Next Article in Journal
Development of a Comprehensive Household Food Security Tool for Families with Young Children and/or Pregnant Women in High Income Countries
Next Article in Special Issue
Exploring the Retail Food Environment Surrounding Two Secondary Schools with Predominantly Pacific Populations in Tonga and New Zealand to Enable the Development of Mapping Methods Appropriate for Testing in a Classroom
Previous Article in Journal
Identification of New Fusarium sulawense Strains Causing Soybean Pod Blight in China and Their Control Using Carbendazim, Dipicolinic Acid and Kojic Acid
Previous Article in Special Issue
Focus on High School: Factors Associated with Creating Harmony between the Educational Transition and Adolescents’ Well-Being
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 17
10.3390/ijerph191710539
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study

by
Hung-Kwan So
1,
Gilbert T. Chua
1,
Ka-Man Yip
1,
Keith T. S. Tung
1,
Rosa S. Wong
1,
Lobo H. T. Louie
2,
Winnie W. Y. Tso
1,
Ian C. K. Wong
3,
Jason C. Yam
4,
Mike Y. W. Kwan
5,
Kui-Kai Lau
6,
Judy K. W. Kong
7,
Wilfred H. S. Wong
1 and
Patrick Ip
1,*
1
Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
2
Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong, China
3
Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
4
Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
5
Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong, China
6
Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
7
Inspiring HK Sports Foundation, Hong Kong, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(17), 10539; https://doi.org/10.3390/ijerph191710539
Submission received: 28 July 2022 / Revised: 16 August 2022 / Accepted: 21 August 2022 / Published: 24 August 2022
(This article belongs to the Special Issue Healthy School Environment to Enhance Health and Well-Being of Students)
Review Reports Versions Notes

Abstract

:
Despite concerns about the negative effects of social distancing and prolonged school closures on children’s lifestyle and physical activity (PA) during the COVID-19 pandemic, robust evidence is lacking on the impact of the pandemic-related school closures and social distancing on children’s wellbeing and daily life. This study aimed to examine changes in the PA levels, sleep patterns, and screen time of school-aged children during the different phases of the COVID-19 outbreak in Hong Kong using a repeated cross-sectional design. School students (grades 1 to 12) were asked to report their daily electronic device usage and to fill in a sleep diary, recording their daily sleep onset and wake-up time. They were equipped with a PA monitor, Actigraph wGT3X-BT, to obtain objective data on their PA levels and sleep patterns. Students were recruited before the pandemic (September 2019–January 2020; n = 577), during school closures (March 2020–April 2020; n = 146), and after schools partially reopened (October 2020–July 2021; n = 227). Our results indicated lower PA levels, longer sleep duration, and longer screen time among participants recruited during school closures than those recruited before the COVID-19 outbreak. Primary school students were found to sleep on average for an extra hour during school closures. The later sleep onset and increased screen time documented during school closures persisted when schools partially reopened. Our findings illustrate the significant impact of social distancing policies during the COVID-19 pandemic on the sleep pattern, screen time, and PA level in school-aged children in Hong Kong. Professionals should urgently reinforce the importance of improving physically activity, good sleep hygiene, and regulated use of electronic devices for parents and school-aged children during this unprecedented time.

1. Introduction

During the coronavirus disease 2019 (COVID-19) pandemic, social distancing policies such as home confinement, school closures, and minimal/no outdoor play for school children, resulted in unusual lifestyle changes in children and adolescents [1]. It has been proposed that the lockdown measures have affected the general well-being of children and adolescents more profoundly than the infection itself [2]. Since the outbreak, there has been increasing interest in the impact of these policies on physical activity (PA), screen time, and sleep patterns among children and adolescents. According to a study in Shanghai, the prevalence of physically inactive school-aged children increased from 21% in January 2020 to 66% in March 2020 [3]. However, countries such as Colombia, Spain, Italy, and Germany observed an increase in PA during the pandemic [4,5], whereas five studies found no significant differences in PA behavior before and during the pandemic [5,6,7]. The status, intensity, and duration of COVID-19 restrictions have varied by country and region and may explain the inter-regional differences in children’s PA during the pandemic [8]. A recent report in the US showed that the non-academic screen time of children doubled during the COVID-19 pandemic from 3.8 to 7.7 h a day [9]. This raises concerns regarding the known potential harms of excessive screen time, mainly the risks to physical and mental health such as greater obesity and higher depressive symptoms, among school-aged children [10]. In addition, evidence showed that school-aged children had a longer sleep duration, with delayed sleep onset and wake-up times, as a result of homeschooling during this period [8,11,12]. Although adequate sleep is beneficial for children, researchers found a pooled prevalence of sleep disturbances of up to 54% in children during the pandemic [13]. Some researchers have suggested that the COVID-19 pandemic could have exacerbated existing lifestyle problems such as physical inactivity and sedentary lifestyles due to missed PA classes and social distancing measures [14,15].
However, the majority of evidence in the current literature is limited; most studies have been based on subjective questionnaire assessments and only looked at the short-term changes across the first year of the pandemic. It would be meaningful to examine the longer-term impact of the pandemic on the lifestyles of children and adolescents. Although the association between lifestyle and well-being in school-aged children has been widely studied, research using objective assessments for parameters such as physical activity and sleep patterns is needed to document quantitative evidence on the lifestyle changes during the COVID-19 pandemic. To the best of our knowledge, only one study has used Actigraph data to evaluate sleep patterns in 16 healthy Japanese preschoolers during the pandemic [16]. In our study on school-aged children in Hong Kong, we objectively measured and compared lifestyle habits, including PA levels, sleep patterns, and screen time, during the different phases of the COVID-19 outbreak in order to examine the impact of policy on children.

2. Materials and Methods

This was a repeated cross-sectional study [17] designed to assess the impact of COVID-19 on school-aged children in Hong Kong over 2 years across 3 time-points, including pre-pandemic (September 2019–January 2020), school closures (March 2020–April 2020), and partial school reopening (October 2020–July 2021). We explored changes in PA levels, sleep patterns, and electronic device usage (screen time) in primary and secondary school students. Repeated cross-sectional data assessed independent samples in each time period, allowing for meaningful inference of changes across a population during the different time periods. Although overlap of cross-sectional samples is advantageous because it minimizes the variance of parameter estimates, considerable sample overlap is not required for reliable conclusions regarding population trends over time [18].

