SLEEP

Despite clear research findings showing that sleeping less than seven hours per night has an array of health consequences, over 1 in 3 American adults report sleeping less than seven hours per night. Many studies exploring the consequences of insufficient sleep are restricted to small sample sizes and are of relatively short duration due to a significant cost of gold-standard polysomnography in terms of participant burden, expense, time, and reliance on trained sleep technicians. Additionally, many studies of short sleep duration use a paradigm of experimental sleep restriction on otherwise healthy sleepers, which excludes people who chronically obtain short sleep duration over months to years. Here, we explore possible solutions to these issues by implementing a sleep extension protocol in 14 adults (average age 20.6{+/-}2.5y; +/- SD) with self-reported habitual sleep duration less than 6.5h/night. Participants completed 2 weeks of baseline monitoring (habitual short sleep duration) and then were instructed to increase time in bed to [≥]8h/night for four weeks. Sleep was monitored using wrist-actigraphy and the Dreem 2 headband, a wireless dry electrode consumer electroencephalography (EEG) device. Compared to wrist-actigraphy, the Dreem 2 shows minimal systemic skew for nights with data quality over 75% (as assigned by the Dreem algorithm). However, Bland Altman analysis shows significant random error with limits of agreement approximately +/- 70 minutes between actigraphy and the Dreem. Exploration of sleep metrics from the Dreem 2 during baseline short sleep versus sleep extension revealed an increase in total sleep time; increase in all recorded sleep stages; and no significant changes in sleep onset latency, wakefulness after sleep onset, or sleep efficiency. Although several limitations of producing high quality data were identified, the Dreem 2 headband shows promise as a home environment sleep research device. With an improvement in data quality, the Dreem headband, or another wireless consumer sleep device, has the potential to help advance the sleep field in ways that were previously inaccessible with clinical PSG.

BackgroundIsolated rapid eye movement (REM) sleep behavior disorder (iRBD) cohorts have provided novel insights in the earliest neurodegenerative processes in -synucleinopathies. Even though polysomnography remains the gold standard for diagnosis, an accurate questionnaire-based algorithm to identify eligible subjects could facilitate efficient recruitment in research. ObjectivesThis study aimed to optimize the identification of subjects with iRBD from the general population. MethodsBetween June 2020 and July 2021, we placed newspaper advertisements including the single-question screen for RBD (RBD1Q). Participants evaluations included a structured telephone screening consisting of the RBD screening questionnaire (RBDSQ) and additional sleep-related questionnaires. We examined anamnestic information predicting polysomnography-proven iRBD using logistic regressions and receiver operating characteristic curves. Results543 participants answered the advertisements and 185 subjects fulfilling in- and exclusion criteria were screened. Of these, 124 received polysomnography after expert selection and 78 (62.9%) were diagnosed with iRBD. Selected items of the RBDSQ, the Pittsburgh Sleep Quality Index, the STOP-Bang questionnaire, and age predicted iRBD with high accuracy in a multiple logistic regression model (area under the curve >80%). Comparing the algorithm to the sleep expert decision, 77 instead of 124 polysomnographies (62.1%) would have been carried out, while 63 (80.8%) of iRBD patients would have been identified. 32 of 46 (69.6%) unnecessary polysomnography examinations could have been avoided. ConclusionsOur proposed algorithm displayed high diagnostic accuracy for polysomnography-proven iRBD in a cost-effective manner and may be a convenient tool for application in research and clinical settings. External validation sets are warranted to prove its reliability.

