Biological Rhythm Research ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/nbrr20 Effect of COVID-19 lockdown on sleep behavior and screen exposure time: an observational study among Indian school children Koumi Dutta , Ruchira Mukherjee , Devashish Sen & Subhashis Sahu To cite this article: Koumi Dutta , Ruchira Mukherjee , Devashish Sen & Subhashis Sahu (2020): Effect of COVID-19 lockdown on sleep behavior and screen exposure time: an observational study among Indian school children, Biological Rhythm Research, DOI: 10.1080/09291016.2020.1825284 To link to this article: https://doi.org/10.1080/09291016.2020.1825284 Published online: 14 Oct 2020. Submit your article to this journal Article views: 2845 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=nbrr20 BIOLOGICAL RHYTHM RESEARCH https://doi.org/10.1080/09291016.2020.1825284 ARTICLE Effect of COVID-19 lockdown on sleep behavior and screen exposure time: an observational study among Indian school children Koumi Duttaa, Ruchira Mukherjeeb, Devashish Senb and Subhashis Sahu a a Ergonomics and Occupational Physiology Laboratory, University of Kalyani, Kalyani, India; bErgonomics and Work Physiology Laboratory, Department of Life Sciences, Presidency University, Kolkata, India ABSTRACT ARTICLE HISTORY Lockdown is an important measure that has been globally adopted to reduce the spread of the contagious disease caused by SARS CoV-2. The imposed schedule and confinement led to extensive use of digital media and rise in sedentary activity drastically. The esca­ lated duration of screen exposure causes disruption in sleep beha­ vior. An online survey was conducted to comprehend the effect of lockdown on sleep behavior and screen exposure time on school children. Screen exposure time involved with various electronic gadgets was also analyzed. It was observed that the social jet lag and sleep debt were significantly less during lockdown than before it. Inertia during lockdown significantly increased. The difference between screen exposure time on weekdays before lockdown and weekends during lockdown was identified to be the highest. Three clusters based on sleep behavior and duration of screen time were identified of which Cluster 2 revealed simultaneous existence of high sleep duration and screen time. These baseline data on sleep parameters and duration of exposure to the screen will help us in devising approaches to mitigate the evident disruption this unpre­ cedented phase has brought about. Received 16 July 2020 Accepted 15 September 2020 KEYWORDS Lockdown; sleep behavior; screen exposure time; school children; circadian realignment Introduction COVID-19 outbreak in India started escalating and lockdown was implemented from 25 March 2020 to resist the transmission of the disease. Since India is a populous country, lockdown was an inevitable method of prevention. The decrees imposed during lock­ down included restrictions on various social practices and behavior. People had to their spend time mostly confined at their homes. School, college, and offices were initially closed and later were partially or fully resumed in virtual platform with the help of electronic devices and Internet facility. Many of our activity patterns and sleep habits had to undergo a noticeable transforma­ tion during this lockdown phase. It has been observed prolonged restriction on inherent practices brought about by this period of confinement may deter health well-being of people (Wang et al. 2020). CONTACT Subhashis Sahu [email protected] University of Kalyani, Kalyani, Nadia-741235, India © 2020 Informa UK Limited, trading as Taylor & Francis Group Ergonomics and Occupational Physiology Laboratory, 2 K. DUTTA ET AL. Extensive use of electronic device has been included as a part of regular schedule. Classes for school and college students, meetings as well as office work are being conducted online. This alteration in the mode of work involving prolonged screen time is apprehended to affect sleep. This disruption in sleep wakefulness cycle may be devoted to the de-synchronization in pace of the “zeitgebers” (Altena et al. 2020). Sleep is a behavior that includes various aspects, of which some can be assessed using multiple quantitative parameters like sleep latency, inertia, sleep duration, and debt