Sleep Health最新文献

Goal and aims: Performance evaluation of automatic sleep staging on two-channel subcutaneous electroencephalography.

Focus technology: UNEEG medical's 24/7 electroencephalography SubQ (the SubQ device) with deep learning model U-SleepSQ.

Reference method/technology: Manually scored hypnograms from polysomnographic recordings.

Sample: Twenty-two healthy adults with 1-6 recordings per participant. The clinical study was registered at ClinicalTrials.gov with the identifier NCT04513743.

Design: Fine-tuning of U-Sleep in 11-fold cross-participant validation on 22 healthy adults. The resultant model was called U-SleepSQ.

Core analytics: Bland-Altman analysis of sleep parameters. Advanced multiclass model performance metrics: stage-specific accuracy, specificity, sensitivity, kappa (κ), and F1 score. Additionally, Cohen's κ coefficient and macro F1 score. Longitudinal and participant-level performance evaluation.

Additional analytics and exploratory analyses: Exploration of model confidence quantification. Performance vs. age, sex, body mass index, SubQ implantation hemisphere, normalized entropy, transition index, and scores from the following three questionnaires: Morningness-Eveningness Questionnaire, World Health Organization's 5-item Well-being Index, and Major Depression Inventory.

Core outcomes: There was a strong agreement between the focus and reference method/technology.

Important supplemental outcomes: The confidence score was a promising metric for estimating the reliability of each hypnogram classified by the system.

Core conclusion: The U-SleepSQ model classified hypnograms for healthy participants soon after implantation and longitudinally with a strong agreement with the gold standard of manually scored polysomnographics, exhibiting negligible temporal variation.

Objectives: Black and older adults have higher risk for sleep problems than their White and younger counterparts. Yet, our understanding of the determinants of sleep problems specifically among older Black adults is severely limited. The aim of this study was to determine whether everyday and major discrimination are longitudinally associated with sleep disturbance in a nationally representative sample of older Black adults.

Methods: Non-Latinx Black respondents aged 51+ were selected from waves 8 (2006) through 15 (2020) of the Health and Retirement Study (baseline N = 1397). Sleep disturbance was measured with the 4-item Jenkins Sleep Questionnaire. The 6-item Everyday Discrimination Scale was used to measure everyday discrimination, and the Major Experiences of Discrimination Scale was used to measure major discrimination. Analyses controlled for sociodemographics, health behaviors, and health conditions. Lagged mixed-effects linear regression models were performed to test the longitudinal associations between baseline discrimination and sleep disturbance over 12years.

Results: Higher baseline everyday discrimination was longitudinally associated with more severe sleep disturbance. Compared to respondents who reported no major discrimination at baseline, those who reported two or more major discrimination experiences had more severe sleep disturbance over time.

Conclusions: This study provides critical information on the possible longitudinal drivers of sleep disparities at the population level. This information has implications for better understanding the mechanisms of health disparities and for attaining health equity.

Objectives: The present study aimed at exploring the association between eveningness and lifestyle-related variables, that is, body mass index, alcohol, and cigarette consumption, in adults (18-40years), focusing on the possible moderator effect of age and the role of sleep disturbances and circadian misalignment (social jetlag).

Methods: A web-based survey was administered to 437 participants, covering demographics, lifestyle-related variables, chronotype, sleep quality, and daytime sleepiness. A subset of 206 participants wore a wrist actigraph for a week, allowing the creation of a sleep health index within the RU-SATED framework. Regression analysis was used to investigate the associations between chronotype and lifestyle-related outcomes, accounting for social jetlag and sleep health; and to explore the lifestyle trajectories over time.

Results: Evening chronotypes showed higher body mass index levels, consumed more alcohol, and smoked more cigarettes than other circadian typologies, in particular after 25 years of age. Poor sleep health and social jetlag significantly contribute to explaining evening types smoking behavior, while not affecting body mass index levels. Social jetlag plays a more important role compared to sleep disturbances and eveningness in predicting more detrimental drinking and smoking behavior.

Conclusions: Participants who maintain the evening trait past the age of 25years are more prone to adopt an unhealthy lifestyle, especially if experiencing poor sleep health and circadian misalignment. Circadian preferences and sleep health should be considered when planning interventions aimed at promoting healthy lifestyles in adults aged 18-40years. Further investigations should explore the effect of modifications in lifestyle in the prevention of noncommunicable diseases.

Objective: This study investigated the associations of sleep status (duration and quality) with all-cause death among centenarians, using data from the China Hainan Centenarians Cohort Study.

Method: The epidemiological distribution of sleep duration and sleep quality (estimated using the Pittsburgh Sleep Quality Index) was described based on the data from the China Hainan Centenarians Cohort Study. Cox regression was used to analyze the association between sleep status and all-cause mortality.

