Browsing by Author "Seixas, Azizi"
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Item 237 Sleep disturbances, online instruction, and learning during COVID-19: evidence from 4148 adolescents in the NESTED study(Oxford University Press, 2021-05) Saletin, Jared; Owens, Judith; Wahlstrom, Kyla; Honaker, Sarah; Wolfson, Amy; Seixas, Azizi; Wong, Patricia; Carskadon, Mary; Meltzer, Lisa; Pediatrics, School of MedicineIntroduction: COVID-19 fundamentally altered education in the United States. A variety of in-person, hybrid, and online instruction formats took hold in Fall 2020 as schools reopened. The Nationwide Education and School in TEens During COVID (NESTED) study assessed how these changes impacted sleep. Here we examined how instruction format was associated with sleep disruption and learning outcomes. Methods: Data from 4148 grade 6-12 students were included in the current analyses (61% non-male; 34% non-white; 13% middle-school). Each student’s instructional format was categorized as: (i) in-person; (ii) hybrid [≥1 day/week in-person]; (iii) online/synchronous (scheduled classes); (iv) online/asynchronous (unscheduled classes); (v) online-mixed; or (vi) no-school. Sleep disturbances (i.e., difficulty falling/staying asleep) were measured with validated PROMIS t-scores. A bootstrapped structural equation model examined how instructional format and sleep disturbances predict school/learning success (SLS), a latent variable loading onto 3 outcomes: (i) school engagement (ii) likert-rated school stress; and (iii) cognitive function (PROMIS t-scores). The model covaried for gender, race-ethnicity, and school-level Results: Our model fit well (RMSEA=.041). Examining total effects (direct + indirect), online and hybrid instruction were associated with lower SLS (b’s:-.06 to -.26; p’s<.01). The three online groups had the strongest effects (synchronous: b=-.15; 95%CI: [-.20, -.11]; asynchronous: b=-.17; [-.23, -.11]; mixed: b=-.14; [-.19, -.098]; p’s<.001). Sleep disturbance was also negatively associated with SLS (b=-.02; [-.02, -.02], p<.001). Monte-carlo simulations confirmed sleep disturbance mediated online instruction’s influence on SLS. The strongest effect was found for asynchronous instruction, with sleep disturbance mediating 24% of its effect (b = -.042; [-0.065, -.019]; p<.001). This sleep-mediated influence of asynchronous instruction propagated down to each SLS measure (p’s<.001), including a near 3-point difference on PROMIS cognitive scores (b = -2.86; [-3.73, -2.00]). Conclusion These analyses from the NESTED study indicate that sleep disruption may be one mechanism through which online instruction impacted learning during the pandemic. Sleep disturbances were unexpectedly influential for unscheduled instruction (i.e., asynchronous). Future analyses will examine specific sleep parameters (e.g., timing) and whether sleep’s influence differs in teens who self-report learning/behavior problems (e.g., ADHD). These nationwide data further underscore the importance of considering sleep as educators and policy makers determine school schedules.Item 238 Adolescent Sleep Variability, Social Jetlag, and Mental Health during COVID-19: Findings from a Large Nationwide Study(Oxford University Press, 2021-05) Wong, Patricia; Wolfson, Amy; Honaker, Sarah; Owens, Judith; Wahlstrom, Kyla; Saletin, Jared; Seixas, Azizi; Meltzer, Lisa; Carskadon, Mary; Pediatrics, School of MedicineIntroduction: 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.Item 675 COVID-19 Instruction Style (In-Person, Virtual, Hybrid), School Start Times, and Sleep in a Large Nationwide Sample of Adolescents(Oxford University Press, 2021-05) Meltzer, Lisa; Wahlstrom, Kyla; Owens, Judith; Wolfson, Amy; Honaker, Sarah; Saletin, Jared; Seixas, Azizi; Wong, Patricia; Carskadon, Mary; Pediatrics, School of MedicineIntroduction: The COVID-19 pandemic significantly disrupted how and when adolescents attended school. This analysis used data from the Nationwide Education and Sleep in TEens During COVID (NESTED) study to examine the association of instructional format (in-person, virtual, hybrid), school start times, and sleep in a large diverse sample of adolescents from across the U.S. Methods: In October/November 2020, 5346 nationally representative students (grades 6–12, 49.8% female, 30.6% non-White) completed online surveys. 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 school start times for IP or O/S days, and bedtimes (BT) and wake times (WT) for each applicable school type and weekends/no school days (WE). Sleep opportunity (SlpOpp, total sleep time proxy) was calculated from BT and WT. Night-to-night sleep variability was calculated with mean square successive differences. Results: Significant differences for teens’ sleep across instructional formats were found for all three sleep variables. With scheduled instructional formats (IP and O/S), students reported earlier BT (IP=10:54pm, O/S=11:24pm, O/A=11:36pm, WE=12:30am), earlier WT (IP=6:18am, O/S=7:36am, O/A=8:48am, WE=9:36am), and shorter SlpOpp (IP=7.4h, O/S=8.2h, O/A=9.2h, WE=9.2h). Small differences in BT, but large differences in WT were found, based on school start times, with significantly later wake times associated with later start times. Students also reported later WT on O/S days vs. IP days, even with the same start times. Overall, more students reported obtaining sufficient SlpOpp (>8h) for O/S vs. IP format (IP=40.0%, O/S=58.8%); when school started at/after 8:30am, sufficient SlpOpp was even more common (IP=52.7%, O/S=72.7%). Greater night-to-night variability was found for WT and SlpOpp for students with hybrid schedules with >1 day IP and >1 day online vs virtual schedules (O/S and O/A only), with no differences in BT variability reported between groups. Conclusion: This large study of diverse adolescents