ABSTRACT
During the COVID-19 pandemic, schools rapidly transitioned from in-person to remote learning. We examined sleep- and mood-related changes in early adolescents, before and after this transition to assess the impact of in-person vs. remote learning. Sleep-wake timing was measured using wrist-actigraphy and sleep diaries over 1–2 weeks in Year 7 students (age M±SD =12.79±0.42 years) during in-person learning (n=28) and remote learning (n=58;n=27 were studied in both conditions). Circadian timing was measured under a single condition in each individual using salivary melatonin (Dim Light Melatonin Onset;DLMO). Online surveys assessed mood (PROMIS Pediatric Anxiety and Depressive Symptoms) and sleepiness (Epworth Sleepiness Scale – Child and Adolescent) in each condition. During remote vs. in-person learning: (i) on school days, students went to sleep 26 min later and woke 49 min later, resulting in 22 min longer sleep duration (all p<0.0001);(ii) DLMO time did not differ significantly between conditions, although participants woke at a later relative circadian phase (43 minutes, p=0.03) during remote learning;(iii) participants reported significantly lower sleepiness (p=0.048) and lower anxiety symptoms (p=0.006). Depressive symptoms did not differ between conditions. Changes in mood symptoms were not mediated by changes in sleep timing. Although remote learning had the same school start times as in-person learning, removing morning commutes likely enabled adolescents to sleep longer, wake later, and to wake at a later circadian phase. These results indicate that remote learning, or later school start times, may extend sleep duration and improve some subjective symptoms in adolescents.
ABSTRACT
Introduction: Circadian rhythms have critical roles in human health. We quantified the effect of time-of-day of COVID-19 vaccination and other covariates on self-reported side effects post vaccination. Methods: The dataset was created from MassGeneralBrigham (MGB) electronic health records and REDCap survey that collected self-reported symptoms for 1-3 days after each immunization. Variables are demographics (age, sex, race, and ethnicity), vaccine manufacturer, clock time of vaccine administration/appointment, any COVID-19 diagnosis/positive test prior to vaccination, any history of allergy, and any note of epinephrine self-injection (e.g., EpiPen) medication. Time of day groupings were morning (6 am10 am), midday (10 am2 pm), late afternoon (2 pm6 pm) or evening (6 pm10 pm). Side effects were classified as Allergic (Rash;Hives;Swollen lips, tongue, eyes, or face;Wheezing) and Non-Allergic (New Headache, New Fatigue, Arthralgias, Myalgias, Fever) symptoms. The study was approved by the MGB IRB.Machine learning (ML) techniques (e.g., extreme gradient boosting) were applied to the variables to predict the occurrence of side effects. Stratified k-fold cross validation was used to validate the performance of the ML models. Shapley Additive Explanation values were computed to explain the contribution of each of the variables to the prediction of the occurrence of side effects. Results: Data were from 54,844 individuals. On day 1 after the first vaccination, (i) females, people who received the Moderna vaccine, and those with any allergy history were more likely to report Allergic side effects;and (ii) females, people who received the Janssen vaccine, those who had prior COVID-19 diagnosis ,and those who received their vaccine in the morning or midday and were more likely to report Non-Allergic symptoms. Older persons had fewer side effects of any type. Conclusion: ML techniques identified demographic and time-ofday- of-vaccination effects on side effects reported on the first day after the first dose of a COVID-19 vaccination. We will use these techniques to test for changes on days 2 and 3 after the first dose, and the first 3 days after the second dose and for the influence of recent night or shiftwork. Future work should target underlying physiological reasons.