable chronic disease with significant health and societal consequences. Many patients face barriers to care due to systemic inequality, poverty, and other contributors to social vulnerability, leading to delayed diagnosis and more severe disease at presentation. Several studies have examined the impacts of social vulnerability on OSA severity using individual-level factors. However, there is comparatively limited work examining how neighbourhood-level indicators may influence OSA severity. This study aimed to determine whether social vulnerability, measured using a neighbourhood-level multidimensional index, is associated with OSA severity at referral to a tertiary sleep centre. MethodsWe conducted a retrospective observational study of adult patients referred to an academic hospital in Calgary, Canada for evaluation of OSA between November 2016 and November 2019. Patient data were linked using residential postal codes to the Canadian Index of Multiple Deprivation (CIMD), a census-based tool designed to reflect dimensions of social vulnerability in Canadian populations. CIMD divides social vulnerability into four dimensions including residential instability, ethnocultural composition, economic dependency, and situational vulnerability. We employed both linear and logistic mixed-effects models to assess the impact of neighbourhood-level social vulnerability on sleep apnea severity, using postal code as the grouping variable. OSA severity was based on home sleep apnea test (HSAT) derived oxygen desaturation index (ODI). Secondary outcomes included severe OSA (ODI [&ge;] 30), sleepiness based on Epworth Sleepiness Scale (ESS), and severe sleepiness (ESS > 15). ResultsThe study included 2,232 patients, 80% of whom had at least mild OSA. ODI was positively associated with situational vulnerability (p < 0.01) and inversely associated with ethnocultural composition (p < 0.01), though both associations lost significance after adjusting for BMI. ESS was independently associated with situational vulnerability (p < 0.01) and inversely with ethnocultural composition (p = 0.01), independent of BMI and ODI. Severe sleepiness was associated with situational vulnerability (p < 0.01) and residential instability (p = 0.02). ConclusionLiving in a socially deprived area was associated with OSA severity at time of referral, though this relationship appeared to be mediated by BMI. Deprivation dimensions were independently associated with sleepiness, highlighting the broader impact of social-related factors on sleepiness. These findings demonstrate the complex interplay between social vulnerability and sleep disorders and suggest that composite indices like the CIMD can enhance our understanding of these relationships.

BackgroundInsomnia disorder is a common sleep disturbance characterized by adverse daytime cognitive and emotional impairments, such as repetitive negative thinking and increased psychological distress. Memory control, a key self-regulatory ability to control or inhibit unwanted thoughts and memories, plays an essential role in supporting cognitive functions and emotional well-being. Here, we delineate the neurocognitive mechanisms underlying memory control among individuals with insomnia. Methods41 participants meeting DSM-5 criteria for insomnia disorder and 40 healthy sleepers completed an emotional Think/No-Think task, during which participants either retrieved (Think) or suppressed the retrieval (No-Think) of aversive memories in response to memory cues while electroencephalograms were recorded. ResultsLinear mixed model analyses with age and depression scores as covariates showed that participants with insomnia exhibited impaired memory control abilities, as evidenced by reduced suppression-induced forgetting in memory recall when compared to healthy sleepers. Electrophysiologically, healthy sleepers showed enhanced right prefrontal theta power in retrieval suppression than in retrieval, indicating elevated needs of inhibitory control during memory control. In sharp contrast, this difference was absent among those with insomnia. Notably, the greater the severity of insomnia symptoms, the smaller the retrieval vs. retrieval suppression theta power differences across participants, linking inefficient top-down control of unwanted memories with low sleep qualities. ConclusionIndividuals with insomnia showed impaired memory control of aversive memories and aberrant electrophysiological activities during retrieval suppression. Future research shall investigate the causal relationship between memory control abilities and insomnia symptoms.

BackgroundEmerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. HypothesisKey predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study DesignCross-sectional study. Level of EvidenceLevel 3. Methods121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). ResultsPhone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. ConclusionsOur findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical RelevanceEducation and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Shared sleeping quarters are commonplace in contexts such as athletes at major sporting events, academic dormitories, and military barracks, yet mismatched sleep preferences can undermine rest and ultimately, human behaviour and performance. We introduce the Roommate Sleep Preference Questionnaire (ROOMPREF), a brief eight-question survey capturing preferences for noise, lighting, and temperature tolerances, snoring behaviour, and chronotype. Responses feed into a free, web-based clustering tool built in Python, which flags preference conflicts, and implements adaptive K-Means clustering within sex-chronotype subgroups. A post-cluster swapping algorithm further mitigates residual mismatches, enhancing the room-matching process. The resource includes distribution charts, group summaries, and optional automated room allocations, with downloadable CSV outputs. We demonstrate its application in a pilot cohort, highlighting its potential to improve sleep outcomes across various use-cases. This free resource has the potential to alleviate mismatched rooming partners, resulting in enhanced sleep and wellbeing outcomes.

ABSTRACT Background Medical students face demanding academic schedules and elevated stress levels, predisposing them to poor sleep quality. Sleep hygiene, a set of behavioural and environmental practices aimed at optimising sleep, has been identified as a modifiable determinant of sleep quality, yet its role among medical students in Sudan remains unstudied. Objectives To assess current sleep hygiene practices among medical students at UMST and determine their association with sleep quality outcomes. Methods A facility based cross-sectional study was conducted at UMST among 240 medical students from three academic batches (3rd, 4th, and 5th year), selected via stratified random sampling. Data were collected using two validated self administered instruments: the Pittsburgh Sleep Quality Index (PSQI) and the Sleep Hygiene Index (SHI). Descriptive statistics, independent sample t tests, one way ANOVA, chi-square tests, Pearson correlation, and binary logistic regression were performed using SPSS version 23. Results Poor sleep quality (PSQI >5) was prevalent in 72.1% of participants (mean PSQI 7.25 +/- 2.66), and poor sleep hygiene (SHI >16) in 92.5% (mean SHI 27.1 +/- 7.9). SHI score (continuous) was the only significant independent predictor of sleep quality on logistic regression (OR = 1.13 per unit increase; 95% CI: 1.08-1.19; p < 0.001), equivalent to a 3.4-fold increase in odds per 10-unit rise in SHI score. Female sex was additionally identified as a significant predictor (OR = 1.88; 95% CI: 1.00-3.53; p = 0.049). A significant positive correlation was observed between PSQI and SHI scores (r = 0.359, p < 0.001). Conclusion Poor sleep hygiene is highly prevalent among UMST medical students and is the most significant modifiable predictor of poor sleep quality, with each unit increase in SHI score increasing the odds of poor sleep quality by 13%. These findings highlight a gap in sleep health education within Sudanese medical institutions and support the integration of targeted sleep hygiene interventions into the medical curriculum. Keywords: sleep hygiene; sleep quality; medical students; Sudan; PSQI; SHI; cross-sectional study

This study compared the effects of a 25-min nap opportunity and a 10-min non-sleep deep rest (NSDR) condition on perceptual, cognitive, and physical performance in physically active young adults. Sixty participants (26 female, 34 male; 22 {+/-} 4 years) were randomly assigned to one of three groups (nap, NSDR, control; n = 20 each). All groups completed identical assessments immediately, 20 min, and 40 min post-intervention. Mixed-effects models, adjusted for sex, prior-night sleep, and weekly physical activity, revealed a significant Group x Time interaction for sleepiness, fatigue, readiness to perform, and handgrip strength (p < 0.05). At 40 min post-intervention, the nap group reported lower fatigue than control and higher readiness to perform than both control and NSDR (p < 0.05). No significant effects were observed for the NSDR condition on perceptual, cognitive, or physical outcomes (p > 0.05). These findings indicate that a short nap can enhance perceived readiness and reduce fatigue after a brief latency period, whereas NSDR did not elicit significant effects under the present conditions.