while daytime sleepiness; nap details provide a qualitative overview. Psychological and physio­ logical impact of compromised sleep is well established (Yoo and Franke 2012). It can culminate into consequences like mental health issues, metabolic disorders, and circadian de-synchronization (Sivertsen et al. 2019). Sleep rhythm is adversely affected by the sedentary activity involving screen exposure time (Hysing et al. 2015). A nongovernmental organization (CRY) working on child rights and health has already reported 88% of the respondents experienced a massive rise in duration in screen exposure among urban children of India Digital media has become indispensable among adolescents which impact sleep duration, cause both bed time and wake time to be late along with other sleep issues (Vanden 2010). Erratic sleep behavior along with increased sedentary activities also may lead to obesity and related co-morbidities (Dutta et al. 2018). In this particular condition strengthening immunity is extremely necessary and for this purpose good sleep practices can be helpful. It has already been observed that immunity and sleep to follow a relationship where both interact to impact each other (Ibarra-Coronado et al. 2015). In this particular and unique predisposition, it is primary prerequisite to comprehend the changes brought about in behavior for several aspects. Detailed report of quantitative and qualitative sleep parameters collated with information on-screen time will help us to be more equipped to implement a contextual and multidimensional ameliorative approach to combat health issues which may emerge during and after the lockdown. Methods Online survey was designed and circulated using social media or electronic mail. Participation was anonymous, voluntary and random. Consent form was placed at the beginning of survey as the first section of the sheet which was distributed among the parents; only on providing parental consent the survey could be completed. This survey was circulated for two weeks from 26th April (after a month of commencement of lock­ down) in India. Prolonged screen exposure has deterring effects on children and adolescents and they are also susceptible to erratic sleep behavior, to address the vulnerability of this age group, the dataset was stratified with respect to age and the responses for age group 8–16 years were included in the study. Total responses received were 272 of which 153 participants were under the 8–16 years of age range and were therefore selected for the study (n = 153). The study was approved by the Institutional Ethical Committee (Human) and data collection was performed in accordance with Helsinki’s Declaration. The comparative analysis presented in the study was majorly performed among four temporal response categories (groups): BIOLOGICAL RHYTHM RESEARCH 3 ● Group I – Weekdays before lockdown (WDBL) ● Group II – Weekends before lockdown (WEBL) ● Group III – Weekdays during lockdown (WDDL) ● Group IV – Weekends during lockdown (WEDL) Sleep parameters (qualitative and quantitativ Bed time, sleep time, wake time, time of leaving bed were noted on weekdays and weekdays, both the time periods, before as well as during lockdown. Sleep latency, inertia, duration for weekdays, and weekdays were computed using established formulas on weekdays and weekends before and during lockdown. Social Jet lag and sleep debt were also calculated during both the time phases i.e before and during lockdown. ‘SJLsc= │Sleep onset on free day – Sleep onset on work days │’ (Jankowski 2017). Sleep Debt = Average sleep need for the day – Average sleep duration on each day (Kantermann et al. 2007). ● Sleep Duration: The amount of time between sleep start and sleep end (Malone et al. 2016). ● Sleep Latency: It is the time taken from going to bed until falling asleep (Wittmann et al. 2006). ● Sleep Inertia: It is defined as the difference between wake-up time and time being fully alert (Wittmann et al. 2006). ● Social Jet Lag (SJL): The dissimilitude between biological and social timing. ● Sleep Debt: It is the cumulative effect of not getting enough sleep. It is calculated by the following formula: In the online survey questions regarding sleep quality, daytime sleepiness, and sleep discontinuity were asked with options