Sleep Advances

Objectives. To test whether one week of self-administered morning bright light therapy (BLT) improves sleep, daytime sleepiness and alertness, mood, and objective cognition in healthy university students. Methods. Thirty-three healthy students completed a two-week randomized within-subject crossover trial comparing one week of morning BLT (30 min of 10,000 lx; melanopic equivalent daylight illuminance of approximately 8,989 lx) with one week of usual-light control in counterbalanced order, with no washout. Sleep was assessed with wrist-worn Fitbit sleep tracking and daily diaries; daytime sleepiness (Karolinska and Stanford Sleepiness Scales), positive and negative affect (PANAS), mood (POMS), and a cognitive battery (Stroop, Flanker, Corsi, verbal span) were also assessed, alongside post-trial semi-structured interviews. Outcomes were analyzed with linear mixed-effects models, with Holm correction across five primary outcomes. Results. BLT reduced daytime sleepiness in a time-of-day-specific manner (condition x time-of-day interaction; largest reduction at 12:00, dz = -0.58, with a smaller but still significant reduction at 15:00), reduced night-to-night variability in sleep duration (dz = -0.52), increased Fitbit sleep efficiency (dz = 0.81), and increased PANAS positive affect (dz = 0.41). Objective cognition was unchanged across all measures. Interviews indicated that participants experienced BLT primarily as a sleep and alertness intervention, with minor tolerability issues. Conclusions. Brief morning BLT improved alertness, sleep regularity and efficiency, and positive affect, but not objective cognition, in healthy students, supporting morning light as a low-burden strategy for daytime functioning while cautioning against overstating cognitive benefits.

Study Objectives To develop and initially validate the Circadian Disruption Index (CDI), a self-report measure of circadian disruption, and obtain preliminary evidence of its responsiveness to circadian health education. Methods In Study 1, 244 participants completed a 22-item CDI version and external measures. The sample was randomly divided for exploratory and confirmatory factor analyses. Internal consistency, external associations, and discrimination of poor sleep quality were examined. In Study 2, 72 postgraduate students completed the CDI before and 1 week after a 16-hour circadian health education program in an uncontrolled pre-post design. Results Analyses yielded a 15-item, three-factor structure comprising rhythm stability and light exposure, behavioral habits and diet, and sleep quality and subjective complaints. Total-score internal consistency was acceptable (Cronbach's = 0.871). Confirmatory factor analysis showed a comparative fit index of 0.902 and a root mean square error of approximation of 0.072, although the Tucker-Lewis index was 0.882. CDI scores correlated with sleep quality, chronotype, corrected midsleep on free days, depression, and anxiety, but not social jetlag. The area under the curve for poor sleep quality was 0.807 (95% confidence interval, 0.753-0.862), with an exploratory cutoff of [≤] 23. In Study 2, CDI scores decreased from 22.26 to 19.88 (p = 0.002; Cohen's dz = 0.36). Conclusions The CDI demonstrated satisfactory internal consistency, a meaningful multidimensional structure, and responsiveness to short-term changes following circadian health education, supporting its potential utility for assessing circadian disruption and monitoring circadian-related behavioral changes.