Obstructive sleep apnoea (OSA) is a disorder marked by repeated episodes of airway collapse during sleep, leading to hypoxaemia and sympathoexcitation. Its impact on brain fluid transport remains unclear. We investigated MRI-visible perivascular spaces (PVS) in healthy controls (n=20; 5 females, mean{+/-}SD age = 52.1{+/-}9.9 years) and OSA patients (n=20; 3 females, mean{+/-}SD age = 54.6{+/-}9.6 years) before and after continuous positive airway pressure (CPAP) therapy in a longitudinal case-control study. MRI-PVS were automatically quantified using a deep learning model called the nnU-Net. At baseline, OSA patients had significantly greater whole-brain PVS volumes and cluster counts than controls (volume: exp({beta})=1.65, 95%CI [1.07, 2.51], p=0.01; cluster counts: exp({beta})=1.51, 95%CI [1.1, 2.04], p=0.01). However, after 12 months of CPAP, these differences were no longer significant (volume: exp({beta})=1.56, 95%CI [1.03, 2.39], p=0.054; cluster counts: exp({beta})=1.39, 95%CI [0.97, 1.92], p=0.072). Similarly, PVS metrics were significantly greater in OSA patients than controls at baseline in the frontal (volume: exp({beta})=1.66, 95%CI [1.02, 2.64], p=0.04; cluster counts: exp({beta})=1.46, 95%CI [1.02, 2.08], p=0.04) and temporal lobe (volume: exp({beta})=1.92, 95%CI [1.2, 3.03], p=0.01; cluster counts: exp({beta})=1.68, 95%CI [1.1, 2.55], p=0.02). After 12 months of CPAP, PVS metrics remained significantly higher in the OSA patients compared to controls in the frontal (volume: exp({beta})=1.68, 95%CI [0.94, 2.87], p=0.085; cluster counts: exp({beta})=1.4, 95%CI [0.92, 2.05], p=0.12) and temporal lobes (volume: exp({beta})=1.54, 95%CI [0.94, 2.38], p=0.085; cluster counts: exp({beta})=1.44, 95%CI [0.95, 2.12], p=0.089). These findings suggest that OSA is associated with PVS enlargement, which may be regionally reversible with CPAP treatment.

ObjectivesChronic insomnia (INS) is particularly prevalent in older adults and females. Sex-and age-related differences in neurophysiological markers of sleep quality (sleep spindles and slow-wave activity [SWA]) may underlie differential vulnerability to INS. This study investigated the effects of sex and insomnia on spindle and SWA beyond aging, to better understand the mechanistic differences contributing to the higher prevalence of INS in females. MethodsAfter a habituation night, one night of sleep assessed with polysomnography was analyzed in 222 adults (aged 18-82) including 119 INS (71% female) and 103 healthy sleepers (HS; 61% female). Spindle density, slow oscillation (SO) density, relative sigma power and SWA were derived during NREM sleep. Age, group, sex, and group-by-sex interactions were examined, with age as a covariate. ResultsAge, insomnia, and sex each contributed uniquely to NREM oscillatory activity. INS primarily reduced spindle and SO density, while sex accounted for differences in SWA. While SWA was higher in females overall, sex differences were not significant within the INS or HS groups. Female INS reported highest rates of insomnia severity as well as lower sigma power than males in the INS group. Spindle and SO density deficits were also present in female INS relative to female HS, as well as male INS relative to male HS. ConclusionsThe combination of reduced sigma power in females with insomnia relative to their male counterparts, as well as less spindle and SO density compared to female healthy sleepers may contribute to greater insomnia severity in females. Statement of SignificanceInsomnia is a growing public health concern that is more commonly reported in females, yet the neural mechanisms underlying this sex difference remain poorly understood. Our findings suggest that specific markers of sleep quality are disproportionately disrupted in females with insomnia, potentially contributing to greater vulnerability and symptom severity. These results provide new insight into how sex influences the neurophysiology of insomnia disorder and identify oscillatory markers that could serve as targets for personalized interventions. Future research should investigate whether these alterations represent persistent dysfunction or reversible changes, which could advance understanding of the biological basis of insomnia and inform strategies to improve sleep health in at-risk populations.