Goal and AimsTo evaluate the performance of 6 wearable devices across 4 device classes (research-grade EEG-based headband, research-grade actigraphy, high-end consumer tracker, low-cost consumer tracker) over 3 age-groups (young: 18-30y, middle-aged: 31-50y and older adults: 51-70y). Focus TechnologyDreem 3 headband, Actigraph GT9X, Oura ring Gen3 running the latest sleep staging algorithm (OSSA 2.0), Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3. Reference TechnologyIn-lab polysomnography (PSG) with consensus sleep scoring. Sample60 participants (26 males) across 3 age groups (young: N=21, middle-aged: N=23 and older adults: N=16). DesignParticipants slept overnight in a sleep laboratory from their habitual sleep time to wake time, wearing 5 devices concurrently. Core AnalyticsDiscrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/REM) classification (devices vs. PSG). Mixed model ANOVAs for comparisons of biases across devices (within-subject), and age and sex (between-subjects). Core OutcomesThe EEG-based Dreem headband outperformed the other wearables in terms of 2-stage (kappa = .76) and 4-stage (kappa = .76-.86) classification but was not tolerated by at least 25% of participants. This was followed by the high-end, validated consumer trackers: Oura (2-stage kappa = .64, 4-stage kappa = .55-.70) and Fitbit (2-stage kappa = .58, 4-stage kappa = .45-.60). Next was the accelerometry-based research-grade Actigraph which only provided 2-stage classification (kappa = .47), and finally the low-cost consumer trackers which had very low kappa values overall (2-stage kappa < .31, 4-stage kappa < .33). Important Additional OutcomesProportional biases were driven by nights with poorer sleep (i.e., longer sleep onset latencies [SOL] and wake after sleep onset [WASO]). For those nights with sleep efficiency [&ge;]85%, the large majority of sleep measure estimates from Dreem, Oura, Fitbit and Actigraph were within clinically acceptable limits of 30 mins. Biases for total sleep time [TST] and WASO were also largest in older participants who tended to have poorer sleep. Core ConclusionThe Dreem band is recommended for highest accuracy sleep tracking, but it has price, comfort and ease of use trade-offs. The high-end consumer sleep trackers (Oura, Fitbit) balance classification accuracy with cost, comfort and ease of use and are recommended for large-scale population studies where sleep is mostly normal. The low-cost trackers, despite poor wake detection could have some utility for logging time in bed.

Study ObjectivesIn older adults, self-reported sleep measures may be inaccurate, but polysomnography (PSG) is burdensome. We assessed the performance of an electroencephalography-measuring headband (HB) or actigraphy (ACT) compared with PSG in older adults with sleep disturbances. MethodsSixty-three adults aged [&ge;]60 years who reported symptoms of insomnia and/or daytime sleepiness [&ge;]once/week completed a week-long, home-based protocol during which they wore the HB for seven nights, an actigraph for seven days and nights, and completed a one-night level II unattended PSG. For the current analysis, we compared total sleep time (TST) and wake after sleep onset (WASO) from all three devices on the PSG night. We calculated absolute differences and intraclass correlation coefficients (ICCs) for TST and WASO between HB and ACT, respectively, vs. PSG. We also evaluated the performance of the HB among subgroups of the poorest sleepers according to the presence of sleep apnea, insomnia, poor sleep quality, and periodic limb movements of sleep. Feasibility of the HB was assessed by measures of adherence (i.e., ability to use the HB over seven nights) and usability (i.e., ratings of items from the WEarable Acceptability Range [WEAR] scale). ResultsThe average age was 72.8 [standard deviation 6.6] years, 63.5% were female, and 63.5% identified as non-Hispanic White. On PSG, averages for TST and WASO were 370.1 [93] and 88.9 [63] minutes, respectively. For the HB vs. PSG, mean differences and ICCs were -11.9 minutes and 0.83 [0.74, 0.89] for TST; and -15.5 minutes and 0.65 [0.48, 0.77] for WASO. For ACT vs. PSG, mean differences for TST and WASO were larger, and ICCs showed lower levels of agreement. The HB performed well among the poorest sleepers, with ICCs >0.65 for TST and WASO. On average, participants wore the HB for 6.5 [0.8] nights, and usability was rated highly. ConclusionsThe HB demonstrated good agreement with PSG, outperforming ACT, including among the poorest sleepers. Devices like the HB might provide feasible measures of sleep that are more accurate than ACT and enhance the management of sleep health in older adults with sleep disturbances. Future research should focus on further validation of these devices in habitual sleep environments.

