Journal of Sleep Research

TitleFactors influencing English general practitioners referrals to specialist sleep services: a qualitative study using the COM-B model ObjectivesThis study explored the factors influencing access to sleep services for individuals with symptoms of OSA and narcolepsy, from the perspective of general practitioners (GP). MethodsA qualitative interview study was conducted with GPs within three areas of England: South London, East Midlands or South West England to explore their views on factors influencing referrals to specialist sleep services. The semi-structured interviews were conducted between November 2024 and April 2025 using an interview guide informed by published research and the COM-B model of behaviour change; this model proposed that Capability (C), Opportunity (O), and Motivation (M) are needed for behaviour (B) change to occur. Data were analysed using exploratory thematic analysis informed by the COM-B model using an iterative approach. ResultsWe conducted 31 interviews, mostly online, with one conducted face-to-face. Our data suggest that the most important factors shaping referral to sleep services are limited capacity of NHS sleep services, limited referral pathways for narcolepsy, inflexible referral pathways for OSA, and limited knowledge of narcolepsy. ConclusionsThis qualitative study with GPs in England highlights that, although sleep disorders are a common concern, the current healthcare system provides limited support for GPs in managing these conditions. Fundamental sleep medicine service reforms are needed to improve referral pathways. These reforms should be guided by data-driven research that assesses current services in relation to population health needs and evaluates the potential health and economic benefits of expanding service capacity.

IntroductionThis study evaluated the performance of a wrist-worn wearable, Verily Study Watch (VSW), in detecting key sleep measures against polysomnography (PSG). MethodsWe collected data from 41 adults without obstructive sleep apnea or insomnia during a single overnight laboratory visit. We evaluated epoch-by-epoch performance for sleep versus wake classification, sleep stage classification and duration, total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings (NAWK). Performance metrics included sensitivity, specificity, Cohens kappa, and Bland-Altman analyses. ResultsSensitivity and specificity (95% CIs) of sleep versus wake classification were 0.97 (0.96, 0.98) and 0.70 (0.66, 0.74), respectively. Cohens kappa (95% CI) for 4-class stage detection was 0.64 (0.18, 0.82). Most VSW sleep measures had proportional bias. The mean bias values (95% CI) were 14.0 minutes (5.55, 23.20) for TST, - 13.1 minutes (-21.33, -6.21) for WASO, 2.97% (1.25, 4.84) for SE, -1.34 minutes (-7.29, 4.81) for SOL, 1.91 minutes (-8.28, 11.98) for light sleep duration, 5.24 minutes (-3.35, 14.13) for deep sleep duration, and 6.39 minutes (-0.68, 13.18) for REM sleep duration. Mean and median NAWK count differences (95% CI) were 0.05 (-0.42, 0.53) and 0.0 (0.0, 0.0), respectively. DiscussionResults support applying the VSW to track overnight sleep measures in free-living settings. Registered at clinicaltrials.gov (NCT05276362).

Although not as prominent as in other animals, also humans experience seasonal variations in for example sleep duration and circadian processes. These variations are likely primarily driven by changes in photoperiod length. Anecdotally, a relevant number of people report experiencing fatigue and low energy levels particularly during spring - at least in Germany, Switzerland, and Austria. Thus, this phenomenon is commonly referred to as "spring fatigue". However, scientific evidence for such a seasonal syndrome is largely missing. We thus investigated temporal variations in fatigue, daytime sleepiness, insomnia symptoms, and sleep quality through an online survey including repeated (i.e., every six weeks) assessments of the same individuals over the course of one year. We hypothesised that fatigue and daytime sleepiness would be higher during shorter photoperiods. We further expected lower sleep quality and more severe insomnia symptoms under shorter photoperiods. Additionally, we explored variations with photoperiod change, across months, and seasons. Hypotheses were tested using Bayesian linear mixed-effects models. The study and analyses were pre-registered. Between April 2024 and September 2025, 418 adults (80% women) completed at least two assessments. Nearly half of participants (47 %) reported experiencing spring fatigue. However, repeated assessments across one year showed no evidence for seasonal or monthly variations in fatigue, sleepiness, insomnia symptoms, or sleep quality. Fatigue during day-to-day activities decreased with longer photoperiods but was independent of photoperiod change. Overall, the results provide evidence against spring fatigue as a genuine seasonal phenomenon. The discrepancy between high self-reports of the phenomenon and stable longitudinal patterns suggests that spring fatigue may reflect cultural labelling and result from cognitive-perceptual biases rather than reflecting a genuine seasonal syndrome.