We completed a video-based, four-night, in-home, level 3 sleep apnea study of healthy, low-risk pregnant participants and their bed partners in order to characterize sleep physiology in the third trimester of pregnancy. Demographic, anthropometric, and baseline sleep health characteristics were recorded, and the NightOwl home sleep apnea test device was used to measure sleep breathing, posture, and architecture parameters. Symptoms of restless legs syndrome were elicited in the exit interview. Forty-one pregnant participants and 36 bed partners completed the study. Bed partners had a significantly higher prevalence of sleep apnea than their pregnant co-sleepers (31% vs. 5.9%). Bed partners also had more severe sleep apnea than their pregnant co-sleepers, and this persisted on an adjusted analysis for baseline differences in factors known to increase risk of sleep apnea. In pregnant participants, increasing gestational age was found to be protective against mild respiratory events but not more severe events. While the correlation between STOP-Bang score and measures of sleep apnea severity was weak, an affirmative response to the witnessed apneas item on the STOP-Bang questionnaire was a strong predictor of more severe sleep apnea for all participants. Smoking history also increased sleep apnea risk. Pregnant participants had lower sleep efficiency and longer self-reported sleep onset latency. Restless legs syndrome was experienced by 39.5% of the pregnant participants but no bed partners. From a sleep breathing perspective, people with healthy, low-risk pregnancies have better sleep than their bed partners despite lower sleep efficiency and higher rates of restless legs syndrome.

Rapid Eye Movement (REM) sleep makes up approximately 20% of sleep in the adult human and is altered in psychiatric and neurodegenerative conditions. REM sleep comprises two substates, during which eye movements do (phasic REM) and do not (tonic REM) occur. Tonic REM makes up 70-90% of REM sleep but its role in regulating brain function, mood and cognition remain underexplored. We investigated how seven nights of insufficient sleep (6 h time in bed), compared to sufficient sleep, alter periodic and aperiodic components of the phasic and tonic REM sleep electroencephalography (EEG), in 542 sleep recordings of 36 young adults. Associations between phasic and tonic REM sleep EEG and mood, cognitive performance, and overnight changes in cortical excitability as indexed by 1/f spectral slopes were assessed. Insufficient sleep predominantly affected tonic REM EEG components, specifically the density of theta, the amplitude, density, and frequency of alpha oscillations and the 1/f slope in the 30 to 45 Hz range. These changes associated with mood and cognitive performances, and with overnight reductions in cortical excitability. These results provide evidence for a role of tonic REM sleep in regulating mood and counteracting cognitive deterioration and excitability changes associated with insufficient sleep.

Lucid dream (LD) induction using external sensory stimulation has most commonly relied on distal cues such as lights or auditory signals, with mixed success rates. In this study, we investigated whether more direct bodily stimulation targeting the muscle and vestibular systems could influence LD induction. We compared electrical muscle stimulation (EMS) and galvanic vestibular stimulation (GVS), each combined with a two-week cognitive training protocol including dream journaling, reality checks, and association training. Twenty-eight participants (14 per group) completed two counterbalanced morning naps: one with stimulation (STIM) during REM sleep and with one sham-stimulation control (SHAM). EMS and GVS stimulation did not lead to increased incorporation of the stimulus. Lucidity rates were high in both EMS conditions, highlighting the substantial role of elevated baseline lucidity in induction studies, cognitive training, and expectation effects. In contrast, GVS stimulation significantly increased externally rated lucidity and DLQ questionnaire scores compared to control. Overall, the findings indicate that galvanic vestibular stimulation can increase dream lucidity. Future work should further examine the mechanisms by which vestibular stimulation influences dream awareness and its potential role in lucid dream induction. O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/711028v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@7de78forg.highwire.dtl.DTLVardef@1ed7628org.highwire.dtl.DTLVardef@e84964org.highwire.dtl.DTLVardef@2a5f5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