BackgroundBrain waves during sleep are involved in sensing and regulating peripheral glucose level. Whether brain waves in patients with diabetes differ from those of healthy subjects is unknown. We examined the hypothesis that patients with diabetes have reduced sleep spindle waves, a form of brain wave implicated in periphery glucose regulation during sleep. MethodsFrom a retrospective analysis of polysomnography (PSG) studies on patients who underwent sleep apnea evaluation, we identified 1,214 studies of patients with diabetes mellitus (>66% type 2) and included a sex- and age-matched control subject for each within the scope of our analysis. We similarly identified 376 patients with prediabetes and their matched controls. We extracted spindle characteristics from artifact-removed PSG electroencephalograms and other patient data from records. We used rank-based statistical methods to test hypotheses. We validated our finding on an external PSG dataset. ResultsPatients with diabetes mellitus exhibited on average about half the spindle density (median=0.38 spindles/min) during sleep as their matched control subjects (median=0.70 spindles/min) (P<2.2e-16). Compared to controls, spindle loss was more pronounced in female patients than in male patients in the frontal regions of the brain (P=0.04). Patients with prediabetes also exhibited signs of lower spindle density compared to matched controls (P=0.01-0.04). ConclusionsPatients with diabetes have fewer spindle waves that are implicated in glucose regulation than matched controls during sleep. Besides offering a possible explanation for neurological complications from diabetes, our findings open the possibility that reversing/reducing spindle loss could improve the overall health of patients with diabetes mellitus. FundingThis research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (ZIA ES101765).

BACKGROUNDSleep disorders pose a major global health burden and are associated with a wide range of adverse health outcomes. Polysomnography (PSG) is the gold standard for sleep assessment, but it is impractical for home-based or long-term monitoring. We investigated whether zero-burden cardiorespiratory signals, when harnessed through foundation model approaches, can enable accurate sleep assessment at scale and capture broader dimensions of multi-organ health. METHODSWe present SleepFounder, a foundation model for zero-burden sleep monitoring built upon cardiorespiratory signals. SleepFounder was developed on the largest curated multi-ethnic sleep dataset to date, comprising over 800,000 hours of recordings from 35 cohorts across the United States and China. We evaluated SleepFounder across downstream tasks ranging from conventional sleep analysis to emerging applications, including demographic profiling and multi-organ disease detection and prediction. We further conducted a real-world study using multi-center ballistocardiography (BCG) data collected with a custom-developed sleep mat system for external validation. RESULTSSleepFounder achieved strong performance across diverse downstream tasks and consistently outperformed baseline models, obtaining the best results in 14 out of 17 dataset-task pairs. For conventional sleep analysis and demographic profiling, averaged across external datasets, it achieved a Cohens Kappa of 0.671 (0.668-0.673) for five-class sleep staging, an area under the receiver operating characteristic curve (AUROC) of 0.917 (0.912-0.922) for moderate-to-severe obstructive sleep apnea detection, a mean absolute error of 6.727 (6.684-6.771) years for age prediction, and an AUROC of 0.865 (0.860-0.870) for sex classification. In multi-organ disease detection, representative AUROCs reached 0.943 (0.917-0.966) for Parkinsons disease, 0.886 (0.841-0.928) for gastroesophageal reflux disease, and 0.881 (0.831-0.922) for heart failure. Additional conditions, including high cholesterol, coronary heart disease (CHD), bipolar disorder, and chronic pain, achieved AUROCs ranging from 0.811 to 0.830 in the held-out test set, with results further validated across five external cohorts. For future disease prediction, concordance indices reached 0.838 (0.797-0.873) for CHD death and 0.837 (0.806-0.865) for cardiovascular disease death, with corresponding metrics of 0.734-0.781 for congestive heart failure, stroke, and angina. In the real-world BCG study, SleepFounder maintained 94% of its performance on average relative to prior external validations conducted on PSG-based datasets. CONCLUSIONSSleepFounder establishes a foundation model that learns from cardiorespiratory signals to enable accurate, scalable, and zero-burden sleep assessment. By linking sleep physiology with multi-organ health, it bridges clinical and home settings and demonstrates that signals traditionally used for sleep monitoring can serve as powerful biomarkers of systemic function and disease risk. These findings highlight a new paradigm for zero-burden sleep and health monitoring in real-world settings.

Sleep arousals trigger rapid autonomic shifts, yet their specific sympathetic signatures remain poorly characterized due to the mixed sympathetic-parasympathetic nature of traditional cardiovascular markers. Electrodermal activity (EDA), driven exclusively by sympathetic sudomotor pathways, offers a more direct opportunity to characterize arousal-related autonomic responses during sleep. This study quantifies the evolution of EDA-based features associated with arousal events in 100 adults using polysomnography and high-resolution EDA recordings. We implemented a time-varying frequency decomposition framework to isolate sleep-specific sympathetic components, extracting statistical and peak-based features from arousal segments and matched stable-sleep controls. Compared to controls, arousal segments exhibited robust sympathetic modulation in EDA persisting 40 seconds post-arousal. While long arousals produced robust responses, short arousals showed negligible sudomotor responses. REM and NREM sleep showed consistent feature trajectories, with greater variability during REM. The observed activation is primarily driven by clustered sympathetic bursts and amplitude enhancement rather than shifts in peak frequency. These findings establish EDA as a highly sensitive marker of sleep-related autonomic activation and provide a quantitative baseline for characterizing sympathetic responses to sleep arousals.