Results: A total of 994 centenarians, with an average age of 102.77 ± 2.75years, were included. The median (Q1, Q3) daytime sleep duration was 1.00 (0.50, 1.50) hour, while nighttime sleep duration and total sleep duration were 8.00 (7.00, 9.00) hours and 9.00 (8.00, 10.50) hours, respectively. By the end of the follow-up period, 517 centenarians had died, with a median follow-up time of 4.2 (1.3-5.0) years. A noteworthy finding emerged: male centenarians with a daytime sleep duration of at least 2 hours had a 97% greater risk of all-cause mortality (HR=1.97, 95%CI: 1.07-3.62, P = .039) than those who got less daytime sleep, after adjusting for potential confounders.

Conclusion: The sleep duration patterns of centenarians in Hainan were comparable to those in other provinces of China. Centenarians who sleep longer had a higher risk of all-cause mortality. This risk plateaued after more than 9 hours of sleep, with no gender differences observed. Furthermore, the duration of daytime sleep was significantly associated with all-cause mortality among male centenarians.

Objectives: Health literacy includes the skills necessary for effective communication with health professionals, understanding health instructions, and recognizing health needs. Our study aimed to create a novel measure of sleep health literacy-an individual's capacity to comprehend sleep-related information and navigate sleep-related healthcare services.

Methods: The Sleep Health Literacy scale consists of two subscales: "Sleep Health Communication" (11 Likert-scale items assessing access to resources and communication with healthcare providers) and "Sleep Health Knowledge" (46 true/false items assessing specific knowledge of sleep health). The Sleep Health Literacy scale was completed by 154 undergraduate students (mean age=20.96years; 79.87% female) in study 1. In study 2, an additional sample of 251 participants (mean age=20.23years; 79.87% female) completed the Sleep Health Literacy scale, along with measures of convergent and discriminant validity.

Results: Exploratory factor analysis results in study 1 revealed a two-factor structure for the "Sleep Health Communication" subscale ("comprehension" and "critical application"). The subscale demonstrated good internal consistency (α = 0.81) and inter-item and item-total correlations. On the "Sleep Health Knowledge" subscale, participants answered 76.36% of items correctly. In study 2, the Sleep Health Literacy had good convergent validity with Sleep Beliefs Scale and the All Aspect of Health Literacy Scale.

Conclusions: The Sleep Health Literacy scale offers a standardized measure to assess sleep health literacy, an understudied domain that has important links to health. This measure may allow researchers and clinicians to better understand how to improve sleep health. Further validation of the Sleep Health Literacy scale is warranted in more diverse samples.

Objective: This study gathered adolescent and caregiver perspectives on sleep hygiene practices and beliefs for Black families living in low-resourced urban communities.

Methods: Semistructured interviews were conducted with adolescents (n = 9) and caregivers (n = 9) from January-March 2021, during the COVID-19 pandemic. Interview questions included adolescent's current sleep habits, barriers, and home and neighborhood influences. Trained researchers coded data using inductive analysis and a constant comparative method to derive themes.

Results: Two themes focused on sleep hygiene practices, including items used to facilitate sleep (e.g., melatonin, electronics) and COVID-19 consequences on sleep schedules. Two themes focused on sleep hygiene beliefs, including a common value on the importance of sleep and influences on caregiver's bedtime rules.

Conclusions: Findings provide important insights on sleep hygiene practices and beliefs in a historically marginalized population of Black adolescents and caregivers in urban communities to inform targeted sleep interventions, policies, and programs for optimal sleep and well-being.

Objective: We analyze the relationship between employment status, income, and sleep in South Africa to address two research questions: (1) How does employment status influence the sleep quantity of the individual and their partner? (2) How does income impact the sleep quantity of the individual and their partner?

Method: Using data from 1600 Black African couples in the South African Time Use Study, we employ the Actor-Partner Interdependence Model to investigate the relationship between employment status, income, and sleep in couples. We categorize nighttime sleep into three categories: recommended sleep (7-9 hours), short sleep (<7 hours), and long sleep (>9 hours).

Results: Employed individuals were more likely to get the recommended amount of sleep and less likely to experience long sleep. However, men whose wives are employed are almost twice as likely as men whose wives are unemployed to experience short sleep, and they experience 47% lower risk of long sleep. Men with a medium level of income have a higher risk of short sleep than those with low income, while those in the highest income category are more likely to get the recommended amount of sleep.

Conclusion: These results highlight the intricate dynamics between managing employment demands and securing economic stability for both men and women in the context of high unemployment and shifting gender norms.