The transition from wake to stable sleep is characterized by multiple neural, physiological, and behavioral changes. How these changes may differ in individuals with difficulties falling asleep such as children with neurodevelopmental conditions is poorly understood. Here, we studied sleep initiation in >2000 nights recorded from 186 children who participated in the Simons Sleep Project (SSP). Data included simultaneous, synchronized recordings of actigraphy, electroencephalography (EEG), photoplethysmography (PPG), and skin temperature. We extracted multiple neural, physiological, and behavioral measures that are known to increase/decrease during the sleep initiation period including EEG delta (1-4Hz) power, movement counts, heart rate (HR), and skin temperature. Transitions from 20 minutes before sleep onset to 40 minutes after sleep onset were modeled with a sigmoid function enabling the quantification of transition timing, speed, and magnitude per measure. Individuals with longer sleep onset latencies (SOL) exhibited smaller increases in EEG delta power and skin temperature as well as smaller decreases in HR and activity counts. These findings indicate that difficulties falling asleep are associated with multiple forms of cortical, physiological, and behavioral hyperarousal that can be measured at home with wearable devices. Importantly, transition magnitudes were key to explaining differences in SOL across participants (26% explained variance) in contrast to transition speed or timing within the sleep initiation period (<13% explained variance). Longer SOL and weaker transitions were particularly prominent in children diagnosed with autism and/or attention deficit hyperactivity disorder (ADHD).

BackgroundNursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. AimsTo explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. MethodsA total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. ResultsPoor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). ConclusionsSocial support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.

To characterize sleeping posture, behaviour, and environment in healthy pregnant participants and their bed partners across multiple nights in the home setting during late pregnancy, we completed a manual review of overnight video recordings from a four-night, in-home, level 3 sleep apnea study. Sleeping postures were scored according to a thirteen-posture classification system to determine the cumulative time per night spent in each sleeping posture. Additional aspects of sleeping posture, behaviour, and environment were also assessed. Forty-one pregnant participants and 36 bed partners completed the study, contributing 168 nights of video. Significant differences were found between the pregnant participants and bed partners in cumulative time spent in each posture as well as frequency and duration of episodes spent in each posture. Pregnancy status, side of the bed, and presence of a pregnancy pillow, bed partner, shared bed sheets, and pets in the sleeping space had various effects on the time spent in each posture. Pregnant participants spent more time in transition postures (going-to-sleep, waking-to-void, returning-to-bed, and waking-in-the-morning) than bed partners. There was a moderately positive correlation in posture changes between pregnant participants and their bed partners. Pets significantly increased the number of posture changes per night for both groups. Pregnant participants had more absences and time absent from bed. Sleep in late pregnancy is characterized by an increased frequency and duration of episodes spent in a restricted number of sleeping postures and is impacted by the sleep environment. Modifying the sleeping environment may improve comfort, minimize disturbances, and benefit sleep. Statement of SignificanceSubjectively-recalled supine going-to-sleep posture in late pregnancy is associated with stillbirth and fetal growth restriction. Sleeping posture, however, is dynamic, and few studies provide comprehensive analyses of sleeping posture in pregnancy using objective measurements. This novel study used a gold-standard objective measure of sleeping posture, was conducted across multiple nights in the participants own homes, and accounts for usual sleeping behaviours and environment by including the participants bed partner. A critical remaining knowledge gap is whether an individuals nightly sleeping posture varies significantly across the third trimester. Future work should use nightly, continuous, and objective methods to measure sleeping posture across the entire third trimester to bridge this gap and investigate the relationship between sleeping posture and pregnancy outcomes.

A systematic review and meta-analysis of sleep studies in schizoaffective disorder were conducted using published articles researched in major databases within the period from inception to December 1, 2025. The sleep parameters: total sleep time, sleep efficiency, sleep latency, wakefulness, REM time and percentage, REM latency, REM density, stage 1, 2, 3 and 4 sleep time and percentage, delta sleep time and percentage, of drug-free schizoaffective patients were analyzed and, where available, compared with case-control data of healthy controls, depressed unipolar patients and schizophrenic patients. Forty studies were identified in the systematic review. Nine case-control studies with 67 schizoaffective patients, 88 schizophrenic patients, 79 healthy controls and 131 depressed patients were included in the meta-analyses. The primary outcome was the standard mean difference. Data were fitted with a random-effects model. Publication bias assessment was checked by Egger's Regression and funnel plot asymmetry. Patients with schizoaffective disorder showed reduced total sleep time, increased sleep latency and wakefulness, along with reduced REM time and shortened REM latency, reduced stage 4 sleep time and percentage compared to healthy controls. Patients with schizoaffective disorder differed from depressed patients only for increased sleep latency, while they did not show any difference compared to patients with schizophrenia. SZA showed a non-significant trend (p=0.08) towards increased REM density compared to SCZ, suggesting the need to better clarify the role of REM density in mood and psychotic disorders.

