Sleep Medicine

Background: Drug-Induced Sleep Endoscopy (DISE) is the current reference standard for identifying the anatomical site of upper airway obstruction in obstructive sleep apnea (OSA), but its invasiveness and limited availability restrict its routine use. Because snoring is generated by vibration of the obstructing upper-airway structures, its acoustic characteristics may provide a non-invasive biomarker of the anatomical site of obstruction. This study investigated whether machine learning could reliably distinguish palatal from epiglottic snoring using acoustic information alone. Methods: A retrospective analysis was performed on 159 DISE recordings obtained from adult patients with moderate, non-positional OSA. Snoring events were independently identified and anatomically classified by two blinded expert examiners. Only events with complete inter-observer agreement were included. The corresponding audio segments were extracted and characterized using spectral, cepstral, temporal and harmonic acoustic features. Feature selection was performed using recursive feature elimination. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers were developed using a patient-independent nested cross-validation framework. Results: The final dataset comprised 1,759 snoring events, including 1,054 palatal and 705 epiglottic recordings. Both classifiers demonstrated robust discrimination between the two anatomical classes under patient-independent validation, achieving ROC AUC values of 0.90 +/- 0.03 (SVM) and 0.90 +/- 0.03 (MLP), with balanced accuracies of 0.83 and 0.81, respectively. The most informative predictors were spectral features, particularly spectral energy distribution, spectral flux, Mel-Frequency Cepstral Coefficients (MFCCs), and spectral flatness, whereas fundamental frequency contributed minimally to classification. Conclusions: Snoring contains reproducible acoustic information reflecting the anatomical origin of upper-airway obstruction. By combining high-confidence DISE-derived anatomical labels with a rigorous machine learning framework, this study demonstrates the feasibility of non-invasive acoustic phenotyping of clinically relevant obstruction sites. Rather than proposing a novel artificial intelligence algorithm, our work establishes a clinically oriented framework that may support future decision-support tools for patient selection, treatment planning, and multicentre development of comprehensive acoustic phenotyping models.

Background: Emerging evidence suggested the role of sleep and circadian rhythm in the development of mental resilience, but the association of an integrated analysis of objective sleep and circadian measures with multifaceted mental resilience in healthy adults has been underexplored. Objective: This study aims to explore the associations between multifaceted mental resilience and sleep and circadian rhythm in healthy adults. Methods: A total of 156 healthy adults (Age: Mean (SD): 29.54 (5.28) years; 60.9% female) underwent 14-day actigraphy, one night polysomnography (PSG) and dim light melatonin onset (DLMO) assessments. Resilience was operationalized as both a coping capacity and a positive mental health outcome in the context of accumulative adversity (life stressful events and childhood adversities). We first identified the covariance patterns between multiple sleep and circadian health domains (Sleep macro- and microstructures, dim light melatonin onset, rest activity pattern) and multifaceted mental resilience using partial least squares regression (PLSR), and followed by univariate analysis as complementary to illustrate the distribution trends of sleep and circadian rhythm parameters across integrative resilience score derived from principal component analysis (PCA). Findings: The PLSR model identified a modest but significant association of greater mental resilience with a sleep and circadian profile (Bootstrapping r = 0.15, 95%CI: 0.02-0.28), characterized by greater frontal-central slow waves activity in NREM sleep, greater frontal theta activity in REM sleep, lower occipital spectral power across fast and slow frequency bands in NREM and REM sleep, and probably earlier dim light melatonin onset timing. Conclusions: Sleep and circadian metrics, specifically frontal slow waves and REM theta activity, attenuated overall occipital spectral power during sleep, and earlier dim light melatonin onset time, represent quantifiable targets that may serve to improve resilience and stress-related mental health in adults. Longitudinal and interventional studies are needed to establish whether sleep interventions can be endorsed as evidence-based approaches for resilience enhancement. Clinical implications: Targeting sleep and circadian aspects may be an important approach to promote stress-related mental health.