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.

BackgroundThe patients suffering of the rapid eye movement sleep behavior disorder (RBD) are in high risk of developing a neurodegenerative disorder, most frequently from the group of alpha-synucleinopathies, such as Parkinsons disease (PD), Dementia with Lewy Bodies (DLB) or multiple system atrophy (MSA). The definitive diagnosis of RBD is based on polysomnographic investigation. Actigraphy is much easier to perform and reflects condition in patients home environment. The aimsThe aim of this study was to find suitable biomarkers for RBD, which can be detectable by actigraphic recording. MethodsHigh resolution actigraphic recording (MotionWatch, CamNtech ltd.) and confirming polysomnographic recording was performed on 45 RBD patients, 30 patients with other sleep-related motor disorders and 20 healthy controls. Each individual file was analysed by software testing for amount of sleep (MotionWare 1.1.20) and secondly for periodic motor activity (PLMS analysis 1.0.16). The 13-item patient self-rating RBD screening questionnaire (RBD-SQ) translated to Czech language was also used for screening purposes. We used an RBD-SQ score of five points as a positive test result, as suggested by the original publication of the scale. ResultsWhen using the actigraphic sleep detection, we encountered significant differences mostly on non-dominant hand, related to sleep fragmentation - most notably increased percentage of Short immobile bouts (47.0% vs. 28.0%, p<0.0001), increased Fragmentation index (72.5 vs. 40.7, p<0.0001) and decreased percentage of Sleep efficiency (72.1% vs. 86.8%, p<0.0001)in RBD subjects compared to other sleep disorders and controls. When analyzing periodic motor activity, we also found surprisingly more periodic hand movements (p=0.028, corrected for multiple testing), but differences on lower extremities using either measurement were not significant. The discrimination function based on RBD-SQ and Short immobile bouts % could allocate correctly the RBD status in 87.6% of cases with Wilks Lambda 0.435 and p<0.0001. ConclusionIn our single-center study in patients from the Czech population, we found that actigraphic recording from upper extremities shows consistently more prominent sleep fragmentation in RBD patients compared to other sleep diagnoses or healthy controls. Actigraphy may be useful in broader screening for RBD.

BackgroundWhile accurate sleep measurement is vital for older adults, the validity of actigraphy (AG) in free-living environments remains controversial, particularly given the flexible sleep-wake schedules common in this demographic. To address this uncertainty, we assessed the accuracy of wrist AG against in-home portable electroencephalography (EEG) among community-dwelling older adults. MethodsCommunity-dwelling older adults underwent concurrent sleep monitoring using a portable EEG device and a wrist-worn AG for five consecutive nights whenever possible, with monitoring extended to up to seven nights when feasible. Key sleep parameters, including total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency, were derived from both devices. Measurement agreement was assessed using Bland-Altman plots and multilevel modeling, while reliability and accuracy were quantified via intraclass correlation coefficients (ICCs) and mean absolute percentage error (MAPE). ResultsForty-nine adults contributed 217 nights of recordings. On average, AG slightly overestimated TST and sleep efficiency and underestimated SOL and WASO compared with EEG. Single-measure ICCs were 0.73 for TST and 0.38 for WASO (0.84 and 0.55 for averages across nights), and the MAPE was 11% for TST but exceeded 50% for SOL and WASO, indicating poor accuracy for these indices. ConclusionIn community-dwelling older adults, wrist AG yielded acceptably accurate estimates of average TST, supporting its use in epidemiological monitoring of sleep duration. However, large errors for SOL and WASO indicate that portable EEG- or polysomnography-based assessment remains indispensable when precise evaluation of sleep initiation and nocturnal wakefulness is required.