ImportanceSleep-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. ObjectiveTo evaluate associations between commonly used sleep questionnaires and cognitive impairment among midlife primary care patients. Design, Setting, and ParticipantsCross-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. ExposuresFive 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 MeasuresThe 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. ResultsAmong 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 RelevancePoor 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. Key PointsO_ST_ABSQuestionC_ST_ABSAmong commonly used brief sleep questionnaires, which measure, if any, best identifies midlife primary care patients at risk of early cognitive vulnerability? FindingsIn this cross-sectional study of 646 primary care patients aged 35-64 years, poor multidimensional sleep health assessed using the RU-SATED questionnaire was the only sleep-wake disturbance phenotype consistently associated with cognitive impairment across two cognitive measures (NIH Toolbox Cognitive Battery and Montreal Cognitive Assessment). MeaningBrief multidimensional sleep health screening may help identify midlife adults with sleep-related early cognitive vulnerability in primary care and may represent a potential target for sleep-focused interventions to promote long-term brain health.

Sleep-onset insomnia is widespread in modern society, and many individuals turn to music to improve their sleep. While clinical studies have shown that music can positively affect sleep quality, the impact on sleep initiation remains unclear. Furthermore, there is limited knowledge on the mechanisms by which music may facilitate sleep. Here, we investigated whether music can facilitate sleep onset and if the effect is related to brain waves synchronizing to the slow beat of the sleep music. We recorded participants with sleep-onset insomnia (N=53) during a 30-minute afternoon rest using electroencephalography (EEG). Participants were randomly divided into two groups. Twenty-four participants listened to music chosen from a sleep music playlist while resting, and 29 rested in silence. We evaluated the transition from wakefulness towards sleep with the delta-alpha ratio of the EEG. To assess neural synchronization to the beat of the music, we used an EEG frequency tagging approach. We found a higher degree of transition towards sleep in the music group compared to silence over the 30-minutes resting period. Furthermore, higher beat stability in the music was reflected in stronger neural frequency tagging at the musical beat. However, the analyses showed no relationship between sleep initiation and neural synchronization to the beat. In sum, our results revealed that music has a positive effect on sleep initiation and that there is neural synchronization to naturalistic sleep music with a steady beat, but we found no indication that this neural synchronization is the central mechanism driving enhanced sleep initiation with music.

Cross sectional and interventional studies have demonstrated that sleep has a significant impact on waking brain function including alertness and cognitive performance. Few studies have assessed whether spontaneous night-to-night variation in sleep associates with variation in brain function within an individual. How this compares to the between-individual variation in sleep and cognition and their associations also remains largely unknown. These questions are of particular interest in the context of ageing because both sleep and cognitive abilities are altered in ageing. Furthermore, older people have been reported to be less sensitive to sleep loss. Here we investigated the relationship between sleep and cognition by quantifying associations between intraindividual variation in sleep and cognition as well as associations between interindividual variation in sleep and cognition in 35 cognitively intact older adults (70.8 {+/-} 4.9 years; mean {+/-} SD; 14 females) living in the community. Subjective and actigraphic sleep measures and daily digital assessments of cognition (nine cognitive tests; 19 variables) were obtained over a two-week period. The cognitive test battery probed a wide range of cognitive functions including reaction time, working memory, attention, and problem solving. Principal Component Analysis (PCA) identified four principal sleep components which were labelled Sleep Duration, Sleep Efficiency, Subjective Sleep Quality, and Nap-Effect. Mixed model analyses were conducted with mean and deviation-from-the-mean cognitive variables to quantify how inter- and intra-individual variation in sleep associated with inter and intra individual variation in cognition. Longer sleep duration was associated with faster reaction times in both the inter- and intra-individual analyses and with reduced errors in the inter-individual analyses. Higher sleep efficiency was associated with faster reaction times in both the intra- and inter-individual analyses. By contrast, aspects of cognition relating to learning, visual memory, verbal reasoning, and verbal fluency did not associate with sleep. The data show that in older people some aspects of waking function are sensitive to normal variation in sleep duration and efficiency which implies that interventions that target these aspects of sleep may be beneficial for waking function in ageing.