Understanding how sleep changes across development requires methods that are both efficient and sensitive to maturational differences in sleep physiology. Spectral sleep scoring is a promising approach, but has primarily been validated in adult populations. This study presents and validates a spectral scoring approach for pediatric sleep. Participants (n = 192; 9 to 18y) completed overnight in-lab sleep studies. Visual scoring was determined by registered polysomnographic technologists and confirmed by board certified sleep physicians following AASM guidelines. Visual stages included: wake, non-rapid eye movement (NREM) stage 1 (N1), NREM stage 2 (N2), NREM stage 3 (N3), and rapid-eye movement (REM) sleep. Spectral activity within each epoch was assessed for the following spectral stages: Wake (40 to 95 Hz), Light (11 to 15.5 Hz), Hi Deep (1 to 3 Hz), Lo Deep (0.1 to 1 Hz), and REM (17 to 26 Hz). Concordance rates were checked across agreement matrices between visual and spectral stages, with expected high agreement between: 1) visual Wake and spectral Wake; 2) visual N2 and spectral Light; 3) visual N3 and spectral HiDeep/LoDeep; and 4) visual REM and spectral REM. Cohens {kappa} was calculated from concordance matrices. There was high agreement between visual and spectral stages (71-89%, {kappa} =.76). Our results provide evidence that spectral scoring is a promising method for efficiently scoring pediatric sleep. Moreover, this method provides additional, clinically relevant information that is not readily captured through traditional visual scoring. This may be particularly valuable for assessing sleep physiology in pediatric clinical populations, where developmental differences in EEG patterns may complicate visual scoring.

Background and Objectives: Area-based hypoxic burden, termed as hypoxic dip area (HDA) has been studied as a novel obstructive sleep apnea (OSA) metric that may better characterize intermittent hypoxemia in OSA and thereby better predict outcomes. However, to be of use in routine clinical care, a physiological normative value is necessary. Here we attempt to use several large cohorts to establish a percentile-based threshold that can classify individuals with normal vs. abnormal physiological profiles. Methods: Data from 10 cohorts (EPISONO, FINS, Dayfun, MrOS, MESA, SHHS, APPLES, WSC, CFS, and AIRS [Mount Sinai Clinical Cohort]; n=14,031 subjects) were included. HDA was defined as the area bounded by SpO2 nadirs ([≥]2% desaturation) flanking left/right peaks. The 97.5th percentile of HDA among asymptomatic subjects (n=136) from EPISONO, FINS, and DAYFUN (aged 20-52 years; 46 [33.8%] male) was used to define normal and elevated HDA levels. Physiological features, clinical comorbidities, and incident cardiovascular disease (CVD) events and mortality were compared between normal/elevated HDA groups. Sensitivity analyses were conducted using age-, sex-, and BMI-adjusted threshold derived from multivariable regression model. Results: The 97.5% percentile cutoff of HDA was 8.6%min/h, classifying 4,500 individuals as normal HDA and 9,531 as elevated HDA. Individuals with elevated HDA ([≥]8.6%min/h) demonstrated greater nocturnal hypoxemia (lower baseline SpO2, higher T90, T85, T80 and oxygen desaturation index), higher AHI (apnea-hypopnea index) and arousal index, and greater daytime sleepiness as well as a higher prevalence of lifetime CVD and hypertension across cohorts. Elevated HDA was related with a higher prevalence of incident CVD events and mortality in unadjusted analyses, although these associations were attenuated after adjustment for confounding factors. Results were similar using alternative thresholds derived from multivariable regression analyses. Conclusions: A physiologically derived cutoff of 8.6% min/h for HDA effectively distinguishes physiological profiles across respiratory, arousal, and oxygenation domains and generalizes across multiple cohorts. Those with elevated HDA had a higher prevalence of incident CVD outcomes, although observed relationships were attenuated after further adjustment. Our findings provide a physiologically interpretable threshold for HDA that can be readily used in clinical use.

