Impact of School Start Time Delays and Learning Modality on Sleep Timing and Duration during COVID-19

Abstract

Objectives.

To assess the impact of a school start time (SST) delay on adolescent sleep health during the COVID-19 pandemic, and if there were differences by learning modality.

Methods.

Data were collected from a longitudinal study evaluating sleep, education, and health among high school students in Georgia in 2020. Paired t-tests and multivariable linear regression analyses were conducted to examine changes in sleep duration and timing among 9^th grade students (n=134) and their association with learning modality (remote vs. in-person learner).

Results.

Students’ school day wake times were 1.5 hours later, school night sleep duration was 1.2 hours longer, and social jetlag was 0.9 hours shorter after the school start time delay (all P<.05). Learning modality was a significant predictor of changes in sleep timing but was not associated with changes in sleep duration.

Conclusions.

Delayed school start time was associated with positive changes in adolescent sleep health during the COVID-19 pandemic. Sleep timing was affected by learning modality, however in-person and virtual students had similar gains in sleep duration. Learning modality may be more beneficial for adolescents with early school start times to promote healthier sleep habits.

Background

The American Academy of Pediatrics (AAP) has recognized sleep deprivation among adolescents as a significant public health issue since 2014 (“School Start Times for Adolescents,” 2014). Teenagers are the least likely of any age group to get sufficient sleep, with only 25% of high school students reporting sufficient sleep duration (8 or more hours) on an average school night in 2017 (U.S. Department of Health and Human Services, 2020). Adolescents are also more likely than adults to experience misalignment in their sleep timing and social jetlag, a measure of inconsistent bedtimes and wake times between the school week and weekend (Mathew, Hale, & Chang, 2020). As adolescent sleep duration has steadily decreased over the last two decades and sleep timing is disrupted by early school start times, adolescent sleep health has become a pressing concern for both parents and educators (Keyes, Maslowsky, Hamilton, & Schulenberg, 2015; U.S. Department of Health and Human Services, 2020).

Poor sleep health has numerous consequences for teenagers. Sleep deprivation in adolescents can impair cognitive function and academic achievement, and lead to decreased alertness, attention span, and working memory (Wolfson & Carskadon, 2003). Poor sleep health can also lead to risky sexual and health behaviors among teens and is associated with a range of adverse physical and mental health outcomes, including obesity, diabetes, depression, and anxiety (Cespedes Feliciano et al., 2018; Gupta, Mueller, Chan, & Meininger, 2002; Lovato & Gradisar, 2014; McKnight-Eily et al., 2011; Pasch, Laska, Lytle, & Moe, 2010). Given the negative health outcomes associated with insufficient adolescent sleep duration and inconsistent sleep timing, it is important to identify positive, modifiable influences on adolescent sleep behaviors.

Prior research conducted around the world has demonstrated that school start times are a major environmental determinant of adolescent sleep health (Wheaton, Chapman, & Croft, 2016). Early school start times, defined by the AAP as before 8:30am, are associated with earlier wake times and shorter weekday sleep duration among high school students (“School Start Times for Adolescents,” 2014; Wheaton et al., 2016). Experimental studies have found that delays in school start time corresponded with increases in sleep duration per school night and improvements in academic performance (Owens, Belon, & Moss, 2010; Wahlstrom et al., 2014). Evidence of the positive impact of school start times delays on adolescent sleep health in the United States and abroad has led the AAP to support school start time delays (Minges & Redeker, 2016).

The school closures associated with the COVID-19 pandemic in March 2020 provide an opportunity to investigate how a less structured daily schedule affects adolescent sleep (Decker, Peele, Riser-Kositsky, Kim, & Harris, 2020). Recent studies have found that adolescents in middle and high school went to bed later, woke up later, and slept longer on school days during early COVID-19 school closures, however there was conflicting evidence of whether increased sleep duration also indicated better sleep quality (Becker et al., 2021; Gruber, Saha, Somerville, Boursier, & Wise, 2020; Ramos Socarras, Potvin, & Forest, 2021; Santos & Louzada, 2022; Stone et al., 2021). Additionally, one study found an association between school start time and adolescent sleep during school closures, with students starting virtual classes later in the morning reporting longer sleep duration, more time spent in bed, and later bedtimes and wake times on weekdays (Weingart et al., 2021).

