Sleep and Biological Rhythms最新文献

Sleep education had inconsistent effects on children and adolescents, and there was a lack of evaluation based on the context of Chinese students. We aimed to explore the outcomes of sleep education on middle-school students in eastern China. We enrolled 239 middle-school students, divided into intervention group with sleep education (125) and control group (114). The sleep education was conducted within 4 months. Interaction effect was determined by generalized linear mixed models to evaluate efficacy of intervention, measured with three indicators (sleep knowledge, attitude and quality). There were no significant intervention effects on sleep knowledge, attitude and quality. Interaction effects (coefficient) were 0.008 (95% CI: -0.125 to 0.141, P = 0.905), 0.0001 (95% CI: -0.052 to 0.052, P = 0.997) and 0.045 (95% CI: -0.146 to 0.236, P = 0.640) on sleep knowledge, attitude and quality, respectively. Besides, no significant difference was found in the changes of three indicators among individuals who exhibited the same change in both groups. Four-month sleep education was not helpful to improve sleep knowledge, attitude and quality of middle-school students in eastern rural China. It is necessary to try suitable and practical intervention methods.

Supplementary information: The online version contains supplementary material available at 10.1007/s41105-025-00570-3.

Sleep quality significantly affects the well-being of older adults. This study aimed to analyse the current state of sleep quality among older adults in China and to examine the effects of living environment and depression on sleep quality, as well as their interrelationship(s). A survey was conducted using a self-designed questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and Patient Health Questionnaire Depression Scale (PHQ-9), targeting a sample of adults ≥ 60 years of age in the central province of China, yielding 6211 valid responses. Correlation analysis indicated that the living environment was negatively correlated with both depression and PSQI score (r = - 0.140, p < 0.01; r = - 0.174, p < 0.01). The living environment (β = - 0.049, p < 0.001) was a negative predictor of PSQI, while depression (β = 0.477, p < 0.001) was a positive predictor. Mediation analysis revealed that depression acted as a mediator between the living environment and sleep quality, contributing 61.7% of the overall effect. Further analysis through hierarchical regression and simple slope plotting indicated that in a favourable living environment, the impact of depression on sleep quality was more pronounced. These findings suggest that a supportive living environment has a protective effect against poor sleep hygiene among older adults and that depression plays a significant mediating role. Intervention strategies should focus on improving living environments and implementing mental health measures to enhance sleep quality among older adults.

Choroid plexus volume change has been suggested as a biomarker for the course of various neurological diseases. However, its role in sleep disorders remains unclear. We analyzed choroid plexus volume changes in patients with isolated rapid eye movement sleep behavior disorder (iRBD) compared with healthy controls. We enrolled 27 patients with iRBD and 27 healthy controls. All participants underwent brain magnetic resonance imaging (MRI), including three-dimensional T1-weighted imaging suitable for volumetric analysis. iRBD was diagnosed based on overnight polysomnography and corresponding clinical history. We compared the choroid plexus volume between patients with iRBD and healthy controls, and investigated the relationship between choroid plexus volume and polysomnographic findings. The mean choroid plexus volume was significantly larger in patients with iRBD than in healthy controls (2.379% vs. 2.116%, p = 0.002). No significant correlation was observed between choroid plexus volume and polysomnographic findings in patients with iRBD. Patients with iRBD demonstrated choroid plexus enlargement compared with healthy controls. This finding could be related with glymphatic system dysfunction in this population.

Over the past few decades, the use of heart rate variability (HRV) has expanded significantly due to its ease of collection, affordability, and its clinical relevance to psychophysiological processes and psychopathological disorders. This study aims to demonstrate the effectiveness of an artificial intelligence approach based on HRV signals for automatic sleep stage classification. This review examines machine learning algorithms for HRV-based sleep stage classification over the past 15 years. It also compares the HRV features extracted, the classification algorithms used, and the evaluation parameters employed. Existing studies indicate that with advances in technology, machine learning algorithms utilizing HRV features for sleep staging achieve high accuracy, sensitivity, and specificity. The use of HRV for sleep analysis via machine learning algorithms is an active area of research with broad application potential. As technology progresses and data accumulation increases, this approach is expected to offer more accurate and personalized solutions for sleep medicine and health management.

Spring transitions into daylight savings time (DST) result in increase of sleep latency and fragmentation and decrease of sleep time and efficiency. We evaluated the effects of DST on patterns of positive airway pressure (PAP) use and its effectiveness by utilizing continuous tracking of PAP therapy available with cloud-based sleep care management systems. 62 compliant adult OSA patients from Jacksonville, Florida, USA on stable continuous PAP (CPAP)/autoPAP (APAP) therapy were enrolled and PAP usage and residual apnea-hypopnea index (AHI) were collected for the "pre-DST period" (Sun-Mon-Tue, 7-9/03/2021) and "DST period" (Sun-Mon-Tue, 14-16/03/2021) from compliance reports generated or downloaded via cloud-based sleep care management systems. Demographic variables, average compliance and effectiveness of PAP during the two weeks that included both intervals of interest (from Wednesday 3rd to Wednesday 17th) were further analyzed. Statistics included repeated measures ANOVA, non-parametric Wilcoxson's rank sum tests, independent and paired T tests, and Chi-square test. Majority of patients were Caucasian (73%); with average age of 57.5 ± 11 years, 443.1 ± 124 min of nightly PAP use, and 0.97 ± 0.06% of PAP compliance. 73% of patients were male, with no significant differences noted for sleep variables between genders. There was significant decrease in PAP usage duration between pre-DST Mondays and DST Mondays (delta_normalized_PAP_duration = -0.18, p = 0.0027). AHI demonstrated significant decrease on DST Mondays, followed by significant increase on DST Tuesdays (deltaAHI = - 0.54 and 0.47 respectively). This study demonstrated that the effects of DST on duration of PAP use and sleep disruption monitored by AHI are seen days after DST transition, even in the zones with very stable light/dark cycles like Florida, USA.