BackgroundPolysomnography (PSG) is the gold standard for diagnosing and monitoring sleep disorders, however, it is time-consuming and costly, as the application of the equipment can only be done by trained sleep technicians and the test must be conducted within a sleep laboratory. In this study we assessed the performance of the first wireless patch-based PSG system, the Onera Sleep Test System (STS), which can be applied by the patient and performed outside of the sleep laboratory in settings such as the home. To achieve this, sleep stage and physiological data from the Onera STS were compared to gold standard in-lab PSG. Materials and methodsThe recordings were unsupervised to simulate a home-use environment. Epoch-by-epoch agreement was assessed by calculating sensitivity, specificity, accuracy, and Cohens kappa coefficient. Pearsons correlation coefficients were calculated for multiple sleep parameters to measure the level of entire night agreement. ResultsSubstantial agreement with a Cohens kappa of 0.69 across all sleep stages was determined, which reached 0.81 when Stage N1 was removed from the analysis. A high accuracy, specificity, and sensitivity were found for wake N2, N3 and REM. Although specificity (95.25%) and accuracy (89.62%) were high for N1, sensitivity was low (27.19%). Sleep parameters calculated by sleep stage transitions, apnea hypopnea index and oxygen desaturation index, showed strong correlations. ConclusionsThe Onera STS provides comparable clinical information to traditional PSG. Moreover, the application time was reduced by 77% which reduces the overall costs of PSG. These results open the possibility for PSG studies to be performed efficiently outside of the sleep laboratory at a larger scale, thus improving access for patients.

Sleep architecture is essential for metabolic and cardiovascular health, yet the impact of day-to-day dietary variation on objective sleep physiology remains unclear. Using 4.8 thousand person-nights with real-time dietary logs and multi-stage wearable sleep recordings, we examined how prior-day nutrition relates to next-night sleep under free-living conditions. Higher fiber density was associated with increased restorative sleep, including +0.59 pp deep sleep, +0.76 pp REM sleep, -1.35 pp light sleep, and -1.14 bpm lower mean nocturnal heart rate. Greater plant diversity and higher whole-plant food intake were similarly associated with lower nocturnal heart rate (-0.72 to -0.94 bpm). Meal-timing behaviors primarily influenced sleep duration, sleep-onset latency, and autonomic tone: heavier evening meals were associated with +7.7 min longer total sleep time and +0.73 bpm higher nocturnal heart rate. In contrast, short-term variation in macronutrient energy distribution and micronutrient consumption showed no robust associations with sleep outcomes. When analyses were restricted to more extreme dietary contrasts, effect magnitudes increased while remaining directionally consistent. These findings indicate that routine daily dietary choices, particularly plant-forward composition and meal timing, have immediate and measurable effects on objective sleep architecture.

Sex differences in sleep architecture are well-documented, with females experiencing longer total sleep time (TST), more slow wave sleep (SWS) and shorter Rapid Eye Movement (REM) sleep duration than males. Although studies imply that sex hormones could affect sleep, effects of exogenous sex hormones on sleep architecture remain unclear. This study examined sleep architecture changes in transgender individuals after 3 months of gender-affirming hormone therapy (GAHT). We assessed sleep architecture in 73 transgender individuals: 38 transmasculine participants who started using testosterone and 35 transfeminine participants who started using estrogens and anti-androgens. Sleep architecture was measured before GAHT and after 3 months of GAHT for 7 nights using an ambulatory single-electrode sleep EEG device. Changes in sleep architecture were analyzed using linear mixed models, and non-normally distributed outcomes were log-transformed and reported as percentages. In transmasculine participants, SWS decreased by 7 minutes (95% CI: -12; -3) and 1.7% (95% CI: -3%; - 0.5%), REM sleep latency decreased by 39% (95% CI: -52%; -22%) and REM sleep duration increased by 17 minutes (95% CI: 7; 26) after 3 months of GAHT. In transfeminine participants, sleep architecture showed no significant changes after 3 months of GAHT. Sleep architecture changes after three months of masculinizing GAHT in line with sleep in cisgender males, while it shows no changes after feminizing GAHT. The sex-specific nature of these changes raises new questions on sex hormones and sleep. Future research should focus on studying possible underlying neural mechanisms and clinical consequences of these changes. Statement of significanceSleep architecture shows differences between men and women, with women showing longer sleep, longer slow wave sleep and shorter REM sleep than men. Rodent research indicates that sex hormones can alter sleep architecture, but research on sex hormones and sleep architecture in humans is still lacking. This study examined effects of three months of gender-affirming hormone use in transgender individuals. Results show that testosterone use in persons assigned female at birth resulted in sleep architecture changes similar to cisgender males, whereas estradiol- and anti-androgen use by persons assigned male at birth did not change sleep architecture. These novel findings indicate that sex hormones could change sleep architecture in a sex-specific manner, warranting further studies into causal mechanisms underlying these changes.