As the pandemic continued during the 2020–2021 academic year, some school districts continued with virtual learning, while others allowed students to choose between in-person and virtual learning (Peele, 2021). The effect of learning modality on adolescent sleep is not well studied and may differentially impact teen sleep health. Virtual students do not have to get ready or commute to school in the mornings, potentially allowing them to sleep later and longer than in-person learners. However, virtual learning may also affect behaviors, such as increased screen time and reduced daily structure, physical activity, and exposure to natural light that would negatively affect adolescent sleep health. Preliminary results from the Nationwide Education and Sleep in Teens During COVID (NESTED) study, a large, cross-sectional study conducted in Fall 2020, demonstrated that in-person students report earlier weeknight bedtimes and weekday wake times and shorter sleep opportunity than virtual students during the COVID-19 pandemic (Meltzer et al., 2021). Yet, there may be confounding factors, such as socioeconomic status or physical and mental health, related to both sleep health and an adolescent’s decision to learn in-person or virtually during the pandemic. Longitudinal analyses are needed to examine changes in adolescent sleep during the COVID-19 pandemic, as well as to more rigorously study the relationship between teen sleep and virtual learning during the pandemic.

In this paper, we investigate the relationship between learning modality and changes in sleep timing and duration during the COVID-19 pandemic. We use self-reported data collected among 9^th grade students before COVID-19-related school closures and approximately 10 months after school closures when students were able to choose between virtual and in-person learning, and high school start times were delayed district-wide by one hour. We hypothesized that the delay in school start time led to delays in bedtimes and wake times and increased sleep duration over the study period. We also hypothesized that these changes differed by student learning modality; we expected virtual students to report larger delays in bedtimes and wake times and a greater increase in sleep duration compared to in-person students following the school start time delay.

Methods

Study Design and Sample

This longitudinal study investigating sleep, education, and health outcomes was conducted among a cohort of 9^th grade students attending two public high schools in a semi-rural school district of north-central Georgia. All 9^th grade students attending these schools during February 2020 were eligible to participate (N=1,133) and were followed through their 10^th grade fall semester. Parents were provided with the opportunity to opt their children out of participating in the study and all subjects indicated assent prior to completing the surveys (n=749).

Online surveys were administered to assess students’ sleep patterns at two time points: (T1) February 17 – March 4, 2020, prior to COVID-19-related school closures when the students were 9^th graders, and (T2) November 16 – December 17, 2020, after the students had become 10^th graders and the school district delayed school start times by one hour (from 7:30 a.m. to 8:30 a.m.). At T2, students and their families chose between two learning modalities – in-person attendance and virtual attendance – and all classrooms included a mixture of both types of learners. Both surveys were administered online during standard time so there should be little influence of biannual changing of the clocks. Among eligible students, 54% completed the survey at T1, 45% completed the survey at T2, and 23% completed the surveys at both time points.

Students who consented to participate provided their individual, school district-assigned student ID number in the surveys. These student ID numbers were then used to match survey respondents to deidentified demographic data provided by the school district’s Office of Student and Data Services. The linked demographic data provided by the school district included gender, race, ethnicity, free or reduced lunch (FRL) eligibility, and grade level.

Measures

Independent Variable

Learning Modality:

At T2, participants reported whether they were currently attending school in-person or virtually. These data were examined as a predictor of potential change in sleep timing and duration between T1 and T2.

Outcome Measures

Sleep Timing:

Five continuous sleep timing measures were assessed, including school night bedtime, school day wake time, weekend bedtime, weekend wake time, and social jetlag. Participants reported typical school day or weekend bedtimes and wake times at each time point. Additionally, social jetlag was measured by the difference between the midpoints of school night and weekend sleep at each time point, from the aforementioned sleep timing variables. Intra-individual changes between the two timepoints for each of these variables were also assessed.