This study investigated bidirectional associations between maternal sleep and depressive symptoms from preconception to postpartum, and whether infant sleep mediated or moderated these associations. We used data from the Generation R Next Study (N=2,294). Maternal sleep (specifically general sleep disturbance, latency, quality, duration, and midpoint) and depressive symptoms were prospectively assessed at five timepoints from preconception to 12-month postpartum. Sleep was self-assessed with the General Sleep Disturbance Scale and Munich Chronotype Questionnaire; depressive symptoms with the Adult Self Report depression/anxiety subscale and Edinburgh Postnatal Depression Scale. Infant sleep (specifically night awakenings, nocturnal sleep duration, and latency) was parent-reported at 1-month postpartum using the Brief Infant Sleep Questionnaire. Bidirectional associations were examined using Autoregressive Latent Trajectory Models with Structured Residuals. The role of infant sleep was examined using mediation and moderation analyses. We found that maternal sleep and depressive symptoms were both stable over time. For sleep quality and disturbance, bidirectional associations suggested slightly stronger effects from depression to sleep (sleep quality:{beta}depression[-&gt;]sleep quality=0.11, 95%CI:0.07 - 0.14; general sleep disturbance:{beta}depression[-&gt;]sleep disturbance=0.14, 95%CI:0.10 - 0.18) than from sleep to depression ({beta}sleep quality/disturbance[-&gt;]depression=0.07 for both, 95%CIs:0.03 - 0.11). For latency, effects were comparable in both directions ({beta}depression[-&gt;]sleep latency=0.06, 95%CI:0.03 - 0.09; {beta}sleep latency[-&gt;]depression=0.05, 95%CI:0.01 - 0.09). The association between depressive symptoms and sleep latency was both mediated (9.7%) and moderated (p<0.05) by infant sleep latency. In conclusion, general maternal sleep disturbance, sleep quality, and sleep latency showed bidirectional associations with depressive symptoms from preconception/early pregnancy onwards. Infant sleep latency may represent a potential modifiable factor within this cycle.

Sleep health reflects interacting demographic, clinical, micro- and macroarchitectural, and neurophysiological factors that may not be captured by single metrics or diagnostic categories. We applied hierarchical clustering to longitudinal Sleep Heart Health Study data from 1,468 adults with complete polysomnographic, demographic/clinical, and pre-sleep electroencephalographic data at two visits separated by 5.19 {+/-} 0.27 years. Forty nonredundant features selected from candidate demographic/clinical, sleep-stage, and pre-sleep spectral measures were clustered independently at each visit. Four reproducible sleep-health phenotypes emerged: a group with preserved deep sleep and favorable mental-health ratings; a large light-sleep group with low N3 and high N1; an older, physically unhealthy group with shorter total and rapid-eye-movement sleep; and a younger, physically healthy group with longer total and rapid-eye-movement sleep. The same population-level structure was evident at both visits, although only 37.7% of participants retained the same cluster assignment, with transitions most directed toward the light-sleep phenotype. An independent analysis of slow-wave morphology, excluded from cluster construction, differentiated all four phenotypes after false-discovery-rate correction. Groups with preserved or healthier sleep showed more numerous, higher-amplitude, steeper, and shorter slow waves, whereas the light-sleep and physically unhealthy groups showed weaker and more prolonged slow waves. Pre-sleep spectral features did not differ significantly across clusters after correction. These findings identify reproducible but individually dynamic sleep-health phenotypes and demonstrate that macro-architectural cluster structure is reflected in independent measures of NREM sleep microarchitecture.

Study Objectives: To characterize neural correlates of memory and emotion reactivity in individuals with insomnia disorder (INS) compared to good sleepers (GS) and to investigate associated short- and long-term changes following cognitive-behavioural therapy for insomnia (CBTi). Methods: This randomized controlled trial included 56 INS and 20 GS. At baseline, participants underwent resting-state and task-based functional magnetic resonance imaging, including working memory, emotion reactivity, and face-name associative memory tasks. They filled out sleep diaries and self-report questionnaires about sleep, cognition, and emotions, and underwent in-lab polysomnography. Subsequently, participants with INS were randomized to either the 3-month treatment group (TX) or wait-list control group (WL). Both TX and WL groups repeated baseline assessments at three months post-randomization and completed sleep diaries and questionnaires one year after finishing CBTi. Results: At baseline, the INS group reported higher insomnia severity, greater subjective cognitive impairment, and perceived sleep-related stimuli as more negative than GS. Contrasting with these subjective evaluations, no group differences were found in memory performance, task-based brain activity, or resting-state functional connectivity. At three months post-randomization and one-year follow-up, CBTi significantly improved subjective, but not objective, cognitive functioning and sleep in adults with INS. No significant treatment-related changes were observed in valence ratings of sleep images, task-based brain activity, or resting-state functional connectivity. Conclusions: Beyond its primary role in improving insomnia symptoms, CBTi may have long-term benefits for individuals with INS who experience concerns about their cognitive functioning, even in the absence of objective changes in neural processing of cognitive and emotional tasks.

