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Screen time among school-aged children of aged 6–14: a systematic review

Global Health Research and Policy volume 8, Article number: 12 (2023) Cite this article

Abstract

Background

Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers. The purpose of this study was to conduct systematic review to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

Methods

Screen time related studies were searched on PubMed, EMBASE, Clinical Trials, Controlled Trials, The WHO International Clinical Trials Registry Platform, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal databases from January 1, 2016 to October 31, 2021. Two researchers independently screened the literature and extracted the data, and adopted a qualitative analysis method to evaluate the research status of the length and usage of screen time of school-aged students.

Results

Fifty-three articles were included. Sixteen articles studied screen time length in the form of continuous variables. Thirty-seven articles studied screen time in the form of grouped variables. The average screen time of schoolchildren aged 6 to 14 was 2.77 h per day, and 46.4% of them had an average screen time ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020.

Conclusions

Excessive screen time has become a common behavior among children and adolescents around the world. Intervention measures to control children's screen use should be explored in combination with different uses to reduce the proportion of non-essential uses.

Background

Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers [1]. With the development of science and technology integrated into social life, smart devices such as mobile phones, computers and tablets are more and more widely used in work, study and daily life. Children are exposed to electronic products at a younger age and their screen time is increasing. Too much screen time can have negative effects on children's physical and mental health. First, the negative effect of screen time on eyesight has been confirmed in many countries’ studies [2, 3]. For example, the study by Hu Jia et al. showed that screen time ≥ 3 h per day (OR = 2.026, 95%CI:1.235 ~ 3.325) was a myopia risk factor for primary and middle school students [4]. Second, excessive screen time will also bring obesity, depression, sleep disorders and other health problems to children and adolescents [4,5,6].

The COVID-19 pandemic is still spreading across the globe, affecting the lives of billions of residents around the world. Various public institutions, including schools, have adopted a range of lockdown measures. More primary and middle schools have conducted online teaching, and the time for school-aged children to use electronic products for online learning has further increased. Diane Seguin et al. found that during the pandemic, the average daily screen time of Canadian children increased from over 2 h (2.6 h on average) to nearly 6 h (5.9 h on average)(t(73) = 9.04, p = 0.001). Screen time increased by a total of more than 3 h, and children's screen time increased further during the pandemic compared to pre-pandemic [7].

Due to the physical development stage of school-aged children, the effect of prolonged screen time on their physical and mental health is more obvious and irreversible than that of adults. The Physical Activity Guidelines for Chinese Children and Adolescents [8] released in 2017 states that, the screen time of Chinese children and adolescents should be limited to 2 h per day. Referring to the guidelines of the American Academy of Pediatrics [9], children under the age of 2 should not use electronic media, while the time of using it for children over 2 years old should be limited to 2 h per day. However, empirical studies on the actual length and use of current screen time of school-aged children are relatively scattered and insufficient. This study used the qualitative systematic review method to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

Methods

Inclusion criteria

The types of literature include cross-sectional studies, cohort studies and case–control studies published in the form of peer-reviewed journal articles. The research subjects of the literature should include primary and secondary school students aged 6 to 14, including male and female. The literature published includes raw data, screen time values, age distribution, time distribution, and the screen use.

Exclusion criteria

Unpublished, unoriginal and non-peer reviewed articles, case reports, letters or comments; the research subjects do not meet the age requirements (under 6 years old, over 14 years old); the literature does not describe screen use time in detail, lacks quantitative data and correlation verification, and is only empirical conclusion.

The strategy of literature search

Search the literature in the public databases on PubMed, Clinical Trials, Controlled Trials, the WHO International Clinical Trials Registry Platform, EMBASE, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal. According to the phrases included the age group, and the screen use, "school-age child"/"primary school"/"junior high school student"/"primary and secondary school student"; "screen time"/" video time "/" electronic equipment "/" electronic products "/" multimedia equipment "/" digital equipment "are searched in the database. At the same time, search the references of the literature for other literature. The search time limit is from January 1, 2016 to October 31, 2021. The types of literature searched include cross-sectional studies, cohort studies and case–control studies. The search was limited to human studies reported either in English or in Chinese. All search phrases were modified according to MeSH terms.

