Screen time among preschoolers: exploring individual, familial, and environmental factors

Sangha Lee; Donghee Kim; Yunmi Shin

doi:10.3345/cep.2023.01746

Clin Exp Pediatr > Volume 67(12); 2024

Lee, Kim, and Shin: Screen time among preschoolers: exploring individual, familial, and environmental factors

Review Article

Developmental and Behavioral Medicine

Screen time among preschoolers: exploring individual, familial, and environmental factors

Sangha Lee, PhD

, Donghee Kim, MD

, Yunmi Shin, MD

Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea

Corresponding author: Yunmi Shin, MD. Department of Psychiatry, Ajou University School of Medicine, 164 World cup-ro, Yeongtong-gu, Suwon 16499, Korea Email: ymshin@ajou.ac.kr

Received December 22, 2023 Revised May 28, 2024 Accepted June 18, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Clinical and Experimental Pediatrics 2024;67(12):641-650.

Published online: September 12, 2024

DOI: https://doi.org/10.3345/cep.2023.01746

Abstract

Screen-based activity refers to the use of screened devices, which are changing from stationary devices such as televisions and desktop computers to newer portable devices such as smartphones and electronic tablets. The exposure of younger children to all types of screened devices has increased. This review aimed to provide an overview of previous studies and identify the correlations associated with screen-based activities in preschoolers. We conducted a systematic literature review of databases including PubMed, Embase, and PsycInfo targeting quantitative studies published between 2010 and 2019. We then analyzed the associations between screen time and various individual, familial, and environmental factors in healthy children aged 0–6 years. We specifically included studies that provided a subgroup analysis for this age category to systematically identify the factors influencing screen time patterns among preschoolers. The search yielded 36 articles that met the inclusion criteria. We investigated age, sex, and social, family, and personal factors as well as media parenting. The results suggested that media parenting for screen-based activities was an important protective factor. Limited research has been conducted on preschoolers' screen-based activities with both stationary and portable screened devices. Most factors have been studied using stationary devices and are too infrequent for solid conclusions to be reached. Parental screen time and the setting of appropriate media limits were crucial protective factors against excessive media exposure. However, information about family and personal factors remain scarce, and further research is needed.

Key words: Screen time, Child, Preschool, Data correlation

Key message

This systematic review examined the correlation between screen time and various factors in preschoolers. Findings suggest that media parenting, including setting appropriate media limits, is crucial in protecting against excessive screen exposure. However, limited research has been done on the impact of family and personal factors, particularly with the increasing use of portable devices among young children.

Introduction

Screen time is usually defined as the duration spent by an individual using electronic/digital media such as televisions (TV), videos, movies, video games, and computers [1]. Screen-based activities include various types of screen media use, such as internet surfing, computer use, mobile phone use, television viewing, and video game playing [2]. The substantial increase in screen time is clinically significant since is associated with a range of adverse outcomes as documented in the literature. Many studies have shown that excessive screen time is associated with language delays, attention problems, obesity, aggressive behavior, and sleep problems in young children [3,4]. Screentime habits formed in early childhood can be predictors of negative psychological and physical health outcomes later in life [5]. Consequently, understanding the implications of screen time is imperative for developing strategies to mitigate its negative impacts while maximizing its potential educational and developmental benefits.

Remarkable technological advances have been made in almost all countries over the last few decades. Portable screen devices such as smartphones and tablets have become more accessible and personalized. Although TV remains the dominant screen-based activity for families, children are now able to watch TV alone using portable screened devices. While portable screened devices may increase their sedentary behaviors, they also increase play opportunities, creating tension for public health providers and parents alike [6]. Chang et al. [7] showed that 39.3% of 390 toddlers watched TV almost daily, whereas only 12.0% of children in South Korea used smartphones daily. On weekdays, 48% of children watched TV for more than 1 hour. On weekends, 63.1% of children watched TV for more than 1 hour. On weekends, 23.4% of children used their smartphones for more than 1 hour. An estimated 31.3% of children used smartphones before 24 months of age.

There is an evident global increase in mobile device usage among children. For instance, a Pew Research Center survey reported that in 2020, 45% of American children under 8 years of age owned tablets, a significant increase from just 1% in 2011 [8]. In Europe, children aged 1–6 years now average 102 min of daily media use, especially in households with less educated parents [9]. This shift from traditional to digital media consumption requires a reassessment of screen time regulations worldwide. As the use of portable devices has become more widespread, traditional screen times, such as TV viewing, are declining. This trend, coupled with the comparative data from South Korea and other regions, highlights the global challenges and implications of managing children’s media use. In fact, current health guidelines recommend that children younger than 2 years of age be exposed to a limited amount of educational mobile screen media, while for those aged 2–5 years, screen time should be less than 1 hour daily [10].

Smart devices with touch-based multimodal interfaces provide an easy-to-use platform for young children, especially compared to desktop computers, as using a mouse and learning keyboard techniques requires fine motor skills. Furthermore, the pleasure children derive from interacting with touch screens may lead to their increased habitual use [11].

