Journal of Sleep Research

Importance: Sleep-wake disturbances in midlife are common and potentially modifiable contributors to long-term brain health, yet primary care lacks a brief, validated tool that reliably identifies adults with early cognitive vulnerability. Objective: To evaluate associations between commonly used sleep questionnaires and cognitive impairment among midlife primary care patients. Design, Setting, and Participants: Cross-sectional analysis of baseline data from the MidCog cohort, an observational study of English-speaking adults aged 35 to 64 years receiving primary care at academic practices or federally qualified health centers in the Chicagoland area. Exposures: Five validated sleep questionnaires were used to assess distinct sleep-wake disturbance phenotypes: (A) unsatisfactory sleep (PROMIS Sleep Disturbance T-score >55), (B) short sleep duration (<6 hours; Munich Chronotype Questionnaire), (C) obstructive sleep apnea (OSA) risk (STOP-Bang [&ge;]3), (D) insomnia symptoms (Insomnia Severity Index [&ge;]15), and (E) poor multidimensional sleep health (RU-SATED [&le;]6). Main Outcomes and Measures: The primary outcome was cognitive impairment defined as an age- and education-adjusted NIH Toolbox Cognition Battery (NIHTB-CB) Fluid Composite T-score <40 ( >1 SD below the population mean). Cognitive impairment defined by the Montreal Cognitive Assessment (MoCA) score <23 served as the secondary outcome. Logistic regression estimated adjusted odds ratios (aOR), controlling for age, sex, education, body mass index, hypertension, hypercholesterolemia, diabetes, smoking, depressive symptoms, and recruitment site. Results: Among 646 participants (mean [SD] age, 52.3 [8.1] years; 62.4% female; 38.0% non-Hispanic Black, 38.4% non-Hispanic White, 16.0% Hispanic), cognitive impairment was present in 18.7% by NIHTB-CB and 22.3% by MoCA. Among five sleep-wake disturbance phenotypes evaluated, only poor multidimensional sleep health was consistently associated with cognitive impairment after multivariable adjustment (NIHTB-CB: adjusted OR [95% CI] = 2.03 [1.25-3.26]; MoCA: 1.98 [1.20-3.26]). Conclusions and Relevance: Poor multidimensional sleep health was associated with cognitive impairment in midlife primary care patients. Brief multidimensional sleep health screening may identify individuals with early cognitive vulnerability and represent a potential strategy for targeting sleep-focused interventions to promote long-term brain health.

PuhrposeTo evaluate the short- and long-term cross-sectional associations between COVID-19 infection and multidimensional sleep health. MethodsData from the COVID-19 Outbreak Public Evaluation (COPE) initiative were used to examine the association between a novel multidimensional sleep health measure (COPE Multidimensional Sleep Health Scale, CMSHS) modeled from the RuSATED instrument and (1) COVID-19 infection and (2) post-acute sequelae of SARS-CoV-2 infection (PASC). ResultsData from 11,326 respondents were used for this study. The cohort was comprised of 51% women, 61% non-Hispanic White, and 17% Hispanic adults. COVID-19 infection was more prevalent among participants who had not received a booster vaccination (55.4% vs. 30.2%, p<0.001); the number of comorbid conditions was higher among those who had been infected (2.2% vs. 1.7%, p<0.001). Participants with COVID-19 infection had significantly lower CMSHS scores indicative of worse sleep health compared with uninfected participants (3.52 {+/-} 1.37 vs. 3.78 {+/-} 1.30; p < 0.001). Participants with PASC had lower CMSHS scores in comparison to those without PASC (2.72 {+/-} 1.30 vs. 3.82 {+/-} 1.28, p<0.001). In adjusted models, a progressive decline in CMSHS scores was observed over 12 months following infection (3.52 {+/-} 0.05 vs. 2.98 {+/-} 0.04; p < 0.001 for <1 month vs. 6-12 months). ConclusionCompared with uninfected individuals, multidimensional sleep health was worse among persons who had a COVID-19 infection. Individuals with PASC had greater and persistent reductions in sleep health for up to 12 months post-infection. Brief summaryO_LISeveral studies have examined the negative effects of COVID-19 on sleep, however the effects of COVID-19 infection on multidimensional sleep health remain poorly understood as do these associations over time. Using a large, population-based cohort, this study evaluates short- and long-term effects of Covid-19 infection on overall sleep health. C_LIO_LIThe study provides evidence that COVID-19 infection is associated with impairments in overall sleep health, with effects persisting up to 12 months post-infection. The findings in this study demonstrate that poor sleep health is an important long-term consequence of COVID-19 infection and emphasizes the need for sleep assessment among patients affected by COVID-19. C_LI