Background: Regularity of sleep patterns over time has increasingly gained traction as an important axis of sleep health. Since sleep habits are under some degree of behavioral control, understanding such patterns in naturalistic settings is particularly important. We quantified sleep variability and tested the hypothesis that regularity correlates with physical activity, resting heart rate (rHR), and heart rate variability (HRV). Methods: We analyzed real-world digital health data from over 81,000 participants (over 18 million nights) who provided informed consent to participate in the Apple Heart and Movement Study and elected to contribute sleep, activity, and heart rate data to the study. Variability was quantified using the standard deviation (SD) computed from total sleep time (TST), sleep start time (S-start), end time (S-end), and midpoint time (MP), as well as the Sleep Regularity Index (SRI). Results: The SD-based variability metrics correlated with one another (R values 0.74-0.92), and with the SRI metric (R values 0.62-0.64). More consistent sleep, by any metric, was associated with more activity and better rHR and HRV. The most consistent tertile for TST variability had higher median TST (6.9 vs 5.9 hours), more daily exercise (32.8 vs 20.4 minutes), lower rHR (62.4 vs 65.6 beats per minute), and higher HRV (40.6 vs 37.3), all p<1e-100. The findings were similar when variability was defined by S-start SD, S-end SD, MP SD, or SRI. Conclusion: Sleep consistency metrics are highly correlated with each other, and consistency by any metric was associated with more activity, lower rHR, and higher HRV. While causality cannot be established, the results of this large, naturalistic observational cohort are consistent with the growing literature on the potential positive health associations of sleep consistency.

Background: Accurate REM identification is critical for diagnosing REM sleep behaviour disorder (RBD), yet many automated sleep staging systems, especially single-channel EEG models trained on healthy cohorts, do not generalise well to real-life polysomnography (PSG) performed in patients. Objective: To compare a feature-based Random Forest (RF) model tuned for RBD with a state-of-the-art single-EEG deep architecture (AttnSleep), and to assess the impact of cohort adaptation and multimodal inputs (EEG, EOG, EMG, ECG). Methods: Experiments used 89 multi-site in-clinic PSGs (SleepWearables Phase-1) plus 53 MASS healthy controls (mean age 63, std 5 years), with 10-fold cross-validation and out-of-fold evaluation. Model performance was assessed using Cohen's kappa, and attention-based modality analysis was performed to quantify signal contributions. Results: When applied out-of-the-box after training on open-source healthy datasets, both models achieved moderate agreement overall (Cohen's kappa = 0.46), but performance declined in RBD, particularly for REM sleep (AttnSleep Cohen's kappa = 0.19 vs RF Cohen's kappa = 0.44), highlighting limited cross-cohort generalisation. The multimodal model improved overall agreement (Cohen's kappa 0.59 - 0.60) and performance in RBD (Cohen's kappa 0.45 - 0.46), with gains most pronounced in REM (Cohen's kappa 0.45 - 0.49). Attention-based modality analysis identified EEG as the dominant signal, increased EOG contribution during REM, and elevated ECG importance during N3. In RBD subjects, EOG weighting increased relative to non-RBD controls (Delta = +0.081). Guided by these weights, a reduced four-channel EEG model matched full multimodal performance in non-RBD subjects, and adding EOG achieved the best overall configuration (Cohen's kappa = 0.61 overall; Cohen's kappa = 0.48 in RBD) with improved REM classification (53% vs 45% recall). Inclusion of EOG also reduced inter-dataset variability in REM staging. Nonetheless, staging performance in RBD remained lower than in controls, particularly for REM. Conclusions: These results highlight the limited generalisability of minimal-sensor models trained on healthy cohorts, the value of mixed cohort-specific training, and the benefit of multimodal integration and attention-guided channel selection, rather than minimal-sensor approaches alone, for robust clinical sleep staging in pathological populations such as RBD.

