Abstract
To find the effect of coronavirus disease 2019 (COVID-19)-related sleep behavior changes using school-based self-reported data from a nationally representative Korean adolescent population. We analyzed web-based self-reported data from the Korean Youth Risk Behavior Web-based Survey in 98,126 participants (51,651 in 2019 [before the COVID-19 pandemic]; 46,475 in 2020 [during COVID-19 pandemic] 12 through 18 years old were included in this study. Self-report questionnaires were used to assess socioeconomic status, health behaviors, psychological factors, and sleep patterns. During the COVID-19 pandemic, Korean adolescents had a later weekend bedtime (≥1:00 am: 68.2% vs 71.5%, P < .001) and late weekend wake time (≤7:00 am: 13.3% vs 10.7%, P < .001) compared to before COVID-19 pandemic. Average sleep duration (434.7 ± 102.6 vs 428.2 ± 100.4 minutes; P < .001) was significantly lower during the COVID-19 pandemic and weekend catch-up sleep >2 hours (42.1% vs 43.7%; P < .001), late chronotype (17.1% vs 22.9%, P < .001) were significantly higher during COVID-19 pandemic. After adjusting for multiple confounding variables, short sleep duration (≦5 hours, odds ratio [OR] 1.14; 95% confidence interval [CI] 1.10–1.19), 6 hours, OR 1.07; 95% CI 1.03–1.12), long weekend catch-up sleep (OR, 1.08; 95% CI, 1.06–1.11) and late chronotype (OR, 1.43; 95% CI, 1.38–1.47) were significantly associated with COVID-19 pandemic. The COVID-19 pandemic was associated with changes in sleep behavior among Korean adolescents, resulting in later bed and wake-up times, increased weekend catch-up sleep, and a shift of chronotype toward eveningness.
Keywords: COVID-19, Korean adolescents, sleep
1. Introduction
In 2020, coronavirus disease 2019 (COVID-19) became a global pandemic, and people’s lifestyle has since been dramatically changed to prevent contagious infections. In particular, school-aged adolescents had a long period of homeschooling via digital devices, since schools were closed during the COVID pandemic under government regulations. The combined effect of changes in lifestyle behaviors; confinement to the home through government restrictions on travel; and elevated depression, anxiety, and stress associated with the current COVID-19 pandemic, may have significant negative impacts on sleep.[1] This has been especially evident in healthcare workers, who may be required to work longer shifts in highly stressful environments.[2,3]
Recent evidence has shown that the COVID-19 pandemic induced changes in sleep habits among adults, especially in places where lockdown was adopted. Despite an increase in time in bed, poorer sleep quality was reported among adults.[4] Adolescents typically have a preference to sleep late. A delayed sleep phase,[5] use of electronic devices, and social life are associated with adolescents’ sleep behavior. A common consequence is sleep restriction and poor school performance.[6] During the COVID-19 pandemic, adolescents have had more flexibility with their schedules, which should help align with their sleep preferences. Online classes begin later than the usual in-person classes and there is no time spent commuting to school. The impact of the pandemic on sleep habits among adolescents has not been adequately characterized and compared with the period previous to the pandemic in adolescents. So, our study aim is to find the effect of COVID-19-related sleep behavior changes using school-based self-reported data from a nationally representative Korean adolescent population.
2. Methods
2.1. Study participants
We used cross-sectional data from the Korea Youth Risk Behavior Web-Based Survey (KYRBWS) which were using adjusted weighted values sampling method to represent Korean adolescents. In 2019, the KYRBWS was investigated from 3 June through 12 July: this period before the COVID-19 pandemic. In the 2020 KYRBWS, data were investigated from 3 August through 13 November; this period enables the mid-pandemic situation.
The aim of KYRBWS survey is to assess Korean adolescent`s health status and health behavior in order to provide basic data for Korean adolescents. KYRBWS survey was composed of a complex survey design with selection probabilities and post-stratification. All adolescents about 400-middle school and 400-high school students with the exception of those with absenteeism and dyslexia were eligible to participate. Since 2015, the ethics approval for the KYRBWS was waived Institutional Review Board by the KCDC Institutional Review Board. The participants investigated the self-administered questionnaires online in the school’s computer room and all participants gathered informed consent. Among the 112,251 total KYRBWS participants (57,303 in 2019; 54,948 in 2020: the following were excluded from this study participants without information sleep time (n = 14,125). Finally, 98,126 participants (51,651 in 2019 (before the COVID-19 pandemic lockdown); 46,475 in 2020 (before the COVID-19 pandemic lockdown) 12 through 18 years old were included in this study.