Sleep regularity is an important but under-measured dimension of sleep health. Objective indices from actigraphy or wearables are robust but resource-intensive. The Sleep Regularity Questionnaire (SRQ) offers a brief subjective tool, but its validity against objective and diary-based indices in healthy adults is unclear. In Part 1, 31 adults wore a smart ring continuously for 21 nights. Device-derived regularity metrics included the Sleep Regularity Index (SRI), interdaily stability (IS), social jetlag (SJL), composite phase deviation (CPD), and the standard deviation of sleep onset and wake time. In Part 2, 52 adults completed a one-week sleep diary, from which variability in sleep timing, total sleep time (TST), SJL and nightly perceived sleep quality were derived. All participants completed the SRQ and Brief Pittsburgh Sleep Quality Index (B-PSQI). In Part 1, associations between SRQ scores and device-derived SRI, IS, SJL, CPD and timing variability were small (absolute r [&le;] 0.36). Higher SRQ Global and Sleep Continuity scores were moderately associated with better B-PSQI global scores (r -0.37 to -0.44). In Part 2, SRQ Global and Circadian Regularity showed small-to-moderate associations with higher diary-rated sleep quality and lower bedtime variability (r {approx} 0.40 and -0.32 to -0.34), while correlations with other diary metrics and B-PSQI were weak (absolute r [&le;] 0.25). The SRQ shows modest convergent validity with diary-based timing variability and perceived sleep quality, but only weak correspondence with smart ring-based sleep regularity indices. It is likely to complement, rather than replace, objective monitoring in healthy adults with relatively regular sleep-wake patterns.

Sleep is essential for childrens health and development, yet sleep problems are common worldwide. Comfort items such as soft toys or blankets are widely used to promote independent sleep, but their effects have not been evaluated in a randomised controlled trial (RCT). The REST trial emerged from a child-led citizen-science study (The Kids Trial) where children co-created and designed the trial. Therefore, this paper had two aims, to assess whether sleeping with a comfort item affected childrens sleep; and to assess the feasibility of conducting an online, child-led citizen-science RCT. The REST (Randomised Evaluation of Sleeping with a Toy or comfort item) trial was an online two-arm, parallel-group, superiority RCT. Children, aged 7 to 12 years, were randomised (1:1) to either sleep with a self-chosen comfort item ( Try-it-Out group) or refrain from using one ("Wait-and-See" group) for one week. The primary outcome was sleep-related impairment (SRI; PROMIS Pediatric Short Form v1.0 SRI 4a). The secondary outcome was overall sleep quality (Single Item Sleep Quality Scale, SQS). Analyses followed an intention-to-treat principle using mixed-effects models adjusted for baseline measures. A total of 139 children from 11 countries were randomised (mean age: 9.8 years; 45% female); 101 children (73%) completed post-test measures at one week. The adjusted mean difference (Intervention minus Control) in SRI T-scores was -0.53 (95% CI: -3.40 to 2.34; p = 0.714), equivalent to approximately -0.05 SD on a scale where 10 points = 1 SD. This indicated a trivial effect, well below the minimal important difference (MID) of 3 points. The adjusted mean difference in SQS was 0.28 (95% CI: 0.01 to 0.55; p = 0.040), suggesting a small and uncertain difference in favour of the intervention group. However, this result was not supported in subsequent sensitivity or exploratory subgroup analyses. No adverse events were reported. Sleeping with a comfort item for one week did not influence sleep-related impairment. A small statistically significant difference in perceived sleep quality was observed in the primary analysis, but was not sustained in the per-protocol analysis. Together, these findings suggest that any benefit of comfort items for sleep is small and uncertain. The trial demonstrated that children can meaningfully engage in online, citizen-science research, supporting the feasibility of child-led RCTs. Trial registrationISRCTN13756306 (registered 10 January 2025)

