Sleep advances : a journal of the Sleep Research Society最新文献

Study objectives: The current study employed natural language processing (NLP) to capture multidimensional and transdiagnostic information in pediatric clinical notes. We present a novel, low-resource sleep vocabulary that can be applied to notes to identify pediatric sleep-related mentions automatically.

Methods: Using a combination of existing medical sleep ontologies, interviews with clinicians, and examination of clinical note narratives, we develop a novel vocabulary of pediatric sleep-related terms and phrases that covers both technical terms, abbreviations, and colloquial keywords used in describing pediatric sleep health. We compare our vocabulary against a set of manually annotated clinical notes to determine the effectiveness of our vocabulary for identifying notes with pediatric sleep-related mentions.

Results: Our vocabulary was able to correctly identify clinical notes with pediatric sleep-related mentions with a recall of 0.992 and a precision of 0.852. Most false positives occurred in notes that either explicitly stated no sleep issues or contained text unrelated to patient sleep health (e.g. medication side effects). Among the text spans annotated as sleep-related mentions, 77.1% include at least one keyword from our vocabulary.

Conclusions: Our vocabulary showed excellent performance for identifying pediatric sleep-related mentions at the clinical note level and decent performance for identifying the specific text containing patient mentions. Our low-resource vocabulary, which can be deployed in almost any compute environment, can serve as an identifying first pass over clinical notes to identify which notes or note sections should be further processed by more advanced models or manual annotation review to identify more narrow mentions.

Central disorders of hypersomnolence (CDoH), including the primary hypersomnolence disorders of narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), idiopathic hypersomnia (IH), and Kleine-Levin syndrome (KLS), as well as secondary hypersomnolence disorders, represent an underdiagnosed and under-treated population. Continuing advancements in understanding and treating CDoH rely on an understanding of the patient and caregiver experience. To address this need, a community-led, patient-owned online research study was launched by the nonprofit organizations Sleep Consortium and Global Genes, using the RARE-X research platform. An expert working group of stakeholders with expertise in hypersomnolence disorders, including clinicians, therapy developers, and patient advocates, was convened to identify key patient- and caregiver-reported clinical outcome measures essential for evaluating CDoH symptoms and impacts. These clinical outcome measures have been implemented as part of an online direct-to-patient study. The measures chosen by the Sleep Consortium Expert Working Group are presented here with the hope of supporting the standardization of clinical outcome assessments being used in CDoH research, especially for primary hypersomnolence disorders.

Study objectives: Chronic sleep loss is widespread and can cause deficits in vigilant attention. There are few practical biomarkers that can be used to assess the impact of chronic sleep loss on alertness and performance. Those that are available, such as tests of vigilant attention, cannot discriminate chronic sleep loss from other impairments, like alcohol intoxication, limiting the use of such tools. We aimed to test whether a suite of behavioral oculometrics would be sensitive, and perhaps specific, to chronic sleep restriction (CSR).

Methods: We conducted a randomized, counterbalanced, cross-over study to compare behavioral oculometrics following a week of 5 h of time in bed versus 9 h of time in bed. Each experimental week was preceded by a washout week of 9 h nightly time in bed. Participants completed a visual tracking task every 2 h that allowed us to evaluate behavioral oculometrics associated with visual motion processing, pursuit, and saccade behavior. We used mixed-effects models to compare outcomes.

Results: Twelve participants completed the 4-week study (6 female). We observed modest decrements in pursuit and tracking smoothness, and maladaptive saccadic behavior, where the rate of saccades increased during CSR, with an apparently compensatory decrease in catch-up saccade amplitude. Visual motion processing of direction, but not speed, was altered.

Conclusions: Eye movements are a sensitive measure that can detect sensory and motor impairments from 1 week of 5 h of CSR, potentially distinguishable from those due to other causes, and thus may be a useful biomarker for identifying the source of impairment.

Study objectives: Craniopharyngioma is a suprasellar brain tumor often associated with hypersomnia, which may be potentially due to alterations in sleep architecture. We performed cross-sectional and longitudinal assessments of sleep architecture in pediatric patients with craniopharyngioma.