Sleep Duration:

Two continuous sleep duration outcomes included school night sleep duration and habitual sleep duration. School night sleep duration was calculated based on school night bedtimes and wake times at each time point. Habitual sleep duration was calculated as the weekly weighted average of school night and weekend sleep duration, weighted 5/7 and 2/7 respectively, at each time point.

Sleep timing and duration measures at each time point were subtracted to calculate intra-individual changes from Spring 2020 (T1) to Fall 2020 (T2), and these change scores were the primary outcomes of interest.

Covariates

Demographics:

Demographic data were collected through the online student surveys as well as provided by the school district’s Office of Student and Data Services. When possible, self-reported demographic data on race, ethnicity, and gender from the T2 survey was used; if the participant did not provide this information, then school district data were used. Race/ethnicity was categorized as non-Hispanic Asian, non-Hispanic Black, Hispanic, non-Hispanic multiracial, or non-Hispanic white. Gender was recorded as either female or male. Students’ free or reduced lunch (FRL) eligibility (“yes” or “no”) was provided exclusively from school district data. Parents’ highest education level was provided exclusively from the T1 survey and dichotomized as “less than college degree” or “at least college degree attained.”

Screen Time:

At T2, students categorically assessed changes in their leisure screen time (not related to class and schoolwork) since the beginning of the COVID-19 pandemic; this variable was recoded as either “increased screen time” or “no change or decreased screen time.”

Parent-Set Bedtime:

At T1, students recorded if they had a parent-set bedtime.

Statistical Analyses

Only students with complete sleep and covariate data at both T1 and T2 were included in analyses (n=134). Students were excluded if they did not complete surveys at both time points (n=486) or if they did not have complete sleep and covariate data from both surveys (n=129). Descriptive statistics of demographic, covariate, and sleep variables were examined for the analytic sample as well as by learning modality. Paired t-tests for the sleep timing and duration variables were conducted to assess significant changes in each outcome measure for the full sample across time points. Analyses included seven multivariable linear regression models for each sleep timing and duration measure using ordinary least squares regression. Outcomes modeled reflected intra-individual changes in sleep timing and duration to account for the 1-hour school start time delay that affected all participants, with positive values representing delayed bedtimes or wake times, increased duration, or increased social jetlag from T1 to T2. All models adjusted for participants’ race/ethnicity, gender, FRL eligibility, parental education, change in screen time, and presence of a parent-set bedtime. All testing assessed statistical significance for each predictor at the alpha 0.05 level. All linear regression models met assumptions of normality and displayed no heteroskedasticity. None of the predictors met conditions for collinearity, which was assessed using variance inflation factors. All data were analyzed in SAS 9.4 (SAS Institute, Cary NC).

Results

Demographics

Analyses include data from 9^th grade students who had complete sleep and covariate data from both surveys at T1 and T2 (n=134). The sample was predominantly non-Hispanic white (53.0%), followed by Hispanic (20.9%), non-Hispanic Black (12.7%), non-Hispanic Asian (7.5%), and non-Hispanic multiracial (6.0%) (Table 1). Approximately 59.0% of participants were female and 47.7% were eligible for FRL. More than half of respondents (56.0%) reported that the highest level of education for both of their parents was less than a college degree. The majority of students reported an increase in screen time since the COVID-19 pandemic (76.9%) and did not have a parent-set bedtime (79.9%).

Table 1.

Demographic and Covariate Characteristics for Total Sample and Learning Modality Groups^†

	Total (n=134)		T2: In-Person (n=95)		T2: Virtual (n=39)
	N	%*	N	%*	N	%*
Race
Asian, NH^	10	7.5	4	4.2	6	15.4
Black, NH	17	12.7	13	13.7	4	10.3
Hispanic	28	20.9	20	21.1	8	20.5
Multiracial, NH	8	6.0	5	5.3	3	7.7
White, NH	71	53.0	53	55.8	18	46.2
Gender
Female	79	59.0	52	54.7	27	69.2
Male	55	41.0	43	45.3	12	30.8
FRL
Yes	64	47.8	43	45.3	21	53.9
No	70	52.2	52	54.7	18	46.2
Parent Education Level
Less Than College Degree	75	56.0	47	49.5	27	69.2
At Least College Degree	59	44.0	48	50.5	12	30.8
Change in Screen Time
Increased Time	103	76.9	71	74.7	32	82.1
No Change or Less Time	31	23.1	24	25.3	7	18.0
Parent Set Bedtime
Yes	27	20.2	21	22.1	6	15.4
No	107	79.9	74	77.9	33	84.6

^†Chi-square and Fisher’s exact tests indicated no significant differences in baseline characteristics between in-person and virtual students except for parent education level.