Alexithymia is the inability to identify and describe emotions, associated with an increased risk of somatic and psychiatric disease. Alexithymia is also characterized by reduced subjective sleep quality. However, it is not well-known if this reduction in sleep quality reflects physiological changes in sleep, a specific alteration in perceived sleep quality not captured by objective metrics, or a non-specific spillover of the negative affective style which characterizes alexithymia. In BSETS, a large (N=228) EEG study of naturalistic sleep, we found that alexithymia symptoms are associated with worse habitual (AIS) and current (GSQS) subjective sleep quality ({beta}AIS=0.207, pAIS=0.002, {beta}GSQS=0.203, pGSQS=0.002). However, we observed no significant association between alexithymia and EEG-based sleep efficiency ({beta}=- 0.017, p=0.791). The association with subjective sleep quality was almost completely attenuated when controlling for either depressive symptoms (PHQ-9, {beta}AIS=0.001, pAIS=0.982, {beta}GSQS=0.044, pGSQS=0.486) or trait neuroticism (ZKPQ, BFI-44, {beta}AIS=0.057, pAIS=0.442, {beta}GSQS=0.048, pGSQS=0.514). A possible exception is the Difficulty Identifying Feelings alexithymia dimension, reflecting complaints about the labeling of interoceptive experiences, which was associated with lower subjective sleep quality even after covariate control. This pattern of findings suggests that alexithymia is characterized only by alterations of subjective sleep quality, and even these reflect a non-specific increase in negative affect which influences self-reports of sleep quality, rather than a specific reduction in perceived sleep quality itself.

Borderline personality disorder (BPD) is a serious mental illness that often emerges in adolescence. Although efficacious treatments have been developed, they are often lengthy, expensive, inaccessible, and require specialized training to deliver. Optimizing BPD prevention and early treatment is an important public health priority. Disrupted sleep is closely linked to key BPD features, and prior research has documented a range of sleep problems among BPD samples that appear to worsen symptom course and duration. This intervention trial (NCT04507308) assessed the effects of a single-session sleep-focused treatment on adolescent sleep and BPD-related symptoms. Fifty-seven adolescents (ages 13-18) with 3+ clinically impairing BPD features and at least some self-reported sleep-related difficulties participated with a caregiver. Participants completed baseline surveys, interviews, and 10-day ecological momentary assessment (EMA) protocol to examine BPD symptoms and sleep in daily life. Youth also collected at-home ambulatory electroencephalogram (EEG) recordings before undergoing the treatment informed by the Transdiagnostic Sleep and Circadian Intervention. Post-treatment, youth practiced their new sleep routine for approximately one month before undergoing reassessment (EMA, EEG, surveys). Many indices of sleep quantity and quality improved across both objective (EEG) and subjective assessments (EMA and self- and caregiver-report). Youth reported an earlier bedtime and waketime, more sleep, and fewer awakenings in daily life. Every sleep-related questionnaire score improved at post-intervention and was maintained at one-month follow-up. Multiple indices of BPD symptoms also improved significantly. Findings suggest sleep may be a promising intervention target for at-risk youth.

Sleep disturbances are a significant but underrecognized burden for individuals with Lennox-Gastaut Syndrome (LGS) and their caregivers. We conducted a large LGS caregiver survey to assess sleep quality, duration, specific sleep-related issues, and management strategies. Quantitative analysis revealed a significant association between nocturnal seizures and overall seizure frequency, but general sleep metrics in individuals with LGS (e.g., sleep hours, sleep quality) were not associated with seizure burden. In contrast, caregiver sleep was frequently disrupted - particularly in families who co-slept or reported frequent seizures. Nearly 40% of caregivers experienced nightly sleep disturbances. Respondents reported a range of strategies to manage sleep problems, including medication, environmental adjustments, and behavioral routines. Many also expressed frustrations over the limited effectiveness of strategies and inadequate support for sleep issues. These findings underscore the dual impact of LGS-related sleep disturbances on patients and caregivers, and highlight the need for comprehensive, family-centered approaches to sleep health.