ABSTRACT Purpose: Survivors of severe COVID-19 commonly experience post-intensive care syndrome (PICS), which includes depression and fatigue. Fatigue is far more common and may inflate depression severity given overlapping symptoms. We sought to disentangle fatigue from depression in PICS. Methods: We conducted a cross-sectional analysis of the RAFT COVID study, a national multicenter longitudinal cohort of severe prolonged COVID-19 survivors. We included participants who completed validated surveys at 1-year from hospitalization for depression (PHQ-9) and fatigue (FACIT-Fatigue). We described correlation of FACIT-fatigue with the PHQ9, and separately with PHQ-2 and PHQ-7, which both omit the two items we hypothesized are influenced by fatigue: tiredness and sleeping. Using a MIMIC model, we performed differential item functioning to evaluate the impact of fatigue on depression directly through these two questions and indirectly with the latent depression construct. We then compared PHQ-7 to PHQ-9 scores by fatigue status. Results: Among 82 participants, 61.0% reported fatigue (reverse-scored FACIT-Fatigue[&ge;]9), and 15.9% moderately severe depression (PHQ-9[&ge;]10). FACIT-fatigue was strongly correlated with PHQ-9 (r=.87, p<.001), but less so for PHQ-2 (r=.76, p<.001) and PHQ-7 (r=.82, p<.001). The MIMIC model identified significant direct effects on tiredness ({lambda}=.89, p<.001) and sleep ({lambda}=.52, p<.001). Among fatigued participants, the rescaled PHQ-7 was lower than the PHQ-9 (median of 4.5, IQR 1.50-9.75, vs 7, IQR 4-9.75). Conclusions: Fatigue significantly inflated depression symptoms in severe COVID-19 survivors through tiredness and sleeping PHQ-9 items. PHQ-2 may better screen for true depressive symptoms in PICS, minimizing the risk of misdiagnosis and overtreatment.

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.

Lucid dreaming (LD), during which the dreamer is aware that they are dreaming, is frequently induced in laboratory settings by delivering sensory cues during rapid eye movement (REM) sleep. These cues should be incorporated into ongoing dreams and can trigger reflective awareness. This approach relies on the continuity between waking experiences and dream content. In sleep laboratories, participants often dream of the experimental setting itself (lab dreaming), providing a predictable context in which lucidity may emerge. The present studies leveraged this phenomenon by explicitly training participants to associate the sleep laboratory with reflective awareness prior to sleep. Across three studies (total N = 101), participants completed a morning nap following verbal LD instructions and presleep audio designed to prime recognition of the laboratory context in dreams. In addition, conditions included immersive virtual reality (VR) rehearsal of the laboratory environment, VR combined with haptic stimulation (HS) during REM sleep, or VR containing subtle fake system errors intended to prompt reflective checking. LD frequency was assessed through external ratings of signal-verified LD (SVLD) dream reports. Lucidity rates were high across all conditions, with approximately 40-45% of dreams externally rated as lucid and 11%-32% SVLDs occurring in every group. However, neither VR rehearsal, haptic stimulation, nor implicit VR errors increased lucidity relative to the baseline laboratory induction procedure. Exploratory analyses investigated the overlap between laboratory dreaming, false awakenings (FAs), and lucidity. These findings suggest that explicit training focused on the predictable context of the sleep laboratory may already provide a powerful pathway to lucidity, with additional technological manipulations offering limited benefit under a single-nap protocol. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/711049v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@191373corg.highwire.dtl.DTLVardef@c1490corg.highwire.dtl.DTLVardef@1a2c193org.highwire.dtl.DTLVardef@52c5d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