2.1. Participants

The study sample was divided into the five main districts of Hong Kong: Hong Kong Island, West Kowloon, East Kowloon, West New Territories, and East New Territories. Using a stratified random sampling method, a list of primary (grades 1–6) and secondary (grades 7–12) schools from each district was randomly generated. The primary and secondary schools in each district were invited to participate according to the list. If the invitation was rejected, the next school on the list was invited until the required sample size was met. One grade of students was randomly selected from each school to participate. This method was adopted in our previous local prevalence study published in a peer-reviewed journal [19].
The first assessment was conducted between September 2019 and January 2020 before the first wave of the COVID-19 outbreak in Hong Kong. Schools that agreed to participate in the study were contacted by mail and telephone. Written informed consent was obtained from the participants’ parents. Each student and parent pair received a set of questionnaires and a wrist-placed activity monitor (Actigraph wGT3X-BT, Pensacola, FL, USA). The Actigraph device is known for its sensitivity and has been used to monitor PA and sleep in children in several peer-reviewed publications [20,21,22,23].
The second assessment was conducted between late February and March 2020, approximately two months after school closures due to the first wave of COVID-19 in Hong Kong. Parents willing to participate in the assessments were contacted by telephone. A set of questionnaires and an Actigraph device were delivered to each participating household. To comply with the social-distancing policies and to minimize infection risk, the consent form, questionnaire, and Actigraph device were left by the door of each household. The signed consent forms, questionnaires, and PA monitoring device were collected at the end of the monitoring period.
The third assessment was conducted between October 2020 and July 2021 after schools were partially reopened. Students were required to attend half-day in-person classes at school and then half-day online lessons at home. Participating schools were contacted by mail and phone. After the signed consent form was returned, the questionnaire and Actigraph set was distributed to students and parents.

2.2. Measurements

2.2.1. Questionnaires

The questionnaires were completed by one parent of the student. Demographic information and children’s electronic device usage (screen time) data were collected [24]. Participants were also asked to fill in a sleep diary daily to record their sleep onset and wake-up times.

2.2.2. Screen Time

Screen time was assessed by asking participants how many hours on average they spent using an electronic device (e.g., TV, online/electronic game, smartphone, computer, tablet) on a weekday and at the weekend. The electronic device usage was calculated using the weighted average formula: [(weekday × 5) + (weekend × 2)]/7.

2.2.3. Actigraph Data

To properly analyze physical activity, subjects were required to have three valid monitoring days. One day’s recording with more than 10 h wear time beyond the sleep period was counted as a valid day [25]. Subjects with data from at least two days and nights were used in the sleep analysis.

Non-Wear Time

The non-wear period was identified using the Choi algorithm [26]. A minimum of 90 consecutive “zero” epochs with a spike tolerance of 2 min and 100 counts/min were used to identify non-wear periods.

Sleep Measurements

Bedtime was determined by five consecutive asleep minutes whereas wake time was determined by ten consecutive awake minutes. Sleep periods beyond the sleep time and wake-up time marked in the students’ sleep diary or over 50% non-wear time were eliminated.
  • Sleep Period Classification
Extracted data was analyzed using 60-s (1 min) epochs. Each 1-min epoch was classified as asleep or awake using the Cole–Kripke algorithm. The Cole–Kripke algorithm adjusts the epoch data by rescaling the count values by 100 and setting values over 300 to 300 [27]. A sleep index less than 1 was classified as asleep and otherwise as awake [28]. Sleep periods were detected using the Tudor–Locke algorithm [25].
  • Sleep Parameters
The measurement of sleep-related parameters was based on previously described studies [21,29]. Parameters included sleep and wake-up time, total sleep duration, sleep latency, sleep efficiency, and sleep fragmentation index (SFI). Sleep efficiency was calculated as the percentage of asleep time divided by the total in-bed time. The SFI indicates restlessness during the sleep period [30] and was calculated as a sum of the movement index and the fragmentation index. The movement index was the percentage of total awake minutes out of the total in-bed time and the fragmentation index was the percentage of one-minute periods of sleep over the entire sleep period. Latency was defined as the difference between the students’ self-reported sleep time and accelerometer-detected sleep time.
  • Physical Activity Levels
Step count and sum of the vector magnitude (VM) in 60-s epochs were processed using ActiLife (software version 6.13.4, ActiGraph, FL, USA). Cut-offs for PA and sedentary behavior according to Chandler et al. were used to quantify the time spent as sedentary (VM < 3672 per min), engaged in light PA (3672 ≤ VM per min < 9816), moderate PA (9816 ≤ VM per min < 23,628), and moderate-to-vigorous PA (MVPA) (VM per min ≥ 23,628) [31].

2.3. Statistical Analysis

All analyses were conducted using R statistical software, version 4.0.3 (R Program for Statistical Computing, Lucent Technologies, NJ, USA). Principal component analysis was used to construct the socioeconomic status (SES) index using household income, parental education level, and occupation. Descriptive analyses were used to compare the demographic characteristics of the students among the three periods. A mixed effect model was used to compare the measurement variables of lifestyle (physical activity, sleep, and electronic device usage) among the three periods, adjusting for age, sex, and SES status, for the whole sample and separately for primary and secondary schools.
Compositional data analysis (CoDA) was conducted using “compositions” and “robCompositions” r packages to explore the relationship between the daily use of time (sleep, MVPA, screen time, and other activities) and sleep quality of children among the three periods. Small non-zero values were used in the imputation to replace zeros, as log ratios cannot be created with zero values [32]. Compositional isotemporal substitution analyses were performed to explore the influence of the reallocation of fixed time durations (15 min) between the daily time use of different activities on children’s sleep quality using SFI as the dependent variable. All models were adjusted for sex and SES status. Statistical significance was set at a two-sided p < 0.05 for all analyses.

2.4. Ethics

This study was approved by The Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 19-516). Informed written consent was obtained from the parents of the participants.

3. Results

3.1. Descriptive Analysis (Table 1)

A total of 577 students (267 primary and 310 secondary students) were recruited before the pandemic (September 2019–January 2020), 146 students (87 primary and 59 secondary students) were recruited during school closures (March 2020–April 2020), and 293 students (137 primary and 156 secondary students) were recruited after schools partially reopened (October 2020–July 2021). Table 1 shows a summary of the participant baseline characteristics. There were no significant differences in students’ anthropometric parameters or SES index among the three periods. The proportion of primary school students recruited during school closures was higher (60% during school closures vs. 46% before the pandemic and 47% after school reopened, p = 0.013). The percentage of female students recruited during school closures and reopening was higher (68% and 77% during school closures and reopening, respectively, vs. 58% in the pre-pandemic phase, p < 0.001). Students recruited during the pre-pandemic period were slightly older than those in the other two periods (12.85 years vs. 12.14 years during school closures, p = 0.006 and 11.93 years after schools partially reopened, p < 0.001).
Table
Table 1. Characteristics of the participants at three time-points.
Table 1. Characteristics of the participants at three time-points.
Pre-PandemicDuring School ClosureSchool Partially Reopened
Data collection periodSeptember 2019–January 2020March 2020–April 2020October 2020–July 2021p-value
N (%)/Mean (SD)N (%)/Mean (SD)N (%)/Mean (SD)
Total valid data collected577146293
School type
 Primary267 (46%)87 (60%)137 (47%)0.013
 Secondary310 (54%)59 (40%)156 (53%)
Sex
 Female333 (58%)100 (68%)227 (77%)<0.001
 Male244 (42%)46 (32%)66 (23%)
Age (years)12.85 (2.61)12.14 (2.90)11.93 (2.11)<0.001
Anthropometric parameters
 Height (z-score)−0.39 (1.50)−0.41 (1.49)−0.29 (1.10)0.535
 Weight (z-score)−0.10 (1.32)−0.03 (1.38)−0.02 (1.25)0.672
 BMI (z-score)0.17 (1.25)0.24 (1.26)0.13 (1.29)0.683
 Body fat (%)20.99 (9.63)21.38 (9.27)20.72 (8.67)0.788
Body Status
 Underweight12 (2%)2 (1%)12 (4%)0.576
 Normal442 (77%)111 (76%)227 (77%)
 Overweight85 (15%)21 (14%)38 (13%)
 Obese34 (6%)7 (5%)15 (5%)
 Missing4 (1%)5 (3%)1 (0%)
Social Economic Status (SES)−0.08 (1.53)0.18 (1.69)0.03 (1.52)0.301