^*Percentages may not add up to 100 due to rounding.

^{^}NH = non-Hispanic.

Sleep Timing and Duration

Overall, students reported a greater delay in sleep timing on school nights than on weekends (Table 2). On average, students went to bed 0.3 ± 1.7 (P = .07) hours later on school nights and woke up 1.5 ± 1.1 (P <.0001) hours later on school days at T2. However, changes in school night sleep timing varied greatly between in-person and virtual students. In-person students reported going to bed 0.1 ± 1.1 hours earlier and waking up 1.1 ± 0.7 hours later on school nights at T2, while virtual students further delayed their bedtimes by 1.2 ± 2.3 hours and wake times by 2.4 ± 1.3 hours on school nights at T2.

Table 2.

Sleep Timing and Duration for Total Sample and Learning Modality Groups, T1 to T2

	Total Sample (n=134)		T2: In-Person (n=95)	T2: Virtual (n=39)
	Mean (STD - hrs)	P	Mean (STD - hrs)	Mean (STD - hrs)
School Night Bedtime
T1	11:00 pm (1.5)		11:10 pm (1.3)	10:35 pm (1.8)
T2	11:16 pm (1.4)		11:03 pm (1.3)	11:47 pm (1.4)
T1 to T2 Change (hrs)	0.3 (1.7)	.07	−0.1 (1.1)	1.2 (2.3)
School Day Wake Time
T1	5:36 am (0.7)		5:35 am (0.7)	5:38 am (0.8)
T2	7:05 am (1.0)		6:41 am (0.7)	8:04 am (1.1)
T1 to T2 Change (hrs)	1.5 (1.1)	<.0001	1.1 (0.7)	2.4 (1.3)
School Night Sleep Duration (hrs)
T1	6.6 (1.6)		6.4 (1.4)	7.0 (2.0)
T2	7.8 (1.4)		7.6 (1.3)	8.3 (1.4)
T1 to T2 Change	1.2 (1.5)	<.0001	1.2 (1.4)	1.2 (2.0)
Weekend Bedtime
T1	12:58 am (1.8)		1:04 am (1.6)	12:43 am (2.1)
T2	12:54 am (1.6)		12:44 am (1.6)	1:19 am (1.7)
T1 to T2 Change (hrs)	−0.1 (1.8)	.67	−0.3 (1.5)	0.6 (2.2)
Weekend Wake Time
T1	9:43 am (1.9)		9:38 am (1.8)	9:57 am (2.2)
T2	9:48 am (1.6)		9:34 am (1.5)	10:21 am (1.6)
T1 to T2 Change (hrs)	0.1 (2.0)	.65	−0.1 (1.9)	0.4 (2.1)
Habitual Sleep Duration (hrs)
T1	7.2 (1.5)		7.0 (1.3)	7.7 (1.8)
T2	8.1 (1.3)		8.0 (1.3)	8.5 (1.3)
T1 to T2 Change	0.9 (1.5)	<.0001	1.0 (1.3)	0.8 (1.8)
Social Jetlag (hrs)
T1	3.1 (1.5)		3.0 (1.3)	3.3 (1.7)
T2	2.2 (1.2)		2.3 (1.2)	1.9 (1.2)
T1 to T2 Change	−0.9 (1.7)	<.0001	−0.7 (1.4)	−1.4 (2.1)

On weekends, all students reported going to bed 0.1 ± 1.8 (P = .67) hours earlier and waking up 0.1 ± 2.0 (P = .65) hours later at T2. However, changes in sleep timing had different directions depending on learning modality. In-person students reported shifting their bedtimes earlier (−0.3 ± 1.5 hours) and their wake times earlier (−0.1 ± 1.9 hours) on weekends at T2, while virtual students reported delaying their bedtimes by 0.6 ± 2.2 hours and wake times by 0.4 ± 2.1 hours at T2 on the weekend.

