Objective: In 2010, American youth aged 8 to 18 spent an average of 7.5 hours daily using entertainment media, an increase of more than an hour compared with 2005. Increase in media use is associated with multiple negative outcomes, including decreased sleep time and increased tiredness, but little research has examined whether media use is associated with poorer sleep efficiency when the individual is actually asleep.
Methods: This study assessed relations between adolescent media use and sleep efficiency. Fifty-five adolescents (mean age = 14.89 years; SD = 0.62; 53% African-American and 47% white) completed self-report measures concerning their media use. Sleep quality was measured by actigraphy for 1 week, and both sleep offset and sleep efficiency were extracted from actigraphy data.
Results: Sleep efficiency was negatively correlated to daily time spent text messaging (r(52) = -0.29; p < .05), media use after bed (r(52) = -0.32; p < .05), and number of nighttime awakenings by mobile phones (r(52) = -0.33; p < .05). Decreased sleep efficiency was related to sleeping later in the morning, presumably to make up for lost sleep at night (r(52) = -0.33; p < .05). In a regression model, media use accounted for 30% of the variance in sleep efficiency (adjusted R = 0.30; F(6,44) = 3.74; p < .01).
Conclusion: Media use after bed, awakenings by a mobile phone at night, and sleep offset associated with adolescents' sleep efficiency. Results support the incorporation of media use habits into adolescent sleep health education and sleep dysfunction interventions. Parental education about the effects of media use on sleep could also mitigate negative effects.
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http://dx.doi.org/10.1097/DBP.0000000000000239 | DOI Listing |
Womens Health (Lond)
January 2025
Faculty of Health Sciences, Department of Physiotherapy, University of Granada, Granada, Spain.
Background: After breast cancer (BC), women may face other severe symptoms such as sleep problems. The use of simple, fast, and reliable scales is necessary in the clinic to improve patient benefits, and sleep is an important aspect to be addressed.
Objective: This study was conducted to adapt and validate the Spanish version of the satisfaction, alertness, timing, efficiency, and duration (SATED) scale for measuring sleep health in women who have completed treatment for BC in Spain (SATED-BC).
Sensors (Basel)
December 2024
GIKAFIT Research Group, University of the Basque Country (UPV/EHU), 01007 Vitoria-Gasteiz, Spain.
The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discriminate between perceived sleep. Ten objective sleep variables from the multisensory sleep-tracker were analyzed.
View Article and Find Full Text PDFSci Rep
January 2025
Center on Aging and Health, Johns Hopkins University, Baltimore, MD, USA.
Although prior studies have examined associations of personality traits with sleep, most have investigated self-reported sleep, been cross-sectional, and focused on younger and middle-aged adults. We investigated associations of personality with actigraphic sleep parameters and changes in sleep in 398 cognitively normal adults aged 40-95 years (M ± SD = 70.1 ± 12.
View Article and Find Full Text PDFSleep Health
January 2025
University of Texas at San Antonio, Department of Sociology and Demography, San Antonio, Texas, United States.
Objectives: Drawing on the socioecological model of sleep health, we formally examine the association between neighborhood disorder and sleep efficiency. While most studies focus on direct associations with neighborhood context, we also consider whether the relationship between religious attendance and sleep efficiency varies as a function of neighborhood disorder.
Design: We use ordinary least squares regression to model cross-sectional survey data.
J Clin Neurol
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
Background And Purpose: Obstructive sleep apnea (OSA) is associated with an increased risk of adverse outcomes, including mortality. Machine-learning algorithms have shown potential in predicting clinical outcomes in patients with OSA. This study aimed to develop and evaluate a machine-learning algorithm for predicting 10- and 15-year all-cause mortality in patients with OSA.
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