2.2. Socioeconomic and demographic factors
The self-administered questionnaires were used to provide information on socioeconomic factors (age, sex, school type [middle, high, or vocational school; southern, girls only, or coeducation school], family income, residence area, family structure, and academic achievement), health behavior factors (smoking status, alcohol drinking, regular exercise, sexual experience, and illicit drug use), psychological factors (self-rated health status, self-rated stress status, depression, suicidal ideation, suicidal plan, suicidal attempt) and co-morbidity (asthma, allergic rhinitis, atopy).
Regular exercise was determined as ≥3 times/week activity in the past 7 days.[7] Illegal drug experience was assessed by experiencing any of the following drugs (glue, butane gas, stimulants, marijuana, amphetamine, heroin, high-dose cold medicine, or anxiolytics for mood elevation, hallucinations, or diet excessively).[8] Smoking and alcohol use was defined as smoking cigarettes or drinking alcohol on >1 day over the last month.[9] Depression was defined using the Korean version of the World Health Organization Composite International Depression was defined using the following question which has been validated for health surveys such as our cross-sectional design[10]: “Have you experienced more than 2 consecutive weeks where you felt sad, blue, or depressed during the last year?.” Co-morbidity (asthma, allergic rhinitis, atopy) was defined as co-morbidity if participants have been diagnosed by a physician.
Suicidal ideation, suicidal plan, and suicidal attempts were assessed if they have ideation, plans, and attempts in last year. This indicator is a well-documented predictor of suicide behaviors that have been previously well-validated in another study.[11]
2.3. Sleep
Self-reported waking time and bedtime were investigated for each participant as weekday and weekend sleep duration. Average sleep duration was calculated as (5 × weekday sleep duration + 2 × weekend sleep duration)/7. Sleep time was divided into 5 subcategories: ≤5, 6, 7, 8, and ≥9 hours. Weekend catch-up sleep was calculated from the weekend sleep duration minus weekday sleep duration. Bedtime delay was calculated from weekday bedtimes minus weekend bedtimes. Long weekend catch-up sleep was calculated from weekend sleep duration minus weekday sleep duration of >2 hours.[12,13]
The average sleep time for participant’s weekdays and weekends was calculated based on the participant’s responses to the Korean version of the Munich Chronotype Questionnaire: “On a weekday (or school days) and on a weekend (school-free days), at what time do you usually go to bed for sleep and at what time do you usually get up?.” To define sleep chronotypes, we calculated the midpoint of sleep on school-free days (MSF), the midpoint of sleep on school days (MSW), sleep duration on school days (SDW), and sleep duration on school-free days (SDF) using the above sleep questions. Midpoint of MSF corrected for sleep extension on school-free days (MSFsc) was used as the chronotype indicator.[14] MSFsc was calculated using the following equation.[15] MSFsc = MSF − ([SDF − (SDW × 5 + SDF × 2)/7)/2. When SDF was shorter than or equal to SDW, MSFsc was the same as MSF. Chronotype was categorized as quintiles of the MSFsc: early chronotype (Q1, lowest MSFsc), intermediate chronotype (second [Q2], third [Q3], and fourth [Q4] quintiles), and late chronotype (Q5, highest MSFsc).[16] Social jetlag was calculated from the equation: social jetlag = MSF − MSW. Sleep satisfaction was defined by answering: “Have you ever had enough sleep to recover from fatigue in the last week?” The answer options using a 5-point Likert scale include more than enough, enough, moderate, not enough, and less than enough. Sleep quality was reclassified into 3 groups: enough (more than enough), moderate, and not enough (less than enough to not enough).
2.4. Data analysis
The general characteristics between the before COVID-19 pandemic lockdown and during COVID-19 pandemic lockdown in Korean adolescents were compared with chi-square test with complex sampling Rao–Scott correction, to represent the entire population, as this study was designed to use weighted values. Multiple logistic regression analysis with complex sampling adjusted for age, sex, school type, family income, residence area, family structure, and academic achievement, health behavior factors (smoking status, alcohol drinking, regular exercise, sexual experience, and illicit drug use, self-rated health status, self-rated stress status, depression, suicidal ideation, suicidal plan, suicidal attempt and co-morbidity (asthma, allergic rhinitis, atopy). P values < 0.05 were considered statistically significant. All data were analyzed using SPSS for Windows (version 21.0; SPSS Inc., Chicago, IL).