Introduction Maintaining long-term positive airway pressure (CPAP) adherence is a major challenge when treating obstructive sleep apnea (OSA). We evaluated factors associated with one-year CPAP adherence and developed models predicting Month-12 adherence and CPAP-use trajectories. Methods CPAP-naive adults with OSA (apnea-hypopnea index[&ge;]5) at Kaiser Permanente Southern California with 364-day observation period were analyzed (development/internal-validation cohort, n=14,906; temporal holdout, n=1,676). Logistic regression evaluated baseline associations with Month-12 adherence (mean[&ge;]4 hours/night during days 331-360). XGBoost models utilized baseline features (clinical, sleep-study, questionnaire data), early CPAP data (days 1-7), or combined to predict long-term CPAP use. A latent-class model identified trajectories over days 8-364. Predictive models were evaluated in both random and temporal holdouts. Results About one-third (34.6%) of the primary cohort (49.7{+/-}13.8 years, 63.2% male) were adherent during Month-12. Higher AHI and longer self-reported sleep duration favored adherence while greater comorbidity burden, depression, and substance-use disorder were associated with lower adherence. In held-out testing, adherence prediction using baseline features yielded an AUROC of 0.678. Using CPAP-only data, AUROCs were higher from 0.787 with seven days of usage data to 0.816 and 0.854 with 14 and 30 days. Models combining CPAP and baseline features increased Day-7 AUROC to 0.810. We identified four one-year trajectories: Early-Decliners (42.9%), Gradual-Decliners (17.4%), Stable-Moderate (19.0%), Stable-High (20.7%). Models predicting patient trajectory revealed macro-average AUROCs of 0.619, 0.756, and 0.770 for baseline, CPAP-only, and combined models. Conclusion Baseline predictors may identify strategies for adherence support, while early CPAP data substantially improved predictive performance. These models may guide personalized follow-up but should not restrict CPAP access. External and implementation validation are needed.

Abstract Importance: Socially vulnerable patients have a high burden of obstructive sleep apnea, but the stage of the referral pathway at which access barriers arise is uncertain. Objective: To determine whether area-level social deprivation was associated with appointment scheduling, wait time, cancellations, or no-shows among adults referred for specialist obstructive sleep apnea care. Design: This was a cross-sectional study evaluating patients referred from December 1, 2016 through November 30, 2019. Data were analyzed from January 30, 2026 to May 16, 2026. Setting: Foothills Medical Centre Sleep Centre in Calgary, Canada. Participants: Adults referred to a tertiary academic sleep centre in Calgary, Alberta, Canada. Eligible patients had valid provincial health insurance and either a scheduled clinic appointment or home sleep apnea test data available. Exposures: Quintiles of the four Canadian Index of Multiple Deprivation domains: residential instability, economic dependency, ethnocultural composition, and situational vulnerability. Main Outcomes and Measures: The primary outcome was receipt of a scheduled specialist appointment. Secondary outcomes were time from referral to the first attended appointment and number of appointment cancellations or no-shows. Results: Among 3111 patients (mean [SD] age, 53.7 [14.3] years; 40.7% female), 1766 (56.7%) were scheduled and 1647 (52.9%) attended an appointment. Each quintile increase in situational vulnerability was associated with lower odds of scheduling (adjusted odds ratio [95% confidence interval] 0.86 [0.81-0.92]), whereas each quintile increase in ethnocultural composition was associated with higher odds (adjusted odds ratio [95% confidence interval] 1.32 [1.22-1.43]). Residential instability and economic dependency were not associated with scheduling. No deprivation domain was associated with time to the first attended appointment, cancellations, or no-shows. Conclusions and Relevance: In this cohort, area-level deprivation was associated with whether patients were scheduled for an appointment but not with wait time or missed visits after scheduling. These findings suggest that equity interventions should focus on completion of referral and scheduling processes.