Background and ObjectivesSleep has been associated with cognition and risk of dementia. However, sleep is a highly complex and multi-dimensional state, and there is uncertainty about which aspects of sleep are most relevant to cognitive performance and dementia risk. We applied a data-driven approach to identify clusters of sleep variables that reflect meaningful sleep composites and examined their association with cognitive performance and dementia risk. MethodsData from the Sleep and Dementia Consortium, consisting of 5 US population-based cohorts were utilized. Participants had methodologically consistent, home-based polysomnography, self-report habitual sleep, neuropsychological assessments, and dementia risk surveillance. The pooled cognitive analysis included 5,958 participants aged [&ge;]45 years, and the incident dementia analysis included 5,471 participants aged [&ge;]60 years. A cluster around latent variables analysis was used to derive 9 latent sleep composites from 44 sleep metrics. Global cognitive composite z-scores were derived from principal component analysis. Linear regression models were used to assess associations between sleep composites and cognitive performance. Cox proportional hazard models assessed associations between sleep composites and incident dementia. ResultsMean (SD) age was 70 {+/-} 11 and 74 {+/-} 12 years for the cognitive and dementia analysis, respectively. There were 1,134 incident dementia cases (median follow-up time of 5-19 years). 9 sleep composites were identified, together explaining 49% of the total variance in the original 44 sleep metrics: Sleep quantity and efficiency, sleep fragmentation, light NREM predominance, N3 predominance, spindle number and duration, REM sleep bouts, respiratory disturbances, slow oscillation-spindle coupling and spindle amplitude. Of these, composites reflecting greater sleep quantity and efficiency (i.e., longer and more consolidated sleep; pooled {beta} per one-unit change in composite, 0.03; 95% CI: 0.004 - 0.06; p=0.033) and stronger slow oscillation-spindle coupling (pooled {beta}, 0.04; 95% CI: 0.003 - 0.07; p=0.039) were associated with better global cognition. However, no significant associations were identified between the 9 sleep composites and dementia risk. DiscussionOur data-driven approach identified longer, more consolidated sleep and stronger slow oscillation-spindle coupling as the composites of sleep most strongly related to cognitive performance. These composites may be useful in guiding further investigations of sleep-brain health relationships.

IntroductionInvestigations of sleep homeostasis often involve tightly controlled experimental sleep deprivation in service of understanding mechanistic physiology. The extent to which the deep sleep response to recent sleep loss occurs in naturalistic settings remains under-studied. We tested the hypothesis that a homeostatic increase in deep sleep occurs on the night following occasional short duration nights that arise in naturalistic settings. MethodsWe analyzed sleep staging data in participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data. The analysis group included n=44,564 participants with at least 30 nights of sleep staging data from Apple Watch, from November 2022 to May 2023, totaling over 5.3 million nights. ResultsShort nights of sleep that were >=2 hours shorter than each participants median sleep duration occurred at least once in 92.9% of the cohort, most often in isolation (<7% of instances were consecutive short nights), and with a median duration of just over 4 hours. We observed that the amount of deep sleep increased on the subsequent night in proportion to the amount of sleep loss on the preceding short night, in a dose response manner for short night definitions ranging from 30 minutes to >=3 hours below the within-participant median sleep duration. Focusing on short nights that were at least 2 hours below the median duration, we found that 58.8% of participants showed any increase in subsequent deep sleep, with a median increase of 12% (absolute increase of 5 minutes). In addition, the variability in deep sleep after short nights markedly increased in a dose response manner. The deep sleep homeostatic response showed little correlation to sleep duration, timing, consistency, or sleep stages, but was inversely correlated with deep sleep latency (Spearman R = -0.28). ConclusionThe results provide evidence for homeostatic responses in a real-world setting. Although the deep sleep rebound amounts are modest, naturalistic short nights are a milder perturbation compared to experimental deprivation, and reactive behaviors potentially impacting sleep physiology are uncontrolled. The marked increase in variability of deep sleep amount after short nights may reflect unmeasured reactive behaviors such as caffeine or napping, which exert opposing pressures on deep sleep compared to the homeostat. The findings illustrate the utility of longitudinal sleep tracking to assess real-world correlates of sleep phenomenology established in controlled experimental settings.