Study objectives Consumer sleep technologies (CSTs) enable low-burden longitudinal sleep monitoring, and their output measures are often interpreted as equivalent to polysomnography (PSG) measures. We applied a measurement reliability-aware approach to determine whether CST-derived 'sleep' measures (1) are interchangeable or device-specific, (2) can reliably assess trait-like sleep characteristics of an individual, (3) can be reduced to latent principal components of sleep, and (4) can be used for classification and biomarker discovery. Methods Data from 74 older adults (20 people living with dementia [PLWD]) were collected at-home (upto 14 nights; Total=752nights) using four tools simultaneously: research-grade actigraphy (Axivity), a wearable (Withings Watch), a nearable (Withings Sleep Analyzer) and Sleep Diary, followed by one in-lab PSG assessment. We used repeated-measures correlation analyses, intraclass correlation coefficients (ICC), principal component analysis (PCA) and binary classification models to address our objectives. Results Single-night between-device correlations and correlations with PSG were moderate (0.3[&le;]r<0.7) for some duration- and timing-related measures, but other associations were weak (r<0.3). Seventy-one percent of sleep measures reached acceptable reliability (ICC[&ge;]0.7) within seven nights of aggregation, but the required aggregation window varied across measures, tools and between PLWD and Controls. Reliability-filtered PCA yielded stable and interpretable principal components, but Duration was the only component showing moderate between-device association. Principal components were successfully used to classify PLWD vs Controls but feature importance varied across devices. Conclusions Aggregation of CST derived measures across 7-14 nights, yielded reliable measures, most of which were device-specific, with duration being the only essential aspect transferable between devices.

Abstract Background Insomnia is a major public health problem affecting an estimated 852 million adults worldwide. Current pharmacological treatments, including benzodiazepines and Z-drugs, carry serious risks of dependency, cognitive impairment, and adverse events. These limitations have driven growing interest in complementary and alternative therapies, particularly herbal sedatives, which are perceived as natural and safer. However, evidence on their safety and efficacy remains insufficient and patchy. Objective: This review evaluated the effectiveness of lesser known herbal sedatives for insomnia. Methods The protocol was registered with PROSPERO (CRD420251101795). Eligibility was defined using the PICO framework: Population: adults aged [&ge;]18 years with insomnia; Interventions: Passiflora incarnata, Hawthorn, Melissa officinalis, Chamomile, Viola odorata, Nelumbo nucifera, Rhodiola rosea, and Eschscholtzia californica. Comparators: placebo or usual care; Primary and Secondary Outcomes: sleep quality (Pittsburgh Sleep Quality Index, Insomnia Severity Index, Epworth Sleepiness Scale), sleep duration, and sleep latency. Databases and registers were searched from January 2005 to July 2025. Randomized controlled trials, nonrandomized controlled trials, clinical trials, and observational studies were included. Five reviewers independently screened studies. Data extraction used a structured Excel spreadsheet. Risk of bias was assessed using RoB 2.0 for randomized trials and ROBINS-I V2 for nonrandomized studies. Random-effects meta-analyses (DerSimonian and Laird) were conducted in RevMan. Narrative synthesis followed SWiM guidelines. Results From 1,294 records, 32 studies met eligibility criteria. Meta-analysis of 23 RCTs demonstrated a statistically significant pooled effect favouring herbal sedatives (SMD -0.77, 95% CI -1.14 to -0.40, p=0.0001), with substantial heterogeneity (I square=92%). Subgroup analysis showed larger effects for chamomile (SMD -1.06) and Melissa officinalis (SMD -0.66). Most RCTs had high overall risk of bias; nonrandomized studies predominantly had critical risk of bias. Conclusions This systematic review provides preliminary evidence that several herbal sedatives, particularly chamomile and Melissa officinalis, may improve insomnia-related outcomes. However, methodological weaknesses, high risk of bias, and substantial heterogeneity limit evidence strength. Future research requires standardized extracts, large multicentre RCTs, and extended follow-up.