Study Objectives: Sleep disturbances are common in youth with anxiety disorders, yet most prior work relies on subjective measures and does not capture underlying neural processes. We examined whether alterations in non-rapid eye movement (NREM) sleep oscillations are present in children with anxiety disorders and whether these features relate to internalizing symptoms and emotional functioning. Methods: Children aged 9-13 years with diagnosed anxiety disorders (ANX; n = 17) and typically developing non-psychiatric comparison participants (NC; n = 26) completed overnight polysomnography. Slow oscillations (SO) and slow and fast sleep spindles were characterized using validated automated detection algorithms. Participants also completed a pre-sleep emotional processing task, and parents provided standardized measures of youth mental health. Group differences and dimensional associations between NREM oscillations, internalizing symptoms, and behavioral indices of emotional processing were examined. Results: Sleep architecture did not differ between groups. Slow spindle duration was shorter in the ANX group. Across the full sample, shorter slow spindle duration was associated with greater internalizing symptoms, whereas higher SO density was positively associated with internalizing symptoms. Children in the ANX group exhibited greater facial emotional arousal and more frequent look-away behavior during the emotional processing task. Across participants, greater emotional arousal and look-away behavior were associated with shorter spindle duration during subsequent sleep. Conclusions: Specific features of NREM microarchitecture were associated with individual differences in internalizing symptoms and emotional functioning during early adolescence. These findings highlight the potential relevance of NREM oscillations for understanding developmental links between sleep and youth psychopathology.

Introduction Sleep is routinely assessed by psychiatric mental health nurses in acute inpatient wards, where overnight observation forms part of everyday clinical care. These records are repeatedly collected, but observational assessment and activity-based monitoring capture different aspects of sleep and may not be interchangeable. Aim To examine the agreement between actigraphy-based total sleep time and routine nursing sleep assessments in an acute psychiatric inpatient ward. Method This cross-sectional observational study included 94 patients admitted to an acute psychiatric ward. Participants wore wrist actigraphy devices for up to seven consecutive days while sleep duration was concurrently documented by nursing staff as part of routine care. Pearson correlation coefficients assessed linear association, and intraclass correlation coefficients (ICC) assessed agreement. Results Across all participants, nursing-assessed and actigraphy-based total sleep time showed a moderate linear association (Pearson r = 0.629) and good ICC (0.767). Bland-Altman analysis showed a mean bias of 0.20 hours, with 95% limits of agreement from -1.79 to 2.19 hours. Agreement varied across diagnostic groups and was strongest among patients with bipolar mania (r = 0.826; ICC = 0.886). Discussion Routine nursing sleep assessments showed little systematic bias relative to actigraphy at the group level, but wide limits of agreement indicate that the methods are not interchangeable for individual patients. Patient-reported sleep was not measured and should be included in future comparisons.

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.

Background: Shiftwork disrupts natural sleep-wake cycles, alters light exposure patterns, and contributes to circadian misalignment. Detrimental health consequences associated with shift work include elevated risk for metabolic disorders, cardiovascular disease, cancer and all-cause mortality. Healthcare workers have one of the highest rates of shift work exposure, yet there are relatively few non-pharmacological interventions (with good evidence) developed to improve sleep outcomes in this population. Objective: A pre-post pilot interventional study assessed the acceptability and perceived effectiveness of commercial noise-masking earbuds on improving subjective sleep characteristics among National Health Service (NHS) healthcare staff working fast rotating shifts. Methods: Noise-masking sleep earbuds (Kokoon NightBuds) were worn for a pilot six-week intervention by twenty-seven NHS nurses (aged 26-43 years, 88.9% female) working fast rotating shifts from the EClocker Study. Sensors inside the earbuds were paired with a smartphone app to monitor sleep. An audio library in the smartphone app delivered personalised relaxation exercises and sleep techniques drawn from cognitive behavioural therapy for insomnia (CBT-I). A pre-post two-week monitoring period with daily smartphone-based Experience Sampling Methods (ESM) captured perceived daily sleep patterns. Acceptability and perceived effectiveness of the earbuds in promoting better sleep outcomes was assessed. Results: Use of the noise-masking sleep earbuds over a six-week period was associated with positive sleep improvement trends and elicited promising acceptability. Almost two thirds of NHS fast rotating shift nurses (63%) subjectively reported reductions in general sleep disturbance symptoms (PSQI Global), one in four experienced perceived sleep quality improvements (SQ; 25.9%), one in five reported sleeping longer (TST; 22.2%), and a third perceived falling asleep faster (SOL; 33.3%), had better sleep efficiency (SE; 33.3%) and improved daytime dysfunction (33.3%) (PSQI subcomponent scores). Sleep diaries (CSD) collected daily using smartphone-based ESM also demonstrated small improvements post-sleep earbud use; nurses reported sleeping an average 18 minutes longer (TST) and fell asleep more easily, on average 11 minutes faster (SOL). Sleep earbuds were generally well tolerated; 56% of nurses reported the earbuds as (somewhat to very) helpful, 52% reported (somewhat to strongly) falling asleep more easily (SOL), 44% felt (somewhat to strongly) their sleep quality was improved (SQ) and 30% agreed (somewhat to strongly) they slept longer (TST) and had less disturbed sleep. Conclusions: To our knowledge, this is the first study in Europe to pilot noise-masking earbuds as a potential non-pharmacological aid to improve sleep-wake behaviours or mitigate fatigue for healthcare staff. Preliminary results showed promising acceptability and (small) perceived sleep improvement trends following a targeted six-week earbud intervention in NHS fast rotating shift nurses.