Objective: Sleep health and depression are interconnected multidimensional constructs, yet their shared determinants remain obscure. Understanding the role of socioeconomic/lifestyle factors in predicting sleep-related depression (SRD) is critical for preventive strategies. This study aimed to identify the key socioeconomic/lifestyle predictors of SRD in the general population and patients with clinical depression. Methods: To characterize SRD, we performed regularized canonical correlation analysis between sleep and depression to identify latent phenotypes of SRD in a general population subsample (GP1; n=87,405) from the UK Biobank. Subsequently, machine-learning predictive models were developed in GP1 to predict SRD using socioeconomic/lifestyle factors. The best-performing predictive model was subsequently validated in GP2 at both baseline and follow-up (GP2; n=5,187), and in clinical depression (n=7,454) to assess its generalizability. Complementary analyses were conducted to assess other latent phenotypes (i.e., depression-related sleep, non-SRD, non-depression-related sleep, overall sleep health, and overall depression). Results: A robust multivariate association was identified between sleep and depression in GP1 (canonical r = 0.42, PFDR < 0.001). Socioeconomic/lifestyle factors moderately predicted SRD (r = 0.25; 95% CI: [0.24, 0.25]; R2 = 0.06; 95% CI: [0.06, 0.06]; rMSE = 1.08; 95% CI: [1.08, 1.09]). The top predictors were less frequency of confiding in others, more sedentary television viewing, less vigorous physical activity, and passive smoking exposure. Out-of-sample validation of the predictive model showed similar patterns in GP2 at baseline, at follow-up, and in clinical depression subsamples. Similarly, less frequency of confiding in others and greater sedentary television viewing were the main predictors of other depression-related profiles, whereas more alcohol consumption frequency, less walking frequency, and less time spent outdoors in winter predicted poor sleep-related profiles. Conclusions: Our generalizable predictive model identifies critical modifiable predictors of the association between sleep health and depression that could serve as potential targets for personalized interventions.

Sleep regularity, the consistency of sleep-wake timing from one day to the next, is more strongly associated with longevity than adequate sleep duration. Whether this relationship persists across common diseases is unknown. We compared sleep regularity vs. sleep duration as risk factors for 199 diseases and disorders, using ten million hours of objective sleep-wake data (N=60,998, age[mean{+/-}SD]=62.8{+/-}7.8, 55% female). Multivariable-adjusted risks of incident diseases/disorders for regular/irregular and short/adequate sleepers were compared across 9.5 years of follow-up. Irregular sleep predicted risks for 131 diseases/disorders, more than double the number predicted by short sleep duration (63). Irregular sleep was a superior predictor than short sleep duration for 90 diseases/disorders, including circulatory, metabolic, digestive, renal, infectious, neurological, and musculoskeletal conditions, and mental disorders, whereas short sleep duration was the superior predictor for only 9 diseases/disorders. For models where short sleep duration explained disease risks, 83% were improved by adding sleep regularity. Sleep regularity was a stronger predictor of diseases/disorders than sleep duration in this cohort and should be considered an essential dimension of sleep health.

Young adults frequently report cognitive complaints often attributed to sleep loss alone. However, subjective cognitive functioning is shaped by broader lifestyle and affective factors. Cross-sectional data were analyzed from 530 young adults (mean age 22.1 +/- 2.3 years) to examine the independent, interactive, and cumulative associations of short sleep duration, low physical activity, and psychological distress with everyday cognitive failures. Cognitive failures were strongly associated with sleep duration, physical activity, sleep quality, and distress in univariate analyses. However, hierarchical regression revealed that psychological distress, poor sleep quality, and short sleep duration were the dominant independent correlates of cognitive failures, collectively explaining a substantial proportion of variance in Cognitive Failures Questionnaire scores (R-squared = 0.585, p < 0.001). In contrast, the apparent protective effect of physical activity was not observed after adjustment for sleep and distress (p = 0.976), and no significant sleep-by-physical activity interaction was observed. Further, cumulative risk modeling demonstrated a robust dose-dependent relationship, with cognitive failures increasing progressively as behavioral and psychological risk factors accumulated (p < 0.001). Individuals exposed to all three risk factors exhibited more than double the cognitive failure burden observed in individuals with no risk factors. These results indicate that the cognitive burden in young adults can best be described by an additive increase of behavioral and psychological risk factors as a function of the co-occurrence, rather than by the presence of compensatory effects of lifestyle risk factors. Interventions aimed at preserving cognitive function may therefore benefit from simultaneously targeting sleep health and psychological well-being rather than relying on physical activity alone to offset cognitive burden.