Social jetlag decreased across the sample between T1 and T2, with an average reduction of 0.9 ± 1.7 (P <.0001) hours at T2. In-person students experienced a smaller reduction in social jetlag (−0.7 ± 1.4 hours) compared to virtual students (−1.4 ± 2.1 hours) at T2.

Both school night sleep duration and habitual sleep duration increased between T1 and T2. On average, students reported 1.2 ± 1.5 additional hours of sleep per school night at T2 (P <.0001), and an additional 0.9 ± 1.5 hours of habitual sleep duration per night at T2 (P <.0001). In-person and virtual students reported similar changes in school night sleep duration (in-person: 1.2 ± 1.4 hours; virtual: 1.2 ± 2.0 hours) and habitual sleep duration (in-person:1.0 ± 1.3 hours; virtual: 0.8 ± 1.8 hours).

Multivariable Models

Seven linear regression models were run for each change in sleep timing and duration outcome between T1 and T2: (M1) change in school night bedtime, (M2) change in school day wake time, (M3) change in school night sleep duration, (M4) change in weekend bedtime, (M5) change in weekend wake time, (M6) change in habitual sleep duration, and (M7) change in social jetlag (Table 3). In four of the models (M1, M2, M4, M7), learning modality was a significant predictor for changes in sleep timing; for the remaining three models there were no significant predictors. On average, virtual students delayed their school night bedtimes by an additional 1.3 hours (M1, 95% CI [0.6, 1.9]) and their school day wake times by an additional 1.4 hours (M2, 95% CI [1.0, 1.7]) compared to in-person students after the school start time change. On weekends, virtual students delayed their weekend bedtimes by 0.9 hours (M4, 95% CI [0.3, 1.6]) more than in-person students from T1 to T2, but there was no significant difference between virtual and in-person students in change in weekend wake times (M5). Virtual students also had a significantly greater reduction in social jetlag at T2 than in-person students (M7, B=−0.7 hours, 95% CI [−1.4, −0.1]). Learning modality was not a significant predictor of school night sleep duration or habitual sleep duration (M3, B=0.1 hours, 95% CI [−0.5, 0.7]; M6, B=0.0 hours, 95% CI [−0.6, 0.6]).

Table 3.