The development of the sleep-wake cycle reflects the progressive structural and functional maturation of the brain. However, the organization of neural dynamics during prematurity remains incompletely understood. In this study, we analyzed the EEG from 54 polysomnographic recordings obtained from 39 preterm infants, grouped according to postmenstrual age (PMA) into three categories: 30-31, 32-33 and 34-35 weeks. Lempel-Ziv Complexity (LZC) and Joint Lempel-Ziv Complexity (JLZC) of the electroencephalogram (EEG) were analyzed during active sleep (AS), quiet sleep (QS), and indeterminate sleep (IS). LZC computed from the raw, unfiltered recordings were significantly higher during QS than during AS and increased with PMA during AS. To further refine the analysis, LZC was also evaluated separately in the low-frequency (1-15.5 Hz) and high-frequency (16-30 Hz) EEG bands. In the low-frequency band, LZC was consistently higher during QS than during AS, an effect that was most pronounced in more immature groups. Furthermore, LZC increased with maturation particularly during AS. Sleep-state comparisons of LZC in the high-frequency EEG band also revealed higher values during QS than during AS across all PMA groups. Moreover, in contrast to the low-frequency band, LZC progressively decreased with advancing PMA both in AS and QS, suggesting that the neural mechanisms underlying low- and high-frequency EEG activity follow distinct maturational trajectories. Interestingly, larger LZC in the temporal cortex and interhemispheric differences were detected in the 32-33 PMA group. On the other hand, JLZC analysis revealed greater joint spatiotemporal dynamics across EEG channels during QS than during AS, with consistently higher JLZC values in temporal regions and lower in occipital regions. Together, these findings show that these complexity metrics distinguishes sleep states and captures maturational changes in EEG activity in preterm infants. These results provide novel insights into early brain development and suggest potential quantitative biomarkers of neonatal brain maturation.

Sleep disorders are highly prevalent and impose a substantial public health burden, yet the neurobiological mechanisms underlying shared and disorder-specific abnormalities remain incompletely understood. We conducted a pre-registered transdiagnostic study to identify shared and distinct normative macroscale connectivity patterns associated with reported brain abnormalities across insomnia disorder, obstructive sleep apnea, narcolepsy, and rapid eye movement sleep behavior disorder, and to characterize their microscale neurotransmitter and cellular correlates. We obtained peak coordinates from 80 experiments reporting meta-analytic brain abnormalities across these four sleep disorders. Convergent connectivity mapping was then applied to these coordinates using dense connectivity matrices derived from 100 unrelated participants from the Human Connectome Project, with connectivity patterns evaluated relative to a null distribution generated from randomly selected foci. We subsequently examined the spatial correspondence of the identified connectivity maps with 19 neurotransmitter systems using curated positron emission tomography-derived density maps and assessed cellular enrichment across 24 cell-type systems using regional gene-expression profiles from the Allen Human Brain Atlas. Statistical significance was assessed using spin-based permutation testing and false discovery rate correction (pFDR < 0.05). Shared convergent connectivity patterns were identified within the default mode network (zmean = 1.103, pspin,FDR = 0.019), as well as the dorsal attention (zmean = -0.674, pspin,FDR = 0.014) and frontoparietal (zmean = -0.239, pspin,FDR = 0.019) resting-state networks. Disorder-specific analyses revealed broadly overlapping connectivity patterns, with some disorder-related variability. Both transdiagnostic and disorder-specific connectivity patterns showed selective spatial correspondence with multiple neurotransmitter systems, including serotonin, dopamine, norepinephrine, and glutamate, and were enriched for excitatory, inhibitory, and non-neuronal cell types (pFDR < 0.05). These findings indicate that sleep disorders share convergent macroscale connectivity architecture while also exhibiting disorder-specific variations, with the identified network patterns linked to distinct neurotransmitter and cellular profiles. Together, the results provide a normative systems-level framework for understanding shared and distinct neurobiological mechanisms across sleep disorders.

