Journal of Sleep Research

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).

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

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.

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.

BackgroundDespite broad interest in the health implications of sleep duration, traditional measurements via polysomnography or actigraphy are often limited to one or a few nights per person. Given the potential variability of sleep duration over time, inferential uncertainty remains an important issue for relatively short observation windows. MethodsWe describe potential limitations of shorter duration sleep tracking by sub-sampling from longer-term observation windows, using a combined approach of simulated data from known distributions, in addition to real-world data (30-365 nights) from over 35,000 participants who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep data to the study. ResultsSimulations demonstrate that the magnitude of deviation from truth, defined using all available observations per individual, as well as the presence and direction of bias, depended on the sub-sample size, the type of simulated distribution (Gaussian versus skewed), and the summary statistics of interest, such as central tendency (mean, median) and dispersion (standard deviation (SD), interquartile range). For example, the SD computed from n=7 observations from a simulated normal distribution (7+1 hours) showed a median 6.7% under-estimation bias (IQR 24% under- to 14.7% over-estimation). Real-world sleep duration data, when under-sampled and compared to longer observations within-participant, showed similar SD bias at 7 nights, and similar convergence rates approaching the true value (based on 90 nights) as longitdunal sample number increases. Shapiro-Wilk tests for normality and log-normality show that 64% of simulated log-normal (skew) distributions fail to reject normality at n=7 samples, while real-world sleep duration data most commonly failed both normality and log-normality tests. Finally, simulated cohorts with sleep durations of 7+1 hours mixed with a subset of 6+1 hours sleepers showed that a random single-night observation of "short sleep" (6 hours) is more likely from random variation of a 7-hour sleeper, than from an actual 6-hour sleeper. Extending the observation to n=7 nights mitigates this mis-classification risk. ConclusionThe results of simulations and empiric data patterns suggests that longer duration tracking provides important and tangible benefits to reduce bias and uncertainty in sleep health research that historically relies on small observation windows.

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.

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.

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)

IntroductionSegmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accurately reflect the true temporal sleep dynamics. We aimed to develop a deep learning-based, high-temporal-resolution sleep-wake classifier leveraging temporally continuous manual reference scoring without fixed epoch boundaries and transfer learning techniques to facilitate progress toward a more physiologically consistent sleep assessment. MethodsThree independent datasets were utilized, of which two included sleep-wake scoring manually conducted in a temporally continuous manner. A U-Net based model was initially trained on a large dataset scored using 30-second epochs, with post hoc scoring modifications (n=2034). It was then fine-tuned via transfer learning using a subset of one of the datasets with temporally continuous scoring (n=39) and validated on both its holdout portion (n=40) and the other independent temporally continuous scoring dataset (n=20). Wakefulness and arousals were consolidated, acknowledging their shared physiological characteristics. Prediction confidence estimates were also generated. ResultsThe model achieved overall concordance of 88.96% ({kappa}=0.78) and 88.23% ({kappa}=0.76) in the holdout and second independent evaluation dataset, respectively, with temporally continuous scoring. Correlation between 1-second automatic predictions and temporally continuous manual scoring was r=0.93 (p<0.001) for total sleep time and r=0.67 (p<0.001) for sleep-to-wake transition index. ConclusionsThese findings support the utility of our model in addressing key limitations of 30-second epoch-based scoring and progressing toward more physiologically consistent sleep-wake assessment by providing a practical basis for subsequent analyses. Misclassifications generally showed lower confidences, indicating additional value for targeted review. Statement of SignificanceConventional sleep scoring remains constrained by fixed 30-second epochs, which may fail to capture the true temporal dynamics of the underlying changes between sleep and wakefulness. In this study, we used polysomnography data manually scored on a temporally continuous basis as the gold standard to develop and validate a deep learning model capable of classifying sleep and wakefulness-like states (consolidating wakefulness and arousal) at high temporal resolution without fixed 30-second epochs. The model demonstrated strong agreement with the gold standard, and as such, lays a practical foundation for deriving improved physiologically meaningful biomarkers of sleep fragmentation and continuity, with potential diagnostic and prognostic value and broad applicability toward a more precise and physiologically consistent sleep assessment.