Literature screening and data extraction

According to the search strategy and inclusion and exclusion criteria, two researchers independently conduct literature screening. After the screening, the two researchers discuss the screening process and the inconsistent parts of the results to form a unified result. If no agreement were to reach, a third party should be consulted. The contents of the research extraction include: author, publishing time, research region, research type, sample characteristics, screen time length, use and influencing factors, research content and main results and conclusions.

Risk evaluation and systematic evaluation of literature bias

The Cochrane risk assessment tool [10] is used to evaluate the literature quality of the included cross-sectional studies from the following aspects: random sequence generation, allocation hiding, blinding method, result data integrity, selective reporting and other biases. The bias risk has three possibilities: low risk, high risk and unknown bias risk. For observational studies, Newcastle–Ottawa Scale (NOS) [11] is used for quality assessment, which is scored from three parts: the selection of study population, comparability, exposure evaluation or result evaluation, and uses the semi-quantitative principle of star level system to evaluate literature quality. Studies with a score of 6 stars or more are defined as high quality and are included in this study. The quality assessment is conducted independently by the above-mentioned three researchers. In case of any dispute, a consensus shall be reached through discussion. In this study, Excel 2016 software was used to count the published literature, and qualitative analysis was performed on the included studies.

Results

Basic information and bias risk evaluation of included research

The preliminary search obtained 1275 relevant literatures. After removing the duplicates and reading the literature titles and abstracts, through rounds of screening, two hundred and twenty-six literatures were excluded due to the lack of screen use data. Seventy-nine literatures were excluded due to inconsistent characteristics such as age and gender of the subjects. Thirty-six literatures were excluded due to inconsistent research types. Eight literatures were excluded due to incomplete content of the full text. Thirteen literatures were excluded because the research data source time was more than five years. Finally, fifty-three literatures [4,5,6,7, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60] were included. Their basic information was shown in Table 1. The literature screening process and results are shown in Fig. 1. Considering the representativeness of the sample population, we made unified screening regulations on the age of the study population, the difficulty in obtaining electronic devices, the family's economic ability, and the parents' education level of the study population. There were 19 Chinese literatures and 34 English literatures. In terms of research time, there were two literatures in 2016, eight literatures in 2017, ten literatures in 2018, seven literatures in 2019, thirteen literatures in 2020 and thirteen literatures in 2021. Nineteen literatures were from China (including Taiwan Province), 6 literatures from other Asian countries, 17 literatures from European countries, 9 literatures from American countries, 1 literature from African countries and 1 literature from Oceania countries. The screen time data in the literature were collected by questionnaire and database. There were 16 literatures with continuous screen time and 37 literatures with classified screen time. The evaluation results of the bias risk of different included studies are shown in Fig. 2.

Table 1 Basic features of the included literatures
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Fig. 1
figure 1

Flow chart of literature screening

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Fig. 2
figure 2

Bias risk evaluation results of different included studies (red indicators high risk, green indicators low risk)

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Average daily length of screen time among schoolchildren aged 6–14 (continuous variable)

In 55 literatures, sixteen of them studied screen time length in the form of continuous variables. Sixteen literatures investigated the average daily length and standard deviation of the group by screen time and other health behavior factors. A total of 105,209 primary and middle school students aged 6 to 14 years were included in the study. Taking the international recommended length of screen time—2 h per day as the control parameter, the average length and standard deviation of the screen time of each literature were entered. Meta-analysis carried out by RevMan software showed that the average screen time of the included literature was + 0.77 h higher than the control parameter and the average screen time was 2.77 h per day (95% CI: 0.32 ~ 1.22).The analysis results are shown in Fig. 3.

Fig. 3
figure 3

Forest plot for screen time of 6–14 year old school children (continuous variable)

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Average daily length of screen time for Schoolchildren aged 6–14 (Classification variable)

Among the 55 literatures, thirty-seven expressed screen time in the form of grouped variables. Screen time < 2 h per day and ≥ 2 h per day were defined as screen time in 35 of the 37 classification variable literatures. Two literatures that only provided data on screen time use were not included in the bar chart. Among the included literatures published in 2021, there were four papers whose actual data collection took place in 2021, while the rest of the literatures published in 2021 reported data was collected in 2020 and before. A total of 472,042 primary and middle school students aged 6 to 14 years were included in the study. With the included literatures presented in chronological order, the bar chart showed the proportion of groups with average screen time ≥ 2 h per day in the whole study population. The results showed that 46.4% of primary and middle school students aged 6 to 14 years had screen time within the range of ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children, who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The statistical results are shown in Fig. 4.