Background: Automatic sleep staging algorithms are increasingly applied in clinical and home-based recordings. However, their performance may degrade when transferred to new montages and clinical populations. This is particularly relevant in reduced-channel portable PSG and in disorders such as REM sleep behaviour disorder (RBD), where altered sleep architecture may challenge pretrained models. Objective: To evaluate and compare multiple open-source sleep staging algorithms on a minimal portable PSG setup in controls and patients with and without RBD, and to assess the impact of fine-tuning on clinic-ascertained data. Methods: Six open-source models were applied to 76 subjects recruited from three clinical sleep medicine sites. Performance was assessed using accuracy, F1 scores, and Cohen's kappa, both overall and per sleep stage. Each model was evaluated out-of-the-box and after fine-tuning on clinical data. Results: Out-of-the-box performance varied substantially across models (Cohen's kappa 0.21-0.54). Fine-tuning consistently improved agreement, with the best-performing model (GSSC) reaching Cohen's kappa = 0.58 indicating moderate to good agreement. Performance was highest in controls and lower in patient groups. N3 was the most reliably classified stage across models, whereas N1 remained consistently challenging. REM classification improved after fine-tuning in several architectures but remained model, and subgroup-dependent, particularly in RBD subjects. Conclusion: Fine-tuning substantially mitigates domain shift, updating model parameters to align with new data distributions, when applying automatic sleep staging algorithms to portable clinical recordings. Model architecture influences robustness, with feature-learning approaches demonstrating greater adaptability than fixed-feature models. Despite moderate agreement after adaptation, performance, especially for REM and N1 remains insufficient for fully automated diagnostic use in clinical populations.

The slow oscillation (SO) of non-rapid eye movement (NREM) sleep has been implicated in the restorative properties of sleep. Slow oscillatory transcranial direct current stimulation (SO-tDCS), involving a positive oscillatory current applied to the scalp at a peak frequency of 0.75 Hz, has been used to enhance SO power during NREM sleep. We examined whether enhancing SO power with SO-tDCS during a restricted nighttime sleep opportunity would accelerate the restorative properties of sleep during an otherwise insufficient sleep period and help sustain performance during subsequent extended wakefulness. A total of twenty-six healthy young adults (ages 18-39, n=16 females) completed a 15-day study. After 7 baseline nights at home and 3 baseline nights in the laboratory, participants entered the laboratory for 5 consecutive days including a baseline day, a 2-hour nighttime sleep period with participants randomized to the SO-tDCS (n=11) or SHAM (n=15) condition, 46 hours of sleep deprivation, and two recovery nights. In the SO-tDCS condition, stimulation was administered for one hour starting exactly 60 minutes after sleep onset, with intervals of five minutes of continuous stimulation followed by one minute of no stimulation. Polysomnographic recordings were conducted during each sleep period. Performance was assessed using the Psychomotor Vigilance Test (PVT) approximately every 75 minutes across baseline, sleep deprivation, and recovery. Prior to the two-hour sleep opportunity, a Paired Words Associate Task was administered. Participants listened to 54-word pairs and were asked to recall 46 of the word pairs, with up to three attempts to successfully recall at least 60% of word pairs (T0). Recall was also assessed 20- (T20) and 120-minutes (T120) after awakening from the two-hour sleep period. Data were analyzed using mixed-effects ANOVA. PVT performance (defined as mean response time and number of response times greater than 1,000 ms) significantly declined across sleep deprivation with performance degradations peaking in the early morning hours. Participants in the STIM condition demonstrated significantly better performance during sleep deprivation relative to the SHAM condition. On the PWAT, participants in the SHAM condition recalled fewer word-pairs upon awakening relative to T0. In sharp contrast, performance of participants in the SO-tDCS condition did not deteriorate at T20 and was actually improved at T120 relative to T0. We conclude that SO-tDCS can robustly accelerate the restorative properties of sleep and can additionally enhance sleep related memory consolidation when sleep opportunity is restricted.