to respond. These options were framed to provide a comparative overview on these before and during lockdown. Nap frequency and duration were also reported with the help of this questionnaire. Epworth Day time sleepiness scale was used to determine excessive daytime sleepiness. Screen exposure time: Most commonly used electronic devices with screens were enlisted and duration of screen exposure was noted in the similar pattern as duration of physical activity was reported. Duration of screen exposure while using other devices was specifically noted. A notion about duration was assessed and similar comparative question was asked to receive information about the change in screen exposure time before and during lockdown. Statistical Analysis Line graphs with the means for the quantitative sleep parameters were plotted. Since each participant had to respond for the sleep parameters keeping in mind four different scenarios 4 K. DUTTA ET AL. (WDBL, WEBL, WDDL, and WEDL), paired t-test was performed to check if the sleep log study parameters differed significantly when days of the week or the lockdown situation was kept constant. ANOVA was performed to check if the sleep parameters differed significantly among the four response categories. Finally, hierarchical clustering analysis was performed to categorize the participants based on their sleep behavior. The percentage change in screen exposure and sleep behavior was depicted by plotting column graph. Results The data collected were for four distinct time periods – weekdays, weekends before lockdown (WDBL, WEBL, respectively), and weekdays, weekends during lockdown (WDDL, WEDL, respectively). Figure 1A and B depicts changes in bed time and wake time for the entire week during lockdown and prior to that. Earliest wake time was recorded on weekdays before lock­ down while the earliest bed time was recorded on weekdays before lockdown. The highest sleep duration was recorded during the weekends while the lowest sleep duration was indexed during the weekdays on workdays before lockdown (Figure 2). Highest sleep latency was recorded during weekday during lockdown while the lowest sleep inertia was recorded on the weekdays before lockdown (Figure 3). Sleep debt and Social Jet Lag (SJL) are calculated based on differences in sleep parameters on weekday and weekends a seen in Figure 4. They were found to be significantly different before and during the lockdown. As per Table 1, parameters differing significantly become more abundant when compared across weekdays and weekends both before and during the lockdown while sleep log study parameters differing significantly are not that abundant when compared for before and during lockdown scenario. As per Table 2, sleep inertia lined on borderline significant difference while bed time and sleep latency were not found to differ significantly among the four groups. The rest of the quantitative sleep parameter studied showed significant variation across the four groups. About 37.1%, 42.9%, and 40% reports same qualitative sleep behavior for sleep quality, discontinuity, and comparative nap occurrence, respectively (Table 3). Only 11.4% of the participants suffered from excessive daytime sleepiness in this lockdown phase (Table 4). Figure 1. Variation in mean clock time for bed time and wake time. BIOLOGICAL RHYTHM RESEARCH 5 Figure 2. Variation in duration of sleep prior to and during lockdown. Figure 3. Variation in sleep latency and sleep inertia. Figure 4. Variation in social jet lag and sleep debt. While 28.6% of the participants did not report any nap throughout the week, another 28.6% reported that they took a nap 3–4 days per week during the lockdown phase as seen in Table 5. As per Table 6, tablet usage was reported to be the lowest while phone usage was the highest reported form of screen exposure both on weekdays and weekends before 6 K. DUTTA ET AL. Table 1. Paired t-test for quantitative sleep parameters. t Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10 Pair 11 Pair 12 Pair 13 Pair 14 Pair 15 Pair 16 Pair 17 Pair 18 Pair 19 Pair 20 Bed time (WDBL) – Bed time (WEBL) Wake time (WDBL) – Wake time (WEBL) Latency (WDBL) – Latency (WEBL) Inertia (WDBL) – Inertia (WEBL) Sleep duration(WDBL) – Sleep duration(WEBL) Bed time (WDBL) – Bed time (WDDL) Wake time (WDBL) – Wake time (WDDL) Latency (WDBL) – Latency (WDDL) Inertia (WDBL) – Inertia (WDDL) Sleep duration(WDBL) – Sleep duration(WDDL) Bed time (WDDL) – Bed time (WEDL) Wake time (WDDL) – Wake time (WEDL) Latency (WDDL) – Latency (WEDL) Inertia (WDDL) – Inertia (WEDL) Sleep duration(WDDL) – Sleep duration(WEDL) Bed time (WEBL) – Bed time (WEDL) Wake time (WEBL) – Wake time (WEDL) Latency (WEBL) – Latency (WEDL) Inertia (WEBL) – Inertia (WEDL) Sleep duration(WEBL) – Sleep duration(WEDL) Sig. (2-tailed) 2.225 −7.882 −1.426 −1.735 −4.163 .349 −7.440 −1.945 −2.309 −3.184 −.990 −2.227 .363 −2.005 −2.583 −2.917 −1.689 −1.315 −2.392 .079 .033* .000** .163 .092 .000** .729 .000** .060 .027* .003* .329 .033* .719 .053 .014* .006* .100 .197 .022* .938 *p < 0.05; significant difference at α = 0.05 ** p < 0.001; significant difference at α = 0.001 Table 2. ANOVA for significant differences in quantitative sleep parameters based on weekdays/weekends and before lockdown/ during lockdown. Bed time Wake time Latency Inertia Sleep duration SJL Sleep debt Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups F 1.332 Sig. .267 13.064 .000** .961 .413 2.455 .066 3.411 .019* 17.896 .000** 41.719 .000** *p < 0.05; significant difference at α = 0.05 ** p < 0.001; significant difference at α = 0.001 Table 3. Percentage prevalence report for qualitative sleep parameters and nap occurrence. Less/deeper than before Same as before More/lighter than before How is your sleep quality during lockdown? 42.9 Nature of sleep disconti­ nuity during lockdown: 25.7 Nap occurrences are more frequent during lockdown than before the lockdown: 5.7 37.1 42.9 40.0 20.0 31.4 54.3 BIOLOGICAL RHYTHM RESEARCH 7 Table 4. Percentage prevalence report for excessive daytime sleepiness and screen time exposure. EDS No 88.6 Yes 11.4 Do you feel that you experience more daytime sleepiness than before, during the lockdown? 62.9 37.1 Do you feel that you are having a longer duration of screen time during lockdown? 34.3 65.7 Table 5. Percentage prevalence report for frequency of nap on weekly basis. Report your nap occurrence during lockdown if any Never 28.6 1–2 days a week 11.4 3–4 days a week 28.6 5–6 days a week 5.7 Every day of the week 25.7 Table 6. Percentage prevalence report for screen exposure on weekdays before lockdown, weekends before lockdown, and during lockdown. < 1 hour 1–2 hours 2–4 hours 4–8 hours Not applicable Daily screen exposure on week­ days before lockdown Daily screen exposure on week­ ends before lockdown Phone 28.6 25.7 11.4 5.7 28.6 Phone 31.4 20.0 17.1 2.9 28.6 TV 34.3 31.4 0.0 2.9 31.4 Laptop 25.7 8.6 5.7 5.8 54.3 Tablet 25.7 0.0 0.0 2.9 71.4 TV 22.9 40.0 5.7 2.9 28.6 Laptop 17.1 14.3 2.9 2.9 62.9 Daily screen exposure during lockdown Tablet Phone TV Laptop Tablet 20.0 28.6 20.0 17.1 22.9 5.7 22.9 25.8 17.1 2.9 0.0 11.4 20.0 8.6 0.0 0.0 14.3 8.6 8.6 2.9 74.3 22.9 25.7 48.6 71.4 lockdown and during the lockdown as well. This was followed by exposure via TV and then laptop as a source. The fifth figure denotes increase, decrease, or similarity in screen exposure time for three different sets of comparisons – screen exposure on weekends compared to that on weekdays before lockdown, during lockdown compared to weekends before lockdown (BL) and during lockdown (DL) compared to weekdays before lockdown. Hierarchical clustering considers one group for all subjects and also a scenario where each individual belongs to a group with only himself/herself, in the process, producing a dendrogram showing linkage between the individuals. Data cluster analysis yielded a dendrogram from which three clusters can be identified each containing 12, 15, and 8 individuals (Figure 6). All the sleep quantitative parameters were found to be significantly different in the three clusters except for social jet lag and sleep debt (Table 7). Figure 7 shows the percentage change in screen exposure, sleep duration, social jet lag, and sleep debt during the lockdown compared to before lockdown for each of the clusters formed. Cluster 1 is characterized by highest percentage of participants reporting no change in screen exposure, more reports of decrease in sleep duration, elevated social jet lag, and no increase in sleep debt. The second cluster is characterized by highest