Zainab, Prome, Leslie, Gil, Sly INTRODUCTION
The recent COVID-19 pandemic affected a worldwide level in social, economic, physical, and mental health. Most governments around the world had to issue social distancing measures, and stay-at-home and lockdown restrictions to preserve the health care of their citizens and to stop spreading the virus. Restaurants, offices, public spaces, and schools were affected due to coronavirus (COVID-19) since March 2020. Thus, the daily routine had to change remotely. Billions of children (5-12 years) and youths (13-17 years) around the world had to implement virtual learning. The limitation of the circumstances was conducting children and youths to a sedentary lifestyle and an increase of the screentime (Musa et al. 2022) that can affect their physical and mental health.
Online teaching-learning and the lack of social interaction as a method to reduce infections of the virus had some counterproductive effects since it increases the time on different types of screens (laptops, tablets, mobile devices, TV). An excess in the use of screentime (>2 hours per day) has long-term consequences such as behavioral problems, and sleep disturbances (Panchali, Jagmeet. 2022).
There are different types of research that study the impacts of COVID-19 due to the increase of screentime in their different areas, and most of them have the same result. Most risky health behaviors include abuse of substances, eating and drinking habits, and lack of physical activity. Those behaviors can also drive to mental disorders such as depression, anxiety, stress, and insomnia. On the other hand, physical impacts (MetS, a set of cardiometabolic risk factors) include obesity, hypertension, dyslipidemia, and glucose intolerance. Mets in children and teenagers has become a huge health concern to the public. (Musa et al. 2022). For this reason, it
would be important to ponder new ways to improve children’s and youths’ routines in order to increase their physical activity and avoid excess screentime.
Therefore, the purpose of this research is to identify what effect does/did extended sedentary behavior during COVID-19 has on the mental and physical health of children and youths.
METHODOLOGY
In a study by Sara Musa et. al (2022) concerning COVID-19 and screen-based sedentary behavior, Electronic bibliographic databases such as PubMed Central/Medline, Cochrane Library, PsycINFO, and Google Scholar were used to conduct a systematic search strategy. The search was conducted between August 2021 and September 2021. Sara et. al (2022) was only able to retrieve unrestricted access to published articles with the following keywords: “screentime” OR “sedentary behavior” OR “television” OR “internet” OR “video games” AND “MetS” OR “cardiometabolic” OR “obesity” AND “adolescents” OR “children” OR “youth” OR “school-aged”. One reviewer screened and assessed the relevancy and suitability of the titles for inclusion. Full-text articles with reference lists were retrieved and examined for appropriateness. All reviewed articles were backtracked by another reviewer for double-checking. Discrepancies or disagreements between both reviewers were resolved by either discussion or a third party. To remove all duplicates, Refworks software was used. Any that were not removed automatically were removed manually.
Sara et. al (2022) included studies that only fulfilled the following eligibility criteria: Observational studies (longitudinal, cross-sectional, case-control, cohort), a population of interest concerning apparent healthy children and adolescents(12-18) years, a measure of ST(screen time) as
an exposure; included studies that reported type of ST, Measure of MetS(metabolic equivalent of tasks) as an outcome and measure of the relationship between MetS and ST as odds ratio (OR’s) or equivalent with their 95% confidence interval (CI).
As an exclusion criterion, Sara et. al (2022) excluded reviews where ST was not defined adequately or where time spent on various screens was not contrasted with other forms of a sedentary lifestyle. Studies that examine sedentary behavior but report findings for ST separately from other forms of sedentary behaviors were included. Studies were excluded if MetS diagnosis was not defined properly, not observational as a study design, no reporting of OR or equivalent, studies including adolescents with pathological conditions, populations younger than 12 years or older than years, and studies examining the relationship of ST with results other than MetS such as obesity, physical inactivity, or cardiovascular risk. Data extraction and full review of eligible studies were cross-checked by two independent authors for accuracy. A standardized data extraction table was created including key characteristics of the identified studies as the following: detailed study characteristics (author, publication year, country, study design, sample size, age, gender), screen type, exposure, and outcome indicator measures. Results were extracted as risk estimates: Odds Ratio or prevalence ratio with corresponding confidence intervals or z-score of MetS. A P-value of <0.05 was considered a cut-off for statistical significance.
To evaluate risk bias, Sara et. al (2022) used the National Institute of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-sectional studies. Only 11 items on the checklist for longitudinal research could be applied to cross-sectional studies out of the 14 items that the checklist is comprised of. Each item of methodological quality was classified as yes, no, or not reported. Based on the number of yes as the total score, studies were classified
according to quality rating: Poor < 50%, fair 50-70%, and good >75%. A consensus among authors resolved possible disagreements regarding the final score. Studies met from 73% to 91% of the quality criteria, with 9 studies (9/10, 90%) meeting good scoring indicating low-risk bias. The main aim, population, and definition of exposure/outcome were clearly stated by all studies. However, 2 studies (2/10, 90%), did not identify key potential confounders in the analysis. Due to the cross-sectional nature of these studies, only Eleven items were applicable to nine studies and one prospective cohort study where all 14 items were applicable.