ABSTRACT Objective To assess the comparative efficacy and acceptability of cognitive behavioural therapy for insomnia (CBT-I), its abbreviated versions and control conditions. Design Systematic review and network meta-analysis. Methods Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Frequentist, random-effects network meta-analyses estimated odds ratios (ORs) or mean differences with 95% confidence intervals (CIs). The primary outcome was insomnia remission post-treatment. Secondary outcomes included dropout and subjective sleep continuity measures. Quality of the evidence for each arm was graded using the confidence in network meta-analysis (CINeMA). Data sources We searched MEDLINE, Embase, PsycINFO and Cochrane CENTRAL from inception to December 15, 2025, with a medical information specialist. Eligibility criteria for selecting studies Randomized controlled trials (RCTs) comparing CBT-I and its abbreviated versions with each other or with control conditions, in adults with insomnia, with or without comorbidities. To reduce clinical heterogeneity related to treatment intensity and adherence, we restricted inclusion to in-person delivery. Results We identified 11,379 records and included 77 RCTs (5,731 participants; mean age 52.2 years; 3,473 female). CBT-I (number of arms k = 53; number of participants n = 2,002), sleep restriction and stimulus control therapy (SRT+SCT; k = 16; n = 549), sleep restriction therapy (SRT; k = 5; n = 196) and stimulus control therapy (SCT; k = 7; n = 144) were associated with higher remission than sleep hygiene, relaxation therapy and other control conditions. These interventions were also effective in improving subjective sleep continuity measures. Cognitive therapy for insomnia (CT-I) was more beneficial than relaxation therapy. Dropout did not differ meaningfully between interventions and controls. Confidence in evidence was moderate for CBT-I, low for SRT&SCT and SRT, very low for SCT. Given the weighted mean proportion of insomnia remission among sleep hygiene arms of 20%, CBT-I probably leads to a remission rate of 41% (95% CI, 34%; 48%), SRT&SCT may lead to a remission rate of 40% (30%; 52%), SCT 43% (25%; 63%), and SRT 41% (26%; 57%). Conclusions CBT-I doubles the absolute insomnia remission compared with sleep hygiene, and its abbreviated behavioural therapies, namely, SRT+SCT, SCT and SRT may offer similar benefits with lower resource requirements, but evidence is less certain. CT-I needs further investigations. Relaxation therapy was inferior to these therapies. Implementation decisions should consider resource requirements and evidence certainty. Systematic review registration The Open Science Framework, https://osf.io/z48r2/.

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.

Background: Adolescent sleep health is a growing public health concern, yet no culturally responsive sleep health program has been developed for Aboriginal and Torres Strait Islander (First Nations) young people. This study reports the outcomes and acceptability of Australia's first co-designed sleep health program for First Nations adolescents. Methods: The Let's Yarn About Sleep adolescent program was co-designed with First Nations community members from 23 Traditional groups, involving 174 Elders, adolescents, parents, carers, and service providers. The program drew on an Aboriginal pedagogical framework and the COM-B behaviour change model, integrating Western and First Nations sleep science, and was delivered by Aboriginal Youth Workers trained as Sleep Coaches. Outcomes included self-reported improvements in sleep knowledge, sleep timing and continuity, sleep quality, overall sleep health, and psychological distress. Post-program changes in outcomes were assessed using linear mixed-effects regression analyses. Program ratings and yarning-based feedback assessed acceptability. Findings: 70 First Nations young people participated in the program (median age 13.0 years, range 12 - 8; 67.1% female). Sleep knowledge improved substantially, with the mean composite score increasing from -0.65 (SD 1.23) at baseline to 0.82 (SD 1.27) at follow-up, a large effect (Cohen's d = 1.18; p < 0.001). A significant improvement was observed in the overall sleep health score, representing a medium effect (Cohen's d = 0.63; {beta} = 0.68, 95% CI: 0.31 - 1.04; p < 0.001). Psychological distress showed a directional reduction that did not reach statistical significance (Cohen's d = 0.32; {beta} =0.49, 95% CI:-1.08 - 0.09; p = 0.097), though a modest beneficial effect cannot be excluded. High acceptability was reflected in program ratings and qualitative feedback, with participants reporting greater sleep awareness, improved sleep behaviours, and strong community engagement with the program. Interpretation: A culturally grounded, co-designed sleep health program can improve sleep knowledge and overall sleep health and achieve high acceptability among First Nations adolescents. Community leadership, local delivery, and the embedding of First Nations worldviews are likely central to achieving impact, highlighting a promising pathway to address sleep health inequities Funding: Medical Research Future Fund (APP1201569). No competing interests