Fig. 4
figure 4

Screen time of 6–14 year old school children (classification variable)

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Main uses of screen time for school-aged children

In the included literatures, twenty-five analyzed the types and uses of screen time among schoolchildren aged 6 to 14. The full text of the literature were read to get the classification of the screen devices, including televisions, mobile phones, tablets and computers. The classification of screen use were put into three categories, namely, learning, entertainment (including watching video and video games) and social interaction. The number of literatures and samples for each kind of use were counted. A total of 330,119 schoolchildren aged 6 to 14 were included in this indicator. Calculated according to the statistical sequence of the sample size of the literature study, the results showed that the main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020, as shown in Table 2.

Table 2 Main uses of school-age children’s screen time
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Discussion

From smartphones and social media to TV and tablet-based online courses, today’s school-aged children are constantly inundated by technology. The primary purpose of this review was to summarize the current situation of length and use of screen time of school-aged children. Our findings show that excessive screen time among schoolchildren aged 6–14 is very common and has become a serious public health problem in high—and middle-income countries. Excessive screen time has a variety of effects on the health of school-aged children, including emotional, sleep, behavioral problems, and affects the growth and cognitive development of school-aged children. Some high-income countries, such as the United States [61] and Germany [62], have developed guidelines for restrictions on digital media overuse across age groups, while some low—and middle-income countries have not developed such screen time guidelines. In 2021, the National Health Commission issued Appropriate Technical Guidelines for Prevention and control of Myopia in Children and Adolescents (updated version) [63], which suggested that families should "not put TV and other video products in children's bedrooms", but did not put forward suggestions on screen duration. This review might be useful for the policymakers in formulating or refining guidelines for limiting the excessive digital-media usage for school-aged groups in these countries.

Instead of school settings, home-based television viewing and home-based computers are two primary types of screen viewing of school-aged children. The home setting, especially parents, plays a vital role in deciding the type and length of screen viewing. Parents’ attitudes, beliefs, norms, and behaviors shape and create a shared social and physical environment in the home setting, and this environment affects children’s possibilities for different types of behaviors [64]. Higher parental self-efficacy to limit screen time is associated with less children’s screen time, whereas availability of media equipment is associated with increased children’s screen time [65]. Therefore, health promotion programs are needed to help raise parents' awareness and ability to help reduce children’s excessive screen time. Among different purposes of screen time for school-aged children, the main purpose is spent on entertainment rather than learning, which offers the possibility of reducing long screen time. Parents could set time limits on the use of entertainment software on electronic devices, or replace screen use with outdoor activities. It is also relevant to study further the screen use preferences of students of different ages, and to distinguish the use time of different screen media such as TV, computer and mobile phone. This knowledge would be valuable for the development of effective interventions aiming to diminish the school-aged children’s screen time.

During disease pandemic such as COVID-19, screen usage may become more prevalent through periods of school closures, lockdowns, social isolation, and online learning classes. Public health policies and health promotion strategies targeting parents are needed to raise awareness of the adverse health effects associated with excessive screen time [66]. From our findings, comparing the literature data before 2020 with those after 2020, the increase in screen time of primary and middle school students in the same countries and regions is obvious. There are also relevant studies [67] that due to the impact of the epidemic, the proportion of children whose screen time of electronic products was longer than 3 h per day rose from 9.16% before the epidemic to 19.20% after the epidemic. When literatures were searched, the publication years of literature included the time of epidemic. Compared with those before 2019, there has been a significant increase in screen time reported in the literature since 2020, which is related to the fact that the children have been forced to stay at home longer, and online teaching has led to increased average exposure to electronic devices during the pandemic. Since the online learning is “required” by schools, it raises a triple dilemma among maintaining school-learning, prevention of communicable diseases, and reducing excessive screen time, which needs further discussion. In addition, healthcare workers could provide health education and health consulting service on appropriate screen use behavior, how to improve digital media environment at home, and raise awareness of adverse health effects of screen time. Fitness and entertainment facilities shall be provided at the community level to reduce screen time, and enhance the physical activity level of children and adolescents. An integration of family, community, school, and health systems should be considered to design for intervention model of screen time behaviors.