Sleep disorders are rising globally, but their lifestyle causes remain unclear. We recorded sleep-wake patterns via actigraphy from 1036 Orang Asli adults across 12 communities in Peninsular Malaysia undergoing market integration, marked by changes in permanent infrastructure (electricity and housing), digital technologies (smartphones), and labor practices (i.e., wage labor). We evaluated associations with sleep timing (onset, offset and regularity), quality (nighttime awakenings and waking after sleep onset) and quantity (sleep duration), while accounting for age and sex. Delayed and destabilized sleep timing was observed in communities with powerline access, also resulting in shorter sleep duration; paradoxically, it also improved sleep quality, suggesting increased homeostatic pressure. Age and sex were strong and consistent predictors of sleep variation: older adults had earlier, shorter, and more consistent, consolidated sleep patterns. Men displayed later and shorter sleep patterns than women, likely reflecting gendered divisions of labor among the Orang Asli. Despite averaging relatively few hours slept (6 hrs), Orang Asli exhibited relatively efficient sleep, potentially challenging the notion that longer sleep is universally beneficial. These findings underscore the complex interplay of biology, ecology, and culture in shaping sleep and circadian rhythms. SignificanceA comprehensive cross-sectional study of sleep across a pronounced lifestyle gradient among Malaysias Indigenous Orang Asli populations reveals new insights into the drivers of human sleep and circadian rhythms. Lifestyle changes with market integration, particularly access to electricity, resulted in delayed bedtime and shortened sleep duration, yet enhanced sleep consolidation. Consistent with cross-cultural evidence, aging resulted in earlier bedtimes, earlier rising times, and less sleep. Our findings contribute to debates about the adaptability of human circadian rhythms and challenge universal models of optimal sleep duration.

BackgroundSocial jet lag (SJL), the discrepancy timing between work nights and free nights, reflects schedule-related circadian misalignment. Time-stamped CPAP adherence records may provide objective, longitudinal estimates of sleep timing and could augment conventional CPAP reports by adding information on sleep regularity and weekday-weekend misalignment. ObjectivesTo quantify CPAP-derived SJL in two independent clinical cohorts, characterize its behavioral correlates and age-related patterns, and assess cross-site reproducibility. MethodsWe analyzed CPAP-derived sleep timing in patients from Rutgers-RWJ Health (RWJ, N = 1,437) and Hackensack Meridian Health (HMH, N = 1,510) with at least 31 valid nights and at least one valid work night and free night. Mid-sleep on work nights (MSW) and free nights (MSF) was estimated using circular statistics. SJL was defined as the absolute circular difference between MSF and MSW and categorized as none (<1 h), moderate (1-2 h), or severe ([&ge;]2 h). Sleep duration, free-night rebound, age-stratified prevalence, and cross-site differences were evaluated using nonparametric and categorical tests. ResultsSJL was right-skewed at both sites, with median values below 0.5 h at RWJ and HMH. SJL >1 h was present in 21.2% and 16.4% of patients, respectively; severe SJL occurred in 4.0% and 2.8%. Moderate and severe SJL were associated with shorter work-night sleep and greater free-night rebound, consistent with weekday restriction and weekend compensation. SJL prevalence and variability were highest in younger and middle-aged adults, particularly those aged 26-50 years, and declined markedly after age 65. Core timing phenotypes, including MSW, MSF, and free-night rebound, were highly reproducible across sites despite modest differences in absolute sleep duration and overall SJL prevalence. ConclusionsIn CPAP-treated cohorts, SJL is common but usually modest, is associated with weekday sleep restriction and free-night rebound, and declines substantially with age. These findings support the use of routinely collected CPAP data as a scalable, low-burden source of device-anchored circadian screening phenotypes. CPAP-derived SJL may augment standard adherence reports by helping identify patients who warrant further behavioral, circadian, or activity-based assessment.

Co-sleeping with pets or children is common, yet its effects on sleep, cardiorespiratory physiology, and behavioral outcomes are understudied. We examined within-person associations between co-sleeping with a dog, cat, or child and sleep characteristics, measures of cardiorespiratory physiology, and next-day physical activity in 1,649,083 person-days from 11,733 adults wearing the WHOOP wearable device. Participants reported nightly co-sleeping via a daily journal in the devices companion smartphone application, and linear mixed-effects models compared nights with and without co-sleeping within the same individual. Co-sleeping was associated with modest improvements in cardiorespiratory physiology, including lower resting heart rate (0.8-1.1 beats/min), lower respiratory rate (0.04-0.07 breaths/min), and higher heart rate variability (1.41-1.95 ms). Sleep outcomes were mixed, with longer sleep duration (5.5-9.6 min) but more disturbances (0.36-0.40 instances) and slightly less restorative sleep (0.23-1.17%). Associations were generally consistent across groups, although child co-sleeping showed greater sleep disruption. Next-day physical activity was higher following dog and cat co-sleeping (7.6 and 7.4 intensity-weighted min, respectively) but lower following child co-sleeping (2.6 intensity-weighted min). Although effect sizes were small ({beta} range: 0.008-0.045), findings suggest that co-sleeping is associated with a trade-off between modest cardiorespiratory benefits and mild sleep disruption, indicating that co-sleeping decisions may be driven more by personal and contextual factors than by concerns about physiologic impact.