3. Results
The general characteristics of the included population are presented in Table 1. In 2020 (during the COVID-19 pandemic lockdown), the population was higher in age (P values < 0.001, Table 1). Health hazard behavior such as smoking, alcohol drinking, substance use, and sexual experience was significantly decreased in 2020 (during the COVID-19 pandemic lockdown) than in 2019 (before the COVID-19 pandemic lockdown). On the contrary, regular exercise decreased after the COVID-19 pandemic lockdown. Psychosomatic change after the COVID-19 pandemic lockdown showed positive results. School violence, depression, suicidal idea, suicidal plans, and suicidal attempts were significantly lower in 2020 (during the COVID-19 pandemic lockdown) compared to those in 2019 (before the COVID-19 pandemic lockdown), respectively (P < .001, Table 1). The differences in the reporting of physician-diagnosed allergic diseases between 2019 and 2020 were analyzed. The reporting of asthma was lower in 2020 (6.2%) than that in 2019 (7.1%, P < .001). The reporting of allergic rhinitis in 2020 was 35.0%, which was also lower than that of 35.4% reported in 2019, P < .001).
Table 1.
General characteristics of participants according to history of asthma.
| Before COVID-19 | During COVID-19 | P value | |
|---|---|---|---|
| (n = 51651) | (n = 46475) | ||
| Sex, n (%*) | .120‡ | ||
| Girl | 25,061 (48.6) | 22,318 (47.8) | |
| Boy | 26,590 (51.4) | 24,157 (52.2) | |
| Age | 14.9 ± 1.8 | 15.1 ± 1.8 | <.001† |
| School, n (%*) | <.001‡ | ||
| Middle school | 26,222 (47.6) | 24,144 (49.0) | |
| Academic high school | 20,334 (43.4) | 17,812 (42.5) | |
| Vocational high school | 4684 (9.0) | 4172 (8.5) | |
| School type, n (%*) | .135‡ | ||
| Southern school | 8790 (17.1) | 8129 (17.9) | |
| Girl school | 8752 (17.1) | 7785 (16.5) | |
| Coeducation | 34,109 (65.8) | 30,561 (65.6) | |
| Residence, n (%*) | <.001‡ | ||
| Rural | 3198 (4.5) | 2904 (4.7) | |
| Urban | 48,465 (95.5) | 43,571 (95.3) | |
| Living, n (%*) | <.001‡ | ||
| Living without parents | 2644 (4.4) | 2221 (3.8) | |
| Living with parents | 49,007 (95.6) | 44,254 (96.2) | |
| Family income, n (%*) | <.001‡ | ||
| Low | 6675 (12.5) | 6006 (12.4) | |
| Medium | 24,846 (47.9) | 22,315 (47.5) | |
| High | 20,230 (39.6) | 18,154 (40.1) | |
| Subjective academic achievement, n (%*) | <.001‡ | ||
| Low | 16,101 (31.3) | 14,931 (32.0) | |
| Middle | 15,625 (30.4) | 14,131 (30.4) | |
| High | 19,925 (38.3) | 17,413 (37.7) | |
| Smoking, n (%*) | 2021 (6.0) | 2566 (5.6) | <.001‡ |
| Alcohol, n (%*) | 12,558 (24.6) | 10,493 (22.7) | <.001‡ |
| Regular exercise, n (%*) | 17,896 (33.9) | 15,090 (31.5) | <.001‡ |
| Sexual experience, n (%*) | 2782 (5.5) | 1930 (4.2) | <.001‡ |
| Substance use, n (%*) | 429 (0.8) | 294 (0.6) | <.001‡ |
| Perceived stress, n (%*) | <.001‡ | ||
| Severe to very severe | 9952 (19.0) | 10,140 (21.5) | |
| Moderate | 21,244 (41.2) | 20,856 (45.1) | |
| None to mild | 20,455 (39.8) | 15,479 (33.4) | |
| Perceived health status, n (%*) | .747‡ | ||
| Healthy | 26,273 (70.2) | 32,185 (70.3) | |
| Moderate | 11,571 (22.6) | 10,317 (22.4) | |
| Bad | 2707 (7.2) | 3343 (7.33 | |
| School violence, n (%*) | 1045 (2.0) | 508 (1.1) | <.001‡ |
| Experiences of depressive mood for 2 or more continuous weeks, n (%*) | 14,266 (27.8) | 11,251 (24.1) | <.001‡ |
| Suicidal idea, n (%*) | 6621 (12.8) | 4789 (10.2) | <.001‡ |
| Suicidal plan, n (%*) | 1919 (3.7) | 1500 (3.2) | <.001‡ |
| Suicidal attempts, n (%*) | 1447 (2.7) | 828 (1.7) | <.001‡ |
| Allergic rhinitis, n (%*) | 17,961 (35.4) | 15,795 (35.0) | <.001‡ |
| Atopy, n (%*) | 11,614 (22.6) | 10,690 (23.3) | <.001‡ |
| Asthma, n (%*) | 3579 (7.1) | 2819 (6.2) | <.001‡ |
COVID-19 = coronavirus disease 2019.