3.2. Changes in PA, Sleep Time, and Screen Time (Table 2)

After adjusting for age, sex, and SES index, the average step count and MVPA time per day decreased during school closures and stayed lower after schools’ partial reopening compared to the pre-pandemic time. Moreover, the average sedentary time per day increased when schools reopened.
Compared to their counterparts prior to the pandemic, students had lower sleep efficiency and higher SFI though with a longer sleep duration during schools’ closure. Students had a longer sleep latency and lower sleep efficiency after schools’ reopening.
Students’ screen time for both academic and non-academic purposes increased during both school closures and after schools’ reopening compared to the pre-pandemic time.
Comparing between SES, a lower SES was associated with higher SFI, lower MVPA time, and longer total screen time pre-pandemic and during school closures. Lower SES was also associated with a longer sedentary time during school closures.
Table
Table 2. Changes in weekly PA, sleep, and screen time.
Table 2. Changes in weekly PA, sleep, and screen time.
Pre-Pandemic (Reference)During School ClosuresSchools Partially Reopened
Mean (SD)Mean (SD)β (SE)p-ValueMean (SD)β (SE)p-Value
Physical activity
 Step count per day10,969.33 (2492.15)8472.51 (3295.39)−2692.83 (235.04)<0.0019547.04 (2455.05)−1562.17 (189.24)<0.001
 Sedentary time per day (h)10.46 (1.45)10.64 (1.83)0.16 (0.16)0.29210.85 (1.69)0.61 (0.12)<0.001
 MVPA time per day (h)0.47 (0.35)0.42 (0.49)−0.09 (0.03)0.0070.44 (0.33)−0.08 (0.03)<0.001
Sleep
 Latency (min)43.66 (50.83)55.61 (81.31)7.56 (6.48)0.24356.83 (52.13)11.96 (4.72)0.011
 Actual sleep duration (h)7.45 (1.03)8.76 (1.28)1.19 (0.11)<0.0017.55 (1.09)−0.07 (0.09)0.438
 Sleep time (HH:MM)23:29 (00:53)00:30 (01:17)62.43 (5.68)<0.00123:39 (00:51)14.92 (4.52)0.001
 Wake time (HH:MM)07:11 (00:39)09:54 (01:31)115.14 (6.26)<0.00107:25 (00:59)12.53 (4.98)0.012
 Efficiency (%)95.72 (4.18)91.07 (8.7)−4.27 (0.54)<0.00195.25 (4.21)−0.86 (0.43)0.047
 SFI27.62 (7.34)35.3 (11.75)7.91 (0.88)<0.00127.56 (7.345)0.72 (0.7)0.306
Screen time
 Level of parental guidance1.68 (0.56)1.65 (0.56)−0.05 (0.06)0.4071.76 (0.54)0.03 (0.04)0.497
 Non-academic screen time per day (h)4.38 (4.3)4.54 (4.55)1.16 (0.4)0.0035.60 (4.22)1.87 (0.32)<0.001
 Academic screen time per day (h)1.01 (1.33)1.69 (2.14)0.6 (0.17)<0.0012.92 (2.24)1.93 (0.14)<0.001
 Overall screen time per day (h)5.39 (5.10)6.23 (5.80)1.79 (0.48)<0.0018.52 (5.40)3.85 (0.38)<0.001
MVPA—Moderate to vigorous physical activity.

3.3. Changes in PA, SLEEP Time, and Screen Time in Primary and Secondary School Students

3.3.1. Primary Schools (Grade 1 to Grade 6) (Table 3)

After adjusting for age, sex, and SES index, the average step count and MVPA time per day decreased during school closures compared with the pre-pandemic time. Students had a longer sleep latency, higher SFI, and increased academic screen time per day during school closures.
After schools partially reopened, compared to the pre-pandemic time, students had lesser step counts per day, shorter sleep duration, poorer sleep efficiency during school days, and an increase in both academic and non-academic screen time in both school days and holidays.
Table
Table 3. Changes in weekly PA, sleep, and screen time among primary school students.
Table 3. Changes in weekly PA, sleep, and screen time among primary school students.
Pre-Pandemic (Reference)During School ClosuresSchools Partially Reopened
Mean (SD)Mean (SD)β (SE)p-ValueMean (SD)β (SE)p-Value
Physical activity
Step count per day11,902.36 (2100.06)9594.29 (2445.65)−1961.05 (312.71)<0.00110,433.02 (2493.44)−1165.94 (258.07)<0.001
 School days11,999.24 (2026.46)---10,682.54 (2663.60)−1127.8 (256.02)<0.001
 Holidays11,713.80 (3599.98)9594.29 (2445.65)−1960.99 (471.21)<0.0019836.68 (3248.60)−1317.95 (411.12)0.001
Sedentary time per day (h)10.12 (1.29)10.07 (1.61)−0.25 (0.19)0.18110.13 (1.46)0.00 (0.15)0.977
 School days10.23 (1.36)---10.33 (1.51)0.21 (0.16)0.202
 Holidays9.91 (1.80)10.07 (1.61)0.03 (0.24)0.8999.56 (2.08)−0.59 (0.21)0.005
MVPA time per day (h)0.64 (0.33)0.50 (0.33)−0.11 (0.05)0.0180.62 (0.35)0.02 (0.04)0.572
 School days0.63 (0.33)---0.61 (0.35)0.01 (0.04)0.881
 Holidays0.68 (0.51)0.50 (0.33)−0.17 (0.07)0.0160.65 (0.49)0.05 (0.06)0.473
Sleep
Latency (min)53.19 (40.88)83.68 (64.82)27.65 (6.78)<0.00160.31 (44.30)5.77 (5.54)0.298
 School days53.96 (44.17)---62.87 (42.91)8.58 (5.52)0.120
 Holidays50.41 (59.49)83.68 (64.82)31.29 (9.13)0.00152.69 (62.63)−0.64 (8.08)0.937
Actual sleep duration (h)7.95 (0.79)8.80 (1.03)0.85 (0.13)<0.0017.77 (0.90)−0.17 (0.1)0.089
 School days7.72 (0.79)---7.44 (0.83)−0.27 (0.1)0.007
 Holidays8.66 (1.47)8.80 (1.03)0.11 (0.21)0.5898.85 (1.37)0.08 (0.18)0.658
Sleep time (HH:MM)23:07 (01:21)00:15 (01:22)64.3 (7.09)<0.00123:21 (00:45)13.25 (5.79)0.022
 School days22:57 (00:45)---23:11 (00:45)13.41 (5.53)0.015
 Holidays23:37 (00:54)00:15 (01:22)35.39 (9.16)<0.00123:52 (01:11)10.77 (8.11)0.184
Wake time (HH:MM)07:16 (00:36)09:22 (01:15)119.96 (6.33)<0.00107:21 (00:39)6.37 (5.17)0.218
 School days06:49 (00:26)---06:51 (00:29)2.95 (3.41)0.388
 Holidays08:43 (01:37)09:22 (01:15)35.78 (13.60)0.00909:00 (01:26)14.83 (12.04)0.218
Efficiency (%)95.81 (3.53)94.13 (4.45)−1.15 (0.50)0.02395.92 (3.35)−0.19 (0.41)0.639
 School days97.13 (2.94)---96.43 (3.66)−0.80 (0.40)0.044
 Holidays91.61 (8.82)94.13 (4.45)2.47 (1.08)0.02294.28 (4.85)1.93 (0.95)0.043
SFI26.89 (6.57)31.11 (7.87)4.01 (1.00)<0.00127.23 (6.70)0.41 (0.82)0.614
 School days25.44 (6.73)---26.85 (6.97)1.24 (0.87)0.153
 Holidays31.87 (12.06)31.11 (7.87)−0.24 (1.54)0.87428.97 (8.95)−2.68 (1.37)0.050
Screen time
Level of parental guidance1.87 (0.51)1.94 (0.49)0.04 (0.08)0.5861.86 (0.52)0.02 (0.06)0.765
Non-academic screen time per day (h)1.81 (2.69)2.65 (3.60)0.56 (0.45)0.2114.5 (3.78)2.67 (0.37)<0.001
 School days1.39 (2.42)---3.87 (3.76)2.27 (0.34)<0.001
 Holidays2.86 (4.09)2.65 (3.60)0.95 (0.66)0.1536.09 (5.27)3.62 (0.54)<0.001
Academic screen time per day (h)0.38 (0.92)0.88 (1.28)0.55 (0.21)0.0102.98 (2.38)2.44 (0.18)<0.001
 School days0.43 (0.91)---3.49 (3.01)3.02 (0.22)<0.001
 Holidays0.54 (1.21)0.88 (1.28)0.7 (0.21)0.0011.71 (1.87)0.95 (0.18)<0.001
Overall screen time per day (h)2.28 (3.31)3.53 (1.65)1.12 (0.58)0.0557.48 (5.32)5.11 (0.48)<0.001
 School days1.83 (3.02)---7.36 (6.02)5.28 (0.49)<0.001
 Holidays3.4 (4.65)3.53 (1.65)1.65 (0.74)0.0277.79 (5.75)4.56 (0.61)<0.001
Latency—Difference between monitor-recorded and self-reported sleep time (min); MVPA—Moderate to vigorous physical activity; SFI—Sleep fragmentation index.

