Sleep最新文献

Study objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature.

Methods: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography (PSG) at a tertiary care sleep lab. Each PSG recording was manually scored and these annotations served as ground truth labels for training and evaluation of our models. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed.

Results: After ensembling, the model attains a 3-class (wake, non rapid eye movement [NREM] sleep, rapid eye movement [REM] sleep) balanced accuracy of 84.02%, F1 score of 84.23%, Cohen's κ of 72.89%, and a Matthews correlation coefficient (MCC) score of 73.00%; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30%, F1 score of 74.10%, Cohen's κ of 61.51%, and MCC score of 61.95%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11%, F1 score of 66.15%, Cohen's κ of 53.23%, MCC score of 54.38%.

Conclusions: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.

Study objectives: E-cigarettes can help smokers quit, but how e-cigarettes used for tobacco smoking cessation impact sleep is still unclear. The primary objective was to evaluate the effect of e-cigarettes for smoking abstinence on sleep quality. Secondary objectives included subscales of sleep quality.

Methods: We conducted a secondary analysis of the Efficacy, Safety, and Toxicology of electronic nicotine delivery systems for smoking cessation (ESTxENDS) randomized controlled trial, which included adult smokers in Switzerland (5 sites, 7.2018-6.2021). The intervention group received free e-cigarettes and e-liquids over 6 months plus standard-of-care smoking-cessation counseling (SOC); the control group received SOC alone. The primary outcome was overall self-reported sleep quality at 6 months, measured by the Pittsburgh Sleep Quality Index (PSQI). We considered a minimal clinically important difference (MCID) of 2.5-5. Secondary outcomes included PSQI subscales. We used adjusted linear regressions with inverse probability of attrition weights (IPAW).

Results: ESTxENDS included 1246 participants. Of these, 831 participants completed the PSQI at follow-up. For the primary outcome, there was no significant difference in PSQI score between groups (b=-0.20, p=.256, adjusted analyses with IPAW). For PSQI subscales, only sleep efficiency was significantly better in the intervention group (b=1.87, p=.018), below MCID.

Conclusion: E-cigarettes added to SOC for tobacco smoking abstinence did not significantly alter participant's self-reported sleep quality compared to SOC alone. Clinicians can inform patients willing to quit smoking with e-cigarettes that their use is not likely to disrupt their perceived sleep quality on average.

Study objectives: Sleepiness and fatigue are common symptoms during illness and may persist after the resolution of illness. To gain insight into the neurochemistry of sickness-induced sleep and to discover therapeutic candidates, we performed a high throughput chemical screen using the animal model Caenorhabditis elegans.

Methods: Worms were irradiated with ultraviolet light to induce sickness and then transferred to wells of a 96-well plate each containing a different bioactive chemical dissolved in an aqueous solution. The fraction of quiescent animals was assessed via stereomicroscopic observation. We performed 12 vehicle-only controls for each 96-well plate and considered sleep-inhibiting chemicals as those with a fraction quiescent at least 3 standard deviations less than controls. We followed up the screen with dedicated mechanistic studies of the drug amitriptyline.

Results: Among 3,683 bioactive chemicals screened, 42 strongly reduced sleep behavior. We retested 26 and replicated 23 chemicals as sleep-disrupting. Among replicated compounds were amitriptyline (AMI) and other tricyclic anti-depressants (TCAs). AMI acts downstream of or in parallel to activation of sleep-promoting neurons. In addition to suppressing sleep in sickness (SIS), AMI also suppressed sleep in health and reduces survival. We tested and rejected the hypothesis that AMI acts by increasing monoaminergic tone, providing evidence that TCAs act via a novel mechanism to block sleep.

Conclusions: This is the first high-throughput screen for chemicals modulating SIS. Elucidating the mechanism by which AMI and other chemicals regulate sleep will lead to new biological insights regarding the mechanisms of sleep.

Study objectives: To investigate how sleep architecture and neurobehavioural functions change during a polyphasic short sleep schedule and to compare these responses to those of a monophasic short sleep schedule with the same total sleep opportunity, as well as to those of a well-rested control group.

Methods: Forty healthy young adults (18 males, age: 18-35) were assigned to either the monophasic short sleep group, which had a single 2-h sleep opportunity, or the polyphasic short sleep group, which followed the "Uberman" sleep schedule and had six 20-min sleep opportunities distributed evenly across 24-h (one every 4 h). Polysomnography was conducted during every sleep opportunity. Neurobehavioural functions were assessed at baseline (before the sleep opportunity manipulation started) and six times thereafter (once every 4 h).

Results: Both short sleep groups experienced greater subjective sleepiness, poorer vigilance and lower positive mood as compared to a well-rested control group. Relative to the monophasic short sleep group, the polyphasic short sleep group showed greater vigilance impairment, particularly in the morning. This was accompanied by greater reductions in total sleep time, longer total sleep onset latency and wake after sleep onset, as well as greater proportions of N1 and N2 but lower proportion of N3 sleep in the polyphasic short sleep group relative to the monophasic short sleep group.

Conclusions: In young adults, the "Uberman" polyphasic sleep schedule substantially reduces total sleep duration and sleep efficiency, even when compared to a monophasic sleep schedule with the same overall sleep opportunity, and may be associated with poorer neurobehavioural performance.

Study objectives: Sleep resilience is the ability to maintain effective emotional, cognitive, and physical functioning despite disruptions to sleep/circadian rhythms. While conceptually related to sleep health, no validated measure currently exists. This cross-sectional survey study aimed to validate a novel self-report measure of sleep resilience, the Sleep Resilience Questionnaire (SRQ), and examine its associations with demographic features, sleep disturbance, and sleep-related impairment.

Methods: Our convenience sample included 455 adults (Mean age=45±17) in the United States who completed a demographic survey, PROMIS Sleep Disturbance (PROMIS-SD) and Sleep-Related Impairment (PROMIS-SRI), and retrospective and prospective SRQ forms (English version 1). Exploratory and confirmatory factor analyses (EFA and CFA) evaluated the SRQ's factor structure and internal consistency, and structural equation modeling examined associations with demographics, PROMIS-SD, and PROMIS-SRI.

Results: EFA and CFA supported a highly correlated two-factor retrospective SRQ structure and a unidimensional prospective SRQ structure with excellent fit and internal consistency (CFI & TLI>.99, RMSEA<.07, ω>.92). Lower sleep resilience was associated with greater sleep-related impairment (p<0.05) but not sleep disturbance. Younger age, men, higher education, greater number of dependents, and higher income were associated with lower sleep resilience in specific retrospective or prospective domains (p<0.05).

Conclusions: Findings suggest sleep resilience is a meaningful dimension of sleep health, associated with demographic and sleep-related impairments, and the SRQ forms appear to validly measure it. Despite limitations of the cross-sectional design, the SRQ may be useful for future research aimed at identifying potential intervention targets to improve sleep-related impairments.