Methods: We evaluated 94 patients (median age 9) enrolled in a clinical trial that included limited surgery with proton radiotherapy or radical surgery with observation. Nocturnal polysomnography and multiple sleep latency testing were performed at baseline and at 36 months after enrollment. Sleep architecture was compared to that of healthy children reported by Zhu et al., as well as longitudinally and across hypersomnia diagnostic groups using standard median comparison tests. Chi-squared tests assessed longitudinal changes in prevalence of hypersomnia disorders.

Results: Pediatric patients with craniopharyngioma exhibited 1.0% and 1.5% lower N1 and 9.21% and 9.59% lower N2 sleep (p < .001) at baseline and 36 months than healthy counterparts. N3 sleep was elevated by 11.84% and 13.47% respectively (p < .001). At 36 months, 2.63% less time was spent in REM (p = .005). Analysis of the entire cohort revealed no statistically significant longitudinal changes in sleep architecture (p > .05). However, in a subset of patients with advancing Tanner stage, N1 sleep decreased and REM sleep increased over 36 months. There were no longitudinal changes in prevalence of hypersomnia disorders (p > .05), nor differences in sleep architecture between patients with and without hypersomnia.

Conclusions: Children and adolescents with craniopharyngioma exhibit atypical sleep architecture following surgery, that persists with long-term follow-up. Further research is needed to determine if sleep architecture mediates hypersomnia.

Individuals working nonstandard hours face a range of negative health, safety, and productivity outcomes, largely driven by sleep disruptions associated with their schedules. Existing interventions to improve sleep often adopt a one-size-fits-all approach, overlooking the diversity of individual needs, preferences, and work contexts. These factors are critical considerations for any intervention aiming to improve the sleep of individuals working nonstandard hours as schedules can differ dramatically, both between individuals and within an individual's schedule. Advances in wearable consumer sleep technology and artificial intelligence, like the use of reinforcement learning and large language models, now offer the opportunity for highly tailored just-in-time-adaptive-interventions (JITAIs), or digital interventions that adapt to individuals' unique contexts to provide personalized, timely behavioral support. This paper proposes that integrating artificial intelligence and wearable consumer sleep technology with JITAIs has the potential to deliver the right support, at the right time, and in the right context for each individual nonstandard-hour worker. By directly responding to the unpredictable and variable hours these workers face, such technologies could set a new standard for personalized health, safety, and productivity interventions. Challenges associated with incorporating artificial intelligence and wearable consumer sleep tracking devices into JITAIs, such as trust, technological and algorithmic inaccuracies, user engagement, and cost, are also discussed as key considerations for successful implementation. This paper is part of the Consumer Sleep Technology Collection.

The non-rapid-eye movement (NREM) sleep power spectrum is composed of rhythmic and arrhythmic components, respectively associated with brain rhythms and scale-free dynamics. Both components are hypothesized to represent distinct processes underlying sleep-dependent memory consolidation as well as other brain complex processes. Recent advancements in spectral parametrization techniques and the use of multifractal models have enabled new insights into these components and their connection to brain networks. Aging impacts NREM sleep oscillations, but no animal studies considered the impact of age on rhythmic and arrhythmic components. In this study, we assessed the effects of age on the power spectrum and its two components using local field potential recordings in mice. We recorded across the cerebral cortex and within the hippocampus-a central brain hub involved in NREM sleep-dependent cognitive processing. Ten younger (7.6 months) and eleven older (15.7 months) C57BL/6 J male and female mice were continuously recorded over a 24-hour period. We extracted the NREM sleep standard and rhythmic spectra, controlled for scale-free activity, and estimated multifractal arrhythmic properties during NREM sleep using the Wavelet Leader and Bootstrap based MultiFractal analysis toolbox. Older mice showed specific alterations in both components compared to younger animals: reduced rhythmic gamma power in the anterior cortex, greater regional differentiation of scaling exponents, and higher multifractal dispersion of the arrhythmic component in the hippocampus. These findings demonstrate that aging alters rhythmic and arrhythmic properties of NREM sleep in distinct brain regions, suggesting that both contribute to age-related changes in sleep-dependent cognition.

Study objectives: Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Among the latter, sleep disturbances are particularly common and include insomnia, obstructive sleep apnea (OSA), and excessive daytime sleepiness. Here, we investigated the shared genetic architecture between PD and sleep-related traits to uncover biological pathways that underpin this relationship.