3.3.2. Secondary Schools (Grade 7 to Grade 12) (Table 4)

During school closure, students had decreased step counts per day, less MVPA time per day, more sedentary time, poorer sleep efficiency, and a higher SFI compared with the pre-pandemic time. Academic and non-academic screen time increased during school closures.
After schools partially reopened, students had lesser step counts, lesser MVPA, and increased sedentary time per day compared with pre-pandemic data. A longer sleep latency, shorter sleep duration, lower SFI, and increased academic plus non-academic screen time were also documented.
Table
Table 4. Changes in weekly PA, sleep, and screen time among secondary school students.
Table 4. Changes in weekly PA, sleep, and screen time among secondary school students.
Pre-Pandemic (Reference)During School ClosuresSchools Partially Reopened
Mean (SD)Mean (SD)β (SE)p-ValueMean (SD)β (SE)p-Value
Physical activity
Step count per day9692.94 (2492.86)6111.86 (2196.68)−4035.39 (381.47)<0.0018795.19 (2157.77)−1519.01 (348.77)<0.001
 School days9997.45 (2422.58)---9454.58 (2218.24)−1333.44 (360.77)<0.001
 Holidays8786.08 (4481.52)6111.86 (2196.68)−2955.5 (612.03)<0.0017550.07 (2575.63)−1514.08 (641.59)0.018
Sedentary time per day (h)10.79 (1.48)11.61 (1.51)0.79 (0.27)0.00311.48 (1.63)0.83 (0.23)<0.001
 School days10.93 (1.64)---11.93 (1.80)1.07 (0.27)<0.001
 Holidays10.62 (1.82)11.61 (1.51)1.11 (0.31)<0.00110.92 (1.68)0.41 (0.32)0.194
MVPA time per day (h)0.32 (0.27)0.17 (0.18)−0.15 (0.04)0.0010.28 (0.22)−0.07 (0.04)0.061
 School days0.31 (0.26)---0.27 (0.20)−0.09 (0.04)0.020
 Holidays0.41 (0.54)0.17 (0.18)−0.24 (0.07)0.0010.28 (0.24)−0.09 (0.07)0.220
Sleep
Latency (min)33.16 (58.25)5.08 (84.24)−22.8 (12.02)0.05853.97 (57.78)23.03 (10.02)0.022
 School days37.32 (59.97)---65.50 (61.85)31.19 (9.86)0.002
 Holidays10.86 (70.18)5.08 (84.24)0.02 (15.86)0.99926.71 (68.40)10.68 (15.66)0.495
Actual sleep duration (h)6.94 (0.95)8.72 (1.64)1.79 (0.22)<0.0017.31 (1.20)−0.01 (0.19)0.976
 School days6.58 (0.92)---6.41 (0.99)−0.35 (0.16)0.030
 Holidays8.65 (1.65)8.72 (1.64)0.23 (0.31)0.4668.87 (1.41)0.39 (0.32)0.235
Sleep time (HH:MM)23:57 (00:52)00:54 (01:03)51.12 (9.47)<0.00123:56 (00:50)8.57 (8.63)0.321
 School days23:52 (00:53)---23:53 (00:54)12.73 (8.75)0.146
 Holidays00:29 (01:05)00:54 (01:03)16.82 (12.59)0.18200:05 (01:05)−21.18 (13.41)0.114
Wake time (HH:MM)07:05 (00:42)10:45 (01:30)207.70 (11.73)<0.00107:29 (01:12)11.63 (10.53)0.269
 School days06:38 (00:27)---06:26 (00:32)−9.84 (4.71)0.037
 Holidays09:35 (01:33)10:45 (1:30)64.15 (18.75)0.00109:22 (01:35)−0.86 (19.64)0.965
Efficiency (%)95.56 (4.31)85.48 (11.69)−9.36 (1.08)<0.00194.58 (4.79)−1.16 (0.98)0.236
 School days96.03 (4.42)---95.73 (4.78)−0.34 (0.81)0.674
 Holidays91.95 (8.08)85.48 (11.69)−6.17 (1.67)<0.00192.27 (6.94)−1.2 (1.75)0.491
SFI28.28 (7.69)43.12 (13.89)14.5 (1.62)<0.00127.82 (7.96)0.03 (1.48)0.986
 School days27.53 (8.06)---25.74 (9.17)1.07 (0.27)<0.001
 Holidays33.46 (11.11)43.12 (13.89)9.38 (2.14)<0.00131.14 (9.00)−0.54 (2.30)0.813
Screen time
Level of parental guidance1.45 (0.53)1.26 (0.40)−0.16 (0.09)0.0671.68 (0.54)0.04 (0.08)0.572
Non-academic screen time per day (h)6.6 (4.19)7.64 (4.27)0.61 (0.77)0.4226.44 (4.36)0.69 (0.64)0.278
 School days5.8 (4.43)---5.62 (4.19)0.39 (0.68)0.559
 Holidays8.62 (5.99)7.64 (4.27)1.36 (1.04)0.1928.48 (5.6)1.46 (0.85)0.087
Academic screen time per day (h)1.49 (1.45)2.99 (2.59)1.29 (0.34)<0.0012.87 (2.13)1.36 (0.28)<0.001
 School days1.49 (1.49)---3.01 (2.28)1.53 (0.3)<0.001
 Holidays1.49 (1.73)2.99 (2.59)1.27 (0.38)0.0012.53 (2.28)1.08 (0.32)0.001
Overall screen time per day (h)8.09 (4.84)10.64 (5.25)1.91 (0.92)0.0399.31 (5.34)2.12 (0.75)0.005
 School days7.29 (5.11)---8.63 (5.3)1.97 (0.81)0.015
 Holidays10.11 (6.55)10.64 (5.25)2.65 (1.17)0.02311.01 (6.38)2.86 (0.95)0.003
Latency—Difference between monitor-recorded and self-reported sleep time (min); MVPA—Moderate to vigorous physical activity; SFI—Sleep fragmentation index.