BackgroundNocturnal hypoglycaemia (NH) is a common and challenging complication in Type 1 Diabetes (T1D), disrupting blood glucose control and sleep physiology. Its real-world impact on sleep architecture remains poorly characterised. Consumer wearables offer a way to examine these associations under free-living conditions, providing detailed insight into behavioural and physiological responses to nocturnal blood glucose fluctuations. This study aims to assess how wearable-derived sleep metrics and physiological features could be used as indicators of NH, including the effects of how low blood glucose levels fall during hypoglycaemic events and the associated pre-event changes. MethodsWe conducted a comparative observational analysis of paired continuous glucose monitoring (CGM) and Garmin smartwatch data collected over 12 weeks from 17 adults with T1D. Nights were categorised as normoglycaemia, hyperglycaemia, or hypoglycaemia Level 1 ([&ge;]3.1 and <3.9 mmol/L), and hypoglycaemia Level 2 (<3.0 mmol/L). Thirteen sleep metrics, including total sleep time, wake after sleep onset (WASO), sleep-stage proportions, fragmentation indices, and physiological features such as heart rate, were compared using non-parametric tests. Pre-hypoglycaemic event analyses examined 60-minute and 15-minute windows preceding hypoglycaemia to identify early deviations in sleep and physiological metrics. ResultsAcross 573 nights, 17.5% involved Level 1 and 7.3% Level 2 hypoglycaemia. Level 2 hypoglycaemia was associated with 31 minutes less wakefulness, 17-25 minutes more REM, and up to 74% more deep sleep compared with normo-glycaemic nights. Sleep efficiency increased during hypoglycaemic events despite greater fragmentation. Pre-hypoglycaemic episode analyses revealed shorter awake and light-sleep bouts, as well as a 9.8% higher heart rate, preceding Level 2 episodes. ConclusionsWearable-derived sleep and physiological signals reveal clear intraindividual changes both before and during NH. Our findings indicate that Level 2 episodes are associated with deeper sleep and reduced behavioural arousal, suggesting that CGM alarms may be less effective at waking individuals during level2 NH. By characterising pre-hypoglycaemic changes that differ based on hypoglycaemia level, this work provides preliminary evidence for personalised, wearable-based early-warning systems. Such approaches could help distinguish nocturnal hypoglycaemic events and support more effective alerting, particularly in settings with limited or no access to CGM. Author SummaryO_ST_ABSWhy was this study done?C_ST_ABSPeople with Type 1 Diabetes (T1D) frequently experience nocturnal hypoglycaemia (low blood glucose at night), a dangerous event that often goes unnoticed because individuals are less able to recognise symptoms or wake up during sleep. These events also disrupt sleep in ways that are not well characterised under real-world conditions. Limited access to continuous glucose monitoring (CGM), especially in low- and middle-income countries, highlights the need for affordable alternatives to ensure nighttime safety. What did we do and find?Using more than 500 nights of paired smartwatch and CGM data, we investigated how sleep features change when blood glucose levels fall overnight. We found that hypoglycaemic nights show distinct alterations in sleep architecture, including increased REM and deep sleep, and greater micro-fragmentation. A key finding was that Level 2 hypoglycaemia was associated with deeper sleep and reduced wakefulness. This pattern indicates that individuals may be less likely to awaken during more severe events, even when alarms are present. Pre-hypoglycaemic episode analysis revealed additional early-warning signals, such as shorter awake and light-sleep bouts and elevated heart rate, before level 2 hypoglycaemia occurred. What do these findings mean?Smartwatches can capture sleep-based changes that appear before and during nocturnal hypoglycaemia. Because deeper sleep during Level 2 episodes may reduce responsiveness to CGM alerts, these results suggest that current alarm approaches could be improved by incorporating sleep features alongside glucose data. Such sleep-informed detection may enhance the reliability of hypoglycaemia alerts, reduce missed events during deep sleep, and provide a foundation for low-cost early-warning systems in settings where CGM is unavailable or unaffordable. Further research is needed in larger and more diverse populations, but this work provides early evidence that wearable-derived sleep features can meaningfully strengthen nocturnal hypoglycaemia detection.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[&ge;] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