Introduction: Daytime sleepiness (DS) can compromise athletic performance; however, its influence on Brazilian Jiu-Jitsu (BJJ) athletes remains poorly understood. Daytime naps have been suggested as a strategy to alleviate the effects of DS; however, their impact on physical and specific performance remains inconsistent. Thus, this study investigated the influence of different levels of daytime sleepiness on the physical and specific performance of BJJ athletes and verified the effects of daytime napping on these outcomes. Methods: This investigation employed a randomized, controlled, crossover clinical trial design involving 19 amateur Brazilian Jiu-Jitsu (BJJ) athletes who exhibited excessive daytime sleepiness. Participants performed two experimental conditions, with a 30-minute nap (N30) and without a nap (NN), in a randomized order. Vertical jump (CMJ and SJ), anaerobic power (Wingate ), kimono grip strength (KGST), and specific performance were evaluated using the Jiu-Jitsu Anaerobic Performance Test (JJAPT). Data were analyzed using mixed linear models. Results: Daytime sleepiness significantly influenced all parameters of specific performance (JJAPT), with worse performance observed in athletes classified as having abnormal sleepiness. The opportunity to nap increased the total number of repetitions in the JJAPT of four sets ({approx}20 repetitions; p < 0.001), without consistent changes in physical performance tests. An interaction between napping and sleepiness was observed in relation to isometric endurance strength, indicating that napping may provide greater benefits to athletes experiencing abnormal sleepiness. Conclusion: High levels of daytime sleepiness are associated with reduced specific performance in BJJ athletes. Daytime naps are a simple and potentially effective strategy to mitigate these effects and improve specific performance, reinforcing the importance of monitoring sleepiness and individualizing recovery strategies.

Background Sleep, mood, and affective states are mutually connected. There is a paucity of studies, however, that have considered bidirectional relationships between daily sleep-affective dyads in naturalistic settings, particularly for shift workers. Objective To evaluate the dynamic and temporal interplay of daily smartphone-based self-reported sleep measurements, dimensions of affective experience and cognitive processing in UK shift working nurses. Methods The EClocker Study prospectively monitored 102 National Health Service (NHS) nurses (aged 25-61 years, 83.3% female) working standard (day shift) and non-standard (fast rotating shifts) schedules over a two-week period. Smartphone-based Experience Sampling Methodology (ESM) recorded daily sleep, mood, momentary affect and cognitive attentional functioning. Self-reported burnout, emotional dysregulation, emotion reactivity and affective dimensions (positive and negative) were also collected. Findings Overall, NHS nurses reported a high prevalence of depressive symptoms, stress, burnout and sleep-circadian rhythm disturbances. Generalised Additive Modelling (GAMs) revealed that NHS nurses higher perceived sleep quality predicted better next-day mood state, while better daytime mood was associated with reduced sleep onset latency, such that participants reported falling asleep faster. In contrast, daytime mood or affect (positive and negative) had no substantial, direct impact on nurses subjective sleep parameters (sleep quality, sleep duration, sleep efficiency). Exposure to fast rotating night shifts across the two-week study was associated with more frequent response errors on a Choice Reaction Time (CRT) cognitive task, while daytime somnolence did not adversely influence nurses momentary reaction time speeds or attentional function. Conclusions Clinically relevant sleep impairments, insomnia-related symptoms, elevated stress, and poor mood were pervasive in a sample of UK NHS nurses, regardless of shift type. Sleep quality impacted next-day mood and daytime mood impacted sleep latency, while rotating shifts led to an increase in cognitive errors. Recognising the impact of shiftwork and designing interventions to promote better sleep quality offer potential to enhance mood and performance in healthcare professionals. Clinical implications We need to implement and evaluate interventions that regularise sleep patterns and promote sleep quality to alleviate mood symptoms among frontline NHS shift workers.