Estimated mean or rate-adjusted recommended weighted value;
linear regression analysis with complex sampling, significance at P < .05;
chi-square test with Rao–Scott correction, significance at P < .05.
Table 2 summarizes sleep parameters, which differed between the groups. During the COVID-19 pandemic lockdown, Korean adolescents slept less (≤5 hours: 26.6% vs 28.3%; P < .001), had a later weekend bedtime (≥1:00 am: 68.2% vs 71.5%), and late weekend wake time (≤7:00 am: 13.3% vs 10.7%) compared to before COVID-19 pandemic lockdown. Average sleep duration (434.7 ± 102.6 vs 428.2 ± 100.4 minutes; P < .001), weekday sleep duration (398.2 ± 103.2 vs 390.8 ± 103.7 minutes; P < .001), weekend sleep duration (525.7 ± 178.2 vs 521.5 ± 163.3 minutes; P < .001) were significantly lower in 2020 (during COVID-19 pandemic lockdown) compared to 2019 (before COVID-19 pandemic lockdown). On the contrary, weekend catch-up sleep duration (127.5 ± 173.7 vs 130.7 ± 162.4 minutes; P < .001) and weekend catch-up sleep >2 hours (42.1% vs 43.7%; P < .001) were significantly higher in 2020 (during COVID-19 pandemic lockdown) compared to 2019 (before COVID-19 pandemic lockdown). Mean sleep duration (434.7 ± 102.6 vs 428.2 ± 100.4 minutes; P < .001), MSF (262.9 ± 89.1 vs 260.8 ± 81.8 minutes; P < .001), MSW (262.9 ± 89.1 vs 260.8 ± 81.8 minutes; P < .001), SDW (398.2 ± 103.2 vs 390.8 ± 104.7 minutes; P < .001), and SDF (525.7 ± 178.3 vs 521.1 ± 121.5 minutes; P < .001) were significantly lower during COVID-19 pandemic lockdown, as was social jetlag (63.7 ± 86.8 vs 65.3 ± 81.2 minutes; P < .003). Late chronotype was significantly more common during the COVID-19 pandemic lockdown (17.1% vs 22.9 %, P < .001). Sleep satisfaction was significantly improved (sleep satisfaction enough: 21.0% vs 30.5%, P < .001) during the COVID-19 pandemic lockdown. Table 3 shows the adjusted odds ratio (OR) for the COVID-19 pandemic lockdown according to sleep duration and chronotype in the adolescent population. After adjusting for multiple confounding variables, short sleep duration (≦5 hours, OR 1.14; 95% confidence interval [CI] 91.10–1.19), 6 hours, OR 1.07; 95% CI 1.03–1.12) was significantly associated with COVID-19 pandemic lockdown compared to a sleep duration of 7 hours. After adjusting multiple confounding variables, long weekend catch-up sleep was significantly associated with COVID-19 pandemic lockdown (OR, 1.08; 95% CI, 1.06–1.11), and weekend “late owl” (weekend bedtime 1 ≥ am, OR, 1.27; 95% CI, 1.233–1.31) and weekend “early bird” (weekend wake time ≤ 7:00 am, OR, 1.29; 95% CI, 1.24–1.34) was significantly associated with an increased frequency of COVID-19 pandemic lockdown. After adjusting multiple confounding variables, the late chronotype was significantly associated with an increased frequency of the COVID-19 pandemic lockdown (OR, 1.43; 95% CI, 1.38–1.47) compared to the intermediate chronotype.