Change in Sleep Timing and Duration from T1 to T2, Model Results

Predictor	Model 1: Change in School Night Bedtime (hrs)	Model 2: Change in School Day Wake Time (hrs)	Model 3*: Change in School Night Sleep Duration (hrs)	Model 4*: Change in Weekend Bedtime (hrs)	Model 5*: Change in Weekend Wake Time (hrs)	Model 6*: Change in Habitual Sleep Duration (hrs)	Model 7*: Change in Social Jetlag (hrs)
	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)
Adjusted R2	0.10	0.31	−0.05	0.05	−0.02	−0.05	0.05
Intercept	−0.1 (−0.8, 0.7)	1.4 (1.0, 1.9)	1.5 (0.7, 2.3)	−0.4 (−1.2, 0.5)	−0.6 (−1.6, 0.4)	1.0 (0.3, 1.7)	−1.1 (−1.9, −0.4)
Learning Modality
Virtual	1.3 (0.6, 1.9)	1.4 (1.0, 1.7)	0.1 (−0.5, 0.7)	0.9 (0.3, 1.6)	0.5 (−0.3, 1.3)	0.0 (−0.6, 0.6)	−0.7 (−1.4, −0.1)
In-Person	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Race
Asian	0.9 (−0.2, 2.0)	0.1 (−0.5, 0.8)	−0.8 (−1.9, 0.3)	0.3 (−0.9, 1.5)	−0.7 (−2.1, 0.7)	−0.9 (−1.9, 0.2)	−0.7 (−1.9, 0.4)
Black	0.1 (−0.8, 1.0)	−0.4 (−0.9, 0.1)	−0.5 (−1.4, 0.4)	0.5 (−0.4, 1.5)	0.6 (−0.5, 1.7)	−0.3 (−1.2, 0.5)	0.5 (−0.4, 1.4)
Hispanic	−0.1 (−0.8, 0.7)	−0.2 (−0.7, 0.2)	−0.2 (−0.9, 0.6)	0.4 (−0.5, 1.2)	−0.2 (−1.1, 0.8)	−0.3 (−1.0, 0.4)	0.2 (−0.6, 1.0)
Multiracial	−0.3 (−1.5, 0.9)	−0.2 (−0.9, 0.5)	0.1 (−1.1, 1.3)	0.8 (−0.5, 2.1)	0.3 (−1.2, 1.8)	−0.1 (−1.2, 1.1)	0.8 (−0.4, 2.1)
White	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Gender
Female	0.1 (−0.5, 0.7)	0.0 (−0.4, 0.3)	−0.1 (−0.8, 0.5)	−0.1 (−0.8, 0.6)	0.3 (−0.5, 1.0)	0.0 (−0.6, 0.6)	0.0 (−0.6, 0.7)
Male	Ref	Ref	Ref	Ref	Ref	Ref	Ref
FRL
Yes	0.2 (−0.4, 0.7)	0.2 (−0.2, 0.5)	0.0 (−0.6, 0.6)	−0.1 (−0.7, 0.5)	0.1 (−0.6, 0.9)	0.1 (−0.5, 0.6)	−0.1 (−0.7, 0.5)
No	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Parent Education Level
Less Than College Degree	−0.2 (−0.7, 0.4)	−0.2 (−0.5, 0.1)	0.0 (−0.6, 0.6)	−0.4 (−1.0, 0.2)	−0.2 (−0.9, 0.5)	0.0 (−0.5, 0.6)	0.0 (−0.6, 0.6)
At Least College Degree	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Change in Screen Time
Increased Time	−0.2 (−0.9, 0.4)	−0.3 (−0.7, 0.1)	−0.1 (−0.7, 0.6)	0.3 (−0.4, 1.0)	0.5 (−0.3, 1.3)	0.0 (−0.6, 0.6)	0.6 (−0.1, 1.3)
No Change or Less Time	Ref	Ref	Ref	Ref	Ref	Ref	Ref
Parent Set Bedtime
Yes	0.3 (−0.5, 1.0)	0.2 (−0.3, 0.6)	−0.1 (−0.9, 0.6)	−0.6 (−1.4, 0.2)	0.1 (−0.8, 1.0)	0.1 (−0.6, 0.8)	−0.5 (−1.2, 0.3)
No	Ref	Ref	Ref	Ref	Ref	Ref	Ref

^*Due to restricted sample size, the power of model may be insufficient to detect the true measure of association.

Discussion

This study examined changes in sleep timing and duration following a school start time delay during the COVID-19 pandemic and assessed if these changes differed by learning modality. The district-wide change to school start times was planned for the 2020–21 school year prior to the COVID-19 pandemic and was not influenced by pandemic-related school closures and re-openings. Our results indicate that the one-hour delay in school start time led to significant changes in both sleep timing and duration. Students’ school day wake times were significantly delayed by 1.5 hours and social jetlag was significantly reduced by 0.9 hours following the school start time delay. Students’ school night and habitual sleep duration also significantly increased after the school start time delay, by 1.2 hours and 0.9 hours respectively. In addition, our data demonstrate that learning modality was a significant predictor of changes in sleep timing during the pandemic but was not associated with changes in school night or habitual sleep duration in the context of a 1-hour school start time delay. In comparison to in-person students, virtual students reported greater changes in sleep timing during the school week, namely they further delayed their bedtimes by an additional 1.3 hours and their wake times by an additional 1.4 hours during the school week. Virtual students also reported that they delayed their bedtimes on the weekend later than in-person students by 0.9 hours. Finally, virtual students had a significantly greater reduction (0.7 hours) in social jetlag than in-person students. This study provides evidence that, while learning modality influences sleep timing among adolescents, it does not influence sleep duration in the context of school start time delays. Moreover, it suggests that learning modality may have a greater benefit on adolescent sleep who live in school districts with early school start times.

