Journal of Sleep Research最新文献

Night shift work can impair attention and increase biomarkers linked to neurodegenerative processes. Understanding factors that influence resilience to, or vulnerability under, sleep loss is therefore essential for identifying groups at particular risk. In this within-subjects study, we examined two potential modulators of vulnerability in 54 healthy, naturally cycling women aged 21-33 years: the ovarian hormone estradiol, known for its neuroprotective properties and the personality trait neuroticism, previously associated with stress sensitivity. Participants completed one night of habitual sleep followed by one night of overnight wakefulness, mimicking a transition from an off- to an on-night shift. Women with higher morning blood estradiol levels or lower neuroticism (indexed by higher emotional stability scores) exhibited non-significant tendencies towards faster reaction times during successful psychomotor vigilance test (PVT) trials, which assess sustained attention. Notably, lower neuroticism was also associated with significantly fewer attentional lapses (reaction times ≥ 500 ms) during the PVT, whereas estradiol levels were not. However, neither trait modulated the overall decline in attentional performance observed following the night-shift condition. In contrast, higher neuroticism-but not estradiol-predicted elevated morning levels of phosphorylated tau at threonine 181 (p-Tau181), a plasma biomarker associated with Alzheimer's disease-related neurodegenerative processes, after the night shift condition. These findings highlight neuroticism as a psychological factor linked to increased neurobiological sensitivity to overnight wakefulness among women.

The cyclic alternating pattern is a hallmark of the dynamic organisation of non-rapid eye movement sleep, reflecting the brain's oscillatory regulation of arousal and sleep stability. Since the original publication of the rules for scoring the cyclic alternating pattern in 2001, major advances in sleep neurophysiology, signal analysis and international sleep staging standards have necessitated a comprehensive revision. This consensus report by a Taskforce of the European Sleep Research Society presents the updated 2025 Atlas and Rules for the scoring of the cyclic alternating pattern in human sleep. The new guidelines preserve the foundational framework of the cyclic alternating pattern while introducing several critical innovations: (i) expanded definitions and a broader catalogue of Phase A electroencephalographic patterns; (ii) refined onset and offset criteria based on amplitude and frequency shifts, with strict inter-lead concordance requirements; (iii) standardised metrics for the quantification and reporting of the cyclic alternating pattern; (iv) formal guidance for scoring the cyclic alternating pattern in paediatric populations and optional scoring in rapid eye movement sleep; and (v) a technical framework for computerised detection and analysis. These updates align scoring with contemporary sleep staging standards, enhancing precision, reproducibility and applicability across research and clinical contexts.

Few studies have objectively measured both screens and sleep in real-world settings. This study uses repeated measures to assess heart rate during evening screen use, providing new insights into how these behaviours relate to adolescent sleep. Screen use was recorded using wearable cameras over four nights in 70 youth (37% indigenous Māori, 42% female) aged 11-14.9 years. Heart rate was measured via a Fitbit Inspire 2. Mixed effects regression models were used to estimate within-person differences in heart rate across screen behaviours and time periods, as well as associations with sleep outcomes. Median heart rate was lower during screen use than during non-screen activities (83 BPM, [IQR: 77-91] vs. 93 [IQR: 87-100]). Social media use most proximal to bedtime was associated with slightly lower heart rate compared to other screen activities (-3 BPM, 95% CI: -5, -1), while communication was associated with slightly higher heart rate (3 BPM, 95% CI: 1, 5). Heart rate in the 2 h before bed was not associated with sleep outcomes except for sleep latency; which increased by 9 min (95% CI: 3, 14) for every 10 BPM increase in heart rate. Evening screen use may be a relaxation tool for youth, with social media, gaming and multitasking having minimal physiological impact. In contrast, communication activities were less conducive to relaxation, though differences were small and effects on sleep only modest. Thus, relationships between screen use and sleep are more likely driven by changes in sleep timing rather than physiological arousal from screen exposure. Trial Registration: Australian and New Zealand Clinical Trials Registry: ACTRN12621000193875.