3.4. Influence of the Reallocation of Time Use on Sleep Quality

Table 5 shows the results of the compositional data analysis of the ‘15-min time reallocation’ effects on sleep quality during the three periods.
Table
Table 5. Compositional data analysis of 15-min time reallocation effects on sleep quality during different periods.
Table 5. Compositional data analysis of 15-min time reallocation effects on sleep quality during different periods.
Predicted Difference (95% Confidence Interval)
Add 15 minRemove 15 minOverallPre-pandemicDuring school closureAfter school partially reopened
Screen timeMVPA0.865(0.563, 1.168)0.332(−0.177, 0.840)2.013(0.481, 3.546)0.546(−0.114, 1.206)
Screen timeSleep−0.025(−0.063, 0.012)−0.016(−0.178, 0.145)0.074(−0.063, 0.212)−0.018(−0.046, 0.009)
Screen timeOther activities0.013(−0.02, 0.045)0.001(−0.152, 0.153)0.099(−0.024, 0.223)0.011(−0.016, 0.039)
MVPAScreen time−0.616(−0.831, −0.401)−0.229(−0.834, 0.376)−1.293(−2.212, −0.374)−0.343(−0.759, 0.072)
MVPASleep−0.638(−0.864, −0.413)−0.242(−0.608, 0.124)−1.171(−2.118, −0.224)−0.362(−0.798, 0.074)
MVPAOther activities−0.600(−0.816, −0.384)−0.225(−0.569, 0.119)−1.146(−2.092, −0.200)−0.332(−0.749, 0.085)
SleepScreen time0.022(−0.031, 0.075)0.012(−0.425, 0.450)−0.123(−0.316, 0.070)0.018(−0.009, 0.046)
SleepMVPA0.890(0.575, 1.205)0.348(−0.179, 0.874)1.939(0.372, 3.506)0.564(−0.116, 1.244)
SleepOther activities0.038(0.024, 0.051)0.017(−0.008, 0.042)0.024(−0.032, 0.081)0.029(−0.008, 0.067)
Other activitiesScreen time−0.015(−0.063, 0.032)−0.004(−0.449, 0.441)−0.148(−0.324, 0.029)−0.010(−0.034, 0.015)
Other activitiesMVPA0.853(0.548, 1.158)0.331(−0.174, 0.836)1.914(0.350, 3.477)0.536(−0.126, 1.197)
Other activitiesSleep−0.038(−0.051, −0.024)−0.017(−0.042, 0.008)−0.025(−0.078, 0.027)−0.028(−0.063, 0.006)
MVPA—Moderate to vigorous physical activity.
Reallocation of MVPA time significantly affected sleep quality during school closures. As MVPA time increased, the sleep fragmentation index decreased and vice versa. There was no significant influence on sleep quality from the reallocation of time for any factors, including sleep, screen time, and MVPA during the pre-pandemic and school partial reopening phases.

4. Discussion

To the best of our knowledge, this is one of the first studies using objective PA and sleep quality monitoring data to demonstrate changes in sleep patterns and PA levels among school-aged children during different phases of the COVID-19 pandemic. Previous studies conducted in other countries relied on self-reported questionnaires and compared their findings with pre-COVID data from other studies, which introduced potential bias [15,33,34,35]. Our study provides robust quantitative data using a repeated cross-sectional design and technology to record data to compare pre-pandemic, school closure, and school partial reopening periods. We objectively demonstrated that children had lower PA levels, longer sleep duration, and longer screen time during school closures (March 2020–April 2020) compared with the pre-pandemic time (September 2019–January 2020). Although some of these parameters improved after schools’ reopening (October 2020 and July 2021), the impacts of physical inactivity, prolonged screen time, and sleep disturbances appeared to persist. This study found that the COVID-19 pandemic is having potentially significant long-term impacts on the lifestyle and physical activity of school-aged children, which adds an important piece of evidence to the current literature.
In Hong Kong, only around 8% of school-aged children fulfill the recommended PA levels set by the World Health Organization in 2013 [36]. Our study found that physical inactivity deteriorated after the COVID-19 outbreak compared to pre-pandemic levels. Given the strong link between childhood physical inactivity and long-term major diseases such as metabolic syndrome, obesity, and cardiovascular problems, urgent attention is required. The promotion of physical activity, encouragement of such environments in schools, and education of childcare providers, parents, and teachers to check for potential lifestyle changes in children during the pandemic [37,38] should be prioritized. Promoting out-of-school PA by guiding students, helping them to set realistic goals, and encouraging them to self-monitor would have long-term benefits [39]. In a recent study conducted by Tso et al., children and their families had more psychosocial problems during the COVID-19 pandemic, with more significant difficulties reported in vulnerable families such as in children with chronic illnesses and those with special educational needs. In particular, the quality of life of these children was found to be negatively correlated with physical inactivity and prolonged screen time [40]. Findings from our study imply that the worsening physical inactivity during prolonged school closures persisted after the reopening of schools, which may pose further long-term health risks to children and future adults.
There were significant changes in sleep patterns during the different study periods. Although sleep duration was longer during school closures than the pre-pandemic time, this was still within the recommendations of the American Academy of Sleep Medicine [41]. However, the overall sleep quality was poorer as evidenced by the delayed bedtime and wake-up time, increased in-bed duration, longer sleep latency, and increased movement and SFI. During school closures, some primary school students reported sleeping after 2 a.m., which was not observed in the pre-pandemic group. These children likely have a disturbed circadian rhythm. The circadian rhythm is an endogenous rhythm that assists humans in keeping their biological clock on a 24-h cycle. [42]. In children and adolescents, circadian rhythm disorders and sleep problems are known to be associated with poorer cognitive performance; behavioral problems such as hyperactivity-impulsivity, inattention, daytime sleepiness, emotional and dysregulation; and physical problems such as obesity [43,44,45,46]. In the present study, replacing 15 min/day of screen time, sleep duration, or other activities with MVPA reduced SFI significantly during school closures. During the school closure period, all students attended online courses from home instead of going to school. Without proper parental supervision, the increase in screen time could cause later sleep and wake-up times, resulting in an overall longer sleep duration. Moreover, although most of the sleep patterns recovered after schools partially reopened, a slight delay in bed time was still found. A delayed sleep phase may result in mental health challenges such as lower learning capacity/academic performance, addiction behaviors, and elevated levels of anxiety and depression. Parental and community education and involvement to ensure good sleep habits in children is vital [47,48].
Excessive screen time was also reported in primary school students. The switch to online teaching meant longer use of electronic devices for academic purposes, but recreational screen time increased concurrently and remained elevated when schools partially reopened compared to the pre-pandemic time [49]. Excessive screen time in school-aged children has been associated with an increased risk of behavioral issues, obesity, cardio-metabolic problems, and myopia [50,51,52]. The American Academy of Pediatrics recommends that families and pediatricians should collaborate to develop a family media use plan to guide their children on media use [53].
The findings of this study need to be interpreted with the following caveats. Firstly, the sample size was relatively small due to the design of objective PA monitoring during the COVID-19 outbreak. Secondly, the study subjects were recruited on a voluntary basis, which suggests selection bias may be a concern. However, the availability of repeated measurements for the lifestyle behaviors, over nearly 18 months, allowed us to assess the associations between the behaviors alone and in combination with the three time periods (pre-pandemic, school closure, and school partial reopening). Our study was able to dynamically capture previously unreported differences in children’s PA levels and sleep patterns using an objective PA monitoring approach.