The impact of the school start time delay on sleep health in our analysis aligns with existing literature. Two recent systematic reviews on the impact of school start times on adolescent sleep found that delays in school start times were significantly associated with increased school night sleep duration, less weekend oversleep, and delays in school day wake times among urban, suburban, and rural student populations (Minges & Redeker, 2016; Wahlstrom & Owens, 2017). Our results reinforce pre-pandemic research on the changes in adolescent sleep health arising from school start time delays and illustrate that this change occurs for in-person as well as virtual learners.

Notably, our study did not find significant differences between virtual and in-person students in change in sleep duration, which is unexpected given prior research on virtual learning. Cross-sectional and longitudinal U.S.-based studies conducted among virtual students during the early phase of the COVID-19 pandemic found that students reported a significant increase in school night and weekend sleep duration after transitioning to online learning (Bates et al., 2020; Becker et al., 2021; Gruber et al., 2020; Ramos Socarras et al., 2021). Several of these studies highlighted that the shift to virtual learning reduced time spent preparing and traveling to school and mirrored the impact of delayed school start times (Becker et al., 2021; Ramos Socarras et al., 2021). In addition, preliminary results from a cross-sectional study assessing learning modality and sleep opportunity found that virtual students reported 0.8 hours greater sleep opportunity per school night than did in-person students (Meltzer et al., 2021). A possible explanation for the discrepancy between our findings and other research is that the school start time for our entire sample was delayed by one hour at the beginning of the 2020–21 academic year. Thus, we speculate that the increase in school night sleep duration (1.2 hours) and habitual sleep duration (0.9 hours) observed cannot be ascribed to learning modality and may instead be due to the school start time delay.

Our findings are consistent with previous studies that virtual students experienced delays in their school week sleep timing. Four cross-sectional and longitudinal studies among U.S. adolescents conducted in Spring 2020 found that virtual students had delayed their school week bedtimes by 1 to 2 hours and their wake times by 1.5 to 2.9 hours after transitioning to online learning (Becker et al., 2021; Gruber et al., 2020; Ramos Socarras et al., 2021; Weingart et al., 2021). However, these studies conducted during the early phases of the COVID-19 pandemic only analyzed changes in sleep timing among virtual students; our data demonstrate that virtual students experienced significantly greater delays in their school week bedtimes and wake times in comparison to in-person students attending the same schools. This finding aligns with preliminary results from the NESTED study, which found that virtual students woke up later than in-person students with the same school start times (Meltzer et al., 2021).

In contrast to school week sleep timing, fewer studies have examined changes in weekend sleep timing and social jetlag among adolescents during the COVID-19 pandemic. Our results show that virtual students had greater delays in weekend bedtimes than those who returned to attend school in-person in Fall 2020, which comport with findings from two studies of virtual learners conducted at the beginning of the pandemic (Becker et al., 2021; Ramos Socarras et al., 2021). No prior study has assessed how virtual learning influences social jetlag among U.S. adolescents. Given that social jetlag is associated with worse academic achievement, unhealthy eating behaviors, increased body mass index, and symptoms of depression and anxiety among adolescents (Díaz-Morales & Escribano, 2015; Henderson, Brady, & Robertson, 2019; Mathew et al., 2020), the reduction of social jetlag from school start time delays and virtual learning can potentially improve students’ academic performance as well as their physical and mental health.

This study is the first to prospectively examine the influence of learning modality during the COVID-19 pandemic on changes in adolescent sleep health following a school start time delay in the United States. In addition, this study includes data collected in Fall 2020 and builds on research conducted in the early months of the pandemic to show which adolescent sleep health trends persisted or dissipated over time. We also examined adolescent sleep health among a semi-rural population, which is often excluded from sleep research, and assessed social jetlag, an important adolescent sleep timing measure that has not been rigorously studied during the COVID-19 pandemic. Nevertheless, this study does have several limitations. All sleep data used in this analysis were self-reported and subject to recall bias and measurement error. Our study did not assess sleep quality measures that have been found to be associated with virtual learning (Ramos Socarras et al., 2021; Verlenden et al., 2021). Finally, the low participation rate of survey respondents and the self-selection of virtual learners might have introduced selection bias into the sample. The distributions of FRL eligibility, race, and ethnicity were similar between our sample and the survey non-respondents, however our sample was disproportionately female, and therefore our results might not be generalizable.