Behavioural responses to COVID-19 lockdown will define the long-term impact of psychological stressors on sleep and brain health. Here we tease apart factors that help protect against sleep disturbance. We capitalise on the unique restrictions during COVID-19 to understand how time of day of daylight exposure and outside exercise interact with chronotype and sleep quality. 3474 people from the UK (median age 62, range 18-91) completed our online SleepQuest Study between 29th April and 13th May 2020 - a set of validated questionnaires probing sleep quality, depression, anxiety and attitudes to sleep alongside bespoke questions on the effect of COVID-19 lockdown on sleep, time spent outside and exercising and self-help sleep measures. Significantly more people (n=1252) reported worsened than improved sleep (n=562) during lockdown (p<0.0001). Factors significantly associated with worsened sleep included low mood (p<0.001), anxiety (p<0.001) and suspected, proven or at risk of COVID-19 symptoms (all p<0.03). Sleep improvement was related to the increased length of time spent outside (P<0.01). Older peoples sleep quality was less affected than younger people by COVID lockdown (p<0.001). Better sleep quality was associated with going outside and exercising earlier, rather than later, in the day. However, the benefit of being outside early is driven by improved sleep in owl (p=0.0002) and not lark (p=0.27) chronotype, whereas, the benefit of early exercise (inside or outside) did not depend on chronotype. Defining the interaction between chronotype, mental health and behaviour will be critical for targeted lifestyle adaptations to protect brain health through current and future crises.

Study Objectives. Sleep characteristics are often estimated using self-reports, which may differ from objective measurements, a phenomenon known as sleep discrepancy. However, the factors associated with the degree of sleep discrepancy remain poorly understood. Methods. In the current study, a large healthy participant sample of the Budapest Sleep, Experiences and Traits Study (total N=267, 1899 nights) completed a 7-day protocol including mobile EEG recordings and sleep diaries, and also provided questionnaire-based reports of habitual sleep. We compared analogous sleep metrics from these three modalities, and used cross-validated LASSO regression to investigate demographic, psychological and lifestyle-related factors associated with increased sleep discrepancy across all three modalities, at both between- and within-participant levels. Results. Daily diaries estimated EEG-based sleep timing accurately (mean r=0.83), but were less accurate for sleep onset latency and quality. In contrast, questionnaire reports of habitual sleep provided inaccurate measures of even sleep timing (mean r=0.49) and considerably misestimated sleep timing and duration. Insomnia and depressive symptoms, napping, co-sleeping and personality traits were associated with increased sleep discrepancy. Conclusion. In healthy adults, questionnaires about habitual sleep provide only moderately accurate and biased estimates of actual sleep. Daily diaries provide considerably more accurate estimates, but sleep onset latency and physiological sleep quality is estimated by all self-reports less accurately than sleep timing. Sleep discrepancy is also present in healthy participants, it is particularly and its degree is affected by non-pathological characteristics. Long-term monitoring by daily diaries or wearables should be preferred to self-report questionnaires to measure sleep.

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

Study objectivesSleep disruption is a growing health problem, affecting a significant portion of the adult population worldwide. Insufficient sleep has various short- and long-term consequences, including an elevated risk of cardiovascular and metabolic diseases. Previous research has demonstrated that resistive respiratory muscle training (RMT) can enhance both sleep quality and cardiovascular health in individuals diagnosed with obstructive sleep apnea, underscoring its efficacy as a non-pharmacological therapeutic strategy for this patient group. However, the effects of RMT on sleep and cardiovascular parameters have not been investigated in non-clinical populations. MethodsThis prospective study investigated the effects of combined inspiratory and expiratory RMT (cRMT) on sleep parameters and cardiovascular biometrics, specifically heart rate variability (HRV), in a non-clinical adult cohort. Utilizing a wearable device for remote data collection, this randomized controlled trial included 67 participants divided into good and poor sleeper groups based on historical sleep data. During a five-week intervention period, participants in the intervention group underwent cRMT using a Breather Fit device, while control group participants did not receive the intervention. ResultsStudy findings demonstrate a significant increase in overnight HRV metrics during the intervention period compared to the baseline, indicating improved autonomic cardiac function. However, no significant changes were observed in any parameters of sleep quality. ConclusionThese results suggest that cRMT may enhance cardiovascular health by improving autonomic function in non-clinical populations without directly affecting sleep quality. This study underscores the potential of RMT as a non-pharmacological intervention to improve cardiovascular health, warranting further investigation in future studies. Brief summaryO_LICurrent Knowledge/Study Rationale: Insufficient, disrupted, or ineffective sleep is prevalent and can increase the risk of developing cardiovascular disease (CVD). While respiratory muscle training has been found effective in improving sleep and cardiovascular parameters in sleep apnea, its effect in healthy people with or without sleep issues is unknown. C_LIO_LIStudy Impact: This study demonstrates significant benefits of RMT on CVD metrics including HR and HRV, indicating improved autonomic cardiac function. These findings highlight the potential of RMT as a non-pharmacological intervention to improve cardiovascular health. C_LI