ObjectivesThe objective of this review was to evaluate the diagnosis and treatment of advanced and delayed sleep-wake phase disorders (ASWPD, DSWPD), both forms of circadian rhythm sleep-wake disorders (CRSWDs) using both human reviewers and ChatGTP to summarize relevant content. MethodsPubMed-Medline, EbscoHost, and Web of Science were searched for peer reviewed articles. Original research articles published in English were searched for full-text studies. Studies that reported quantitative data on ASWPD and DSWPD diagnosing tools and treatment options in individuals of all ages were assessed. We assessed ChatGTP 4.0s capacity to extract and summarize data from these studies and evaluated it alongside human reviewers. ResultsOur review of 49 articles on CRSWD from the past 20 years found that 91% focused on DSWPD. The most common diagnostic tools were the MEQ, MCQ, Pittsburgh Sleep Quality Index, and Epworth Sleepiness Scale, with primary methods including actigraphy and dim-light-melatonin-onset. Melatonin, often combined with light therapy and CBT, was the predominant treatment. ChatGPT-4.0 facilitated the review process with about 92% accuracy but required manual oversight for optimal results. ConclusionsOur review identified key diagnostic tools, such as MEQ and MCQ surveys, and common treatment options for ASWPD and DSWPD, including melatonin, light therapy, and CBT. The findings underscore the need for comprehensive and individualized treatment approaches for CRSWDs, with a particular focus on expanding research on ASWPD and increasing data representation across age groups. While ChatGPT shows potential in streamlining data extraction for CRSWDs, it still requires human oversight to ensure accuracy.

BackgroundExcessive daytime sleepiness (EDS) is a major yet under-recognized contributor to road traffic accidents. Traditional diagnostic tools, such as the Maintenance of Wakefulness Test (MWT), assess wakefulness under passive conditions but may not accurately reflect real-world driving risks. To address this gap, we have piloted a Driving Simulation-based MWT (DS-MWT), designed to evaluate sleepiness in an ecologically valid driving scenario. The present study aims to validate the novel DS-MWT against the classical MWT in a functionally relevant cohort of patients with obstructive sleep apnoea (OSA). MethodsThe present monocentric, randomized, controlled, within-subject crossover trial will include 54 participants: 36 patients with OSA undergoing[&ge;] 7-day CPAP withdrawal (W) or continuation (C), and 18 healthy controls. The study employs a well-established CPAP-withdrawal model in patients with prior optimal treatment adherence to transiently induce EDS under controlled conditions. A healthy control group is included to enable between-group comparisons. The primary expected outcome is the difference in mean latencies between MWT and DS-MWT, determined during four standardized test sessions per condition. Secondary exploratory outcomes are defined as the presence, direction, and magnitude of differences or correlations between treatment status (CPAP withdrawal vs. continuation) and driving performance metrics (e.g., lateral position, speed, lane departures, etc.), EEG and eye-tracking features, subjective sleepiness scores, at-home polysomnography (PSG) parameters, and metabolomic biomarkers (saliva, exhaled breath and dried blood spots). Data will be analyzed using linear mixed models, repeated-measures ANOVA, and predictive modeling with cross-validation. DiscussionThis trial addresses a critical limitation in sleep and traffic medicine by introducing a realistic, supposedly more ecologically valid alternative to standard sleepiness assessment tools. The DS-MWT may enhance clinical decision-making regarding fitness to drive (FTD) and provide a framework for identifying physiological and behavioral markers of sleepiness in realistic conditions. Trial registrationClinicalTrials.gov Identifier: NCT06872593, released on 12.03.2025, https://clinicaltrials.gov/study/NCT06872593 Swiss National Clinical Trial Portal SNCTP000006301, released on 19.03.2025, https://www.humanforschung-schweiz.ch/en/trial-search/study-detail/66469