5. Conclusions

This study found that there were adverse changes in sleep patterns, screen time, and physical activity levels during the period of school closures, which persisted when schools partially reopened. As Hong Kong is now in the fifth wave of COVID-19, since early January 2022, the Hong Kong Government has again tightened social distancing measures, which may further increase the long-term risks for children. The improvement in sleep quality with a 15-min increase in MVPA is worth further attention. Further study is needed to longitudinally follow up these children and their families. Professionals should provide parents and children with appropriate advice on maintaining physical activity, good sleep discipline, and appropriate use of electronic devices during this challenging period, particularly during school closures.

Author Contributions

Conceptualization, P.I. and W.H.S.W.; methodology, H.-K.S., G.T.C. and K.-M.Y.; formal analysis, K.-M.Y. and W.H.S.W.; writing—original draft preparation, H.-K.S. and G.T.C.; writing—review and editing, K.T.S.T., R.S.W., L.H.T.L., W.W.Y.T., I.C.K.W., J.C.Y., M.Y.W.K., K.-K.L., J.K.W.K., W.H.S.W. and P.I.; supervision, P.I. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Collaborative Research Fund, University Grants Committee (Reference No.: C7149-20GF, Project Title: Impact of COVID-19 pandemic on Hong Kong children and support to families during crisis) and also supported by the Chow Tai Fook Enterprises.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 19-516).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors thank the school principals, teachers, parents, and students who participated in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cowling, B.J.; Ali, S.T.; Ng, T.W.Y.; Tsang, T.K.; Li, J.C.M.; Fong, M.W.; Liao, Q.; Kwan, M.Y.; Lee, S.L.; Chiu, S.S.; et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: An observational study. Lancet Public Health 2020, 5, e279–e288. [Google Scholar] [CrossRef]
  2. Ashikkali, L.; Carroll, W.; Johnson, C. The indirect impact of COVID-19 on child health. Paediatr. Child Health 2020, 30, 430–437. [Google Scholar] [CrossRef] [PubMed]
  3. Xiang, M.; Zhang, Z.; Kuwahara, K. Impact of COVID-19 pandemic on children and adolescents’ lifestyle behavior larger than expected. Prog. Cardiovasc. Dis. 2020, 63, 531–532. [Google Scholar] [CrossRef]
  4. Ruíz-Roso, M.B.; de Carvalho Padilha, P.; Matilla-Escalante, D.C.; Brun, P.; Ulloa, N.; Acevedo-Correa, D.; Peres, W.A.F.; Martorell, M.; Carrilho, T.R.B.; de Oliveira Cardoso, L.; et al. Changes of Physical Activity and Ultra-Processed Food Consumption in Adolescents from Different Countries during COVID-19 Pandemic: An Observational Study. Nutrients 2020, 12, 2289. [Google Scholar] [CrossRef]
  5. Wunsch, K.; Nigg, C.; Niessner, C.; Schmidt, S.C.; Oriwol, D.; Hanssen-Doose, A.; Burchartz, A.; Eichsteller, A.; Kolb, S.; Worth, A.; et al. The Impact of COVID-19 on the Interrelation of Physical Activity, Screen Time and Health-Related Quality of Life in Children and Adolescents in Germany: Results of the Motorik-Modul Study. Children 2021, 8, 98. [Google Scholar] [CrossRef]
  6. Di Renzo, L.; Gualtieri, P.; Pivari, F.; Soldati, L.; Attinà, A.; Cinelli, G.; Leggeri, C.; Caparello, G.; Barrea, L.; Scerbo, F.; et al. Eating habits and lifestyle changes during COVID-19 lockdown: An Italian survey. J. Transl. Med. 2020, 18, 229–02399. [Google Scholar] [CrossRef]
  7. Dragun, R.; Veček, N.N.; Marendić, M.; Pribisalić, A.; Đivić, G.; Cena, H.; Polašek, O.; Kolčić, I. Have Lifestyle Habits and Psychological Well-Being Changed among Adolescents and Medical Students Due to COVID-19 Lockdown in Croatia? Nutrients 2020, 13, 97. [Google Scholar] [CrossRef]
  8. Yomoda, K.; Kurita, S. Influence of social distancing during the COVID-19 pandemic on physical activity in children: A scoping review of the literature. J. Exerc. Sci. Fit. 2021, 19, 195–203. [Google Scholar] [CrossRef]
  9. Nagata, J.M.; Cortez, C.A.; Cattle, C.J.; Ganson, K.T.; Iyer, P.; Bibbins-Domingo, K.; Baker, F.C. Screen Time Use Among US Adolescents During the COVID-19 Pandemic: Findings From the Adolescent Brain Cognitive Development (ABCD) Study. JAMA Pediatr. 2021, 176, e214334. [Google Scholar] [CrossRef]
  10. Stiglic, N.; Viner, R.M. Effects of screentime on the health and well-being of children and adolescents: A systematic review of reviews. BMJ Open 2019, 9, e023191. [Google Scholar] [CrossRef]
  11. Kaditis, A.G.; Ohler, A.; Gileles-Hillel, A.; Choshen-Hillel, S.; Gozal, D.; Bruni, O.; Aydinoz, S.; Cortese, R.; Kheirandish-Gozal, L. Effects of the COVID-19 lockdown on sleep duration in children and adolescents: A survey across different continents. Pediatr. Pulmonol. 2021, 56, 2265–2273. [Google Scholar] [CrossRef] [PubMed]
  12. Sinha, M.; Pande, B.; Sinha, R. Impact of COVID-19 lockdown on sleep-wake schedule and associated lifestyle related behavior: A national survey. J. Public Health Res. 2020, 9, 1826. [Google Scholar] [CrossRef] [PubMed]
  13. Sharma, M.; Aggarwal, S.; Madaan, P.; Saini, L.; Bhutani, M. Impact of COVID-19 pandemic on sleep in children and adolescents: A systematic review and meta-analysis. Sleep Med. 2021, 84, 259–267. [Google Scholar] [CrossRef] [PubMed]
  14. Pavlovic, A.; DeFina, L.F.; Natale, B.L.; Thiele, S.E.; Walker, T.J.; Craig, D.W.; Vint, G.R.; Leonard, D.; Haskell, W.L.; Kohl, H.W. Keeping children healthy during and after COVID-19 pandemic: Meeting youth physical activity needs. BMC Public Health 2021, 21, 485–10545. [Google Scholar] [CrossRef]
  15. Guo, Y.-F.; Liao, M.-Q.; Cai, W.-L.; Yu, X.-X.; Li, S.-N.; Ke, X.-Y.; Tan, S.-X.; Luo, Z.-Y.; Cui, Y.-F.; Wang, Q.; et al. Physical activity, screen exposure and sleep among students during the pandemic of COVID-19. Sci. Rep. 2021, 11, 8529–88071. [Google Scholar] [CrossRef] [PubMed]
  16. Lokhandwala, S.; Holmes, J.F.; Mason, G.M.; Laurent, C.W.S.; Delvey, C.; Hanron, O.; Andre, C.; Rodheim, K.; Kaur, S.; Spencer, R.M.C. Sleep and Coping in Early Childhood During the COVID-19 Pandemic. Front. Pediatr. 2021, 9, 716608. [Google Scholar] [CrossRef]
  17. Steel, D. Repeated cross-sectional design. In Encyclopedia of Survery Research Methods; Layrakas, P.J., Ed.; SAGE Publications: Thousand Oaks, CA, USA, 2008; pp. 714–716. [Google Scholar]
  18. Beidas, R.S.; Williams, N.J.; Becker-Haimes, E.M.; Aarons, G.A.; Barg, F.K.; Evans, A.C.; Jackson, K.; Jones, D.; Hadley, T.; Hoagwood, K.; et al. A repeated cross-sectional study of clinicians’ use of psychotherapy techniques during 5 years of a system-wide effort to implement evidence-based practices in Philadelphia. Implement. Sci. 2019, 14, 67. [Google Scholar] [CrossRef]
  19. Lo, C.K.M.; Ho, F.K.; Wong, R.S.; Tung, K.T.S.; Tso, W.W.Y.; Ho, M.S.P.; Chow, C.B.; Chan, K.L.; Ip, P. Prevalence of Child Maltreatment and Its Association with Parenting Style: A Population Study in Hong Kong. Int. J. Environ. Res. Public Health 2019, 16, 1130. [Google Scholar] [CrossRef]
  20. Aadland, E.; Johannessen, K. Agreement of objectively measured physical activity and sedentary time in preschool children. Prev. Med. Rep. 2015, 2, 635–639. [Google Scholar] [CrossRef]
  21. Cole, R.J.; Kripke, D.F.; Gruen, W.; Mullaney, D.J.; Gillin, J.C. Automatic sleep/wake identification from wrist activity. Sleep 1992, 15, 461–469. [Google Scholar] [CrossRef]
  22. Evenson, K.R.; Catellier, D.J.; Gill, K.; Ondrak, K.S.; McMurray, R.G. Calibration of two objective measures of physical activity for children. J. Sports Sci. 2008, 26, 1557–1565. [Google Scholar] [CrossRef] [PubMed]