Table 2.
Descriptive statistics of sleep variables.
| Before COVID-19 | During COVID-19 | P value | |
|---|---|---|---|
| (n = 51651) | (n = 46475) | ||
| Sleep duration, average, min | 434.7 ± 102.6 | 428.2 ± 100.4 | <.001† |
| Sleep time, n (%*) | <.001‡ | ||
| ≤ 5h | 12,894 (26.6) | 12,399 (28.3) | |
| 6h | 11,255 (22.6) | 10,676 (23.6) | |
| 7h | 10,152 (19.4) | 9235 (19.4) | |
| 8h | 9397 (17.2) | 7776 (15.8) | |
| ≥9h | 7953 (14.2) | 6449 (12.9) | |
| Weekday | |||
| Sleep duration, min | 398.2 ± 103.2 | 390.8 ± 104.7 | <.001† |
| Bedtime, n (%*) | <.001‡ | ||
| ≤21:00 | 769 (1.2) | 721 (1.4) | |
| 22:00 | 4001 (6.9) | 3316 (6.2) | |
| 23:00 | 9546 (17.0) | 7428 (14.9) | |
| 24:00 | 11,465 (22.0) | 9761 (20.5) | |
| 1:00 | 12,676 (25.2) | 11,206 (24.7) | |
| ≥2:00 | 13,294 (27.7) | 14,043 (32.3) | |
| Wake time, n (%*) | <.001‡ | ||
| ≦5:00 | 1645 (3.0) | 1282 (2.6) | |
| 6:00 | 14,956 (28.5) | 12,504 (26.3) | |
| 7:00 | 30,146 (60.0) | 27,095 (58.3) | |
| 8:00 | 4004 (8.5) | 5594 (12.8) | |
| ≥9:00 | 0 (0) | 0 (0) | |
| Weekend day | |||
| Sleep duration, min | 525.7 ± 178.2 | 521.5 ± 163.6 | <.001† |
| Bedtime, n (%*) | <.001‡ | ||
| ≤21:00 | 743 (1.3) | 651 (1.3) | |
| 22:00 | 1881 (3.2) | 1433 (2.8) | |
| 23:00 | 5703 (10.2) | 4239 (8.5) | |
| 24:00 | 9061 (17.1) | 7153 (14.9) | |
| 1:00 | 10,570 (20.7) | 8605 (18.7) | |
| ≥2:00 | 23,613 (47.5) | 24,393 (53.8) | |
| Wake time, n (%*) | <.001‡ | ||
| ≤5:00 | 586 (1.1) | 449 (1.0) | |
| 6:00 | 1682 (3.1) | 1200 (2.5) | |
| 7:00 | 4840 (9.1) | 3397 (7.2) | |
| 8:00 | 10,252 (19.8) | 7807 (16.8) | |
| ≥9:00 | 34,291 (66.9 | 33,622 (72.5) | |
| Weekend-weekday difference | |||
| Sleep time difference | 127.5 ± 173.7 | 130.7 ± 162.4 | <.001† |
| Weekend catchup sleep, ≥2 h, n (%*) | 21,467 (42.1) | 20,137 (43.7) | <.001‡ |
| Bedtime delay | 49.5 ± 124.7 | 55.4 ± 119.0 | <.001† |
| Wake time delay | 163.8 ± 137.1 | 179.5 ± 139.0 | <.001† |
| Midpoint of sleep on school-free days (MSF) | 262.9 ± 89.1 | 260.8 ± 81.8 | <.001† |
| Midpoint of sleep on school days (MSW), | 199.1 ± 51.6 | 195.4 ± 52.3 | <.001† |
| Sleep duration on school days (SDW) | 398.2 ± 103.2 | 390.8 ± 104.7 | <.001† |
| Sleep duration on school-free days (SDF) | 525.7 ± 178.3 | 521.5 ± 121.5 | <.001† |
| Chronotype | <.001‡ | ||
| Q1: early | 11,222 (20.9) | 8335 (17.4) | |
| Q2: intermediate | 11,021 (21.4) | 8726 (18.9) | |
| Q3: intermediate | 10,494 (20.7) | 8973 (19.6) | |
| Q4: intermediate | 10,052 (19.9) | 9719 (21.2) | |
| Q5: late | 8862 (17.1) | 10,722 (22.9) | |
| Social jetlag, min | 63.7 ± 86.8 | 65.3 ± 81.2 | <.001† |
| Sleep satisfaction, n (%*) | <.001‡ | ||
| Enough | 11,201 (21.0) | 14,321 (30.5) | |
| Moderate | 16,864 (32.5) | 16,015 (34.3) | |
| Unenough | 22,586 (46.5) | 16,139 (35.2) |
COVID-19 = coronavirus disease 2019.
