Occupational burnout and poor subjective sleep quality are each linked to adverse cardiovascular outcomes, yet their combined effects on arterial stiffness remain unclear. This study examined whether poor sleep is associated with arterial stiffness, measured by aortic pulse wave velocity, in physicians with burnout. Sixty male physicians (30 with clinical burnout, 30 without) completed the Pittsburgh Sleep Quality Index and underwent aortic pulse wave velocity assessment using the Arteriograph. Multivariable regression tested independent and interactive associations of burnout and sleep quality, adjusting for heart rate, systolic blood pressure, age, body mass index, and physical activity. Poor sleepers were more frequent in burnout (40%) than controls (10%). Although neither burnout nor sleep quality predicted aortic pulse wave velocity, a significant interaction indicated that poorer, particularly shorter, sleep was associated with greater arterial stiffness only in burnout. Findings highlight the potential value of sleep-focused interventions to improve cardiovascular health in occupational stress.
Background and Objective Circadian rhythms play a crucial role in human health, with disruptions linked to various mental health issues. The COVID-19 pandemic has amplified these concerns, particularly among university students who faced significant changes in their daily routines due to lockdowns and remote learning. This study aims to investigate the impact of the pandemic on chronotype and mental health among Indonesian university students. Methods A comparative analysis was conducted involving two student populations: those affected by COVID-19 (n=257) from 2021 and a pre-pandemic comparison group (n=325) from 2019. Participants completed the Indonesian version of the Munich Chronotype Questionnaire and the Depression, Anxiety, and Stress Scale. Statistical analyses were performed using SPSS version 20.0, employing chi-square tests, Mann-Whitney U-tests, and Spearman’s rank correlation tests. Results We observed significant increases in depression, anxiety, and stress levels during the pandemic compared to pre-pandemic conditions for both sexes (all p<0.01). Average sleep duration increased significantly during the pandemic for both males (6:40 to 7:13 hours) and females (6:46 to 7:13 hours), while mid-sleep times shifted later across all groups. Interestingly, traditional measures of chronotype using mid-sleep time on free days corrected for sleep debt did not reflect significant changes during this period. Conclusions This current study highlights profound impacts on mental health and sleep patterns among university students due to COVID-19-related disruptions. Under extreme conditions such as a pandemic, it is necessary to adapt assessment tools like questionnaires to accurately capture shifts in chronotype and associated mental health outcomes.
Sleep disorders impact human health and their well-being which thereby necessitate an accurate and efficient diagnosing approach. Although traditionally, these disorders are diagnosed using clinical interviews, polysomnography test, etc., but they are time-consuming and costly. This paper focuses on the role of multiple machine-learning models in classifying different types of sleep disorders such as insomnia and sleep apnea. A comprehensive methodology is employed like data preprocessing, class balancing using SMOTE on the dataset containing clinical and behavioral attributes. which includes physical activity level, sleep duration, body mass index, stress level, blood pressure, and heart rate. Multiple models are examined, such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Näive Bayes, alongside advanced ensemble methods like Voting Classifier, CatBoost, XGBoost, and HistGradient Boosting. Among all models, ensemble techniques, particularly the Voting Classifier and HistGradient Boosting, determined superior performance by obtaining an accuracy of 93.94%. The highest observed values of recall, precision, as well as F1-score are 0.94, computed by Voting and HistGradient Boosting, indicating strong model reliability across multiple classes. These results emphasize the importance of ensemble machine learning methods in building effective tools for automated sleep disorder diagnosis, contributing to more timely and accurate clinical assessments.
Background and Objective Adolescent sleep deprivation, increasingly driven by digital media, poses a significant threat to cardiovascular health by promoting risks like obesity and hypertension. To address these modifiable factors, this study measures daily screen time duration and sleep patterns among adolescents to identify behavioural precursors to early heart disease. Methods In this cross-sectional study, 483 adolescents aged 12–17 were participated. Pittsburgh Sleep Quality Index and an Electronic Media Use Questionnaire were applied to assess sleep and screen pattern. Chi-square tests and regression analysis were employed to evaluate associations between screen exposure and sleep quality, with significance set at p<0.05. Results Of the 483 participants, 49.7% exhibited poor sleep quality. Poor sleepers reported significantly higher daily media duration and social media frequency compared to good sleepers. Logistic regression identified device usage before sleep (odds ratio [OR]=1.64, p=0.008), obsessive urge to use devices (OR=1.81, p=0.002), and next-morning tiredness (OR=1.73, p=0.006) as significant predictors of poor sleep quality. Conclusions Excessive and compulsive electronic media use, particularly before bedtime, significantly degrades adolescent sleep quality. Given the established link between sleep deprivation as an early cardiometabolic risk factor, the study founds limiting evening screen exposure creates a vital preventive cardiology strategy to mitigate the future burden of cardiovascular disease.