percentage of subjects reporting increase in screen exposure and sleep duration, more decrease in social jet lag and elevated sleep debt. Cluster 3 denotes participants having almost similar screen exposure despite the lockdown situation, comparative decrease in sleep duration, least number of participants having unchanged social jet lag, and sleep debt. 8 K. DUTTA ET AL. Table 7. ANOVA for significant differences between clusters. Latency Wake time Sleep duration Inertia SJL Sleep debt Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups Between Groups Within Groups F 3.793 Sig. .025* 18.429 .000** 3.077 .049* 5.576 .005* .390 .679 .346 .709 *p < 0.05; significant difference at α = 0.05 ** p < 0.001; significant difference at α = 0.001 Discussion The latest wake time and bed time were recorded in the weekends during lockdown and before lockdown, respectively (Figure 1). Intrinsic periodicity of sleep wakeful behavior abides by 24-hour duration but during the lockdown situation, this intrinsic periodicity has been impaired. This interval of repetition has sometimes been stretched much longer than 24 hours while at other times, curtailed to be much lesser than that. For some cases, episodes of daytime sleeping have been reported to increase simultaneously for some missing a single episode of sleep in 24 hours is also not rare. Arrhythmic patterns or improper lengths of circadian period have long been identified to be associated with depression and mania (Martynhak et al. 2015). This unique time phase of lockdown has been marked with erratic sleep behavior especially pertaining to the bed time among the participants which can incur baneful physiological and psychological consequences. The sleep duration during weekends for both, before and during lockdown is compar­ able with no significant difference between them, portraying a difference of less than 1 minute (Figure 2). Intriguingly, weekends portray significantly higher sleep duration than weekdays both before and during lockdown which confirms the continuation of existing trend of oversleeping on weekends (Valdez et al. 1996). Both sleep inertia and sleep latency follow the same pattern (Figure 3). They were both lower before lockdown and higher during the lockdown. For sleep latency, any of these differences were not significant, but sleep inertia differed significantly before and during lockdown on weekdays as well as weekends. Sleep debt and social jet lag lowered significantly during the lockdown (Figure 4). It is important to note that these parameters, the higher values of which depict more dis­ crepancies in sleep behavior between weekdays and weekends are lowered during lock­ down specifying somewhat homogenous sleep behavior throughout the week during the lockdown period. School-related sleep restriction is recovered during the vacations (Bei et al. 2014) this similar characteristic of sleep behavior is observed during the lockdown. Also, during the lockdown period these two parameters have almost comparable values with the difference being less than a minute. All parameters except sleep latency were found to be significantly different when weekdays and weekends were compared before lockdown (Table 1). Sleep latency was BIOLOGICAL RHYTHM RESEARCH 9 only found to be significantly different before and during lockdown on the weekends. Least number of parameters (only bed time and sleep inertia) was found to be signifi­ cantly different when weekends before lockdown were compared to weekends during lockdown. This finding further strengthens the notion that sleep behavior during lock­ down is comparable to that during weekends. Moreover, about 50% of the participants reported that they feel their routine during the lockdown matches to that of the week­ ends, i.e. on their day off of work. While 42.9% of respondents experienced deeper sleep during lockdown, 31.4% experi­ enced more discontinuous sleep during the lockdown phase. Sleep behavior as per selfreport is deeper, more incidents of wake after sleep onsets which may reflect in higher occurrence of nap during the lockdown (Table 3). About 37.1% reported more daytime sleepiness during the lockdown phase (Table 4). Higher frequencies of nap with 25.7% of participants taking daily naps were noticed. Reportedly, screen