  23. Smith, C.; Galland, B.; Taylor, R.; Meredith-Jones, K. ActiGraph GT3X+ and Actical Wrist and Hip Worn Accelerometers for Sleep and Wake Indices in Young Children Using an Automated Algorithm: Validation With Polysomnography. Front. Psychiatry 2020, 10, 958. [Google Scholar] [CrossRef] [PubMed]
  24. Wong, R.S.; Tung, K.T.S.; Rao, N.; Leung, C.; Hui, A.N.; Tso, W.W.; Fu, K.W.; Jiang, F.; Zhao, J.; Ip, P. Parent Technology Use, Parent-Child Interaction, Child Screen Time, and Child Psychosocial Problems among Disadvantaged Families. J. Pediatr. 2020, 226, 258–265. [Google Scholar] [CrossRef] [PubMed]
  25. Tudor-Locke, C.; Barreira, T.V.; Schuna, J.M., Jr.; Mire, E.F.; Katzmarzyk, P.T. Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl. Physiol. Nutr. Metab. 2014, 39, 53–57. [Google Scholar] [CrossRef] [PubMed]
  26. Choi, L.; Liu, Z.; Matthews, C.E.; Buchowski, M.S. Validation of accelerometer wear and nonwear time classification algorithm. Med. Sci. Sports Exerc. 2011, 43, 357–364. [Google Scholar] [CrossRef]
  27. Rich, C.; Geraci, M.; Griffiths, L.; Sera, F.; Dezateux, C.; Cortina-Borja, M. Quality control methods in accelerometer data processing: Defining minimum wear time. PLoS ONE 2013, 8, e67206. [Google Scholar] [CrossRef]
  28. Quante, M.; Kaplan, E.R.; Cailler, M.; Rueschman, M.; Wang, R.; Weng, J.; Taveras, E.M.; Redline, S. Actigraphy-based sleep estimation in adolescents and adults: A comparison with polysomnography using two scoring algorithms. Nat. Sci. Sleep 2018, 10, 13–20. [Google Scholar] [CrossRef]
  29. Sadeh, A.; Sharkey, K.M.; Carskadon, M.A. Activity-based sleep-wake identification: An empirical test of methodological issues. Sleep 1994, 17, 201–207. [Google Scholar] [CrossRef]
  30. Knutson, K.L.; Van, C.E.; Zee, P.; Liu, K.; Lauderdale, D.S. Cross-sectional associations between measures of sleep and markers of glucose metabolism among subjects with and without diabetes: The Coronary Artery Risk Development in Young Adults (CARDIA) Sleep Study. Diabetes Care 2011, 34, 1171–1176. [Google Scholar] [CrossRef]
  31. Chandler, J.L.; Brazendale, K.; Beets, M.W.; Mealing, B.A. Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children. Pediatr. Obes. 2016, 11, 120–127. [Google Scholar] [CrossRef]
  32. Palarea-Albaladejo, J.; Martin-Fernandez, J.A. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 2015, 143, 85–96. [Google Scholar] [CrossRef]
  33. Dunton, G.F.; Do, B.; Wang, S.D. Early effects of the COVID-19 pandemic on physical activity and sedentary behavior in children living in the U.S. BMC Public Health 2020, 20, 1351–09429. [Google Scholar] [CrossRef]
  34. Moore, S.A.; Faulkner, G.; Rhodes, R.E.; Brussoni, M.; Chulak-Bozzer, T.; Ferguson, L.J.; Mitra, R.; O’Reilly, N.; Spence, J.C.; Vanderloo, L.M.; et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: A national survey. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 85. [Google Scholar] [CrossRef]
  35. Pombo, A.; Luz, C.; Rodrigues, L.P.; Ferreira, C.; Cordovil, R. Correlates of children’s physical activity during the COVID-19 confine.ement in Portugal. Public Health 2020, 189, 14–19. [Google Scholar] [CrossRef]
  36. Census and Statistics Department. Healthy Exercise for All Campaign—Physical Fitness Test for the Community; Hong Kong Monthly Digest of Statistics: Hong Kong, China, 2013. [Google Scholar]
  37. Ho, F.K.; Louie, L.H.; Chow, C.B.; Wong, W.H.; Ip, P. Physical activity improves mental health through resilience in Hong Kong Chinese adolescents. BMC Pediatr. 2015, 15, 48. [Google Scholar] [CrossRef]
  38. Ip, P.; Ho, F.K.; Louie, L.H.; Chung, T.W.-H.; Cheung, Y.-F.; Lee, S.-L.; Hui, S.S.-C.; Ho, W.K.-Y.; Ho, D.S.-Y.; Wong, W.H.-S.; et al. Childhood Obesity and Physical Activity-Friendly School Environments. J. Pediatr. 2017, 191, 110–116. [Google Scholar] [CrossRef]
  39. Gobbi, E.; Maltagliati, S.; Sarrazin, P.; Di Fronso, S.; Colangelo, A.; Cheval, B.; Escriva-Boulley, G.; Tessier, D.; Demirhan, G.; Erturan, G.; et al. Promoting Physical Activity during School Closures Imposed by the First Wave of the COVID-19 Pandemic: Physical Education Teachers’ Behaviors in France, Italy and Turkey. Int. J. Environ. Res. Public Health 2020, 17, 9431. [Google Scholar] [CrossRef]
  40. Tso, W.W.Y.; Wong, R.S.; Tung, K.T.S.; Rao, N.; Fu, K.W.; Yam, J.C.S.; Chua, G.T.; Chen, E.Y.H.; Lee, T.M.C.; Chan, S.K.W.; et al. Vulnerability and resilience in children during the COVID-19 pandemic. Eur. Child. Adolesc. Psychiatry 2020, 31, 161–176. [Google Scholar] [CrossRef]
  41. Paruthi, S.; Brooks, L.J.; D’Ambrosio, C.; Hall, W.; Kotagal, S.; Lloyd, R.M.; Malow, B.A.; Maski, K.; Nichols, C.; Quan, S.F.; et al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. J. Clin. Sleep Med. 2016, 12, 785–786. [Google Scholar] [CrossRef]
  42. Leng, Y.; Musiek, E.S.; Hu, K.; Cappuccio, F.P.; Yaffe, K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol. 2019, 18, 307–318. [Google Scholar] [CrossRef]
  43. Fatima, Y.; Doi, S.A.; Mamun, A.A. Sleep quality and obesity in young subjects: A meta-analysis. Obes. Rev. 2016, 17, 1154–1166. [Google Scholar] [CrossRef] [PubMed]
  44. Hou, Y.; Liu, L.; Chen, X.; Li, Q.; Li, J. Association between circadian disruption and diseases: A narrative review. Life Sci. 2020, 262, 118512. [Google Scholar] [CrossRef]
  45. Rivkees, S.A. Mechanisms and clinical significance of circadian rhythms in children. Curr. Opin. Pediatr. 2001, 13, 352–357. [Google Scholar] [CrossRef]
  46. Touchette, E.; Petit, D.; SÃguin, J.R.; Boivin, M.; Tremblay, R.E.; Montplaisir, J.Y. Associations between sleep duration patterns and behavioral/cognitive functioning at school entry. Sleep 2007, 30, 1213–1219. [Google Scholar] [CrossRef]
  47. Saxvig, I.W.; Pallesen, S.; Wilhelmsen-Langeland, A.; Molde, H.; Bjorvatn, B. Prevalence and correlates of delayed sleep phase in high school students. Sleep Med. 2012, 13, 193–199. [Google Scholar] [CrossRef]
  48. Lopez-Bueno, R.; Lopez-Sanchez, G.F.; Casajus, J.A.; Calatayud, J.; Tully, M.A.; Smith, L. Potential health-related behaviors for pre-school and school-aged children during COVID-19 lockdown: A narrative review. Prev. Med. 2021, 143, 106349. [Google Scholar] [CrossRef]
  49. Wong, C.W.; Tsai, A.; Jonas, J.B.; Ohno-Matsui, K.; Chen, J.; Ang, M.; Ting, D.S.W. Digital Screen Time During the COVID-19 Pandemic: Risk for a Further Myopia Boom? Am. J. Ophthalmol 2021, 223, 333–337. [Google Scholar] [CrossRef]
  50. Enthoven, C.A.; Tideman, J.W.L.; Polling, J.R.; Yang-Huang, J.; Raat, H.; Klaver, C.C.W. The impact of computer use on myopia development in childhood: The Generation R study. Prev. Med. 2020, 132, 105988. [Google Scholar] [CrossRef]
  51. Poulain, T.; Ludwig, J.; Hiemisch, A.; Hilbert, A.; Kiess, W. Media Use of Mothers, Media Use of Children, and Parent-Child Interaction Are Related to Behavioral Difficulties and Strengths of Children. Int. J. Environ. Res. Public Health 2019, 16, 4651. [Google Scholar] [CrossRef]
  52. Vanderloo, L.M.; Keown-Stoneman, C.D.; Sivanesan, H.; Parkin, P.C.; Maguire, J.L.; Anderson, L.N.; Tremblay, M.S.; Birken, C.S. Association of screen time and cardiometabolic risk in school-aged children. Prev. Med. Rep. 2020, 20, 101183. [Google Scholar] [CrossRef]
  53. Council on Communcations and Media. Media Use in School-Aged Children and Adolescents. Pediatrics 2016, 138, e20162592. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