ObjectiveTo investigate the association of objective long sleep duration (LS) and insomnia with objective short sleep duration (ISSD) with mortality in older persons. MethodsIn 3,054 men (average age 76.4{+/-}5.5; mean follow-up=12.1 years) and 3,048 women (average age 83.6{+/-}4.8; mean follow-up=5.4 years), Cox proportional hazards models examined the association of LS (actigraphy-estimated sleep duration>8h) and ISSD (insomnia [difficulty initiating or maintaining sleep and/or sleep medication use [&ge;]3/week] and concurrent actigraphy-estimated sleep duration<6h) with mortality. Other phenotypes (insomnia with normal sleep duration [INSD; insomnia and sleep duration 6-8h]; asymptomatic short sleep [AS; no insomnia and sleep duration<6h]) were also examined. Participants with normal sleep (NS; no insomnia and sleep duration 6-8h) served as the reference group. Models were adjusted for demographics and comorbidities. ResultsIn unadjusted models, LS was associated with increased mortality in men and women when compared with NS. In women only, LS was associated with higher mortality after adjustment for demographics and comorbidity compared with NS (HR 1.30 [1.07, 1.59]). In demographic-adjusted models and across cohorts, ISSD was significantly associated with an increased hazard of mortality compared with NS (HR 1.25 [1.10, 1.43] for men; 1.36 [1.11, 1.67] for women). This association was not significant in either cohort after adjusting for comorbidity. Persons with INSD or AS did not have increased mortality risk compared with NS. ConclusionLS and ISSD are at-risk phenotypes in older persons. Associations with mortality may be mediated by chronic diseases. Future work should examine whether sleep improvements decrease mortality in older persons. STATEMENT OF SIGNIFICANCEPrior studies have reported inconsistent results on the association between insomnia with objective short sleep duration (ISSD) and mortality in older persons. While long sleep duration (LS) has been associated with mortality, self-reported assessments may be subject to bias. Through analysis of two large cohorts of community-dwelling older men and women with objective sleep measurements and robust longitudinal data, we found ISSD and LS to be associated with all-cause mortality. After controlling for the presence of chronic conditions, however, only LS in women was significantly associated with mortality. Our findings suggest that underlying comorbidities mediate the risk of ISSD and LS in older persons and that objective measures may be needed to differentiate risk among older persons with insomnia symptoms.

Background and ObjectivesAPOE4, a genetic risk factor for Alzheimers Disease (AD), is associated with reduced functional connectivity of brain regions that regulate sleep, which may predispose persons to AD via altering sleep architecture. However, little is known about differences in sleep architecture by APOE genotype. MethodsThis cross-sectional study examined the association between APOE genotype and sleep architecture among middle-aged and older adults, using polysomnography (Sleep Heart Health Study, N=3,132). APOE genotype included: APOE4 heterozygotes, APOE4 homozygotes, APOE2 carriers, and APOE3 homozygotes. Macro sleep architecture was quantified using the percentage of time spent in rapid eye movement sleep (%REM), N1 (N1%), N2 (%N2), N3 (%N3), and arousal index. Micro sleep architecture was quantified as odds ratio product (ORP; a continuous measure of sleep depth) for overall sleep and each sleep stage, and spindle characteristics (power, density, and frequency). Linear regression was used, adjusting for covariates. ResultsThe mean age was 67, 53 percent were female, 24% were APOE4 heterozygotes, 2% were APOE4 homozygotes, 14% were APOE2 carriers, and 60% were APOE3 homozygotes. Macro sleep architecture did not vary across genotypes. Compared with APOE3 homozygotes, APOE4 homozygotes exhibited fewer arousals with age ({beta}=-0.33 per hour/year, p=0.04), resulting in significantly fewer arousals at age 70+. ORP decreased in a dose-response pattern with the number of APOE4 alleles during overall sleep and across all sleep stages (ORPAPOE3/3=0.94, ORPAPOE3/4=0.91, ORPAPOE4/4=0.87), and these group difference widened with each year of age. Finally, there was a trend for lower spindle density and power in APOE4 homozygotes relative to APOE3 homozygotes (ps=0.06). ConclusionsArousal threshold increased in a dose-response manner with each APOE4 allele, as evidenced by the findings on ORP. The differences in ORP between APOE3 homozygotes and APOE4 carriers widened further with age, paralleling age-related declines in arousal index among APOE4 homozygotes. Despite these indications of elevated arousal thresholds that might suggest less sleep fragmentation in APOE4 carriers, APOE4 homozygotes exhibited poorer sleep micro architecture, including trends toward reduced sleep spindle activity. Taken together, reduced arousability in APOE4 carriers may reflect abnormalities in cortical activation that blunt arousal rather than an indicator of healthier sleep.