exposure does not change in a high percentage of subjects before and during lockdown. However, it is important to note that highest increase in screen exposure (phone, TV, laptop, and tablet exposure increasing by 13%, 4%, 9%, and 2%, respectively) is noted when the during lockdown scenario is compared to the weekdays before lockdown with exposure (Figure 5). This again reinforces the previous observations that suggest week­ end routine conforms to the lockdown routine. Moreover, as seen in Figure 7, there are no participants that report same sleep duration before and during the lockdown. Both increase and decrease in the sleep duration is indicative of disrupted sleep behavior. Given that 100% of the participants undergo sleep disruptions, it is of utmost importance to look into the matter with higher resolution because sleep alteration is related to a multitude of derogatory manifestations. Desynchronized sleep behavior with simultaneous exposure to screen exposure is proven to be detrimental to health and cognitive performance (Cajochen et al. 2011). The fact that all days are seemingly free days during lockdown, discrepancies between weekdays and weekends are lowered thereby decreasing the social jet lag and sleep debt. Additionally, it indicates disrupted sleep on all days of the week with no possible scope for sleep recovery thereby, posing a greater threat. Cluster analysis was performed on the basis of sleep behavior and screen exposure time. It reveals that cluster 2 was character­ ized by simultaneous increase screen exposure and sleep duration. So, it can be Figure 5. Percentage change in screen exposure. 10 K. DUTTA ET AL. Figure 6. Hierarchical clustering dendrogram. Figure 7. Percentage change in screen exposure and quantitative sleep parameters per cluster. recommended that along with realignment of the sleep-wakefulness cycle for all the participants reporting disturbances in sleep schedule, it is important that screen exposure time in their daily routine should not exceed 2 hours (Dutta et al. 2019). A potentiate observation reported from India on office workers and students had also demarcated the role of pandemic and subsequent lockdown on sleep efficacy, mental health, and somatic pain with exaggerated exposure to screen that further aid in deterioration (Majumdar et al. 2020). Another topical assessment among Indian Facebook Messenger users BIOLOGICAL RHYTHM RESEARCH 11 revealed higher online activity but no change in circadian rhythm with respect to the acrophase of online activity was observed (Swain et al. 2020) It can be concluded that all the participants reported sleep disturbances and simultaneous screen exposure can exacerbate the condition. Awareness regarding the detrimental effects of erratic sleep behavior that may have been inflicted during this phase with subsequent circadian reorientation may alleviate the consequences. In order to inculcate or device efforts to realign sleep schedule the knowledge of the extent of disruption an individual is experiencing is a primordial step, which is addressed by this work. The limitations of the study are: It will be more conclusive if we can reach more number of children to involve in the online survey and more respondents could complete the survey. Almost no information regarding sleep disturbances among the respondents before the lockdown phase could be obtained. Acknowledgments Authors are thankful to the participants for providing consent to participate and patiently respond­ ing to the online questionnaire. The technical support provided by Ahan Karmakar (Presidency University) in executing the online survey using Google forms is hereby acknowledged. Author’s contribution Koumi Dutta developed the study concept, contributed to the study design, data collection, data analysis, interpreted the data, drafted the manuscript. Ruchira Mukherjee performed data analysis, interpreted the data, drafted the manuscript. Devashish Sen developed the study concept and approved the final version for submission. Subhashis Sahu developed the study concept and approved the final version for submission. Disclosure statement No potential conflict of interest was reported by the authors. Funding The funding for this work has been provided from SVMCM (Swami Vivekananda Merit Cum Means fellowship, Government of West Bengal (IN) received by Koumi Dutta.Personal Research Grant provided by University of Kalyani to Dr. Subhashis Sahu. ORCID Subhashis Sahu http://orcid.org/0000-0002-1584-4551 References Altena E, Baglioni C, Espie C, Ellis A, Gavriloff J, Holzinger D, Schlarb A, Frase L, Jernelöv S, Riemann D. 2020. 