K-complexes (KCs) are hallmark waveforms of non-rapid eye movement stage 2 (NREM2) sleep, associated with sleep maintenance and memory consolidation. KC density and amplitude decline with ageing and are further altered in amnestic mild cognitive impairment (aMCI). Manual scoring, while considered the gold standard, is labour intensive and subjective. Existing automated KC detectors, often trained on small public datasets of young healthy subjects using single-channel electroencephalography (EEG), may underperform in elderly aMCI individuals whose KC morphology is more variable. Hence, the goal of this study is to develop and validate AdaPatchFormer, an automated multi-channel Transformer-based KC detection algorithm optimised for elderly individuals with aMCI. AdaPatchFormer integrates a period embedding module, which adaptively identifies physiologically relevant rhythms across multiple frequency bands, with a multi-granularity encoder that progressively fuses temporal features across channels. The model was trained on full-night polysomnography (PSG) recordings from 268 elderly aMCI patients and evaluated against expert-labelled gold standards on four independent test datasets: private aMCI and cognitively normal cohorts, plus two public elderly cohorts. Across all datasets, AdaPatchFormer outperformed the two open-access detectors by Chambon et al. and Lechat et al., achieving higher recall, precision, specificity, accuracy, F1 score, Matthews correlation coefficient (MCC) and a well-balanced recall-precision profile. Moreover, the KC density and amplitude detected by AdaPatchFormer closely mirrored expert annotations. These results suggest that AdaPatchFormer is a robust, accurate, and objective algorithm for KC detection in elderly individuals, with the potential for supporting early and cost-effective identification of aMCI in real-world settings.

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application to EEG recorded by wearable devices remains limited by non-conventional referencing montage and the lack of benchmarking against PSG. Here, we tested whether an ensemble of state-of-the-art staging algorithms can reliably classify sleep from a customised configuration of the ZMax headband, adapted to record a fronto-mastoid EEG channel. Thirty-five nights of simultaneous ZMax and PSG recordings were acquired in a home setting, yielding 250.02 h of data from 10 healthy participants. PSG data were scored according to AASM criteria by two independent experts, with discrepancies resolved to obtain a consensus hypnogram. ZMax signal was processed using four machine-learning algorithms (YASA, U-Sleep, SleepTransformer, DeepResNet), whose predictions were combined into a final ensemble scoring through soft voting. Quantitative/qualitative analyses of NREM slow waves and spindles evaluated the preservation of microstructural features across recording systems. The ensemble scoring achieved almost perfect agreement with human consensus staging (night-level mean ± SD; accuracy = 88.83% ± 2.84%, Cohen's κ = 84.10% ± 4.52%, and Matthews Correlation Coefficient = 84.54% ± 4.23%). It showed excellent classification efficiency for REM (F1-score = 93.99%), N3 (89.53%), N2 (87.93%), and wakefulness (86.37%), with lower performance for N1 (53.20%). Microstructural comparisons confirmed strong correspondence between ZMax and PSG signals. These findings support the deployment of an ensemble scoring approach based on state-of-the-art sleep staging algorithms on ultra-minimal EEG setups. This paradigm advances the integration of data from modern wearable technologies into traditional PSG-based sleep research, overcoming longstanding barriers to ecological, large-scale sleep monitoring.

Reciprocal relations between physical activity and sleep in early childhood are inconsistent relative to reports in adolescents and adults. Compositional data analysis research highlights the need to examine 24-h behaviours holistically. Yet, studies often focus on daytime metrics, neglecting sleep components. This study aimed to determine if the compositions of overnight sleep, 24-h sleep, and nap sleep stages are associated with physical activity in preschool children and if behaviours of a complete 24-h cycle (sleep and wake) vary by sex or nap habituality. Actigraphy data pooled from two studies (n = 272 children; 4.2 ± 0.8 years) provided estimates of sleep and physical activity. Night and 24-h sleep composition included sleep onset latency, duration, and wake after sleep onset. Nap sleep was measured in 31 children with polysomnography (wake and non-REM sleep stages 1, 2 and 3). Nap sleep stage compositions were not associated with movement behaviours. Six multivariate regression models explored differences in compositional 24-h time use between sex and nap habitually groups. Time-use compositions that included both wake and sleep components varied by age, sex, and nap habituality for all components except total sleep time. This study demonstrates the value of CoDA for understanding 24-h behaviour patterns, revealing that nap habits, age, and sex are linked to specific sleep and activity components in preschoolers. Future research should explore these compositional associations in more diverse populations, consider additional physical activity indicators, and incorporate overnight polysomnography assessments.