Polysomnography (PSG) is the gold standard for recording sleep. However, the standard PSG systems are bulky, expensive, and often confined to lab environments. These systems are also time-consuming in electrode placement and sleep scoring. Such limitations render standard PSG systems less suitable for large-scale or longitudinal studies of sleep. Recent advances in electronics and artificial intelligence enabled wearable PSG systems. Here, we present a study aimed at validating the performance of ZMax, a widely-used wearable PSG that includes frontal electroencephalography (EEG) and actigraphy but no submental electromyography (EMG). We analyzed 135 nights with simultaneous ZMax and standard PSG recordings amounting to over 900 hours from four different datasets, and evaluated the performance of the headbands proprietary automatic sleep scoring (ZLab) alongside our open-source algorithm (DreamentoScorer) in comparison with human sleep scoring. ZLab and DreamentoScorer compared to human scorers with moderate and substantial agreement and Cohens kappa scores of 59.61% and 72.18%, respectively. We further analyzed the competence of these algorithms in determining sleep assessment metrics, as well as shedding more lights on the bandpower computation, and morphological analysis of sleep microstructural features between ZMax and standard PSG. Relative bandpower computed by ZMax implied an error of 5.5% (delta), 4.5% (theta), 1.6% (alpha), 0.5% (sigma), 0.8% (beta), and 0.2% (gamma), compared to standard PSG. In addition, the microstructural features detected in ZMax did not represent exactly the same characteristics as in standard PSG. Besides similarities and discrepancies between ZMax and standard PSG, we measured and discussed the technology acceptance rate, feasibility of data collection with ZMax, and highlighted essential factors for utilizing ZMax as a reliable tool for both monitoring and modulating sleep.

Study ObjectivesHigh-frequency EEG activity during sleep (cortical hyperarousal), is a transdiagnostic feature across psychiatric disorders, including nightmare disorder. It is discussed as a target of intervention; however, specific treatment options are yet unavailable. We tested whether exposure to relaxation-associated odor cues during sleep would reduce cortical hyperarousal, i.e. beta (16.25 - 31 Hz), gamma (31.25 - 45 Hz), spindle activity and nightmare occurrence in participants with frequent nightmares. MethodsTwenty-five (21 female, mean age (SD) = 24.94(5.01)) participants, recruited from undergraduate students at University of Luebeck, with [&ge;]1 nightmare / week received a deep breathing relaxation intervention for one week coupled with an odor. On two subsequent nights in the sleep laboratory, the associated odor (A), or control odor (B) were presented in randomized order in a crossover design with randomization at baseline; participants were blinded to intervention. ResultsN = 11 participants were allocated to AB and n = 14 to BA sequence. Exposure to relaxation-associated odor cues during sleep did not affect beta or gamma activity while spindle count and density were significantly reduced. Reduction in spindle count during reactivation nights correlated with reduced subjective wake-after-sleep-onset. There was no additional impact on nightmare symptoms. There were no adverse events or side effects. ConclusionsThe reactivation of relaxation-associated states with odor cues during sleep may be associated with changes in spectral activity, specifically spindle activity. Future studies should implement multiple nights of reactivation and include different patient groups with cortical hyperarousal to test the transdiagnostic potential of this new intervention.

