Sleep最新文献

Traditional sleep staging, guided by the American Academy of Sleep Medicine (AASM) scoring manual, categorizes sleep into five discrete stages based on visual analysis of electrophysiological signals by human expert. However, the rationale for the staging number remains underexplored, and sleep scoring results show low inter-rater agreement, due to such possible factors as subjective judgment, expertise variability among human experts, and limited number of signal features in the AASM manual. To address these limitations, we developed an unsupervised clustering framework incorporating a large set of features from electroencephalogram, electrooculogram, and electromyogram signals, including but not limited to the AASM visual features, and performing sleep staging without relying on pre-defined scoring rules. This data-driven approach shows that the sleep data can be optimally partitioned into five clusters, which correspond well to the five sleep stages defined in the AASM scoring manual. Importantly, the algorithm recognizes over 80% of AASM visual features, and additionally uncovers many features not mentioned in the AASM scoring manual. Detailed analysis into epochs inconsistently scored by the algorithm and by the human expert shows that the algorithm provides more interpretable results. The present study offers well-grounded evidence supporting that sleep should be partitioned into five stages. The findings also suggest that more features in the sleep data should be utilized in addition to those included in the AASM scoring manual for more accurate sleep scoring. Statement of Significance While the American Academy of Sleep Medicine (AASM) scoring manual services the gold standard for sleep staging, the neurophysiological basis for five rather than other number of sleep stages and the sufficiency of visual features for sleep staging remain underexplored. This study introduces an unsupervised clustering framework to explore the natural clustering in the polysomnography data. Five clusters are found to optimally classify the data, and they well correspond to the five sleep stages. The algorithm not only covers most of the AASM visual features, but also reveals critical features not visually apparent. Crucially, the extensive physiological features-based algorithm offers more interpretable staging. This study provides well-grounded evidence to support AASM's five sleep stages and highlights the necessity of expanding features for accurate sleep staging.

Reactivation of waking neuronal activity during sleep holds a functional role in memory consolidation. Reprocessing of daytime memory in dreams might aid later memory performance in a similar way. Numerous findings hint at a link between dreaming and sleep-dependent memory processing; however, studies investigating day-residue incorporation in dreaming led to mixed results so far. In this study, we used a naturalistic learning paradigm aimed at biasing dream content by manipulating presleep experience. Participants listened to one of four different audiobooks while falling asleep and were awoken several times during the night to report their dreams. Afterward, we tested how well they remembered the content of the audiobook. We then asked three blind raters to guess, based solely on anonymized dream reports, which audiobook someone had listened to before experiencing a dream. Our findings show that dreams across the whole night and from both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep awakenings contain specific information about the content of narratives studied before sleep. Moreover, participants with stronger incorporation of the audiobook in their dreams showed a tendency to recognize more audiobook content across the sleep period. Together, these findings suggest that salient day-time experiences resurface in dreams and that content selected for consolidation during sleep is more strongly incorporated.

Wearable sensors are commonly used to study the effects of free-living light exposure on physiological outcomes; however, rigorous validation of their performance has been limited. To address this gap, we quantified the accuracy and precision of light measurements from four commonly used wearable sensors (LYS, ActLumus, GENEActiv, and ActiGraph wGT3X-BT). Eight devices from each manufacturer (32 total) were compared to a criterion spectrometer under light-emitting diode-generated cool (peak ~460 nm) and warm (peak ~600 nm) white light at indoor intensities (2-1000 lux) and broad-spectrum sunlight (5 k-100 k lux). We found linear responses for all sensors between 50 and 20 k photopic lux, irrespective of spectrum, but detection ranges varied: ActLumus (2-100 k lux), LYS (2-60 k lux), GENEActiv (2-40 k lux), and ActiGraph (50-20 k lux). Accuracy-percent difference to criterion-also differed: ActLumus (+3.3 ± 13.4%, mean ± SD), LYS (+19.5 ± 42.0%), and below -20% for GENEActiv and ActiGraph. Interdevice variability was consistently lowest for ActLumus, and melanopic illuminance was less accurate with LYS than ActLumus, particularly in warm indoor light. Laboratory findings were compared to 1-week free-living light measurements in 21 individuals concurrently wearing the most and least accurate sensors in randomized positions on the nondominant wrist. The mean 24 h lux per participant was correlated (r = 0.91, p < .001) but lower for ActiGraph (77.3 ± 68.5 lux) than ActLumus (515.0 ± 436.0 lux; p < .001) with the greatest differences ≤100 lux (p < .001), consistent with the laboratory results. Thus, differences in illuminance range and accuracy can lead to large disparities in free-living measures across manufacturers, suggesting a need for greater technical standardization. Clinical Trial: A Natural History Study of Metabolic Sizing in Health and Disease. https://clinicaltrials.gov/study/NCT05398783 (NCT05398783). Statement of Significance Emerging evidence suggests light exposure has a greater impact on human health than previously appreciated. Wearable sensors embedded with light-measuring capabilities can play a crucial role in studying the relationship between light and physiological health. Thus, the performance of light sensors should be thoroughly evaluated. We tested the detection limits, accuracy, and variability of four commonly used wearable light sensors to indoor and outdoor intensities and spectra reflective of the real-world light environment and assessed the translatability of laboratory-based observations in a free-living pilot study. Our findings demonstrate broad variation in accuracy within and across manufacturers that emphasizes the need for rigorous sensor evaluation and highlights the challenges of interpreting illuminance measures across studies deploying different light sensors.