This study has some limitations. First, according to the research types included in the literature, this study selected the international mainstream methodological quality scale for quality evaluation, but the quality of the relevant original research methodology was limited and not rigorous. It may have reduced the credibility of the conclusions. Second, in the included studies, national conditions and medical systems vary from country to country. The included literatures mainly focus on the health effects of screen time. The standards of screen time data collection and classification were not uniform among studies, which made the statistical results may deviate from the actual situation. In addition, the age range of some study subject included in the literature is not completely in the age range of 6–14 years old. Although only the data of the study subjects in accordance with the age group were selected in the data analysis, there were cases where a single data represented the level of the entire age group, and the sample size of the study subjects of each age group was not balanced, which may cause some bias to the conclusion. Only published literatures were searched, which may lead to incomplete data acquisition and potential publication bias. Third, because of the exclusion of literature published in languages other than English and Chinese, the research results were not representative in these language regions. Last, seventeen of the included literature were published after January 2020, but their data was collected before January 2020. New papers investigating screen time during COVID-19 pandemic have been published after our target date. Those latest data collection could be continued in the future to fully reflect the impact of the pandemic on screen time.

Conclusions

Focusing on school-aged children, this study systematically assessed the specific length and main uses of screen time in school-aged children aged 6–14, providing a baseline reference level for excessive screen time in school-aged children. It also provides ideas for interventions to reduce long screen time. However, the quality of the existing research is uneven, and the research types and quantity are relatively scarce. Further empirical research is needed to confirm the above conclusions.

Availability of data and materials

The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

ST:

Screen time: time spent using the computer, watching TV, playing video games and other multimedia screens

References

  1. Barber SE, Kelly B, Collings PJ, et al. Prevalence, trajectories, and determinants of television viewing time in an ethnically diverse sample of young children from the UK. Int J Behav Nutr Phys Act. 2017;14(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Alvarez-Peregrina C, Sánchez-Tena M, Martinez-Perez C, et al. The relationship between screen and outdoor time with rates of Myopia in Spanish Children. Front Public Health. 2020;8: 560378.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Landreneau JR, Hesemann NP, Cardonell MA. Review on the myopia pandemic: epidemiology, risk factors, and prevention. Mo Med. 2021;118(2):156–63.

    PubMed  PubMed Central  Google Scholar 

  4. Hu J, Ding ZY, Han D, et al. Analysis of influencing factors of myopia among primary and middle school students in Suzhou. China J Pre Med. 2021;33(03):241–5.

    Google Scholar 

  5. An MJ, Chen TJ, Ma J. The relationship between screen time and overweight among primary and middle school students in Fangshan District, Beijinh. Chin J Sch Health. 2018;39(04):506–8.

    Google Scholar 

  6. Liu ZH, Liu ZY, Lv SH. The relationship between video time and self-harming behavior among pupils in five provinces of China. Chin J Sch Health. 2021;42(03):363–6.

    Google Scholar 

  7. Seguin D, Kuenzel E, Morton JB, et al. School’s out: Parenting stress and screen time use in school-age children during the COVID-19 pandemic. J Affect Disord Rep. 2021;6: 100217.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang YT, Ma SX, Chen C, et al. Physical activity guidelines for children and adolescents in China. Chin J Evid Based Pediatr. 2017;12(06):401–9.

    Google Scholar 

  9. Che N. International cutting edge: updated guidelines from the American Academy of Pediatrics. Fashion Baby. 2017;01:42–3.

    Google Scholar 

  10. Gu HQ, Wang Y, Li W. The application of the Cochrane Bias risk assessment tool in the meta-analysis of randomized controlled studies. Chin Circul J. 2014;29(02):147–8.

    Google Scholar 

  11. Ai FL, Hu KR, Shi YL, et al. Quality evaluation of smoking cohort studies in China based on the Newcastle-Ottawa scale. Chin J Dis Control Prev. 2021;25(06):722–9.

    Google Scholar 

  12. Bel-Serrat S, Ojeda-Rodríguez A, Heinen MM, et al. Clustering of multiple energy balance-related behaviors in school children and its association with overweight and obesity-WHO European Childhood Obesity Surveillance Initiative (COSI 2015–2017). Nutrients. 2019;11(3):511.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bogl LH, Mehlig K, Ahrens W, et al. Like me, like you—relative importance of peers and siblings on children’s fast food consumption and screen time but not sports club participation depends on age. Int J Behav Nutr Phys Act. 2020;17(1):50.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Garcia-Conde MG, Marin L, Maya SR, et al. Parental attitudes to childhood overweight: the multiple paths through healthy eating, screen use, and sleeping time. Int J Environ Res Public Health. 2020;17(21):7885.