So, H.-K.; Chua, G.T.; Yip, K.-M.; Tung, K.T.S.; Wong, R.S.; Louie, L.H.T.; Tso, W.W.Y.; Wong, I.C.K.; Yam, J.C.; Kwan, M.Y.W.; et al. Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study. Int. J. Environ. Res. Public Health 2022, 19, 10539. https://doi.org/10.3390/ijerph191710539

AMA Style

So H-K, Chua GT, Yip K-M, Tung KTS, Wong RS, Louie LHT, Tso WWY, Wong ICK, Yam JC, Kwan MYW, et al. Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study. International Journal of Environmental Research and Public Health. 2022; 19(17):10539. https://doi.org/10.3390/ijerph191710539

Chicago/Turabian Style

So, Hung-Kwan, Gilbert T. Chua, Ka-Man Yip, Keith T. S. Tung, Rosa S. Wong, Lobo H. T. Louie, Winnie W. Y. Tso, Ian C. K. Wong, Jason C. Yam, Mike Y. W. Kwan, and et al. 2022. "Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study" International Journal of Environmental Research and Public Health 19, no. 17: 10539. https://doi.org/10.3390/ijerph191710539

APA Style

So, H.-K., Chua, G. T., Yip, K.-M., Tung, K. T. S., Wong, R. S., Louie, L. H. T., Tso, W. W. Y., Wong, I. C. K., Yam, J. C., Kwan, M. Y. W., Lau, K.-K., Kong, J. K. W., Wong, W. H. S., & Ip, P. (2022). Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study. International Journal of Environmental Research and Public Health, 19(17), 10539. https://doi.org/10.3390/ijerph191710539

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop