Background: Sleep quality is influenced by various environmental factors, yet their impact across different regions and seasons in China remains understudied.

Objective: This study evaluated how environmental factors affect sleep quality, duration, and disorders among Chinese urban residents in different regions and seasons.

Methods: Using multi-stage stratified random sampling, 9229 adults from 29 provinces were included. Data were analyzed using analysis of variance, chi-square tests, and multinomial logistic regression.

Results: Sleep quality varied significantly by season and region. Poor sleep was more common in spring (OR = 1.343 vs. summer) and in the southwest (OR = 2.618) and northwest (OR = 2.636) compared to Huanghuai. Short sleep duration was less frequent in autumn (OR = 0.565) and winter (OR = 0.454) versus summer, but more common in southern China (OR = 1.851) and northeast China (OR = 1.591). Sleep disorders were higher in spring (OR = 1.357) and in the Southwest (OR = 1.252).

Conclusions: The influence of season and geographic region on sleep demonstrates multidimensional and specific characteristics. Overall, the majority of regions exhibited better sleep quality in autumn and winter compared to spring and summer. Meanwhile, the southwestern region showed higher risks in terms of sleep disturbances and sleep quality.

Study objectives: To investigate the use of automated analysis to detect heart rate accelerations (HRAs) to screen for sleep-disordered breathing (SDB) among patients who use chronotropic medications.

Methods: Nocturnal polysomnography (NPSG) recordings from 933 participants in the Sleep Heart Health Study were analyzed using an automated method to detect HR changes with pulse signals. Participants were included in the study if aged 40 years or older, had complete NPSG and used chronotropic medications prior to enrolling in the study. Nocturnal respiratory-related HRAs were analyzed for any correlation to apnea-hypopnea index (AHI). The heart rate acceleration index (HRAI) is determined by the HRAs based on pulse signal per hour for an entire NPSG.

Results: The total HRAI has a mean ± SD value of 30 ± 19/h. Total AHI and respiratory HRAI has a Spearman correlation coefficient of 0.79 (p < 0.001). In the receiver operating characteristics curve, the area under the curve is greatest when AHI = 5 events/h, yielding a value of 0.96. This indicates that respiratory-related HRAI had the greatest screening ability when AHI = 5 events/h, as compared to other cut-off points of AHI.

Conclusion: The novel automated analysis for nocturnal HR changes correlates with AHI in patients with SDB who use chronotropic medications and provides an accurate prediction for the screening of SDB among these patients. This method may be used for patients irrespective of chronotropic medication use.

Aims: Insomnia increases the risk of cardiovascular diseases (CVDs), but whether insulin resistance (IR) or its related traits mediate the underlying associations is unclear. We conducted a two-step two-sample Mendelian randomization (MR) study to address these questions.

Methods and Results: We selected genetic variants of insomnia, IR, and its traits as instrumental variables, whereas summary-level data of five CVDs served as the main outcomes, which were derived from previous genome-wide association studies. In the MR analysis, genetically predicted insomnia symptoms were significantly associated with five CVD risks and six IR-related traits after correcting for multiple tests, whereas genetically predicted IR and its related traits, such as T2DM, TG, and high-density lipoprotein cholesterol (HDL-C), were associated with four CVD risks. In the mediation analysis, we found strong evidence for the mediating effects of IR, TG, HDL-C, and T2DM in the causal pathway from insomnia to four CVDs, except for atrial fibrillation. The multivariable MR analysis provided further evidence supporting the potential mediation effects of IR and its related traits in the causal pathway between insomnia and CVDs.

Conclusions: These results suggest that genetically predicted insomnia symptoms are associated with a higher risk of CVDs, with considerable mediation by IR and T2DM.

Objectives: To investigate the association between the sleep duration with cognitive impairment in middle-aged and elderly patients with cerebral small vessel disease (CSVD).

Methods: This cross-sectional study enrolled hospitalized patients aged ≥55 years diagnosed with CSVD between November 2021 and August 2023. Comprehensive neuropsychological assessments were conducted using seven standardized measures: the mini-mental state examination (MMSE), clock drawing test (CDT), verbal fluency test (VFT), auditory verbal learning test (AVLT), digit span test (DST), self-rating anxiety scale (SAS), and self-rating depression scale, along with objective sleep monitoring using a portable sleep monitoring device. Participants were stratified into three sleep duration groups based on self-reported nocturnal sleep: short (≤6 h; n = 86), normal (6-9 h; n = 77), and long duration (≥9 h; n = 53). Additionally, they were categorized by daytime napping patterns: no nap (n = 97), short nap (≤1 h; n = 59), and long nap (>1 h; n = 60). Multivariate linear regression models were used to calculate 95% confidence intervals (95% CIs) for cognitive impairment and emotional disorders.

Results: The study cohort comprised 216 participants (56.50% male) with a mean age of 66.83 ± 8.16 years. Both short and long sleep durations were associated with poorer MMSE and CDT scores compared to normal sleep duration (short vs. normal sleep: β = −0.201, 95% CI [−2.388, −0.360], p < 0.001 for MMSE; β = −0.201, 95% CI [−0.480, −0.061], p < 0.05 for CDT; long vs. normal sleep: β = −0.355, 95% CI [−3.879, −1.643], p < 0.001 for MMSE; β = −0.329, 95% CI [−0.735, −0.273], p < 0.01 for CDT). Compared to short nap duration, both no nap and long nap durations showed worse performance on MMSE, VFT, AVLT, and DST (no nap vs. short nap: MMSE β = −0.304, 95% CI [−6.106, −2.078], p < 0.001; VFT β = −0.240, 95% CI [−5.808, −1.246], p < 0.01; AVLT β = −0.253, 95% CI [−6.136, −1.598], p < 0.01; DST β = −0.209, 95% CI [−1.172, −0.160], p < 0.05; long nap vs. short nap: MMSE β = −0.304, 95% CI [−6.106, −2.078], p < 0.001; VFT β = −0.200, 95% CI [−5.881, −0.625], p < 0.05; AVLT β = −0.188, 95% CI [−5.809, −0.580], p < 0.05; DST β = −0.207, 95% CI [−1.371, −0.151], p < 0.05). Short nighttime sleep duration was associated with higher SAS scores compared to normal sleep duration (β = 0.172, 95% CI [0.105, 2.542], p < 0.05), whereas long sleep duration showed no significant association.

Conclusion: Individuals with nighttime sleep duration of 6-9 h and daytime napping ≤1 h exhibited the best overall cognitive scores. This finding supports the U-shaped relationship model of “moderate sleep-cognitive protection.”

Background: This study presents SIMSleepSM, a novel single-channel electroencephalography (EEG) sleep staging model. It addresses two primary challenges: insufficient modeling of long-range temporal dependencies combined with limited multi-scale feature extraction, and poor accuracy in identifying the N1 stage.

Methods: SIMSleepSM extends the SleePyCo architecture through three principal innovations. First, the spatial-channel synergistic attention (SCSA) module is adapted into a 1D variant, SCSA_1D, tailored for EEG signals and inserted into every feature layer of the backbone network. The spatial attention extracts local temporal dependencies across various time scales, whereas the channel attention captures relationships among feature channels. Together these attentions strengthen temporal dependency modeling and emphasize N1-specific features. Second, an interactive convolution block (ICB) is integrated into the feature pyramid. The ICB employs a two-branch interactive convolution to refine multi-scale feature extraction. Finally, a bidirectional Mamba-based classifier is designed. Its bidirectional state space mechanism captures long-range temporal dependencies in the EEG and thereby strengthens representation of sleep-stage dynamics.

Results: On the Sleep-EDF-20, Sleep-EDF-78, and Sleep Heart Health Study (SHHS) datasets, SIMSleepSM achieves accuracy values of 88.1%, 86.2%, and 84.1%; records macro F1 scores of 82.7%, 81.0%, and 77.9%; obtains Cohen's Kappa coefficients of 0.839, 0.810, and 0.791; and attains F1-scores on the N1 stage of 53.7%, 54.4%, and 50.9% for Sleep-EDF-20, Sleep-EDF-78, and SHHS, surpassing the second-best models by 1.3%, 4.0%, and 4.8%, respectively.

Conclusion: Experimental results demonstrate that SIMSleepSM outperforms thirteen state-of-the-art baseline models, with particularly notable improvements in N1-stage identification. These results indicate that SIMSleepSM provides an effective and reliable solution for automatic sleep staging using single-channel EEG, highlighting it as a robust and high-performing model.

Objective: This study aims to investigate the sleep quality of medical staff in a tertiary hospital, explore the influencing factors, and provide a reference for relevant departments to formulate policies that ensure the health of medical personnel.

Methods: In July 2024, a survey was conducted using the Pittsburgh Sleep Quality Index (PSQI) and the Depression, Anxiety, and Stress Scale among medical staff (mainly young and middle-aged) in a tertiary hospital. The survey assessed their sleep quality, time to fall asleep, sleep disturbances, and daytime dysfunction.

Results: (1) Among the respondents, 528 (57.64%) had sleep disorders, whereas 388 (42.35%) did not. The overall sleep quality of the medical staff was poor, with a PSQI score of 9.65 ± 6.354. (2) Univariate analysis showed that sleep quality was significantly different across gender, exercise habits, and body mass index (BMI) (p < 0.05). Memory decline was also statistically associated with poor sleep quality (p < 0.05). (3) Sleep efficiency and sleep duration varied significantly across different age groups (p < 0.05). (4) There were significant differences in subjective sleep quality among staff with different blood pressure control statuses (p < 0.05). Additionally, medical staff with unhealthy habits, such as smoking and drinking, had significantly higher rates of sleep disorders (p < 0.05). (5) The level of anxiety among healthcare workers is negatively correlated with sleep quality, whereas the duration (number) of exercise is positively correlated with sleep quality. Anxiety was identified as a primary factor affecting sleep quality.

Conclusion: The sleep quality of medical staff in this tertiary hospital is generally poor, especially among young and middle-aged staff, married individuals, females, and those who smoke or drink. Anxiety was closely related to sleep disturbances among these healthcare workers. The hospital should intervene by addressing anxiety and other factors to improve the sleep quality of its medical staff, enhancing their work efficiency.

Objectives: This study aimed to explore the association between excessive daytime sleepiness (EDS) and frequent nightmares and the risk for suicidal ideation (SI) among psychiatric inpatients.

Methods: In this cross-sectional study, 650 inpatients were consecutively recruited from the inpatient department of Shantou University Mental Health Center. EDS was defined as Epworth Sleepiness Scale (ESS) > 10. Frequent nightmares were defined as nightmares ≥1 time per week during past 1 month. Pittsburgh Sleep Quality Index (PSQI) was used to evaluate nightmares and sleep-related features. Beck Scale for Suicide Ideation-Chinese Version (BSI-CV) was used to assess SI.

Results: Among the 650 inpatients, 39 (6.00%) presented SI. Patients with SI had higher proportion of EDS (p < 0.001) and total scores of ESS (p < 0.001) compared to those without SI. Patients with EDS and frequent nightmares had higher odds for SI (OR = 5.203, p = 0.005; OR = 3.077, p = 0.012) after adjusting for the confounders. Similarly, dose-response associations between the higher ESS scores (p for trend < 0.001), frequent nightmare (p for trend = 0.014) and higher risk of SI were also observed.

Conclusions: EDS and frequent nightmares are risk factors for SI among psychiatric inpatients. These findings underscore the importance of assessment and treatment of EDS and frequent nightmares for identifying and mitigating SI among psychiatric inpatients.

The bidirectional interplay between sleep and metabolic homeostasis is fundamental to physiological health. While the roles of glucose and lipid metabolism in sleep regulation have been extensively characterized, bile acids (BAs), which are traditionally viewed as digestive surfactants, are emerging as critical metabolic messengers with distinct circadian rhythmicity and pleiotropic signaling functions. This review systematically elucidates the signaling network of the gut-liver-brain axis mediated by BAs through the nuclear Farnesoid X receptor and the membrane Takeda G protein-coupled receptor 5. Accumulating evidence suggests that BAs are not only precisely regulated by the hepatic circadian clock but also modulate the central nervous system function by crossing the blood-brain barrier or via vagal afferent pathways. Specifically, recent findings highlight that aberrantly elevated BAs can infiltrate the central nervous system to disrupt the master circadian clock and modulate neurocircuitry governing arousal, thereby contributing to sleep fragmentation and circadian misalignment. Furthermore, this review discusses the potential of BA profiles as systemic biomarkers in obstructive sleep apnea, chronic insomnia, and related metabolic comorbidities. Finally, we propose that targeting BA metabolic receptors and the gut microbiota represents a promising translational strategy for the management of sleep disorders and their metabolic consequences.

Introduction: Nightmare Disorder (ND) is a prevalent sleep disorder linked to nocturnal and daytime impairments. Image Rehearsal Therapy (IRT) shows moderate effects, and evidence for other psychological interventions, including eye movement desensitization and reprocessing (EMDR), remains scarce in adults with idiopathic ND (IND).

Methods: This case study explored EMDR therapy's potential for treating IND. Nightmare symptoms, nightmare-related distress, and sleep quality were assessed pre- (T1) and post-intervention (T2).

Results: Score changes were clinically meaningful and reliable within a 95% confidence interval, with T2 scores falling within the normative range.

Conclusions: Specialized nightmare treatment reduces functional impact and chronicity; further investigation of EMDR therapy could improve intervention.