Conclusions

This study demonstrates that delayed school start times can lead to positive changes in adolescent sleep timing and duration during the COVID-19 pandemic and offers new insight as to how these changes differ by learning modality. In particular, while prior studies have shown sleep health benefits associated with virtual learning, its positive effect may differ depending on school start times; virtual learning may be more beneficial for adolescents who are chronically sleep deprived in the context of early school start times, and less beneficial among students typically obtaining sufficient sleep in the context of later school start times. Future research is needed to assess how learning modality affects other behaviors associated with sleep health and learning, including preparation time before school and bedtime routines, and how learning modality delivery (e.g., asynchronous or synchronous) affects adolescent sleep. Our findings are particularly relevant to policymakers and school administrators tasked with determining school start times and learning modality options available in the future.

Biographies

Biographical Notes:

Cassandra Bryan received an MPH in Global Epidemiology from Emory University in 2021. She is currently a Project Coordinator in the Departments of Global Health at Rollins School of Public Health of Emory University, where she manages NIH-funded study on tuberculosis and diabetes in Tbilisi, Georgia. Her areas of interest include mental health, infectious disease epidemiology, and maternal and child health.

Rachel Weingart received an MPH in Behavioral, Social, and Health Education Sciences from Emory University in 2021.

Alyssa Lindsey received an MPH from Emory University in 2022. She works as a research associate with the National Asian Pacific American Women’s Forum (NAPAWF) coordinating several research projects focused on the sexual and reproductive health of Asian American and Pacific Islander (AAPI) populations. Her research interests include maternal and child health and the reproductive life experiences of AAPI and Latin populations in the United States.

Lauren Hale, PhD (Professor of Family, Population, and Preventive Medicine; Core Faculty, Program in Public Health; Renaissance School of Medicine, Stony Brook University) studies the social patterning of sleep health and how it contributes to inequalities in health and well-being with current or previous funding from NICHD, NIDDK, NHLBI, and NIA. Dr. Hale serves on the Board of Directors (Chair) of the National Sleep Foundation and is the founding Editor-in-Chief of the journal Sleep Health. She also serves on the Scientific Advisory Panel of the Pajama Program and the Children and Screens Institute.

Dayna A. Johnson, PhD, MPH, MSW, MS is a sleep epidemiologist and Assistant Professor in the Department of Epidemiology at the Rollins School of Public Health, Emory University in Atlanta GA. She received her doctorate degree in Epidemiologic Science from the University of Michigan and completed a postdoctoral fellowship in Sleep and Circadian Disorders at Harvard Medical School. Her research is aimed at understanding the determinants and health consequences of sleep health disparities by 1) addressing the social and environmental determinants of sleep disorders and insufficient sleep; and 2) investigating the influence of modifiable factors such as sleep disorders and disturbances on various health outcomes. More specifically, Dr. Johnson’s research quantifies the contribution of social, household-level and neighborhood-level factors with objective and well-validated subjective measures of insufficient sleep using data from different epidemiologic cohort studies. She also investigates associations of sleep health and sleep disorders with hypertension, diabetes, metabolic syndrome, and cognition. Dr. Johnson is also exploring how stress reduction interventions can improve sleep and reduce subsequent risk of poor health outcomes.

Julie A. Gazmararian, PhD, MPH, is a Professor in the Department of Epidemiology with joint appointments in the Departments of Behavioral Sciences and Health Education and Health Policy and Management at Rollins School of Public Health of Emory University. Her main areas of interest include physical activity, health disparities, health literacy, maternal and child health, nutrition, and obesity. Dr. Gazmararian is also a member of the Cancer Prevention and Control Research Program at Emory University’s Winship Cancer Institute.

Data Availability

Data will be made available upon request.