Sleep extension has previously been shown to acutely benefit athletic performance. However, studies investigating the effects of sleep extension on fatigue and cognitive performance are lacking. In this randomised crossover trial, 22 elite youth hockey players (17 ± 1 years; 1.83 ± 0.07 m; 82 ± 7 kg) took part in a 3-week protocol during the pre-season. The first week included two familiarisation sessions. During the second and third week of the protocol, athletes underwent a testing session before and after a night of normal sleep vs. sleep extension (10 h of time in bed). For each athlete, these conditions were randomised across weeks. Each testing session consisted of 2 h of hockey training followed by a 30-min colour Multi-Source Interference Task (cMSIT). Three countermovement-jumps (CMJs), three handgrip contractions and 3-min psychomotor-vigilance tasks (PVTs) were performed pre-training, post-training, and post-cMSIT. Sleep was objectively monitored using actigraphy and sleep logs. Athletes slept normally 7:04 ± 0:39 h:mm. In the sleep extension condition, athletes increased their sleep by 16% ± 11% (p < 0.001; η_p ² = 0.72). Sleep onset latency, WASO, and sleep efficiency were similar across conditions (all p > 0.016; η_p ² ≤ 0.11). cMSIT performance and fatigue improved by 8% (p < 0.001; η_p ² = 0.21) and 23% (p = 0.022; η_p ² = 0.02), respectively, following sleep extension. Performance on the PVTs, CMJs and handgrip contractions, while changing between pre-training, post-training and post-cMSIT, remained similar across conditions (p > 0.13; η_p ² ≤ 0.01). These results suggest that acute sleep extension is beneficial for improving perceived fatigue and performance on a long and demanding cognitive task (cMSIT), with no changes in less demanding cognitive tasks (PVT) or short physical tests.

Polysomnography annotations in Sleep Heart Health Study (SHHS), Osteoporotic Fractures in Men Study (MrOS), and Multi-Ethnic Study of Atherosclerosis (MESA) scored apneas and hypopneas solely by flow reduction rather than following the American Academy of Sleep Medicine's (AASM) comprehensive criteria. To address this, we developed a standardized annotation pipeline that integrates sleep staging, oxygen desaturation, and arousal events in accordance with AASM guidelines. This retrospective study analyzed polysomnography data from SHHS1 (n = 5793), SHHS2 (n = 2651), MrOS1 (n = 2907), MrOS2 (n = 1026), MESA (n = 2054), and Korea Image-based Sleep Study (KISS) (n = 7745). We compared reported apnea-hypopnea indices (AHIs) with those derived from original annotations and recalculated values adjusted for sleep stage, desaturation, and arousal. The impact of precise annotation was demonstrated by training two deep learning models, one with original and the other with refined annotations, and comparing their performance in classifying obstructive sleep apnea (OSA) severity. AHIs from original annotations consistently overestimated reported values in SHHS, MrOS, and MESA, with mean absolute errors (MAEs) ranging from 10.3 to 23.6 events/h. After refining the annotations, MAEs were reduced significantly to 0.56-1.29 events/h. KISS, adhering to contemporary scoring guidelines, exhibited high baseline accuracy with an MAE of 0.6 events/h and required no additional refinement. With refined annotations, OSA severity classification F1 score rose from 0.5 to 0.69. Our standardized approach improves cross-cohort consistency, supports both clinical research and AI-based analysis, and enables more reliable use of existing sleep datasets in accordance with current clinical guidelines.