    Article  PubMed  Google Scholar 

  15. Garriguet D, Colley R, Bushnik T. Parent-Child association in physical activity and sedentary behaviour. Health Rep. 2017;28(6):3–11.

    PubMed  Google Scholar 

  16. Zhang SX, Tan KY, Huang SZ, Chen Z, Liang JH, Chen YJ. Current situation and influencing factors of primary school students' video behavior in Guangdong Province during the COVID-19 epidemic. Chin J School Health, 2012;42(08):1148–1151+1155.

  17. Guerrero MD, Barnes JD, Chaput JP, et al. Screen time and problem behaviors in children: exploring the mediating role of sleep duration. Int J Behav Nutr Phys Act. 2019;16(1):105.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Langøy A, Smith ORF, Wold B, et al. Associations between family structure and young people’s physical activity and screen time behaviors. BMC Public Health. 2019;19(1):433.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Latomme J, Van Stappen V, Cardon G, et al. The Association between Children’s and Parents’ Co-tv viewing and their total screen time in Six European Countries: cross-sectional data from the feel4diabetes-study. Int J Environ Res Public Health. 2018;15(11):2599.

    Article  PubMed  PubMed Central  Google Scholar 

  20. López-Bueno R, López-Sánchez GF, Casajús JA, et al. Health-related behaviors among school-aged children and adolescents during the Spanish Covid-19 confinement. Front Pediatr. 2020;8:573.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Malisova O, Vlassopoulos A, Kandyliari A, et al. Dietary intake and lifestyle habits of children aged 10–12 years enrolled in the school lunch program in Greece: a cross sectional analysis. Nutrients. 2021;13(2):493.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Motamed-Gorji N, Qorbani M, Nikkho F, et al. Association of screen time and physical activity with health-related quality of life in Iranian children and adolescents. Health Qual Life Outcomes. 2019;17(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Myszkowska-Ryciak J, Harton A, Lange E, et al. Reduced screen time is associated with healthy dietary behaviors but not body weight status among Polish adolescents. Report from the Wise Nutrition-Healthy Generation Project. Nutrients 2020;12(5):1323.

  24. Pérez-Farinós N, Villar-Villalba C, López Sobaler AM, et al. The relationship between hours of sleep, screen time and frequency of food and drink consumption in Spain in the 2011 and 2013 ALADINO: a cross-sectional study. BMC Public Health. 2017;17(1):33.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Stearns JA, Carson V, Spence JC, et al. The role of peer victimization in the physical activity and screen time of adolescents: a cross-sectional study. BMC Pediatr. 2017;17(1):170.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tanaka C, Tanaka M, Okuda M, et al. Association between objectively evaluated physical activity and sedentary behavior and screen time in primary school children. BMC Res Notes. 2017;10(1):175.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Varagiannis P, Magriplis E, Risvas G, et al. Effects of three different family-based interventions in overweight and obese children: the “4 your family” randomized controlled trial. Nutrients. 2021;13(2):341.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ye S, Chen L, Wang Q, et al. Correlates of screen time among 8–19-year-old students in China. BMC Public Health. 2018;18(1):467.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Li PH, Lv Y, Wang M. Status of sit-in behavior of children and adolescents in Beijing. Chin J Sch Health. 2016;37(10):1476–9.

    Google Scholar 

  30. Abe T, Kitayuguchi J, Okada S, et al. Prevalence and correlates of physical activity among children and adolescents: a cross-sectional population-based study of a rural city in Japan. J Epidemiol. 2020;30(9):404–11.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ahluwalia N, Frenk SM, Quan SF. Screen time behaviours and caffeine intake in US children: findings from the cross-sectional National Health and Nutrition Examination Survey (NHANES). BMJ Paediatr Open. 2018;2(1): e000258.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Alturki HA, Brookes DS, Davies PS. Does spending more time on electronic screen devices determine the weight outcomes in obese and normal weight Saudi Arabian children? Saudi Med J. 2020;41(1):79–87.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Beck H, Tesler R, Barak S, et al. Can health-promoting schools contribute to better health behaviors? Physical activity, sedentary behavior, and dietary habits among Israeli adolescents. Int J Environ Res Public Health. 2021;18(3):1183.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Bucksch J, Kopcakova J, Inchley J, et al. Associations between perceived social and physical environmental variables and physical activity and screen time among adolescents in four European countries. Int J Public Health. 2019;64(1):83–94.