Motor memory retention is impaired in Parkinson's disease (PD), affecting long-term rehabilitation outcomes. It appears that NREM sleep could be beneficial for consolidation processes in PD, and could be leveraged with non-invasive sleep interventions. This study examined the effect of auditory targeted memory reactivation (TMR) during NREM sleep on the retention of a motor sequence learning finger tapping task in 20 PD and 20 healthy older adults (HOA). TMR was applied during a 2-hour nap and its effect on motor retention was post-nap, after 24-hours and with a dual-task. The impact of TMR on sleep electrophysiology was also evaluated. Results showed no effect of TMR on motor retention or dual-tasking, with no difference between the groups. However, the TMR intervention did increase slow-wave density and decreased spindle density in both groups, and slow-wave amplitude during the presentation of the auditory cues was positively associated with performance in HOA. In conclusion, TMR applied during a 2 hour nap did not enhance motor retention, but the changes in sleep physiological features could be linked to a possible underlying effect on memory processing that warrants further investigation.

Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because current diagnostic testing relies on specialist in-patient sleep investigations: overnight polysomnography (PSG) followed by a multiple sleep latency test (MSLT), interpreted according to International Classification of Sleep Disorders criteria (ICSD-3-TR). These investigations are expensive, labour intensive, and available in a limited number of centres, contributing to delays and inequity of access. Automated analysis of sleep-stage probabilities (hypnodensity) using neural networks has shown promising diagnostic performance in research cohorts but still requires hospital-based PSG acquisition. The Dreem 3 headband (DH) is a comfortable, dry-montage EEG device designed for home use. Combined with its proprietary machine learning classification of sleep stages, it may offer accurate ambulatory sleep physiology assessments and support clinical decision making. Methods This was a single-centre, prospective, observational study recruiting 60 participants aged 10 to 35 years undergoing investigation for hypersomnolence within GSTT sleep services and scheduled for PSG and MSLT as part of routine care. Exclusion criteria included physician-diagnosed medical or psychiatric disorder that could independently account for excessive daytime sleepiness; and/ or regular use of prescribed or recreational medication known to affect sleep architecture. Participants first wore the DH at home for five weeknights, followed by a continuous 48-hour weekend recording using two devices in rotation. They then underwent routine in-patient PSG and MSLT. PSG and MSLT were interpreted according to ICSD-3 by an experienced sleep physician and a final diagnosis determined by a sleep physiology consultant. The primary outcome is accuracy of ambulatory DH-based assessment of sleep physiology and subsequent diagnosis of sleep disorders. We evaluate proprietary and in-house developed machine learning methods to detect SOREM epochs and predict narcolepsy diagnosis from PSG, PSG+MSLT and DH data. All algorithmic outcomes will be compared to clinical outcomes derived from current clinical standard of care. Discussion This study will provide proof-of-concept evidence for a home-based wearable EEG approach to narcolepsy diagnosis. Patient and public involvement work with young people with confirmed narcolepsy indicates high acceptability of the DH protocol: in a survey of ten young people, eight reported they would be willing to wear a sleep headband nightly at home for five days (two were unsure), and seven reported they would be willing to wear it continuously for 48 hours over a weekend (two were unsure; one said no). These findings informed the decision to restrict continuous wear to the weekend, reflecting feedback that daytime wear during school or work hours would be unacceptable. If validated, this approach could reduce delays to diagnosis, improve equity of access, and support development of a subsequent multicentre study. Trial registration IRAS Project ID: 321547. Registered October 2022. Recruitment was completed on 30 January 2026.