Study objectives: Growing evidence indicates that subjective cognitive decline, characterized by self-reported cognitive deterioration without measurable cognitive impairment, may be an early indicator of Alzheimer's Disease. This study investigated the association between baseline sleep disturbance and a 10-year trajectory of global cognitive performance in adults with subjective cognitive decline and examined if this association was moderated by age (50-64 years and ≥65 years) and sleep treatment.

Methods: Using six waves (2010-2020) of the Health and Retirement Study, we included individuals aged ≥50 years who reported subjective cognitive decline but had no objective cognitive impairment at baseline (2010) and had the final wave of cognitive data (N = 1372). Latent growth curve modeling was employed to examine the associations between self-reported sleep disturbance and cognitive trajectories from 2010 to 2020, controlling for sociodemographic and health-related factors.

Results: In the full sample, baseline sleep disturbance was not significantly associated with cognitive change. However, a significant interaction between sleep disturbance and age group was found (β = -0.04, 95% confidence interval [-0.08, -0.003]). Stratified analyses showed that poorer sleep was associated with faster cognitive decline in those aged ≥65 years (β = -0.04, 95% confidence interval [-0.07, -0.005]; n = 558), and using sleep treatment was associated with a reduced impact of sleep disturbance on cognitive decline (β = 0.31, 95% confidence interval [0.02, 0.60]). These associations were not significant in those aged 50-64 years (n = 814).

Conclusions: Sleep disturbance was an independent risk factor of future cognitive decline in older adults ≥65 years with subjective cognitive decline. Sleep treatment may mitigate this decline, offering a potential intervention strategy.

Study objectives: We aimed to describe the characteristics of standard maintenance of wakefulness test (MWT), outside of clinical trials, in a sample of drug-naïve patients with narcolepsy type 1 (NT1) and type 2 (NT2).

Methods: Consecutive drug-naïve patients with narcolepsy underwent two days of continuous PSG recording, the multiple sleep latency test (MSLT), then night-PSG and, on the following day, MWT. MWT results were correlated with MSLT and Epworth sleepiness scale (ESS). Patients in the two lower tertiles of MWT mean sleep latency (mSL) were compared to those in the upper tertile.

Results: Seventy-eight NT1 (30.6 ± 11.4 years, 35 males) and 19 NT2 (31.0 ± 9.9 years, 12 males) were included. MWT results showed a bimodal distribution with a large peak with reduced mSL and a small peak with values toward 40 min. MWT mSL was lower in NT1 than in NT2 (10.7 ± 10.8 min vs 23.9 ± 11.5 min, p < .001). In the entire sample, lower MWT mSL was moderately correlated with lower MSLT mSL (Rho = 0.347, p = .001) and higher ESS (Rho = -0.398, p < .001). Patients with NT1 with MWT mSL in the two lower tertiles (≤11.2 min) had higher ESS than those in the upper tertile, without any difference in other clinical or neurophysiological features. In NT2, no significant correlations emerged between MWT, MSLT, and ESS.

Conclusions: MWT mSL is reduced in drug-naive narcolepsy, more severely in NT1 than in NT2. However, a minority of patients show normal MWT results. MSLT, MWT, and ESS measure different aspects of sleepiness in narcolepsy, and none can be considered a comprehensive measure of its severity. Statement of Significance This observational study explored the characteristics of maintenance of wakefulness test (MWT) in 97 drug-naïve patients with narcolepsy type 1 (NT1) and type 2 (NT2) at diagnosis. MWT mean sleep latency was lower in NT1 than in NT2. Unexpectedly, a minority of patients, both NT1 and NT2, managed to resist >30 min (n = 15) and up to 40 min (n = 8). MWT results showed moderate correlation with Epworth sleepiness scale and with multiple sleep latency test results, with no correlations with other clinical and neurophysiological markers. These findings define the performance of patients with drug-naïve narcolepsy on MWT and highlight the heterogeneity of NT1 and NT2 in terms of sleepiness assessed with different tools.