    Article  CAS  PubMed  Google Scholar 

  35. Chong KH, Parrish AM, Cliff DP, et al. Cross-sectional and longitudinal associations between 24-hour movement behaviours, recreational screen use and psychosocial health outcomes in children: a compositional data analysis approach. Int J Environ Res Public Health. 2021;18(11):5995.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gallant F, Thibault V, Hebert J, et al. One size does not fit all: identifying clusters of physical activity, screen time, and sleep behaviour co-development from childhood to adolescence. Int J Behav Nutr Phys Act. 2020;17(1):58.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Guo YF, Liao MQ, Cai WL, et al. Physical activity, screen exposure and sleep among students during the pandemic of COVID-19. Sci Rep. 2021;11(1):8529.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kelly S, Stephens J, Hoying J, et al. A systematic review of mediators of physical activity, nutrition, and screen time in adolescents: Implications for future research and clinical practice. Nurs Outlook. 2017;65(5):530–48.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Krist L, Roll S, Stroebele-Benschop N, et al. Determinants of physical activity and screen time trajectories in 7th to 9th grade adolescents—a longitudinal study. Int J Environ Res Public Health. 2020;17(4):1401.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lazzeri G, Panatto D, Domnich A, et al. Clustering of health-related behaviors among early and mid-adolescents in Tuscany: results from a representative cross-sectional study. J Public Health (Oxf). 2018;40(1):e25–33.

    Article  PubMed  Google Scholar 

  41. Lin YC, Tsai MC, Strong C, et al. Exploring mediation roles of child screen-viewing between parental factors and child overweight in Taiwan. Int J Environ Res Public Health. 2020;17(6):1878.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ng KW, Augustine L, Inchley J. Comparisons in screen-time behaviours among adolescents with and without long-term illnesses or disabilities: results from 2013/14 HBSC Study. Int J Environ Res Public Health. 2018;15(10):2276.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Pearson N, Griffiths P, Biddle SJ, et al. Clustering and correlates of screen-time and eating behaviours among young adolescents. BMC Public Health. 2017;17(1):533.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Pons M, Bennasar-Veny M, Yañez AM. Maternal education level and excessive recreational screen time in children: a mediation analysis. Int J Environ Res Public Health. 2020;17(23):8930.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Silveira JFC, Barbian CD, Burgos LT, et al. Association between the screen time and the cardiorespiratory fitness with the presence of metabolic risk in school children. Rev Paul Pediatr. 2020;38: e2019134.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Souza Neto JM, Costa FFD, Barbosa AO, et al. Physical activity, screen time, nutritional status and sleep in adolescents in northeast Brazil. Rev Paul Pediatr. 2021;39: e2019138.

    Article  PubMed  Google Scholar 

  47. Tambalis KD, Panagiotakos DB, Psarra G, et al. Screen time and its effect on dietary habits and lifestyle among schoolchildren. Cent Eur J Public Health. 2020;28(4):260–6.

    Article  PubMed  Google Scholar 

  48. Tsujiguchi H, Hori D, Kambayashi Y, et al. Relationship between screen time and nutrient intake in Japanese children and adolescents: a cross-sectional observational study. Environ Health Prev Med. 2018;23(1):34.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wachira LM, Muthuri SK, Ochola SA, et al. Screen-based sedentary behaviour and adiposity among school children: results from International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE)—Kenya. PLoS ONE. 2018;13(6): e0199790.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wang S, Hao X, Ma X, et al. Associations between poor vision, vision-related behaviors and mathematics achievement in Chinese Students from the CNAEQ-PEH 2015. Int J Environ Res Public Health. 2020;17(22):8561.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Yan H, Zhang R, Oniffrey TM, et al. Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int J Environ Res Public Health. 2017;14:596. https://doi.org/10.3390/ijerph14060596.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zeng ZP, Wu HP, Bi CJ, et al. Correlation between physical exercise video time and mental sub-health among Chinese adolescents. Chin J Sch Health. 2021;42(01):23–7.