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.

Sleep health is essential for older adults. However, validity of wrist- and waist-worn devices for assessing sleep under free-living conditions remains unclear. This study evaluated the accuracy of a wrist-worn smartwatch (Silmee W22) and a waist-worn activity monitor (MTN-221) in measuring key sleep parameters, using portable electroencephalography (EEG; Insomnograf K2) as the reference. Healthy older adults wore all devices simultaneously for at least three nights. Total sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency were analyzed using Bland- Altman plots, multilevel models, and intraclass correlation coefficients (ICCs). Fifty-five participants completed the study, yielding valid EEG-paired data for 49 participants with Silmee W22 (238 nights) and 53 with MTN-221 (265 nights). Silmee W22 overestimated total sleep time by 35 min and sleep efficiency by 8.1%, whereas MTN-221 overestimated it by 3 min and sleep efficiency by 1.0%. Both devices underestimated sleep onset latency and wake after sleep onset, with greater discrepancies observed as the estimated values increased. ICCs for total sleep time were 0.60-0.75 for Silmee W22 and 0.66-0.79 for MTN-221, while agreement for sleep onset latency and wake after sleep onset remained lower. While Silmee W22 did not provide sufficiently accurate estimates of total sleep time, MTN-221 yielded estimates that may offer practical benefits for large-scale sleep monitoring in older adults. In both devices, estimates of sleep onset latency, wake after sleep onset, and sleep efficiency should be interpreted with caution due to misclassification of quiet wakefulness. Further algorithm refinement is warranted.

BackgroundSlow-wave sleep is critical for sleep quality, cognitive function, and mood. Slow-wave entrainment (SWE) via rhythmic sensory stimulation can enhance slow-wave activity. However, existing implementations rely on EEG systems, thereby limiting accessibility and scalability. Consumer smartwatches offer an opportunity to deliver SWE in home settings without EEG hardware. ObjectiveThis study evaluated whether smartwatch-delivered sensory stimulation applied during smartwatch-estimated deep sleep elicits acute changes in frontal slow-wave EEG activity during home sleep, and whether individual differences in neural responsiveness to stimulation are associated with next-day behavioral and sleep measures. MethodsIn a randomized crossover design, participants recruited offline from the Boston area slept at home for two nights while wearing a consumer smartwatch for stimulation delivery and a portable EEG headband for neural recording. On a single night, participants received block-wise auditory, vibrotactile, or combined stimulation, guided by an automated on-watch sleep-staging model based on heart rate and motion. On the other night, no stimulation was delivered. Event-related changes in frontal delta (1-4 Hz) power were quantified relative to pre-stimulation baselines. Sleep disruption, subjective sleep quality, mood, and cognitive performance were assessed using questionnaires and a computerized Trail Making Test emailed to participants and completed online. ResultsInitiation of sensory stimulation was associated with significant increases in frontal delta power relative to pre-stimulation baseline and matched non-stimulation blocks.Stimulation blocks exhibited lower disruption rates than non-stimulation blocks, suggesting improved sleep stability during stimulation periods. No significant group-level differences were observed between stimulation and non-stimulation nights on measures of sleep quality, mood, or cognition. However, across participants, larger stimulation-evoked increases in delta power were associated with more favorable next-day subjective sleep and mood ratings and fewer clicks to complete the Trail Making Test. 68/93 participants were stimulated overnight. ConclusionsSmartwatch-based slow-wave entrainment delivered during home sleep can elicit reproducible delta EEG responses without sleep disruption. Individual differences in neural responsiveness to stimulation were associated with next-day behavioral measures, suggesting that wearable-based SWE may represent a scalable and accessible approach for improving sleep health. Trial RegistrationThe experiment was retrospectively registered at ISRCTN (registration number pending)

