Study Objectives: To determine the effects of changes in nocturnal sleep and daytime nap durations on all-cause mortality among older adults.
Methods: Two-thousand four-hundred forty-eight community-dwelling older Singaporeans (≥60 years) reported their nocturnal sleep and daytime nap durations at baseline (2009) and the 2-year follow-up. At each phase, they were grouped into the recommended (7-8 hours), short (≤6 hours), and long (≥9 hours) sleep duration categories, and the none (0 hour), short (≤1 hour), and long (>1 hour) nap duration categories. Cox regression analysis was conducted to quantify the associations of changes in sleep and nap durations over 2 years with all-cause mortality risk in the subsequent 4 years (till end of 2015). Multivariable fractional polynomial regression, which treated sleep and nap durations as continuous variables was conducted as a supplementary analysis.
Results: Relative to individuals who had the recommended sleep durations at both baseline and follow-up, the risks of all-cause mortality were higher among older adults who reported considerable changes in sleep duration (from short to long sleep and vice versa, hazard ratio [HR] = 2.14-2.56). Furthermore, compared to those who did not nap at both time points, significantly higher mortality risks were found in individuals who showed any increase in nap duration (HR = 1.86-2.16), or reduced their nap from long to short duration (HR = 1.86). Supplementary analysis revealed similar findings.
Conclusions: In addition to the change in nocturnal sleep duration, change in daytime nap duration can also predict risks of all-cause mortality among older adults. It is crucial to track older adults' sleep and nap durations longitudinally.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/sleep/zsy087 | DOI Listing |
JMIR Public Health Surveill
January 2025
Clinical Research Institute, Affiliated Hospital of Nanjing University of Chinese Medicine (Jiangsu Province Hospital of Chinese Medicine), 155 Hanzhong Road, Nanjing, 210029, China, 86 13770784000.
Background: The association between social media usage and the risk of depressive symptoms has attracted increasing attention. WeChat is a popular social media software in China. The impact of using WeChat and posting WeChat moments on the risk of developing depressive symptoms among community-based middle-aged and older adults in China is unknown.
View Article and Find Full Text PDFMult Scler Relat Disord
December 2024
Multiple Sclerosis Centre, Department of Neuroscience, University Hospital of Padua, Italy.
Background: Self-perception of cognitive functioning in pediatric MS (pedMS) needs to be evaluated with specific questionnaires, currently lacking. This study aimed to develop a self-reportwhich investigates cognitive status in pedMS.
Methods: Twenty-seven pedMS patients (mean age±standard deviation=15 years±1.
Environ Res
January 2025
Department of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China. Electronic address:
Background: Exposure to residential greenness has been linked with improved sleep duration; however, longitudinal evidence is limited, and the potential mediating effect of ambient fine particulate matter (PM) has yet to be assessed.
Methods: We obtained data for 19,567 participants across seven counties in a prospective cohort in Ningbo, China. Greenness was estimated using Normalized Difference Vegetation Index (NDVI) within 250-m, 500-m and 1000-m buffer zones, while yearly average PM concentrations were measured using validated land-use regression models, both based on individual residential addresses.
Med J Armed Forces India
December 2024
Medical Cadet, Armed Forces Medical College, Pune, India.
Background: Sleep deprivation leads to decreased performance, alertness and degradation in the health status of a person. Often the person remains unaware of the reduced alertness and may end up taking inaccurate decisions. There was a need to study the sleep duration of college goers and to study the effect of total night-time sleep duration on daytime Electroencephalogram (EEG) characteristics.
View Article and Find Full Text PDFHum Reprod
December 2024
Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
Study Question: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
Summary Answer: A combination of a machine learning-based tissue analysis software for tissue segmentation and a deep learning-based algorithm for segmentation-independent cell identification shows strong performance on the automated histological analysis of endometriosis sections.
What Is Known Already: Endometriosis is characterized by the complex interplay of various cell types and exhibits great variation between patients and endometriosis subtypes.
Study Design, Size, Duration: Endometriosis tissue samples of eight patients of different subtypes were obtained during surgery.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!