    Google Scholar 

  53. Cheng L, Li Q, Gao AY, et al. The relationship between overweight and obesity and video behavior and medium and high intensity activity among grade 3 ~ 5 primary school students. Chin J Sch Health 2016;37(08):1143–1146.

  54. Huang WH, Lu S, Yang SY, et al. The correlation between video time and eating behavior of middle school students in Guangzhou city. Chin J Sch Health, 2020, 41(04): 528–530+534.

  55. Lin LZ, Gao AY, Wang D, et al. Study on the relationship between sleep time and video time and childhood obesity in primary school students. Chin J Child Heal Care. 2018;26(09):948–51.

    Google Scholar 

  56. Liu WJ, Xiong LH, Lin R, et al. The relationship between static behavior and sports quality of primary school students in Guangzhou city. Chin J Sch Health, 2017;38(01):42–44+47.

  57. Ren TT, Liu J. Correlation between screen time and overweight and obesity among Uygur children and adolescents in Kashgar. Chin J Sch Health. 2018;39(11):1694–6.

    Google Scholar 

  58. Sun JL, Tan J, Tian LN, et al. Present situation and influencing factors of poor vision among primary and middle school students in Jinshui District, Zhengzhou City. South Chin J Pre Med 2021;47(06): 811–813+816.

  59. Wang J, Yang R, Li DL, et al. Association between health literacy, video time and depressive symptoms among middle school students in Shenyang. J Hyg Res. 2019;48(05):765–71.

    Google Scholar 

  60. Wang LM, He XG, Xie H, et al. The correlation between myopia-related health beliefs and screen time among primary and middle school students. Chin J Sch Health. 2021;42(02):181–4.

    CAS  Google Scholar 

  61. Reid Chassiakos YL, Radesky J, Christakis D, Moreno MA, Cross C; Council on Communications and Media. Children and Adolescents and Digital Media. Pediatrics. 2016;138(5):e20162593.

  62. Hansen J, Hanewinkel R, Galimov A. Physical activity, screen time, and sleep: Do German children and adolescents meet the movement guidelines? Eur J Pediatr. 2022;3:1–11.

    Google Scholar 

  63. Tao FB. Thematic interpretation of appropriate technical guide for prevention and control of myopia in children and adolescents. Chin J Sch Health, 2020;41(02):166–168+172.

  64. Määttä S, Kaukonen R, Vepsäläinen H, et al. The mediating role of the home environment in relation to parental educational level and preschool children’s screen time: a cross-sectional study. BMC Public Health. 2017;17:688.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Jago R, Wood L, Zahra J, et al. Parental control, nurturance, self-efficacy, and screen viewing among 5- to 6-year-old children: a cross-sectional mediation analysis to inform potential behavior change strategies. Child Obesity. 2015;11(2):139–47.

    Article  Google Scholar 

  66. Musa S, Elyamani R, Dergaa I. COVID-19 and screen-based sedentary behaviour: Systematic review of digital screen time and metabolic syndrome in adolescents. PLoS ONE. 2022;17(3): e0265560.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Liu X, Liu Z, Li YQ. Study on electronic screen exposure of 276 children aged 3–12 in Xi ’an during winter vacation in 2020. Chin J Woman Child Health Res. 2021;32(10):1541–7.

    Google Scholar 

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Acknowledgements

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Funding

National Natural Science Foundation of China 72274002.

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Authors and Affiliations

  1. School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, People’s Republic of China

    Jingbo Qi & Hui Yin

  2. China-Japan Friendship Hospital, 2 East Yinghuayuan Street, Chaoyang District, Beijing, 100029, People’s Republic of China

    Yujie Yan

Authors
  1. Jingbo Qi
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  3. Hui Yin
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Contributions

JQ and HY designed the study. JQ and YY reviewed the relevant articles and extracted important data. JQ analyzed the data and drafted the manuscript. All authors contributed to the interpretation of the findings and manuscript revision. All authors read and approved the final manuscript.

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Correspondence to Hui Yin.

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Qi, J., Yan, Y. & Yin, H. Screen time among school-aged children of aged 6–14: a systematic review. glob health res policy 8, 12 (2023). https://doi.org/10.1186/s41256-023-00297-z

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Keywords

Global Health Research and Policy

ISSN: 2397-0642

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