Estimated mean or rate-adjusted recommended weighted value;
linear regression analysis with complex sampling, significance at P < .05;
chi-square test with Rao–Scott correction, significance at P < .05.
Table 3.
Adjusted odds ratio for COVID sleep change according to sleep chronotype in a young adult population.
| OR (95% CI) | |
|---|---|
| Sleep duration | |
| ≤ 5 h | 1.14 (1.10–1.19) |
| 6 h | 1.07 (1.03–1.12) |
| 7 h | Reference |
| 8 h | 0.90 (0.86–0.94) |
| ≥ 9 h | 0.89 (0.85–0.93) |
| Long weekend catch-up sleep | 1.08 (1.06–1.11) |
| Weekend sleep time, ≥1:00 am | 1.27 (1.23–1.31) |
| Weekend wake time, ≦7:00 am | 1.29 (1.24–1.34) |
| Sleep chronotype | |
| Q2–4 (intermediate) | Reference |
| Q1 (early) | 0.86 (0.84–0.89) |
| Q5 (late) | 1.43 (1.38–1.47) |
Data are presented as odds ratios (OR) and 95% confidence intervals (CI).
Adjusted for age, sex, smoking, alcohol, regular physical activity, household income, residence, school type, sexual experience, drug experience, academic achievement, family structure, stress, health status, depression, and comorbidity (asthma, allergic rhinitis, atopy).
4. Discussion
In the present study, we showed that association between the COVID-19 pandemic lockdown and sleep behavior change in Korean adolescents. The major findings were as follows: students delayed bedtime and wake-up time during the COVID-19 pandemic; however, sleep duration decreased and sleep quality improved before the pandemic; long weekend catch-up sleep was increased during the pandemic; chronotype shifted toward eveningness.
Our study showed delayed sleep onset and delayed wake-up time in Korean adolescents during the COVID-19 pandemic lockdown which was consistent finding: work-free days versus work days wherein sleep-wake-up times on weekends were delayed than weekdays.[14] This sleep behavior change has been due to compensating for the accumulated sleep debt during working (or school) days.[14]
Early school start time has been shown to restrict adolescents’ sleep duration and increase the risk of behavioral and emotional disturbances.[17] The COVID-19 pandemic put the world into a naturalistic experiment of multiple dimensions. School closure eliminated commuting and imposed online classes that started later than regular onsite classes. Added to home confinement, changes in school schedule allowed adolescents to modify their sleep schedule. We showed that Korean adolescents delayed their sleep habits during the pandemic compared with before the COVID-19 pandemic. This finding is consistent with previous observations among preschoolers,[18] university students,[19] and adults.[20] Previous studies[18–20] assessing sleep behavior among participants other than teenagers reported longer sleep duration during the pandemic as compared with a previous period. In the present study, sleep duration was decreased during the pandemic as compared with before the COVID-19 pandemic. Taken together, home confinement and school closure delayed bed and wake-up time in concert with adolescent preferences.