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. 29(4). 12 K. DUTTA ET AL. Bei B, Allen NB, Nicholas CL, Dudgeon P, Murray G, Trinder J. 2014. Actigraphy-assessed sleep during school and vacation periods: a naturalistic study of restricted and extended sleep opportunities in adolescents. J Sleep Res. 23(1):107–117. doi:10.1111/jsr.12080. Cajochen C, Frey S, Anders D, Späti J, Bues M, Pross A, Mager R, Wirz-Justice A, Stefani O. 2011. Evening exposure to a light-emitting diodes (LED)-backlit computer screen affects circadian physiology and cognitive performance. J Appl Physiol. 110:1432–1438. doi:10.1152/ japplphysiol.00165.201. Dutta K, Mukherjee R, Das R, Chowdhury A, Sen D, Sahu S. 2019. Scheduled optimal sleep duration and screen exposure time promotes cognitive performance and healthy BMI: a study among rural school children of India. Biol Rhythm Res. doi:10.1080/09291016.2019.1646505. Dutta K, Sen D, Sahu S. 2018. Food intake rhythm and its implication on obesity, and related comorbidities among adolescents: a mini review. Biol Rhythm Res. 1–11. doi:10.1080/ 09291016.2018.1424773. Hysing M, Pallesen S, Stormark K, Jakobsen R, Lundervold A, Sivertsen B. 2015. Sleep and use of electronic devices in adolescence: results from a large population-based study. BMJ Open. 5(1): e006748–e006748. doi:10.1136/bmjopen-2014-006748. Ibarra-Coronado EG, Pantaleon-Martinez AM, Velazquez-Moctezuma J, Prospero Garcia O, MendezDiaz M, Perez-Tapia M, Pavón L, Morales-Montor J. 2015. The bidirectional relationship between sleep and immunity against infections. J Immunol Res. 678164. Jankowski KS. 2017. Social jet lag: sleep-corrected formula. Chronobiol Int. 34(4):531–535. doi:10.1080/07420528.2017.1299162. Kantermann T, Juda M, Merrow M, Roenneberg T. 2007. The human circadian clock’s seasonal adjustment is disrupted by daylight saving time. Curr Biol. 17(22):1996–2000. doi:10.1016/j. cub.2007.10.025. Majumdar P, Biswas A, Sahu S. 2020. COVID-19 pandemic and lockdown: cause of sleep disruption, depression, somatic pain, and increased screen exposure of office workers and students of India. Chronobiol Int. 1–10. doi:10.1080/07420528.2020.1786107. Malone SK, Zemel B, Compher C, Souders M, Chittams J, Thompson AL, Pack A, Lipman TH. 2016. Social jet lag, chronotype and body mass index in 14–17-year-old adolescents. Chronobiol Int. 33 (9):1255–1266. doi:10.1080/07420528.2016.1196697. Martynhak BJ, Pereira M, de Souza C, Andreatini R. 2015. Stretch, shrink, and shatter the rhythms: the intrinsic circadian period in mania and depression. CNS Neurol Disord Drug Targets. 14 (8):963–969. doi:10.2174/1871527314666150909115203. Sivertsen B, Vedaa Ø, Harvey AG, Glozier N, Pallesen S, Aarø LE, Lønning KJ, Hysing M. 2019. Sleep patterns and insomnia in young adults: A national survey of Norwegian university students. J Sleep Res. 28(2):e12790. doi:10.1111/jsr.12790. Swain RK, Minz S, Parganiha A, Diwan A, Pati AK. 2020. Circadian rhythm in the pattern of online usage of Facebook messenger during the COVID-19-triggered lockdown: a sequel to the pre-pandemic study. Biol Rhythm Res. doi:10.1080/09291016.2020.1812284. Valdez P, Ramírez C, García A. 1996. Delaying and EXTENDING SLEEP DURING WEEKENDS: SLEEP RECOVERY OR CIRCADIAN Effect? Chronobiol Int. 13(3):191–198. doi:10.3109/07420529609012652. Vanden BJ. 2010. The effects of media on sleep. Adolesc Med State Art Rev. 21(3):418–429. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, Ho RC. 2020. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in china. Int J Environ Res Public Health. 17(5):1729. doi:10.3390/ ijerph17051729. Wittmann M, Dinich J, Merrow M, Roenneberg T. 2006. Social jetlag: misalignment of biological and social time. Chronobiol Int. 23(1–2):497–509. doi:10.1080/07420520500545979. Yoo H, Franke WD. 2012. Sleep habits, mental health, and the metabolic syndrome in law enforce­ ment officers. J Occup Environ Med. 55:99–103. doi:10.1097/JOM.0b013e31826e294c.