Reviews results and conclusions were summarized to begin synthesis. A systematic search of the database identified 3521 abstracts; of these, 2137 were excluded during initial screening for unrelated topics, meeting the exclusion criteria, and duplicate studies from different databases. 62 full-text articles were assessed to examine their eligibility for inclusion in the current review, and finally, after reviewing the full texts, ten studies were included in the data extraction.
To study the impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms, a cross-sectional study was conducted among 10-15 years old adolescents attending grades 6 to 10 of six private schools and four government-aided schools in the city of Mumbai, India by Panchali Moitra and Jagmeet Madan (2022). Through a purposive sampling method, the study sites were selected. Due to the ongoing pandemic-induced closure of educational institutes in India since late March 2020, an online survey was conducted to collect data as surveying in person was not possible. Panchali and Madan (2022) sent to the parents of the participants information leaflets pertaining to the details about the study and a link to provide parental consent. The parents were informed to provide consent within a week of receiving the information sheets. Adolescents who provided parental consent (n=1512) were invited to join individual virtual meetings scheduled separately for each study site. 1298 adolescents completed
the online survey in the presence of investigators, research staff, and school representatives. Data were collected from January 2021 to March 2021 after obtaining ethical approval from an independent ethics committee, Intersystem biomedical committee, Mumbai. Based on a recent study reported the prevalence of excessive ST (using screens >2hours/day) in urban adolescents in India as 68% and after using a 95% confidence level, a 5% margin of error, a non-response rate of 25%, and a proportional representation of adolescents from private and government schools, the final sample size was estimated as 805. Panchali and Madan (2022) administered an online survey detailing questions related to socio-demographic characteristics, eating habits, snacking behaviors, physical activity levels, screen time and screen addiction, sleep patterns, and depression symptoms through google forms on a virtual meeting platform. The survey requested adolescents to provide demographic information, such as gender, date of birth, class of study, type of living arrangement, father’s occupation, mother’s working status, and type of a number of screens (television/mobile phones/computers/laptops/tablets) owned by them and their families.
Panchali and Madan (2022) defined the term ‘screen time as the time spent working/studying/playing using any screen device, and ‘screen usage’ as the different screen devices, such as laptops, mobile phones, television, and more, that were used by adolescents. The frequency, type, and duration of screen usage were reported on a brief five-item questionnaire, that was developed by the researchers after an extensive review of similar instruments used in previous studies amongst adolescents. The frequency of using different screens was reported from ‘never (0 days)’, to ‘every day (7days)’, and the time spent using these screens on a typical weekday and a typical weekend was reported as minutes per day. As an inquiry to estimate the daily time spent on screen-related activities, the participants were asked “In the last 7 days how
much time did you spend in the following screen activities?” and to evaluate the participant’s addiction to screen usage, a five-point Likert scale (strongly disagree to strongly agree) was used. A request for two statements was added in addition to gauge the increase/decrease in their screen usage and ST during the lockdown.
Panchali and Madan (2022) evaluated eating habits amongst participants by asking them to report the frequency of consuming breakfast, having lunch or dinner with family, watching television while having meals, eating out with family and/or friends, and ordering takeaways in the past week. Response options were comprised of the following: never, 1-2 days, 3-4 days, 5-6 days, and every day. They were scored 0-4. A brief 24 item food questionnaire, which was validated in a previous study by Panchali and Madan (2022) for the same population estimated the consumption of fruits, freshly prepared fruit juices, packaged 100% fruit juices, vegetables, unhealthy snacks (foods high in fat, salt, and sugar), and carbonated beverages. Participants were asked, “In the last seven days how many days did you consume the following foods/beverages?” All responses were evaluated on a five-point scale, from never to 2 or more than twice a day, scored 0 to 4 for fruits and vegetables, and reverse coded as 4-0 for unhealthy snacks and carbonated beverages to ensure that higher scores indicated healthy habits. Participants listed if their consumption of each of the food listed had increased, decreased, or remained the same during the pandemic when compared to before the pandemic.