Our study showed that the late sleep chronotype increased during the COVID-19 pandemic. Late chronotype is generally characterized in adolescents, this phenomenon reflecting a late biological preference for sleep and activity.[21] In an interplay with early morning obligations (e.g. early school start times), adolescents normally have short school day sleep duration (late bedtimes combined with early rise times), as well as increased social jetlag.[17,22] Further, late chronotypes have been found to have the shortest school day sleep duration and increased social jetlag compared to the other circadian types (intermediate and early chronotype).[23]
While further studies are needed to understand the impact of the COVID-19 lockdown on sleep behavior change, the COVID-19 pandemic lockdown could have an impact on sleep behavior changes in adolescents. In the COVID-19 pandemic lockdown, illness or hospitalization due to the COVID-19 virus can have an impact on sleep behavior change.[24] COVID-19 confinement result in increased sedentary behaviors, less exercise, and increased food eating, which impact weight gain,[25] and eventually sleep behavior change. Adolescents in COVID-19 pandemic lockdown might cause increased stress, family financial situations change, and health concern due to the COVID-19 virus. These concerns eventually result in sleep behavior changes.[26] Besides, a social distancing strategy to stay at home might reduce sunlight exposure, which is so important in establishing consistent sleep behaviors.
In spite of adolescents experiencing worsened sleep behaviors change during the COVID-19 pandemic crisis, some adolescents might have had improved sleep behavior change in certain domains. First, late chronotype may benefit from greater flexibility by home learning. Second, there may be more opportunities for obtaining sufficient sleep since less time is spent traveling to and from school or engaging in social and extracurricular activities. These considerations may point to a silver lining for adolescents’ sleep in particular: in-person schools are closed, meaning that many adolescents no longer experience early school start times and may thus be able to establish and maintain a schedule more aligned with their endogenous circadian rhythm, in turn also reducing social jetlag (given more consistency between weekday and weekend sleep).[17]
This study had some limitations. First, the cross-sectional study design cannot establish a cause-and-effect relationship between the COVID-19 pandemic and sleep behaviors. Second, we used self-reported data, so the sleep time and wake time may be unreliable despite most adolescents correctly reporting their sleep time and wake time. Further prospective studies that employ more precise investigations of sleep time such as actigraphy are required to address this limitation. Finally, our study populations were Korean adolescents only but the COVID-19 pandemic is a global problem. So, our study results were very limited.
Although the above are some limitations, this present study used the KYRBWS data gathered from a nationwide population-based weighted sampling, so our study had a strength that might represent the entire Korean adolescent. Almost all participants had the same ethnic background, which minimizes other possible confounding factors.
In conclusion, the COVID-19 pandemic lockdown was associated with changes in sleep behaviors among Korean adolescents, resulting in later bed and wake-up times, increased weekend catch-up sleep, and a shift of chronotype toward eveningness.
Author contributions
Conceptualization: Chang Hoon Han, Sujin Lee, Jae Ho Chung.
Data curation: Sujin Lee.
Abbreviations:
- COVID-19
- coronavirus disease 2019
- KYRBWS
- Korea Youth Risk Behavior Web-Based Survey
- MSF
- sleep on school-free days
- MSFsc
- midpoint of MSF corrected for sleep extension on school-free days
- MSW
- midpoint of sleep on school days
- OR
- odds ratio
- SDF
- sleep duration on school-free days
- SDW
- sleep duration on school days
This work was supported by National Health Insurance Service Ilsan Hospital grant NHIMC-2022-CR-013.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Han CH, Lee S, Chung JH. Association between COVID-19 lockdown and sleep behaviors in Korean adolescents. Medicine 2023;102:21(e33809).
Contributor Information
Chang Hoon Han, Email: chang122@nhimc.or.kr.
Sujin Lee, Email: neuroneuro@naver.com.
References
- [1].Altena E, Baglioni C, Espie CA, et al. Dealing with sleep problems during home confinement due to the COVID-19 outbreak: practical recommendations from a task force of the European CBT-I academy. J Sleep Res. 2020;29:e13052. [DOI] [PubMed] [Google Scholar]
- [2].Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 2020;288:112954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Xiao H, Zhang Y, Kong D, et al. Social capital and sleep quality in individuals who self-isolated for 14 days during the coronavirus disease 2019 (COVID-19) outbreak in January 2020 in China. Med Sci Monit. 2020;26:e923921. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Cellini N, Canale N, Mioni G, et al. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy. J Sleep Res. 2020;29:e13074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Laberge L, Petit D, Simard C, et al. Development of sleep patterns in early adolescence. J Sleep Res. 2001;10:59–67. [DOI] [PubMed] [Google Scholar]
- [6].Lund L, Sølvhøj IN, Danielsen D, et al. Electronic media use and sleep in children and adolescents in western countries: a systematic review. BMC Public Health. 2021;21:1598. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].2008 physical activity guidelines for Americans: be active h, and happy! 2018. Available at: http://fitprogram.ucla.edu/workfiles/Documents/Fit%20for%20residents%20curriculum/Step_5/2008_Physical_Activity_Guidelines_for_Americans.pdf. [Access date July 20, 2016].