from across the U.S. found scheduled school start times were associated with early wake times and shorter sleep opportunity, with greatest variability for hybrid instruction. Study results may be useful for educators and policy makers who are considering what education will look like post-pandemic.Item COVID-19 instructional approaches (in-person, online, hybrid), school start times, and sleep in over 5,000 U.S. adolescents(Oxford University Press, 2021-12-10) Meltzer, Lisa J.; Saletin, Jared M.; Honaker, Sarah M.; Owens, Judith A.; Seixas, Azizi; Wahlstrom, Kyla L.; Wolfson, Amy R.; Wong, Patricia; Carskadon, Mary A.; Pediatrics, School of MedicineStudy objectives: To examine associations among instructional approaches, school start times, and sleep during the COVID-19 pandemic in a large, nationwide sample of U.S. adolescents. Methods: Cross-sectional, anonymous self-report survey study of a community-dwelling sample of adolescents (grades 6-12), recruited through social media outlets in October/November 2020. Participants reported on instructional approach (in-person, online/synchronous, online/asynchronous) for each weekday (past week), school start times (in-person or online/synchronous days), and bedtimes (BT) and wake times (WT) for each identified school type and weekends/no school days. Sleep opportunity was calculated as BT-to-WT interval. Night-to-night sleep variability was calculated with mean square successive differences. Results: Respondents included 5,245 racially and geographically diverse students (~50% female). BT and WT were earliest for in-person instruction; followed by online/synchronous days. Sleep opportunity was longer on individual nights students did not have scheduled instruction (>1.5 h longer for online/asynchronous than in-person). More students obtained sufficient sleep with later school start times. However, even with the same start times, more students with online/synchronous instruction obtained sufficient sleep than in-person instruction. Significantly greater night-to-night variability in sleep-wake patterns was observed for students with in-person hybrid schedules versus students with online/synchronous + asynchronous schedules. Conclusions: These findings provide important insights regarding the association between instructional approach and school start times on the timing, amount, and variability of sleep in U.S. adolescents. Given the public health consequences of short and variable sleep in adolescents, results may be useful for education and health policy decision-making for post-pandemic secondary schools.Item Implementation of Sleep and Circadian Science: Recommendations from the Sleep Research Society and National Institutes of Health Workshop(Oxford, 2016-12-01) Parthasarathy, Sairam; Carskadon, Mary A.; Jean-Louis, Girardin; Owens, Judith; Bramoweth, Adam; Combs, Daniel; Hale, Lauren; Harrison, Elizabeth; Hart, Chantelle N.; Hasler, Brant P.; Honaker, Sarah M.; Hertenstein, Elisabeth; Kuna, Samuel; Kushida, Clete; Levenson, Jessica C.; Murray, Caitlin; Pack, Allan I.; Pillai, Vivek; Pruiksma, Kristi; Seixas, Azizi; Strollo, Patrick; Thosar, Saurabh S.; Williams, Natasha; Buysse, Daniel; Pediatrics, School of MedicineItem Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine(American Academy of Sleep Medicine, 2023) Bandyopadhyay, Anuja; Bae, Charles; Cheng, Hao; Chiang, Ambrose; Deak, Maryann; Seixas, Azizi; Singh, Jaspal; Pediatrics, School of MedicineSince the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions.Item The Associations Between Instructional Approach, Sleep Characteristics and Adolescent Mental Health: Lessons from the COVID-19 Pandemic(Elsevier, 2024) Wong, Patricia; Meltzer, Lisa J.; Barker, David; Honaker, Sarah M.; Owens, Judith A.; Saletin, Jared M.; Seixas, Azizi; Wahlstrom, Kyla L.; Wolfson, Amy R.; Carskadon, Mary A.; Pediatrics, School of MedicineObjectives: To test whether adolescents' mental health during the COVID-19 pandemic is associated with the combination of their instructional approach(es) and their sleep patterns. Design: Cross-sectional. Setting: Adolescents were recruited through social media outlets in October and November 2020 to complete an online survey. Participants: Participants were 4442 geographically and racially diverse, community-dwelling students (grades 6-12, 51% female, 36% non-White, 87% high schoolers). Measurements: Participants completed items from the PROMIS Pediatric Depressive Symptoms and Anxiety scales. Participants reported their instructional approach(es), bedtimes, and wake times for each day in the past week. Participants were categorized into five combined instructional approach groups. Average sleep opportunity was calculated as the average time between bedtime and waketime. Social jetlag was calculated as the difference between the average sleep midpoint preceding non-scheduled and scheduled days. Results: Emotional distress was elevated in this sample, with a large proportion of adolescents reporting moderate-severe (T-score ≥ 65) levels of depressive symptoms (49%) and anxiety (28%). There were significant differences between instructional approach groups, such that adolescents attending all schooldays in-person reported the lowest depressive symptom and anxiety T-scores (P < .001, ηp2 = .012), but also the shortest sleep opportunity (P < .001, ηp2 = .077) and greatest social jetlag (P < .001, ηp2 = .037) of all groups. Adolescents attending school in person, with sufficient sleep opportunity (≥8-9 hours/night) and limited social jetlag (<2 hours) had significantly lower depressive (ηp2 = .014) and anxiety (ηp2 = .008) T-scores than other adolescents. Conclusions: Prioritizing in-person education and promoting healthy sleep patterns (more sleep opportunity, more consistent sleep schedules) may help bolster adolescent mental health.