Study ObjectivesNarcolepsy type 1 (NT1) is characterized by excessive daytime sleepiness and cataplexy. Previous studies have implicated the amygdala, thalamus, brainstem and hippocampus in the pathophysiology of NT1. We here aimed to examine more detailed subregional case-control differences in MRI-based segmentations of these brain regions to gain deeper insights. MethodsWe obtained 3T MRI brain scans from 54 NT1 patients (39 females, mean age 21.8 {+/-} 11.0 years, 51 with confirmed hypocretin-deficiency and three patients that had not performed this measure) and 114 healthy controls (77 females, mean age 23.2 {+/-} 9.0 years). Automated segmentation of the hippocampus, amygdala, thalamus, and brainstem was performed on T1-weighted MRI data using FreeSurfer. Case-control volume differences were tested using general linear models and permutation testing. The false discovery rate was controlled at 5% with the Benjamini-Hochberg procedure. ResultsThe analysis revealed no significant case-control differences for any of the subregions in the hippocampus, thalamus, amygdala and brainstem after correction for multiple testing. ConclusionsBased on a detailed automated MRI-based segmentation analyses in a relatively large national sample, NT1 patients had no significant changes in any amygdala, thalamus, brainstem or hippocampus subregions compared to controls. In the future large multi-site studies could be performed to achieve sufficient power to detect more subtle group differences.

BackgroundAutistic individuals experience higher rates of sleep problems throughout their lives, and there is considerable heterogeneity in manifestations of these issues that remains unexplained. Here, we examine associations over time of heterogenous sleep trajectories with autism diagnosis, and behavioural and genetic factors related to autism. MethodWe used data from the Avon Longitudinal Study of Parents and Children (N=13,886, autistic n=150). The primary outcome was parent and self-reported night-time sleep duration, measured on 10 occasions (between 0.5y and 15.5y). The independent variables were autism diagnosis, autism polygenic score (PGS) and four parent-reported autistic traits: repetitive behaviour, social communication, speech coherence, and sociability. Latent class growth analysis was conducted to identify heterogenous classes of sleep trajectories, and these trajectory classes were regressed onto the independent variables. ResultsFour night-time sleep duration trajectory subclasses were identified; shorter (n=512, 4.1%), longer (n=1654, 13.1%), intermediate-shorter (n=3630, 28.8%), and intermediate-longer (used as the reference class; n=6825, 54.1%). An autism diagnosis was associated with a shorter or intermediate-shorter sleep duration trajectory, compared to the reference class. Similarly, higher scores in domains of repetitive behaviour, speech coherence and social communication were associated with shorter sleep duration trajectories. The autism PGS and sociability were not associated with any sleep trajectories compared to the intermediate-longer sleep trajectory (reference group). ConclusionAn autism diagnosis and specific autistic traits were associated with poorer long-term sleep outcomes across childhood and adolescence, highlighting the need for early, sustained sleep interventions, and the potential of trait-specific mechanisms for sleep problems. HighlightsO_LIFour distinct night-time sleep duration trajectories were identified across development C_LIO_LIAutism diagnosis predicted shorter and intermediate-shorter sleep trajectories C_LIO_LISpecific (but not all) autistic traits were linked to shorter sleep trajectories C_LIO_LIAutism PGS did not predict sleep duration trajectories C_LI

Scientists have for decades attempted to automate the manual sleep staging problem not only for human polysomnography data but also for rodent data. No model has, however, succeeded in fully replacing the manual procedure across clinics and laboratories. We hypothesize that this is due to the models limited ability to generalize to data from unseen laboratories. Our findings show that despite the high performance of four state-of-the-art models reported in initial publications, the published models struggle to generalize to other laboratories. We further show a significant improvement in model performance across labs by re-training them on a diverse dataset from five different sites. To assess the contribution of variability in manual scoring, ten experts from five laboratories all labelled the same nine mouse sleep recordings. The result revealed substantial scoring variability, particularly for rapid eye movement (REM) sleep, both within and between labs. In conclusion our study demonstrates that key challenges in the generalizability of state-of-the-art sleep scoring models are signal variability and label noise. Our study highlights the need for a standardized set of mouse sleep scoring guidelines to enable consistency and collaboration across the field. Until such a consensus is reached, we present four sufficiently robust models trained on diverse datasets that can serve as standardized tools across labs.