The Physical Activity Questionnaire for Children and/or Adolescents (PAQ-C/-A) is a validated self-reported instrument that Panchali and Madan (2022) used to assess the physical levels of the participants. The PAQ-C/A is typically administered in children ages 8 to 14 and is comprised of 9 items providing a 7-day recall of the type of frequency of activities performed in a spare time, during physical education (PE) classes and recess breaks, right after school, in the
evenings, and on weekends. The PAQ-A for adolescents > 14 years is a modified version of the PAQ-C that includes the same items except for the question about activities performed during recess. Each item is scored from 1 to 5 in both questionnaires to derive item-specific composite activity scores. The mean of this composite score is used to determine PAQ summary score that ranges from 1-to 5 with higher scores (> or = 2) indicating moderate-vigorous levels of PA and scores <2 as light PA. Participants were asked to report whether perceived changes concerning the changes in the frequency and duration of screen usage, the frequency of intake of specific food items, and engagement in different physical activities during COVID-19 as compared to before. Responses to these questions generated quantitative data that helped estimate the impact of the pandemic on the selected measures.
To gauge the effect the pandemic had on sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep dysfunction, and daytime dysfunction, Panchali and Madan (2022) used the Pittsburgh Sleep Quality Index (PSQI). The PSQI is comprised of the Likert-type and open-ended questions that are scored from 0 to 3 with a maximum score of 21. A total score >5 is considered indicative of poor sleep quality. The frequency of experiencing depression symptoms was assessed using Patient Health Questionnaire-2 or PHQ-2. PHQ or PHQ-2 is a screening tool widely used as a screening tool for major depressive disorders amongst adolescents. The PHQ-2 includes the two items that inquire about the frequency of ‘having little pleasures in doing things’ and ‘feeling down, depressed, or hopeless in the past two weeks. The frequency options for each item include ‘not at all to ‘nearly every day (scored 1 to 3). Participants reporting an overall score of > or = 3 are at risk for depression.
In Panchali and Madan’s (2022) study, all analyses were performed using SPSS version 24. Descriptive statistics were calculated as mean and standard deviations of (n%). Comparison of
categorical and continuous variables were performed using the chi-squared tests and one-way ANOVA, stratified by sex and age categories (10-12 years and 13-15 years). Multiple linear regression analyses were performed to assess the association of ST items, such as frequency and duration of screen usage, screen-related activities, and screen addiction scores with the dependent variables- eating habit (by aggregating eating habits and snacking behavior items scores, totally healthy eating habit was calculated,) physical activity levels (mean summary PAQ-C/A scores), sleep patterns (mean global PSQI scores) and depression symptoms (mean PHQ 2 scores) while controlling for participants age, sex, type of school attended (used as a proxy for socioeconomic status) as covariates. To determine the unadjusted effect of factors associated with each of the dependent variables, univariable regression analyses were performed. After, independent variables with a significance < 0.1 were entered into the mixed-effects multivariable regression models to determine the ST variables that were associated significantly with eating habits, PA, sleep, and depression symptoms. Panchali and Madan (2022) used analysis of residuals to confirm assumptions of linearity. The lowest values of Akaike’s and Schwarz’s Bayesian information criteria measures were used to test the goodness of fit of the final models as indicated in previous studies. To test multicollinearity, Pearson correlation coefficient (r) values > 0.5 or the variance inflation factor (VIF) value > 10 were used as the diagnostic tests. The VIF values ranged from 2.12 to 8.15 (mean 5.44) for most variables, except for an ST-related activity item (reading/listening to music) and an ST addiction statement (I use screens for a longer duration than is good for me), so these variables were excluded in the final model. Results were reported as standardized regression coefficients (β) and standard error (SE). All tests were two-tailed and considered statistically significant at a p-value ≤ 0.05.
DATA AND RESULTS
In a study conducted by Sarah Musa et. al (2022) concerning screen-based sedentary behavior, synthesis began by summarizing review results and conclusions. The reviews were categorized by the type of exposure which was screen time, and the desired outcome which was the Mets and related risk factors. Conclusions were examined of the included research to see which one was more credible. Each included publication was subjected to descriptive analysis. ST exposure was specified in hours per day or week, as well as the observed prevalence of MetS in percentages. For the connection between ST and MetS, adjusted estimates of OR or MetS z-score were derived, along with a 95% confidence interval. The OR of the dose-response gradient effects was also calculated.
The results of the study conducted by Sarah Musa et.al (2022) were shown in figure 1. It includes a flow chart of research identification and selection. A systematic database search yielded 3521 abstracts, of which 2137 were eliminated after initial screening due to unrelated topics, exclusion criteria, and duplicate research from other databases. In total, 62 full-text papers were evaluated to determine their eligibility for inclusion in the current analysis, and ten studies were finally included in the data extraction after full-text evaluation.
