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
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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
skcsahu@yahoo.co.in
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
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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
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