Methods: We analyzed genome-wide association study (GWAS) summary statistics for PD (~37.7 K cases, ~18.6 K proxy cases, ~1.4 M controls) and eight self-reported sleep-related traits (each with n > 300 000): ease of getting up, chronotype (morningness), napping, insomnia, OSA, snoring, daytime dozing, and sleep duration. Genetic correlations were estimated using Linkage Disequilibrium (LD) score regression, and GWAS-pairwise analysis was used to identify genomic segments harboring shared causal variants. We then mapped these variants to protein-coding genes.

Results: We observed a genome-wide genetic correlation between PD and daytime dozing (p < .05). A separate, local-level analysis identified six genomic regions harboring shared causal variants between PD and other sleep-related traits (primarily ease of getting up and napping). The most statistically significant of these local associations was observed at a single locus on chromosome 17, which contains the majority of mapped protein-coding genes, including ARHGAP27, PLEKHM1, CRHR1, and MAPT. These genes are implicated in neurodegeneration and circadian rhythm regulation.

Conclusions: These findings suggest that the MAPT locus, beyond its established role in PD, may also contribute to sleep-wake regulation via shared biological pathways, including tau pathology, stress response, and chromatin remodeling. Our results highlight sleep disturbances as a potential early marker or risk factor of PD.

Study objectives: National prevalence estimates for idiopathic hypersomnia (IH) are difficult to obtain. This study estimated the diagnosed IH prevalence among US adults.

Methods: Symphony Integrated Dataverse claims (01/2015-12/2023) were analyzed. Eligible patients were aged ≥18 years with at least one medical/prescription claim in the year of interest (2019-2023) and prior year. IH was defined by ≥1 medical claim with an IH diagnosis code. Prevalence was estimated among all eligible patients in two ways: annual (IH diagnoses during year of interest) and all-time (IH diagnoses looking back all-time in the database from 2015 through year of interest). Age- and sex-adjusted prevalence estimates were also calculated using the US Census Bureau.

Results: Over 179, 182, 193, 205, and 198 million adults were assessed for diagnosed IH prevalence in each respective year 2019-2023. Unweighted annual prevalence of diagnosed IH from 2019 to 2023 was 12.1, 11.1, 11.0, 10.5, and 11.1 per 100 000 persons, respectively. Unweighted all-time lookback prevalence of diagnosed IH from 2019 to 2023 was 32.7, 37.3, 40.6, 43.3, and 49.0 per 100 000 persons, respectively. From 2019 to 2023, estimated standardized numbers of US adults diagnosed with IH were 30 563, 27 975, 27 859, 26 624, and 28 754 based on annual prevalence, and 82 027, 93 768, 101 766, 107 763, and 124 905 based on all-time prevalence.

Conclusions: Annual prevalence estimates (i.e. proportions of individuals with diagnosed IH during each year of interest) remained consistent across the follow-up period, ranging from 10.5 to 12.1 per 100 000 persons, signifying the rarity of the diagnosis.

Study objectives: Sleep monitoring outside of clinics could enhance care for insomnia and other sleep disorders but requires home systems that are easily operable and provide consistent data quality over multiple nights. We assessed the Waveband for usability by participants and feasibility of obtaining multi-night sleep data in the home setting.

Methods: 15 subjects with insomnia wore the Waveband electroencephalogram headband and an FDA-cleared wearable home sleep testing device, WatchPAT ONE ("WP1") for three nights. Usability was assessed via the System Usability Scale (SUS). Feasibility of participants to collect data was evaluated by examining stability of measured total sleep time in relation to measurements from the reference device (WP1) and data quality as evaluated by three human experts.

Results: Average SUS score was 69.7, meeting the 68-point threshold for good usability. Total sleep time recorded by the Waveband and WP1 devices showed a correlation of 87.3 per cent. All the recordings had an average of over 7 scorable hours of data per night.

Conclusions: Waveband demonstrated good usability by patients, was operable by patients, and generated interpretable data that provided stable sleep estimates across nights, comparable to an established home sleep testing device. The device has potential to advance patient care, sleep research, and clinical trials by enabling longitudinal ambulatory sleep assessment.