An overview of studies by Sarah Musa et.al (2022) shows the overall characteristics of the studies that were considered in table 2. Except for one study that used a prospective cohort design, most of the studies (90%) used a cross-sectional study design. This study included information from 41, 687 people in total. The sample size ranged from 474 to 33,900 people with an average age of 12 to 18 years. Six of the studies [31,32,36,40-42] used a school-based setting, while the other four were part of nationwide surveys. The major goal of the included research
was to find a significant link between ST (of any kind) and MetS in adolescents, with all studies considering single or multiple exposures such as PA, eating habits, or sleep length.
According to different databases, screen time during Covid 19 on eating habits, physical activities, sleep, and depression have affected children across the world. Here is a notable example of a country like India and the effects it had on Indian adolescents. The study by Panchali Moita and Jagmeet Madan (2021) discusses and reports creating an online survey to figure out the pattern and activities adolescents have performed from January through March, so they set a specific period for their outcomes to be more effective. Studies of 1512 adolescents that were used have shown that overall, 33.4% have spent 6 hours doing homework, and 65.4% have at least used it for more than 2 hours on other activities which affected their daily life. It has been discussed how screen time takes away valuable time from adolescents which leads to sleep deprivation, eating habits, screen addiction, and other issues.
CONCLUSION
Throughout the Covid-19 pandemic, the use of screen time influenced the sedentary behavior of individuals, particularly those in the adolescent age range. Not only was this effect on because triggered through mental aspects, but certain physical aspects also played a key role in the development of these specific adolescents, as there has been a decline in physical activity. Due to the safety of individuals, while the virus was spreading rapidly, most were subject to stay inside to protect themselves from sickness, and this caused screen usage to soar because of the closing of schools and isolation from social interaction.
Most health agencies try to make it known to parents that it is up to them to promote strategies to prevent their children from uptake screen time and to focus on any unusual effects of
sedentary behavior due to screen time. Parents infer with their child’s use of screen exposure is key to preventing an increase in unusual behavior, as it should be substituted with physical activity. Certain effects that parents should be on the lookout for are signs of high cholesterol and obesity.
Not only does an increase in on-screen time have mental and physical health on a child, but it also disrupts eating habits as the more time you spend on a device the less prone to hunger one will be. Specific lifestyle behaviors like sleep and eating can cause an increase in depression and anxiety. Although the knowledge researchers have on the impact of screen time is limited, with more studies, especially through a pandemic, there has been open clarity on the lasting effects.
In conclusion, when going through the studies concerning the studies based on screen time affecting the sedentary behavior of adolescents, specifically between the ages of 10 and 18, researchers have come to find that because of a worldwide pandemic has caused a soar in the use of devices. With this soar in the use of devices come the decline in physical activity and the decrease in mental health, creating social anxiety for those who are not ready to return to a life outside their house. Depression is also an effect as well as health concerns that may be detrimental to the growth of an adolescent.
References
Berki, T., & Piko, B. F. (2021, December). A SEDENTARY LIFESTYLE MAY CONTRIBUTE TO THE RISK OF DEPRESSION DURING THE COVID-19 PANDEMIC. European Journal of Mental Health, 16(2), 99+.https://link.gale.com/apps/doc/A688815000/AONE?u=bron88970&sid=bookmark-AONE&xid= 5361b91c
Article Source: Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents
Moitra P, Madan J (2022) Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents. PLOS ONE 17(3): e0264951. https://doi.org/10.1371/journal.pone.0264951
Musa, S., Elyamani, R., & Dergaa, I. (2022). COVID-19 and screen-based sedentary behavior: Systematic review of digital screen time and metabolic syndrome in adolescents. PLoS ONE, 17(3), e0265560. https://link.gale.com/apps/doc/A697667105/AONE?u=bron88970&sid=bookmark-AONE&xid= aa88334a
Reflection:
I’m incredibly fortunate to have this class since, despite any difficulties that I may have experienced with the project, I learned more than I expected. We have worked hard and spent a great amount of time to fulfill the expectation of this project. Although not everything was new to me because I have heard of the terms like introduction, methodology, data/results, and conclusion and I have used them in my previous science project in high school but using them in this writing of science class was quite different yet fascinating. Since this was a group assignment, we had to manage each other’s time and put effort into the writing. Being patient and making small changes were also essential for our writing to be more effective and concise. While writing about some of the difficulties that we have faced as a group, finding the correct articles and following the structure was also troublesome but based on the information that was provided to us during the class was extremely beneficial. It did help us with our writing plus it personally helped me to understand how to create an amazing research paper that will benefit me to write adequately in the future. While working on this project I have learned some rules that will be beneficial for example I was able to finish it on time and was able to work well with my group since I achieved these two important factors which also could be described as strengths. Apart from that, I also learned about the audience because it is essential to know who your audience is and your purpose, and how your piece of writing contributes to the world. I was able to improve my research skills while writing this piece. Also, I’ve figured out how to use evidence in my writing.