BackgroundRecently observational studies have consistently shown an association between gastroesophageal reflux disease (GERD) and sleep disorders. In this study, Mendelian randomization (MR) analysis was performed to determine the genetic causal relationship between GERD and the risk of sleep disorders. MethodsThe summary statistics of GERD and sleep disorders were obtained through large-scale genome-wide association studies (GWAS). In addition to exploring sleep disorders, a deeper analysis was conducted on some major categories of sleep disorders such as sleep apnoea and insomnia. Various MR analysis methods including inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode were performed, and the results of IVW were taken as the primary results. In addition, sensitivity analyses including heterogeneity test and pleiotropy test were also performed to test the robustness of the MR results. ResultsAfter removing the ineligible SNPs and the palindromic SNPs, IVW detected a significant effect of GERD on sleep disorders (OR = 1.436, 95% CI: 1.309-1.576, p = 2.099E-14) and sleep apnoea (OR = 1.486, 95% CI: 1.341-1.647, p = 4.409E-14). However, there was no genetic causality in the effect of GERD on insomnia (IVW OR = 1.146, 95% CI: 0.877-1.498, p = 0.319). Furthermore, the heterogeneity test and pleiotropy test found no evidence of bias, which indicated the results were robust. ConclusionsOur study found that the presence of GERD increased the risk of sleep disorders and sleep apnoea, but not the risk of insomnia. Further research is needed to identify the specific mechanisms of causal relationships.

INTRODUCTIONAgitation is a common and burdensome neuropsychiatric symptom in dementia that fluctuates from day to day, but objective tools for short-term risk stratification are limited. We examined whether nocturnal physiological signals from unobtrusive under-mattress sensors predict next-day daytime agitation and whether associations differ for agitation occurrence versus severity. METHODSWe extracted cardiorespiratory, movement, and sleep-proxy features from two long-term care cohorts (N=55; 333 nights) and one external home-monitoring cohort (N=18; 803 nights). A two-part mixed-effects framework was used to model next-day agitation episodes. RESULTSLower nocturnal respiratory rate and greater activity instability independently predicted higher odds of next-day agitation occurrence. Associations were stronger for motor than verbal agitation. Respiration-related predictors were validated externally. Conversely, no nocturnal features significantly predicted agitation severity. DISCUSSIONPassive sleep monitoring identified reproducible, physiologically interpretable markers of next-day agitation occurrence, supporting the potential of under-mattress sensing for short-term risk stratification and more proactive dementia care.

Isolated REM Sleep Behaviour Disorder (iRBD) represents an early stage of -synucleinopathy, often preceding Parkinsons disease, dementia with Lewy bodies or Multiple System Atrophy. Growing evidence shows that iRBD patients are not only characterized by sleep disturbances but also dysautonomia. However, whether the brain-body coupling reflecting the interaction between neural and peripheral physiological activity is altered in iRBD remains unknown. Leveraging whole-night polysomnography data from thirty-six participants, we quantified heart-brain coupling via heartbeat evoked potentials (HEPs) across wakefulness, NREM, and REM sleep. HEPs were compared between individuals with isolated REM sleep behavior disorder (iRBD; n = 13) and healthy controls (HC; n = 23). In addition, heart rate variability (HRV) and other ECG-derived features were analyzed. During wakefulness, iRBD patients showed altered HEP compared to HC, between 230 and 445 ms after the R-peak. over frontal regions. The duration of RBD symptoms was positively associated with the magnitude of these HEP alterations. HEP alterations were specific to wakefulness, as no differences were observed during NREM or REM sleep. Additional analyses showed that HEP alterations in iRBD during wakefulness were not driven by ECG differences. We corroborate previous findings of altered heart rate variability (HRV) in iRBD patients during REM sleep. We demonstrate that brain-body coupling, as indexed by HEP, is altered during wakefulness in iRBD patients. These diurnal HEP may represent a novel quantitative biomarker of iRBD, and could, in the future, serve as a marker for the phenoconversion to -synucleinopathy.