Background: Sleep disruption is prevalent in people with neurodevelopmental disorders such as autism but is not clear whether it occurs as an endophenotype or secondary to other behaviours. The ABCD Study is a population-based longitudinal study that monitors the health, demography and lifestyle of over 11,000 children in the US. In this study we leverage these data to investigate whether traits consistent with autism (social responsiveness) are associated with sleep disruption independent of lifestyle and other behavioural measures. Methods: Autistic traits were assessed using the Social Responsiveness Scale at age 11, and sleep disruption and behavioural outcomes were assessed at ages 11 and 13 years using the Sleep Disturbance Scale, and the Child Behaviour Check List, respectively. Demographic, health and lifestyle-related variables were assessed by caregiver questionnaires. Regression models were applied to investigate associations between autistic traits and sleep outcomes. Results: There was a significant cross-sectional association between sleep disturbance and SRS at age 11 years old that was independent of sex, ethnicity, socioeconomic position, physical activity, sedentary behaviour and anxiety/depression ({beta} = 0.12, 95% CI (0.07, 0.17); p < 0.001), that persisted at age 13, and that was modulated by chronotype, with evening types showing a stronger association. Discussion: Social responsiveness assessed in early adolescence (age 11) were associated with sleep disruption independent of multiple confounding factors and were prospectively associated with sleep disruption at age 13 years. These findings contribute to the evidence that disruption of sleep and circadian timing may have a primary role in the neurobiological mechanisms that mediate autistic traits.

Chronic partial and acute total sleep loss have a distinct impact on sleep architecture. Namely, acute sleep deprivation primarily leads to a strong rebound of slow wave sleep, while chronic sleep restriction results in an increased propensity of REM sleep. The aim of this work was to examine whether these different effects would translate into quantifiable changes in sleep state boundaries and dynamics using a model-based method. Besides conventional sleep stage scoring, we applied an EEG model (state space approach) for dynamic analysis of nocturnal EEG recordings in 14 healthy subjects under experimental chronic sleep restriction (last of 7 nights with 5 hours of time in bed) and after acute sleep deprivation (sleep following 40 hours of wakefulness), in comparison to baseline sleep. Subjects under chronic sleep restriction revealed increased similarities in the frequency composition of REM sleep and wakefulness and thus, a decreased differentiation of state boundaries between the two behavioral states. Contrarily, acute sleep deprivation affected the spectral composition of NREM sleep. Only acute sleep deprivation resulted in more stable slow wave sleep. Our explorative study confirmed that the distinct effects of increased REM sleep and slow wave sleep propensity following acute total and chronic partial sleep loss are reflected in differential changes of behavioral state boundaries and sleep dynamics. This suggests that these sleep structure characteristics are state dependent, which may allow using such measures in the future to track treatment effects in clinical populations characterized by sleep behavioral state dysregulation.

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.