IntroductionThe interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. ObjectivesThis study investigates the combined impact of sleep determinants on individuals daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and interindividual variability. Methods and analysisThis study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (n = 6). Every four weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ("bursts") to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts, and visual function will be tested in the laboratory. The bursts are separated by two at-home electroencephalogram (EEG) recordings each night. Circadian phase and amplitude will be determined during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously. Ethics and disseminationThe Ethics Committee of the Technical University of Munich approved this study (#2023-653-S-SB). We adhere to research standards including the Declaration of Helsinki and open science principles. Results will be made available as future peer-reviewed publications and contributions to conferences. Article summary - Strengths and LimitationsO_LIThis study investigates human sleep in the natural environment across 12 months incorporating multi-domain sleep determinants to understand their combined contribution to the subsequent sleep episode. C_LIO_LIThe study integrates novel and state-of-the art data collection methods, including wearable at-home EEG, continuous glucose measurement (CGM) and personalised light logging, as well as hair follicle-derived circadian amplitude and phase. C_LIO_LIThe study focuses on longitudinal and high-resolution intra-individual data (N = 12) going beyond sparse resolution. Assessments include home-based EEG recordings twice per month, monthly circadian phase and amplitude assessment, 3-days of four daily ecological momentary assessment per month, and continuous actimetry, continuous light logging and continuous bedroom temperature/humidity/air pressure monitoring. C_LIO_LIDue to the lack of experimental manipulations, drawing direct causal inferences from the data will not be possible. C_LIO_LIThe participant burden to generate the within-subject data is high due to the intensive sampling and long participation duration. C_LI

Insomnia is a common sleep disorder, and many individuals seek alternative treatments like homeopathy. However, evidence for its effectiveness remains controversial. This systematic review and meta-analysis evaluated the effectiveness of homeopathic interventions for insomnia and sleep-wake disorders. A comprehensive search of PubMed, MEDLINE, CINAHL, and the Cochrane Library was conducted for studies published between 2010 and 2025. We included randomized controlled trials (RCTs) and non-randomized studies involving adults ([&ge;]18 years) with primary insomnia receiving any homeopathic intervention compared to placebo, no treatment, or active care. Primary outcomes included validated sleep quality measures (e.g., Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI). Four reviewers independently performed study selection, data extraction, and risk of bias assessment using RoB 2.0 and ROBINS-I. A random-effects meta-analysis was conducted for controlled trials, and a narrative synthesis for non-randomized studies. Certainty of evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). The search yielded 1304 records; 12 studies (nine RCTs and three non-randomized) met inclusion criteria. Meta-analysis showed a large, statistically significant positive effect of homeopathy on sleep outcomes (SMD = 0.81, 95% CI [0.24, 1.38], p = 0.0055), with substantial heterogeneity (I{superscript 2} = 86.04%) and publication bias (Eggers test, p = 0.0079). Most studies had high or critical risk of bias, and overall certainty was low. Homeopathic interventions showed a large positive effect on sleep outcomes, but due to high bias, heterogeneity, and publication bias, evidence remains low-certainty and insufficient to support effectiveness. High-quality RCTs are needed. Systematic Review RegistrationPROSPERO CRD42025649926.

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.