Aim: To investigate the effects of the coronavirus disease 2019 (COVID-19) lockdown on sleep habits in the Croatian general population.
Methods: In this cross-sectional study, 1173 respondents from the general population (809 women) completed a self-report online questionnaire that gathered demographic data and data on sleep habits and mood changes before and during the COVID-19 lockdown.
Results: During the lockdown, bedtime (from 23:11±1:07 to 23:49±1:32 h, P<0.001) and waketime were delayed (from 6:51±1:09 to 7:49±1:40 h, P<0.001). Sleep latency increased from 10 (5-20) to 15 (10-30) minutes (P<0.001). Bedtime and waketime delays were more pronounced in women and respondents younger than 30. Compared with other age groups, respondents younger than 30 more frequently reported insomnia for the first time during the lockdown and had less frequent night-time awakenings (P<0.001), less common problems falling asleep (P<0.001), less frequently felt calm (P<0.001) and rested (P<0.001), but more frequently felt sadness (P<0.001) and fear (P=0.028).
Conclusion: The effect of the lockdown on sleep needs to be better understood. Sleep hygiene education could serve a first-line lifestyle intervention for people in lockdown experiencing sleep disruption.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468728 | PMC |
http://dx.doi.org/10.3325/cmj.2022.63.352 | DOI Listing |
BMC Endocr Disord
January 2025
Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Background: Menopause is a significant phase in women's health, in which the incidence of obstructive sleep apnea (OSA) is significantly increased. Body fat distribution changes with age and hormone levels in postmenopausal women, but the extent to which changes in body fat distribution affect the occurrence of OSA is unclear.
Methods: This research performed a cross-sectional analysis utilizing data from the 2015-2016 National Health and Nutrition Examination Survey (NHANES).
BMC Public Health
January 2025
Amsterdam UMC location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, the Netherlands.
Background: Developing interventions along with the population of interest using systems thinking is a promising method to address the underlying system dynamics of overweight. The purpose of this study is twofold: to gain insight into the perspectives of adolescents regarding: (1) the system dynamics of energy balance-related behaviours (EBRBs) (physical activity, screen use, sleep behaviour and dietary behaviour); and (2) underlying mechanisms and overarching drivers of unhealthy EBRBs.
Methods: We conducted Participatory Action Research (PAR) to map the system dynamics of EBRBs together with adolescents aged 10-14 years old living in a lower socioeconomic, ethnically diverse neighbourhood in Amsterdam East, the Netherlands.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFMetabolomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Gestational exposure to non-persistent endocrine-disrupting chemicals (EDCs) may be associated with adverse pregnancy outcomes. While many EDCs affect the endocrine system, their effects on endocrine-related metabolic pathways remain unclear. This study aims to explore the global metabolome changes associated with EDC biomarkers at delivery.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!