Study Objectives: To evaluate a framework using wearable data to personalize the definition of short sleep, comparing its temporal and functional characteristics against a fixed threshold. Methods: 462 healthy adults wore sleep trackers and provided daily ecological momentary assessments for a year. Short sleep was defined using either a fixed threshold of <6 h/night (fSS) or personalized thresholds anchored to individual sleep-duration distributions (pSS). Temporal patterns of consecutive short-sleep nights were characterized. Linear mixed-effects models examined associations between accumulating short-sleep nights and short- and long-term markers. Sleep patterns across six other countries were also evaluated. Results: pSS and fSS produced similar average thresholds and overall prevalence of short-sleep nights. However, pSS showed larger effect estimates for short-term outcomes, including alertness, sleep satisfaction, stress, sleep heart rate, HRV, and sedentariness. Effects increased with successive short-sleep nights. Proportion of pSS showed stronger association with blood pressure and arterial stiffness. Isolated short nights were common, whereas longer runs were uncommon and typically followed by incomplete recovery sleep. Personalized thresholds distinguished stable short sleepers with few pSS nights from individuals experiencing recurrent sleep shortfall and highlighted vulnerability among those achieving recommended sleep duration but with high variability. Despite marked cross-country differences in sleep habits, the distribution of short-sleep runs, and termination patterns were remarkably similar. Conclusion: Anchoring short sleep to individual habitual sleep distribution captures relative sleep shortfall beyond absolute duration, better characterizing the functional impact of short sleep. Preventive strategies may benefit from limiting pSS accumulation together with addressing sporadic inadequate sleep.

High-dimensional Mendelian randomization (MR) screens can prioritize candidate dietary and immune pathways for insomnia, but their interpretation is constrained by multiple testing, cross-dataset instability, and limited correspondence between genetic constructs and measured population variables. We conducted an exploratory cross-design analysis that combined MR screening of 231 dietary traits and 731 immune phenotypes, targeted cross-release genetic follow-up in FinnGen R12 and R13, and population-based analyses in NHANES and CHARLS. The targeted R13 follow-up prioritised an omelette-related dietary signal (OR 0.773, 95% CI 0.651-0.917; within-layer FDR q=0.00783), a mixed-fruit signal (OR 1.285, 95% CI 1.102-1.498; within-layer FDR q=0.00683), and CD33- and HLA-DR-related immune-cell traits. In NHANES, mapped omelet/scrambled-egg intake was associated with lower odds of sleep problems in 2017-March 2020 (OR 0.746, 95% CI 0.600-0.927; FDR=0.033) and doctor-reported sleep disorder in 2005-2006 (any intake: OR 0.313, 95% CI 0.157-0.624; FDR=0.008; per 50 g: OR 0.721, 95% CI 0.569-0.914; FDR=0.019). Mixed-fruit proxies were not directionally concordant. Higher C-reactive protein (CRP) was associated with sleep problems in NHANES (OR 1.192, 95% CI 1.085-1.309; FDR=0.001) and frequent restless sleep in CHARLS (OR 1.097, 95% CI 1.049-1.147; FDR<0.001). These findings provide exploratory genetic prioritization and population-based association evidence for selected dietary constructs and systemic inflammatory proxies. They do not establish a causal diet-immune-insomnia mechanism, confirm flow-cytometry immune-cell phenotypes, or support dietary intervention recommendations.

Maternal sleep disturbance is an underrecognized risk factor for adverse offspring outcomes. Prolonged sleep disruption can elicit inflammation, an established risk factor for neuropsychiatric disorders in offspring. Sleep disruptions and inflammation elevate tryptophan degradation via the kynurenine pathway (KP), increasing kynurenic acid (KYNA), a metabolite that inhibits glutamatergic and cholinergic neurotransmission and may thereby affect neurodevelopment. Because KYNA is elevated in the brains of individuals with neurodevelopmental psychotic illnesses, we hypothesize that prenatal KYNA elevation may represent a mechanism link between disturbed maternal sleep, inflammation, and adverse offspring neurodevelopmental health. To test this hypothesis, we employed a novel maternal sleep fragmentation (SleepFrag) paradigm during the final week of gestation. We found that six days of SleepFrag increased maternal plasma inflammatory markers, placental KP metabolism, sex-specific placental inflammation, and fetal brain KP metabolism, including elevated KYNA, without altering KP metabolism in maternal plasma or brain. A parallel embryonic kynurenine (EKyn) model was tested to increase prenatal KP metabolism via a maternal kynurenine-supplemented diet. EKyn increased maternal plasma kynurenine and KYNA, and fetal brain KYNA, with a male-specific increase in fetal brain KYNAto-kynurenine ratio, despite minimal effects on maternal sleep-wake architecture or inflammation. Together, these findings identify elevated fetal brain KYNA as a convergent outcome through which maternal sleep disruption, inflammation, and KP activation may influence sex-specific neurodevelopment. They further support the EKyn model as a translational tool for isolating consequences of increased prenatal KP metabolism. Protecting maternal sleep and stabilizing fetal brain KYNA levels may promote long-term offspring brain health.

