Clocks & Sleep最新文献

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

Evidence is gradually accumulating in support of the hypothesis that a process of thermostatic brain cooling and warming underlies sleep cycles, i.e., the alternations between non-rapid-eye-movement and rapid-eye-movement sleep throughout the sleep phase of the sleep-wake cycle. A mathematical thermostat model predicts an exponential shape of fluctuations in temperature above and below the desired temperature setpoint. If the thermostatic process underlies sleep cycles, can this model explain the mechanisms governing the sleep cyclicities in humans? The proposed nested doll model incorporates Process s generating sleep cycles into Process S generating sleep-wake cycles of the two-process model of sleep-wake regulation. Process s produces ultradian fluctuations around the setpoint, while Process S turns this setpoint up and down in accord with the durations of the preceding wake phase and the following sleep phase of the sleep-wake cycle, respectively. Predictions of the model were obtained in an in silico study and confirmed by simulations of oscillations of spectral electroencephalographic indexes of sleep regulation obtained from night sleep and multiple napping attempts. Only simple-inverse exponential and exponential-functions from the thermostatic model were used for predictions and simulations of rather complex and varying shapes of sleep cycles during an all-night sleep episode. To further test the proposed model, experiments on mammal species with monophasic sleep are required. If supported, this model can provide a valuable framework for understanding the involvement of sleep-wake regulatory processes in the mechanism of thermostatic brain cooling/warming.

Background: Bright light therapy (BLT) has not been well-studied in adolescents with major depressive disorder, particularly in outpatient settings.

Methods: We conducted an 8-week clinical trial of BLT in adolescents recruited from a primary care practice with moderate to severe major depression. Acceptability and feasibility were defined by daily use of the light box and integration into daily routines. To assess treatment effects, we utilized the Short Mood and Feelings Questionnaire (SMFQ) and actigraphic sleep variables.

Results: Of the nine enrolled adolescents, the rate of daily use of the light therapy box was 100% at week 2, 78% at week 4 (n = 7), and 67% at weeks 6 and 8 (n = 6). Participants were better able to integrate midday BLT compared to morning BLT into their day-to-day routines. Mean depression scores improved during the 2-week placebo lead-in (dim red light-DRL) and continued to show significant improvement through 6 weeks of BLT. Sleep efficiency increased significantly (p = 0.046), and sleep onset latency showed a trend toward a significant decrease (p = 0.075) in the BLT phase compared to the DRL phase.

Conclusion: Bright light treatment that was self-administered at home was feasible, acceptable, and effective for adolescent outpatients with depression. Findings support the development of larger, well-powered, controlled clinical trials of BLT in coordination with primary care.

The purpose of this paper is to investigate the impact of circadian lighting-induced melatonin suppression on patients with psychiatric and neurological disorders in hospital wards by using an ad-hoc metrology framework and the subsequent metrics formalized by the CIE in 2018. A measurement scheme was conducted in hospital ward rooms in the Department of Neurology, Zealand University Hospital, at Roskilde in Denmark, to evaluate the photometric and colorimetric characteristics of the lighting system, as well as its influence on the circadian rhythm of the occupants. The measurement scheme included point measurements and data logging, using a spectrophotometer mounted on a tripod with adjustable height to assess the newly installed circadian lighting system. The measured spectra were uploaded to the Luox platform to calculate illuminance, CCT, MEDI, etc., in accordance with the CIE S026 standard. Furthermore, the MLIT based on MEDI data logging results was calculated. In addition to CIE S026, we have investigated the usefulness of melatonin suppression models for the assessment of circadian performance regarding measured light. From the results, the lighting conditions in the patient room for both minimal and abundant daylight access were evaluated and compared; we found that access to daylight is essential for both illumination and circadian entrainment. It can be concluded that the measurement scheme, together with the use of the Luox platform and Canva template, is suitable for the accurate and satisfactory measurement of integrative lighting that aligns with CIE requirements and recommendations.

Rapid eye movements (REM) sleep density is the parameter proposed to explain the variability in the amount of eye movements during REM sleep. Alterations in REM sleep density have been proposed as a screening criterion for individuals with depression and other mental health conditions, but its accuracy has not been properly evaluated. The lack of consensus and the variability of the methods used to score it reduces the external validity of the results, hindering an adequate analysis of its diagnostic accuracy and clinical applicability. This scoping review aimed to identify and quantify the methods used to score REM sleep density, describing their main characteristics. A literature search was conducted in PubMed, Scopus, PsycInfo, and Web of Science. Only studies with objective measures for REM sleep density analysis in individuals with depression were considered eligible. The final sample comprised 57 articles, covering 64 analyses of REM sleep density. The relative frequency methods were the predominant measurement parameter for analyzing REM sleep density across studies. The most frequently adopted REM estimation unit was the number of REM events followed by mini-epochs containing REM. The most common unit of measurement were frequency/time measures. The results demonstrate that there is no consistency in the methods used to calculate REM sleep density in the literature, and a high percentage of studies do not describe their methods in sufficient detail. The most used method was the number of REM episodes per minute of REM sleep, but its use is neither unanimous nor consensual. The methodological inconsistencies and omissions among studies limit the replicability, comparability, and clinical applicability of REM sleep density. Future guidelines should discuss and include a specific methodology for the scoring of REM sleep density, so it can be consensually implemented in clinical services and research.