Sleep, especially NREM sleep depth is homeostatically regulated, as sleep pressure builds up during wakefulness and diminishes during deep sleep. Previous evidence from this phenomenon, however, mainly stems from experimental studies which may not generalize to an ecologically valid setting. In the current study, we used a dataset of 246 individuals sleeping for at least seven nights each with a mobile EEG headband according to their ordinary daily schedule to investigate the effect of time spent in wakefulness on sleep characteristics. Increased time in wakefulness prior to sleep was associated with decreased sleep onset latency, increased sleep efficiency, a larger percentage of N3 sleep, and higher delta activity. Moreover, increased sleep pressure resulted in an increase in both the slope and the intercept of the sleep EEG spectrum. As predicted, PSD effects were most prominent in the earliest hours of sleep. Our results demonstrate that experimental findings showing increased sleep depth after extended wakefulness generalize to ecologically valid settings, and that time spent awake is an important determinant of sleep characteristics on the subsequent night. Our findings are evidence for the efficacy of sleep restriction, a behavioral technique already widely used in clinical settings, as a simple but powerful method to improve the objective quality of sleep in those with sleep problems.

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

The state of becoming aware that one is dreaming within an ongoing dream, referred to as lucid dreaming (LD), can occur spontaneously. Yet, since the occurrence of spontaneous LD is relatively rare, various methods have been proposed to induce LD. Existing scientific literature, however, has been constrained by either small sample sizes with limited generalizability, or by reliance on subjective measures without physiological signals. To address these limitations, we recorded verifiable LD using 2-channel EEG and an open-source dream engineering toolbox (Dreamento) in a large sample size of 60 participants collected across a multi-center study in the Netherlands (NL), Italy (IT), and Canada (CA). We employed a novel combination of the senses-initiated lucid dreaming (SSILD) method and a targeted lucidity reactivation (TLR) protocol. Our final sample consists of 60 participants who came twice to the lab for morning naps with a pre-sleep lucidity training paired with multimodal sensory cues (visual, auditory, tactile). Cues were presented again in REM sleep in one of the two naps (stimulation and sham conditions counterbalanced). This preprint reports results from NL and IT in 40 participants: we successfully induced signal-verified lucid dreams (SVLD) in 65% and 45% of NL and IT participants, respectively. Among these, 45% and 35% (NL and IT) of REM cueing and 35% and 15% (NL and IT) of REM sham sessions resulted in at least one SVLD. In NL, the REM cueing sessions yielded 37 predefined eye signals with an average continuously verified lucidity duration of 78.75 {+/-} 54.85 s. The REM sham sessions resulted in 15 eye signals in the presence of LD report (i.e, SVLD) and had an average duration of 47.80 {+/-} 22.49 s. In IT, 48 predefined eye signals were identified within REM cueing sessions, with an average overall duration (i.e., from the first to the last predefined eye signal) of 506.33 {+/-} 643.73 s and an average continuously verified (consecutive eye signals) duration of 91.13 {+/-} 70.87 s. In contrast, 10 predefined eye signals were identified during REM sham sessions, with an average overall duration of 546.5 {+/-} 744.58 s and a single continuously verified episode that lasted 20s. Preliminary findings suggest that REM cueing aids the initiation and maintenance of lucidity, facilitates objective estimation of LD duration, and increases dream control. Future research should focus on automating the tools we provided and conducting larger-scale fully automatised studies at home to further explore factors contributing to such high success rates.

This study aimed to explore night-to-night variability of multiscale sleep patterns by analyzing subcutaneous electroencephalography (sqEEG) from 20 healthy participants over one year (205-388 nights per participant, 6,429 nights in total). We utilized the time series of aperiodic slopes, sigma and slow-wave power as a new whole-night unit of sleep macrostructure. Using dynamic time warping, we calculated the distances (differences) between those time series to assess night-to-night sleep macrostructure dissimilarity. We found that the overall sleep macrostructural patterns were relatively similar across nights (20% dissimilarity), while their temporal alignment was quite variable (time series warped by ~60% for the best alignment). Lower variation in macrostructure dissimilarity was associated with better subjective sleep quality (r=-0.25). Then, we qualitatively compared yearlong variation in macroscale, microscale (sleep stage proportions, mean spectral power) and mesoscale (sleep cycle duration) metrics. We found that intra-individual night-to-night variation was '"low (coefficients of variation < 20%) for spectral power, sleep duration, N2 and REM sleep; ''medium'' (20-40%) - for N3 and macrostructure dissimilarity; and "high" (>40%) - for sleep cycle duration, wake and N1. In summary, different sleep metrics showed differential night-to-night variability, which was more metric-specific than scale-dependent. This might reflect a distinction between more trait-like versus more dynamically varying features of sleep, although this assumption needs further clarification.

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.