Background: Many individuals use the Internet, including generative artificial intelligence like ChatGPT, for sleep-related information before consulting medical professionals. This study compared responses from sleep disorder specialists and ChatGPT to common sleep queries, with experts and laypersons evaluating the responses' accuracy and clarity.

Methods: We assessed responses from sleep medicine specialists and ChatGPT-4 to 140 sleep-related questions from the Korean Sleep Research Society's website. In a blinded study design, sleep disorder experts and laypersons rated the medical helpfulness, emotional supportiveness, and sentence comprehensibility of the responses on a 1-5 scale.

Results: Laypersons rated ChatGPT higher for medical helpfulness (3.79 ± 0.90 vs. 3.44 ± 0.99, p < .001), emotional supportiveness (3.48 ± 0.79 vs. 3.12 ± 0.98, p < .001), and sentence comprehensibility (4.24 ± 0.79 vs. 4.14 ± 0.96, p = .028). Experts also rated ChatGPT higher for emotional supportiveness (3.33 ± 0.62 vs. 3.01 ± 0.67, p < .001) but preferred specialists' responses for sentence comprehensibility (4.15 ± 0.74 vs. 3.94 ± 0.90, p < .001). When it comes to medical helpfulness, the experts rated the specialists' answers slightly higher than the laypersons did (3.70 ± 0.84 vs. 3.63 ± 0.87, p = .109). Experts slightly preferred specialist responses overall (56.0%), while laypersons favored ChatGPT (54.3%; p < .001). ChatGPT's responses were significantly longer (186.76 ± 39.04 vs. 113.16 ± 95.77 words, p < .001).

Discussion: Generative artificial intelligence like ChatGPT may help disseminate sleep-related medical information online. Laypersons appear to prefer ChatGPT's detailed, emotionally supportive responses over those from sleep disorder specialists.

Study objectives: We investigated profiles of community support and challenges in relation to insomnia symptoms among the Bhutanese living in the United States.

Methods: Using data from the pilot Bhutanese Community of Central Ohio Health Study (N = 495; 51.5% men, 69.8% aged 18-44years), we used latent class analysis to identify distinct profiles of neighborhood social cohesion, social support, and community challenges (e.g., limited access to healthcare services and transportation, crime and safety issues, substance use, intimate partner violence) and their associations with insomnia symptoms. Insomnia symptoms were self-reported as difficulty falling and staying asleep and dichotomized as "not at all" vs. "some to always." Identified classes/profiles were further differentiated by self-reported sociodemographic, socioeconomic, health, acculturative, and discrimination factors.

Results: Latent class analysis revealed four distinct classes/profiles. The High Cohesion (class 1) profile (30.1% of sample) had the lowest likelihood of insomnia symptoms at 6.5%, followed by class 2 or High Support (23.6%) with a 15.3% likelihood. Class 3 or High Challenges profile (11.5%) had a moderate likelihood of insomnia symptoms at 49%. Class 4 or the Low Cohesion/Support profile (34.7%) had a 100% likelihood of reporting insomnia symptoms. Class 4 when compared to class 1 was more likely to report cardiometabolic conditions, experiences of everyday discrimination, limited English linguistic proficiency, and not using telehealth.

Conclusion: Community social cohesion and support may play an important role in mitigating insomnia symptoms among Bhutanese refugees. Further investigations are warranted.

Objective: To investigate the associations of sleep behaviors with the risk of rheumatoid arthritis, and whether the associations differ among individuals with low, intermediate, or high genetic risk.

Methods: We included participants who were free of rheumatoid arthritis at baseline based the UK Biobank. We evaluated the associations of five sleep behaviors with the risk of rheumatoid arthritis using Cox proportional hazard regression models. We then generated a sleep risk score which combined five sleep behaviors and assessed its association with the risk of rheumatoid arthritis. We finally generated a genetic risk score and examined the joint effects of sleep patterns and genetic susceptibility on the risk of rheumatoid arthritis.

Results: Of the 375,133 participants at baseline, 4913 incident rheumatoid arthritis cases were identified over a median follow-up of 11.73years. We found that insomnia and daytime sleepiness were associated with a 33% and a 38% increased risk of rheumatoid arthritis. A U-shaped association was observed between sleep duration and the risk of rheumatoid arthritis, with a 29% higher risk for those with short sleep and a 30% higher risk for those with long sleep. Participants with unfavorable sleep patterns had a 63% increased risk of rheumatoid arthritis compared with those with favorable sleep patterns. Participants with unfavorable sleep patterns and high genetic risk showed the highest risk of rheumatoid arthritis although no statistically significant multiplicative or additive interaction was found.

Conclusions: Our study suggested that insomnia, daytime sleepiness, and short or long sleep duration, as well as sleep risk score were associated with an increased risk of rheumatoid arthritis.