BackgroundAs sleep data from wearable devices are increasingly available in health research, there are new opportunities to understand sleep regulation behaviors as modifiable risk factors for disease. At such a large scale (tens of thousands of people over millions of day-level observations), prioritizing and interpreting sleep behaviors is challenging while maintaining biological relevance and modifiability. In this work, we aim to address this challenge by proposing a framework to interpret Fitbit data through a well-known neurobiological framing of sleep regulation, the two-process model. MethodsWe use data from the All of Us Research Program, a national biobank with passively collected Fitbit data for 32,292 people across 15,754,893 total days. We map Fitbit behaviors (b) to either circadian (C) or homeostatic (S) processes. Using iterative exploratory factor analysis to obtain weights, the Fitbit Cb and Sb are then weighted at the level of each day to create Cb and Sb scores. FindingsCb and Sb scores were found to align with expected real-world relationships with age, seasonality, shift work, and napping. Cb and Sb scores were interpreted with relation to depression, where it was found that Sb scores are highly associated with likelihood of diagnosis (OR = 1.5, p < 2e-16) while Cb and Sb scores are equally associated with severity (Sb score {beta} = 0.2, Cb score {beta} = 0.21, p < 2e-16). InterpretationCb and Sb scores support longitudinal interpretation (e.g., changes in Sb around treatment), aggregation (e.g., differences in Cb between two groups), and actionable modification (e.g., reduce naps to improve poor Sb). Overall, our behavior scores allow for interpretation of wearables sleep data and can be utilized across many disease contexts to better understand how sleep influences health. FundingThis work was supported by NIH training grant T32GM145734 and NIH R21HL172038.

Background/ObjectivesElectromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for Rapid eye movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM-sleep motor activity. MethodsSeventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3-second mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. ResultsIn participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. Across 12,941 3-second mini epochs, EMG, actigraphy, and video detected 1,703, 1,613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG-actigraphy ({kappa} = 0.27 {+/-} 0.10) to actigraphy-video ({kappa} = 0.41 {+/-} 0.12) and EMG-video ({kappa} = 0.45 {+/-} 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. ConclusionActigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patient, with actigraphy showing the highest sensitivity and manually scored video the lowest.

Deep sleep is widely considered to be the most recuperative component of sleep restoration. Accordingly, a positive relationship between naturally occurring deep sleep and function (e.g., cognitive performance) is often assumed. However, this assumption warrants closer examination--particularly given the rise of sleep tracking that emphasizes traditional sleep metrics and their implied predictive value. We present evidence that while clinical deep sleep scoring provides no predictive value, slow-wave activity (SWA) exhibits a paradoxical association with both improved and worsened neurobehavioral fatigue following sleep deprivation. Specifically, we found that SWA-based models account for approximately 50-60% of the inter-individual variance in recovery from sleep deprivation. Remarkably, when regressed against recovery from sleep deprivation, SWA during the baseline sleep night showed a negative association (normalized {beta} = (-)0.5, p = 0.001) while in the same model SWA during the subsequent wakefulness period showed an opposite positive association (normalized {beta} = 0.5, p = 0.001). Furthermore, although the group-averaged SWA while behaviorally awake increased with impairment across the sleep deprivation period, individual-level data revealed an inverse relationship: individuals more resilient to sleep deprivation exhibited greater SWA in-between mental test sessions and less corresponding impairment during wakefulness suggestive of a protective effect. These findings identify a Deep Sleep Dual Indeterminacy Problem -- simultaneous measurement and causal indeterminacy -- that explains why clinical sleep staging fails as a functional biomarker across a wide range of outcomes, and provide a principled framework for next-generation sleep metrics grounded in continuous electrophysiology and temporal modeling.

ObjectiveIsolated rapid-eye-movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video-polysomnography. We assessed a two-stage screening strategy combining a brief questionnaire on rapid-eye-movement sleep behavior disorder symptoms and other prodromes with wrist actigraphy across multiple case-control cohorts. MethodsParticipants aged 40-80 without neurodegenerative disease were recruited from five cohorts; all cases were confirmed by video-polysomnography. The questionnaire was administered to 289 participants, and 236 underwent [&ge;]14 nights of home wrist actigraphy. The wearable-based algorithm was built on four movement features (mean motor activity, activity index, short or long immobile bouts, twitch activity). Models were trained with nested cross-validation using XGBoost. ResultsThe full retrospective cohort included 396 participants (99 cases, 297 controls; mean age 64 {+/-} 11; 55% male). The dream enactment question alone achieved an area under the curve of 0.85, which improved to 0.86 using the four-item questionnaire. Actigraphy alone achieved 82% sensitivity and 84% specificity. In the subgroup completing both assessments (75 cases, 54 controls), the two-stage protocol--questionnaire followed by actigraphy--yielded 68% sensitivity and 100% specificity using the dream-enactment question alone, and 73% sensitivity and 100% specificity using the four-item questionnaire. InterpretationA two-stage protocol combining questionnaire and actigraphy demonstrated high specificity and good sensitivity for detecting isolated rapid-eye-movement sleep behavior disorder in this multicenter cohort. This low-cost, scalable strategy is compatible with widely used wearable devices and warrants validation in community-based populations.