Sleep disorders are the most distressing non-motor problems among Parkinson's disease (PD), whereas obstructive sleep apnea (OSA) is a type of sleep disorder that commonly occurs in Parkinson's disease. Therefore, we conducted this meta-analysis to estimate the prevalence of OSA in Parkinson's disease. A comprehensive search in CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, was conducted from inception to September 2023 to identify studies on the prevalence of PD patients with OSA. A Comprehensive Meta-Analysis software version 3.0 and R software with random-effects models were used for pooled prevalence and prediction interval analysis. We assessed heterogeneity using I ² test and Cochran's Q-statistic and study quality with Hoy's risk of bias. Moreover, meta-regression and subgroup analysis identified factors contributing to prevalence. A total of 42 studies with 3342 participants were included. The pooled prevalence of OSA among Parkinson's disease patients was 45% (95% CI: 39-52%). Based on 95% prediction interval, the future prevalence of OSA in PD was expected to vary from 13 to 83%. Moderator analyses showed that male sex, higher BMI, and country geographical area were significantly associated with OSA prevalence, in which South and North America are the top two continents that have a high prevalence of OSA. This study gives a comprehensive overview of the high prevalence of OSA and associated factors in Parkinson's disease, which can be used for planning care and future research.

Supplementary information: The online version contains supplementary material available at 10.1007/s41105-024-00561-w.

This study evaluates the diagnostic accuracy of Somfit against polysomnography (PSG) for detecting obstructive sleep apnoea (OSA). Somfit is a wearable home-sleep monitoring device attached to the forehead, combining pulse arterial tonometry, oximetry, and actigraphy with sleep staging, arousals, and total sleep time (TST) derived from frontal neurological signals. Ninety-two participants suspected of having OSA were assessed using Somfit and simultaneous overnight PSG recordings at three Australian sites. Each PSG study was manually scored by three independent scorers. The reported statistics include standard measures of agreement between Somfit's TST, Oxygen-Saturation Index (ODI), Apnoea-Hypopnea Index (AHI), and the average of those metrics from the three PSG scorers. The overall inter-scorer agreement was 76% (kappa = 0.772). TST, ODI, and AHI from Somfit were highly correlated with similar metrics from PSG (all r > 0.84, p < 0.001). Sensitivity, specificity, and accuracy were 90.5%, 75.0%, and 89.1% respectively, with a diagnostic odds ratio (DOR) of 28.5 for AHI ≥ 5. For AHI ≥ 15, sensitivity, specificity, and accuracy were 78.0%, 73.8%, and 76.1%, respectively, with a DOR of 9.99. For AHI ≥ 30, sensitivity, specificity, accuracy, and DOR were 72.4%, 90.5%, 84.8%, and 24.9, respectively. The area under the curve (AUC) at different PSG AHI cut-offs ranged between 0.86 and 0.93. Coupled with oximetry and EEG-based derivation of TST, Somfit's performance is comparable to PSG in detecting OSA severity.

In the first comprehensive examination of its kind, we investigated the association between maternal depression and preschoolers' sleep problems, as well as factors that influence children's sleep. A cross-sectional questionnaire survey was administered to mothers of children in nine kindergartens and nursery schools across six Japanese prefectures. The sample included 232 mother-child pairs (children's average age = 61.72 months; 51.29% boys; mothers' average age = 38.38 years). Mothers' depression and parenting behaviors were assessed using the Patient Health Questionnaire-9 and Parenting Behavior Checklist to Promote Preschoolers' Sleep, respectively. Children's sleep problems and emotional and behavioral problems were assessed using the Japanese Sleep Questionnaire for Preschoolers and the Strengths and Difficulties Questionnaire, respectively. Mothers reported on their children's duration of daytime activity and screen time. Thirty-two percent of mothers had more than minimal depressive symptoms. Path analysis revealed that children's emotional and behavioral problems, and sleep problems were associated with maternal depression (β = 0.24, β = 0.21). Furthermore, children's emotional and behavioral problems, duration of daytime activity, and parenting behaviors to promote children's sleep were associated with children's sleep problems (β = 0.45, β =  - 0.09, β =  - 0.45). The values of GFI (0.99), AGFI (0.95), and RMSEA (0.05) showed a good fitness of the model. The results suggest the necessity of assessing children' sleep problems and emotional and behavioral problems when considering mothers' mental health problems in the future.