Sleep supports episodic memory consolidation, yet it remains unclear how naturalistic post-encoding sleep quality relates to the neural reinstatement of episodic representations across adulthood. The present study examined whether sleep discontinuity during the retention interval predicted delayed context memory and encoding-retrieval similarity (ERS) of EEG in younger and older adults. Participants completed an object-scene context memory task with immediate and delayed retrieval, while EEG was recorded during encoding and retrieval. Actigraphy was used to measure sleep across the post-encoding retention period, and principal component analysis identified sleep discontinuity and sleep time components. Behavioral results showed that greater post-encoding sleep discontinuity, but not sleep time, was associated with poorer delayed memory accuracy for mismatching object-context pairs across age. ERS analyses further showed that greater sleep discontinuity was associated with reduced ERS for correctly rejected mismatching pairs across frontal and posterior spatiotemporal clusters. Age moderated sleep-ERS associations: greater sleep discontinuity was generally related to lower ERS in younger adults, whereas some spatiotemporal clusters showed positive associations in older adults, potentially reflecting compensatory or effortful retrieval-related processing in poorer sleepers. Together, these findings suggest that sleep continuity during the post-encoding retention interval is important for preserving high-fidelity episodic representations needed for later context discrimination. More broadly, the results demonstrate that naturalistic sleep fragmentation is linked to both behavioral memory outcomes and neural reinstatement across adults.

Background and Objective: Diabetes mellitus (DM) causes autonomic neuropathy, which may alter nocturnal respiratory rate (NRR). To test the association between DM and NRR, we analyzed elective polysomnograms of four large observational cohorts. Research Design and Methods: We performed cross-sectional analysis of over 25,000 individuals with polysomnograms (PSGs) from the Sleep Heart Health Study (SHHS), Hispanic Community Health Study/Study of Latinos (HCHS/SOL), Osteoporotic Fractures in Men Study (MrOS), and Wisconsin Sleep Cohort (WSC). Patient-level NRRs were derived from inductance plethysmography waveforms. DM status was determined by self-report, physician diagnosis, medication use, or laboratory values, depending on the cohort. We related DM and NRR (continuous and dichotomized) using logistic regression models and adjusted for potential confounders. Cohort-specific results were combined using random-effects meta-analysis. Results: Meta-analysis of unadjusted models showed a pooled odds ratio (OR) of 1.10 (95% CI:1.04-1.17) for each breath-per-minute (brpm) increase in NRR. This association remained significant after multivariable adjustment (OR:1.06, 95% CI:1.02-1.11). Dichotomized analyses similarly showed higher odds of DM across dichotomization thresholds ranging from 15 to 21 brpm. At a threshold of 18 brpm, the unadjusted pooled OR was 1.77 (95% CI:1.23-2.55, P=0.0022), and the adjusted OR was 1.49 (95% CI:1.10-2.02, P=0.0098). Conclusions: Clinically stable outpatients with elevated NRR have an increased prevalence of DM. Additional studies are needed to investigate whether the mechanism is autonomic neuropathy and whether monitoring NRR can detect early complications of DM.