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.

BackgroundPreterm birth is associated with an increased risk for neurodevelopmental impairments, requiring brain monitoring using amplitude-integrated electroencephalography (aEEG). While established tools detect severe dysfunction (e.g., Hellstrom-Westas classification), methods for assessing mild to moderate impairments--such as Burdjalov scoring or expert-based Sleep-Wake Cycle identification--are subjective and require specialized training. Automated neonatal sleep-staging models usually rely on polysomnography from term infants, a resource-intensive method rarely feasible in NICUs, where simplified single-channel aEEG is standard. MethodsaEEG recordings from 40 neurologically healthy neonates (32-42 weeks PMA) were collected and annotated for quiet (QS) and non-quiet sleep (NQS) by an expert clinician. Signals were bandpass filtered, segmented into 30 s epochs, and cleaned using impedance thresholds. 69 temporal, spectral, wavelet, EMG-inspired, and aEEG-envelope features were extracted. The 5 most relevant features were selected for QS/NQS classification using several machine-learning models validated with leave-one-subject-out cross-validation. A partial least squares model was then trained on QS-derived features to predict postmenstrual age and assess correlations with brain maturation. ResultsThe k-Nearest Neighbors (KNN) classifier showed the best QS/NQS discrimination, with mean Cohens{kappa} = 0.69 {+/-} 0.14 for preterm (32-37 weeks PMA) and 0.48 {+/-} 0.21 for term infants. QS-derived features correlated strongly with postmenstrual age (PMA). The PLS model predicted PMA with an average error of 0.88 weeks (MSE = 1.33 weeks, r = 0.91), while the fully automated version using predicted QS segments yielded an error of 1.08 weeks (r = 0.86). ConclusionAutomated QS/NQS detection from single-channel aEEG is feasible in preterm neonates. Despite reduced accuracy in term infants, QS-derived features closely track brain maturation, supporting the potential of aEEG-based models for objective, early detection of neuromaturation delays in preterm infants

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.

RationaleObstructive sleep apnea (OSA) is linked to cardiovascular, metabolic, and cognitive morbidity. Although COVID-19 has been associated with long-term respiratory and neurological sequelae, its role in precipitating new-onset OSA remains unclear. ObjectivesTo evaluate whether SARS-CoV-2 infection increases risk of developing OSA up to 4.5 years post-infection and how risk varies by hospitalization status, demographics, comorbidities, and vaccination status. MethodsThis retrospective cohort study used electronic health records from the Montefiore Health System in the Bronx. Adults tested for SARS-CoV-2 between March 1, 2020, and August 17, 2024, were classified as hospitalized COVID+, non-hospitalized COVID+, or COVID-. Patients with prior OSA or inadequate follow-up were excluded. Inverse probability weighting adjusted for demographic, clinical, socioeconomic, and vaccination covariates. New-onset OSA was assessed using weighted Cox proportional hazards models. Secondary outcomes including hypertension, myocardial infarction, heart failure, stroke, arrhythmia, pulmonary hypertension, type 2 diabetes, and obesity were evaluated with Poisson regression. Sensitivity analysis used a pre-pandemic control cohort. ResultsAmong 910,393 eligible patients, hospitalized [HR 1.41 (95% CI 1.14-1.73)] and non-hospitalized [HR 1.33 (95% CI 1.22-1.46)] COVID+ patients had higher adjusted risk of new-onset OSA versus COVID- controls. Similar findings were observed using historical controls (n=621046). After OSA onset, hospitalized COVID+ patients had higher risks of heart failure and pulmonary hypertension, while non-hospitalized COVID+ patients had higher risk of obesity vs COVID- patients. ConclusionsSARS-CoV-2 infection is independently associated with increased risk of new-onset OSA. These findings support targeted screening in post-COVID populations.