ObjectivesLight is a key regulator of the human circadian system, yet conventional photopic illuminance does not reflect the spectral sensitivity of intrinsically photosensitive retinal ganglion cells (ipRGCs). Melanopic equivalent daylight illuminance (EDI) provides a more biologically relevant metric. We examined whether melanopic EDI better predicts real-world sleep outcomes than photopic illuminance and whether these associations differ by chronotype. MethodsFifty-nine young adults wore actigraphs for seven days to monitor light exposure and sleep. Light exposure was quantified as photopic illuminance and melanopic EDI. Chronotype was classified using the Morningness-Eveningness Questionnaire (MEQ) as non-evening type (nET) and evening type (ET). Hierarchical regression assessed the added predictive value of melanopic EDI, and linear mixed-effects models examined temporal associations and chronotype effects. ResultsMelanopic EDI improved the prediction of sleep outcomes across all time windows. The largest delta R2 occurred for sleep quality in the afternoon (1.55%) and at night (1.00%), deep sleep at night (0.93%), and fragmentation in the afternoon (0.51%). Nighttime exposure (01:00-03:00) was associated with poorer sleep quality in both chronotypes (nET: p=0.018 (01:00), p=0.015 (03:00); ET: p=0.043 (01:00)). Morning exposure (10:00) improved sleep quality (p=0.002) and reduced sleep fragmentation (p=0.031) in nET, whereas evening exposure (18:00-24:00) was associated with lower sleep quality (p=0.002) and greater sleep fragmentation (p=0.027) in ET. ConclusionsMelanopic EDI is more sensitive to sleep than photopic illuminance. Morning light benefited nET, whereas evening light disrupted sleep in ET, supporting melanopic metrics and chronotype-based light strategies to improve sleep health. Statement of SignificanceLight, especially short-wavelength blue light, exerts non-visual effects on human physiology through ipRGCs that synchronize the circadian system. However, conventional light metrics, such as photopic illuminance, do not capture these spectral characteristics, limiting their ability to predict physiological outcomes. Using real- world data, this study demonstrates that melanopic EDI is a more sensitive predictor of sleep quality and structure than photopic illuminance. Temporal and chronotype-specific analyses showed that morning melanopic light improves sleep in nET, whereas evening exposure disrupts sleep in ET. These findings address a gap by demonstrating the ecological validity and chronotype-dependent impact of melanopic-sensitive light metrics. These results underscore the need to incorporate melanopic metrics and chronotype considerations into personalized sleep hygiene strategies, clinical recommendations, and public health guidelines, and point toward developing targeted interventions that leverage spectral light properties to optimize circadian and sleep health.

Background and ObjectivesNarcolepsy Type 1 (NT1) is a neurological disorder caused by hypocretin deficiency, leading to excessive daytime sleepiness and cataplexy. This study characterized dynamic functional connectivity (dFC) states in NT1 patients, acute sleep-deprived (SD) individuals, and healthy controls, and explored how these states relate to clinical measures of sleepiness and arousal. MethodsIn this study, resting-state co-fluctuation analysis was employed to identify recurring brain states and compare group differences in state dwell time, transition probabilities, and interaction strength. Associations between dFC properties and clinical metrics (Epworth Sleepiness Scale [ESS] scores, mean sleep latency from MSLT) were also investigated. ResultsFive distinct resting-state co-fluctuation states were identified. NT1 patients showed significantly longer mean dwell time and higher fraction rate in State 3, characterized by synchronized activity between the salience/ventral attention network (SN/VAN) and sensorimotor network (SMN) with antagonistic co-fluctuations to the visual network (VIS), compared to both SD and control groups. They also exhibited increased reciprocal transition probabilities between State 3 and State 5. Group-specific differences in co-fluctuation strength were observed across multiple states, with NT1 showing distinct alterations in interactions involving the striatum, limbic system, and attentional networks. Moreover, the fraction rate of State 5 negatively correlated with ESS scores, while the fraction rate of State 3 negatively correlated with mean sleep latency from MSLT in NT1 patients, indicating that increased occupancy of certain states was associated with less subjective sleepiness and greater arousal instability. ConclusionThese findings highlight the role of chronic hypocretin-mediated arousal failure versus acute homeostatic sleep pressure in shaping network co-fluctuation patterns, characterized by thalamocortical disconnection, cortical dysregulation, and enhanced striatal-limbic connectivity. This state might be specific to hypocretin deficiency and suggests that dFC states may serve as potential biomarkers for sleep-wake disorders.