- [8].Park S, Kim Y. Prevalence, correlates, and associated psychological problems of substance use in Korean adolescents. BMC Public Health. 2016;16:79. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Lim MK, Kim HJ, Yun EH, et al. Role of quit supporters and other factors associated with smoking abstinence in adolescent smokers: a prospective study on Quitline users in the Republic of Korea. Addict Behav. 2012;37:342–5. [DOI] [PubMed] [Google Scholar]
- [10].Gigantesco A, Morosini P. Development, reliability and factor analysis of a self-administered questionnaire which originates from the World Health Organization’s Composite International Diagnostic Interview – Short Form (CIDI-SF) for assessing mental disorders. Clin Pract Epidemiol Mental Health. 2008;4:8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Gaynes BN, West SL, Ford CA, et al. Screening for suicide risk in adults: a summary of the evidence for the U.S. preventive services task force. Ann Intern Med. 2004;140:822–35. [DOI] [PubMed] [Google Scholar]
- [12].Pallesen S, Saxvig IW, Molde H, et al. Brief report: behaviorally induced insufficient sleep syndrome in older adolescents: prevalence and correlates. J Adolesc. 2011;34:391–5. [DOI] [PubMed] [Google Scholar]
- [13].Komada Y, Inoue Y, Hayashida K, et al. Clinical significance and correlates of behaviorally induced insufficient sleep syndrome. Sleep Med. 2008;9:851–6. [DOI] [PubMed] [Google Scholar]
- [14].Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: daily temporal patterns of human chronotypes. J Biol Rhythms. 2003;18:80–90. [DOI] [PubMed] [Google Scholar]
- [15].Roenneberg T, Kuehnle T, Pramstaller PP, et al. A marker for the end of adolescence. Curr Biol. 2004;14:R1038–9. [DOI] [PubMed] [Google Scholar]
- [16].Sato-Mito N, Sasaki S, Murakami K, et al. The midpoint of sleep is associated with dietary intake and dietary behavior among young Japanese women. Sleep Med. 2011;12:289–94. [DOI] [PubMed] [Google Scholar]
- [17].Crowley SJ, Wolfson AR, Tarokh L, et al. An update on adolescent sleep: new evidence informing the perfect storm model. J Adolesc. 2018;67:55–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Liu Z, Tang H, Jin Q, et al. Sleep of preschoolers during the coronavirus disease 2019 (COVID-19) outbreak. J Sleep Res. 2021;30:e13142. [DOI] [PubMed] [Google Scholar]
- [19].Wright KP, Jr., Linton SK, Withrow D, et al. Sleep in university students prior to and during COVID-19 stay-at-home orders. Curr Biol. 2020;30:R797–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Lee PH, Marek J, Nálevka P. Crowdsourced smartphone data reveal altered sleep/wake pattern in quarantined Chinese during the COVID-19 outbreak. Chronobiol Int. 2020;37:1181–90. [DOI] [PubMed] [Google Scholar]
- [21].Randler C, Faßl C, Kalb N. From Lark to Owl: developmental changes in morningness-eveningness from new-borns to early adulthood. Sci Rep. 2017;7:45874. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Gariepy G, Danna S, Gobiņa I, et al. How are adolescents sleeping? Adolescent sleep patterns and sociodemographic differences in 24 European and North American Countries. J Adolesc Health. 2020;66:S81–s8. [DOI] [PubMed] [Google Scholar]
- [23].Saxvig IW, Evanger LN, Pallesen S, et al. Circadian typology and implications for adolescent sleep health. Results from a large, cross-sectional, school-based study. Sleep Med. 2021;83:63–70. [DOI] [PubMed] [Google Scholar]
- [24].Besedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99:1325–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Rundle AG, Park Y, Herbstman JB, et al. COVID-19-related school closings and risk of weight gain among children. Obesity (Silver Spring, Md). 2020;28:1008–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Tsai KM, Dahl RE, Irwin MR, et al. The roles of parental support and family stress in adolescent sleep. Child Dev. 2018;89:1577–88. [DOI] [PMC free article] [PubMed] [Google Scholar]