The gut microbiota (GM) plays a crucial role in human health. The bidirectional interaction between GM and the central nervous system may occur via the microbiota-gut-brain axis, possibly regulating the sleep/wake cycle. Recent reports highlight associations between intestinal dysbiosis and sleep disorders, suggesting that probiotics could ameliorate this condition. However, data are poor and inconsistent. The aim of this quantitative metanalytic study is to assess the GM composition in sleep disturbances and evaluate probiotics' effectiveness for managing sleep disorders. A systematic review was carried out until July 2022 in online databases, limiting the literature research to human studies and English language articles. No significant GM diversity between patients with sleep disturbances versus healthy controls was found, revealed by α-diversity, while β-diversity is missing due to lack of proper reporting. However, probiotics supplementation significantly reduced the self-assessed parameter of sleep quality and disturbances Pittsburgh Sleep Quality Index (PSQI) score compared with the placebo. No difference in the Epworth Sleepiness Scale (ESS) score was found. While available data suggest that GM diversity is not related to sleep disturbances, probiotics administration strongly improves sleep quality as a subjective perception. However, heterogeneity of data reporting in the scientific literature should be considered as a limitation.

Background: The gold standard for diagnosing sleep bruxism (SB) and obstructive sleep apnea (OSA) is polysomnography (PSG). However, a final hypermotor muscle activity often occurs after apnea episodes, which can confuse the diagnosis of SB when using portable electromyography (EMG) devices. This study aimed to compare the number of SB episodes obtained from PSG with manual analysis by a sleep expert, and from a manual and automatic analysis of an EMG and electrocardiography (EKG) device, in a population with suspected OSA.

Methods: Twenty-two subjects underwent a polysomnographic study with simultaneous recording with the EMG-EKG device. SB episodes and SB index measured with both tools and analyzed manually and automatically were compared. Masticatory muscle activity was scored according to published criteria. Patients were segmented by severity of OSA (mild, moderate, severe) following the American Academy of Sleep Medicine (AASM) criteria. ANOVA and the Bland-Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the intraclass correlation coefficient (ICC).

Results: On average, the total events of SB per night in the PSG study were (8.41 ± 0.85), lower than the one obtained with EMG-EKG manual (14.64 ± 0.76) and automatic (22.68 ± 16.02) analysis. The mean number of SB episodes decreases from the non-OSA group to the OSA group with both PSG (5.93 ± 8.64) and EMG-EKG analyses (automatic = 22.47 ± 18.07, manual = 13.93 ± 11.08). However, this decrease was minor in proportion compared to the automatic EMG-EKG analysis mode (from 23.14 to 22.47). The ICC based on the number of SB episodes in the segmented sample by severity degree of OSA along the three tools shows a moderate correlation in the non-OSA (0.61) and mild OSA (0.53) groups. However, it is poorly correlated in the moderate (0.24) and severe (0.23) OSA groups: the EMG-EKG automatic analysis measures 14.27 units more than PSG. The results of the manual EMG-EKG analysis improved this correlation but are not good enough.

Conclusions: The results obtained in the PSG manual analysis and those obtained by the EMG-EKG device with automatic and manual analysis for the diagnosis of SB are acceptable but only in patients without OSA or with mild OSA. In patients with moderate or severe OSA, SB diagnosis with portable electromyography devices can be confused due to apneas, and further study is needed to investigate this.

Explaining the complex structure and dynamics of sleep, which consist of alternating and physiologically distinct nonREM and REM sleep episodes, has posed a significant challenge. In this study, we demonstrate that a single-wave model concept captures the distinctly different overnight dynamics of the four primary sleep measures-the duration and intensity of nonREM and REM sleep episodes-with high quantitative precision for both regular and extended sleep. The model also accurately predicts how these polysomnographic measures respond to sleep deprivation or abundance. Furthermore, the model passes the ultimate test, as its prediction leads to a novel experimental finding-an invariant relationship between the duration of nonREM episodes and the intensity of REM episodes, the product of which remains constant over consecutive sleep cycles. These results suggest a functional unity between nonREM and REM sleep, establishing a comprehensive and quantitative framework for understanding normal sleep and sleep disorders.