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SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A95-A95
Author(s):  
Patricia Wong ◽  
Amy Wolfson ◽  
Sarah Honaker ◽  
Judith Owens ◽  
Kyla Wahlstrom ◽  
...  

Abstract Introduction Adolescents are vulnerable to short, insufficient sleep stemming from a combined preference for late bedtimes and early school start times, and also circadian disruptions from frequent shifts in sleep schedules (i.e., social jetlag). These sleep disruptions are associated with poor mental health. The COVID-19 pandemic has impacted education nationwide, including changes in instructional formats and school schedules. With data from the Nationwide Education and Sleep in TEens During COVID (NESTED) study, we examined whether sleep variability and social jetlag (SJL) during the pandemic associate with mental health. Methods Analyses included online survey data from 4767 students (grades 6-12, 46% female, 36% non-White, 87% high school). For each weekday, participants identified if they attended school in person (IP), online-scheduled synchronous classes (O/S), online-no scheduled classes (asynchronous, O/A), or no school. Students reported bedtimes (BT) and wake times (WT) for each instructional format and for weekends/no school days. Sleep opportunity (SlpOpp) was calculated from BT and WT. Weekday night-to-night SlpOpp variability was calculated with mean square successive differences. SJL was calculated as the difference between the average sleep midpoint on free days (O/A, no school, weekends) versus scheduled days (IP, O/S). Participants also completed the PROMIS Pediatric Anxiety and Depressive Symptoms Short Form. Data were analyzed with hierarchical linear regressions controlling for average SlpOpp, gender, and school-level (middle vs high school). Results Mean reported symptoms of anxiety (60.0 ±9.1; 14%≧70) and depression (63.4 ±10.2; 22%≧70) fell in the at-risk range. Shorter average SlpOpp (mean=8.3±1.2hrs) was correlated with higher anxiety (r=-.10) and depression (r=-.11; p’s<.001) T-scores. Greater SlpOpp variability was associated with higher anxiety (B=.71 [95%CI=.41-1.01, p<.001) and depression (B=.67 [.33-1.00], p<.001) T-scores. Greater SJL (mean=1.8±1.2hrs; 94% showed a delay in midpoint) was associated with higher anxiety (B=.36 [.12-.60], p<.001) and depression (B=.77 [.50-1.03], p<.001) T-scores. Conclusion In the context of system-wide education changes during COVID-19, students on average reported at-risk levels of anxiety and depression symptoms which were associated with greater variability in sleep opportunity across school days and greater social jetlag. Our findings suggest educators and policymakers should consider these sleep-mental health associations when developing instructional formats and school schedules during and post-pandemic. Support (if any) T32MH019927(P.W.)


2021 ◽  
Vol 61 (2) ◽  
pp. 404-405
Author(s):  
Amy A. Gelfand ◽  
Alexandra C. Ross ◽  
Sara Pavitt ◽  
Christina L. Szperka ◽  
Samantha L. Irwin ◽  
...  

Sleep Health ◽  
2020 ◽  
Vol 6 (6) ◽  
pp. 749-757 ◽  
Author(s):  
Runa Stefansdottir ◽  
Vaka Rognvaldsdottir ◽  
Sunna Gestsdottir ◽  
Sigridur L. Gudmundsdottir ◽  
Kong Y. Chen ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Matias Berthelon ◽  
Diana Kruger ◽  
Catalina Lauer ◽  
Luca Tiberti ◽  
Carlos Zamora

2019 ◽  
Vol 15 (8) ◽  
pp. 541-547 ◽  
Author(s):  
Timothy A. Brusseau ◽  
Ryan D. Burns ◽  
You Fu ◽  
R. Glenn Weaver

2018 ◽  
Author(s):  
Rachel Sippy ◽  
Diego Herrera ◽  
David Gaus ◽  
Ronald E. Gangnon ◽  
Jonathan A. Patz ◽  
...  

AbstractSeason is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009—2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05—1.51) and Thursdays (RR: 1.25, 95% CI 1.02—1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65—1.01) and Sundays (RR: 0.74, 95% CI 0.58—0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46—5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly absolute minimum temperature, an interaction between total precipitation and monthly precipitation days, and a three-way interaction between minimum temperature, total precipitation, and precipitation days. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.Author summaryDengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009— 2016 medical record data from rural Ecuador, we studied the short- and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year in mid-March, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.


2018 ◽  
Vol 50 (1) ◽  
pp. 78-84 ◽  
Author(s):  
Fernando Mazzilli Louzada ◽  
Sofia Isabel Ribeiro Pereira

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