Our understanding of the impact of smart device use on young children’s physical, cognitive, and social development is limited for several reasons [12]. According to Holloway et al. [13], the use of smart devices by young children has not been studied comprehensively because smart devices were introduced relatively recently and their usage is difficult to evaluate.

There are limited published data on screen-based activities that include both stationary and portable devices as well as associated and protective factors among preschool children. This review aimed to document recent studies of screen-based activity and its associated factors and summarize the possible risk and protective factors among preschool children.

Methods

1. Data sources and search strategies

An electronic search was conducted to identify relevant publications. Due to the recent proliferation of various new devices that have expanded the potential exposure of infants and young children to smart devices and minimized the impact of the coronavirus disease 2019 pandemic, only studies published in 2010–2019 were retrieved to address the need for research in this area. Thus, we searched PubMed, Embase, and PsycINFO for relevant articles published between January 2010 and December 2019.

The following terms were used in the literature search and were developed according to Patient/Population, Intervention, Comparison, and Outcome guidelines: “video games,” “mobile phones,” “smartphone,” “addictive behavior,” “internet use,” “excessive computer use,” “TV viewing,” “screen time,” “digital screen time,” “screen use,” “screen-based activity,” and “screen activity” were all within term group 1. These search terms were combined with “OR.” In addition, terms from group 2, such as “infant,” “child,” “preschool,” and “children” were also combined with “OR” as terms for the objects. The 2 above mentioned term groups were combined with “AND.” This was a literature-based descriptive study; therefore, neither institutional review board approval nor informed consent from the participants was required. For the documentation and screening of the articles retrieved from the databases, all information was exported to EndNote software (versionX9; Analytics Clarivate, Philadelphia, PA, USA).

2. Eligibility criteria

The a priori formulated eligibility criteria were as follows. First, the study population consisted of healthy children aged 0–6 years or those with an average age within this range. Second, this study examined the correlates or associations between screen time and other demographic, contextual, and behavioral variables. Third, only studies with a quantitative design were included (e.g., cross-sectional, longitudinal; no qualitative studies were included), and only peer-reviewed journal articles published in English were included. Case reports, expert opinions, editorial letters, reviews, unpublished studies, and dissertations were excluded.

3. Study selection and data extraction

Two independent researchers performed the study selection process in 3 steps: (1) title, (2) abstract, and (3) full-text screening. Two independent reviewers (SH and YM) manually screened the titles. Disagreements were resolved by consensus. Titles that clearly had no relevance, including book chapters, case reports, conference abstracts, comments, editorials, journal notes, grey literature, and news sources, were excluded during the title screening step. The studies were screened based on the eligibility criteria.

A data extraction form was used to obtain information including study design, country, participant ages, study variables (e.g., sex, age, parental perceptions, parenting skills, home environment, and children's physical activity levels), and outcomes (i.e., screen-based characteristics [duration, frequency, and pattern]).

Fig. 1 summarizes the search and inclusion processes used herein. The database search identified 981, 625, and 270 manuscripts in PubMed, Embase, and PsycInfo, respectively. Of these articles, 978 duplicates were excluded. The title and abstract review resulted in the exclusion of 831 studies that did not meet the inclusion criteria. The reference lists of the remaining studies were manually reviewed to identify any other relevant articles. Finally, 36 articles were included in this review.

Results

1. Included studies

The results of the 36 studies on screen-based activity measured by screen time are summarized in Table 1. Among them, 44.4% (n=16) were conducted in Europe, 25% (n=9) in the United States, and 19.4% (n=7) in Australia. Other studies were conducted in Canada, South Korea, Singapore, and Taiwan. The study sample sizes ranged from 62 to 18,577. Most (n=23 [63.9%]) were cross-sectional studies, while the rest (n=13 [36.1%]) were longitudinal studies. The children’s screen-based activity levels were measured by parental self-reports in all but one study, which measured mealtime TV viewing using videos recorded by mothers [14].

2. Factors associated with screen-based activities

1) Individual factors

Gender, ethnicity, and age: Research has consistently associated differences in screen device usage with factors such as gender [15-17], ethnicity [15,16,18,19], and younger maternal age [20]. For instance, mothers who are younger or of certain ethnicities may allow longer screen time for their children.

Child behavior: Peer relationship issues during childhood [21] and individual habits, such as eating in front of the screen [22,23], were identified as risk factors for increased screen time among preschoolers.

2) Familial factors

Parental educational level: A lower educational level in mothers [24-28] and parents in general [17,29,30] is correlated with higher screen time among children.

Parental screen time: Parental screen time significantly affects children's screen exposure and has emerged as a consistent risk factor across multiple studies [1,17,19,22-24,31-34].

Parental mental health and employment: Children whose parents have mental health issues [15,35] or are unemployed [36] are prone to higher screen usage.

Family structure: Single motherhood [37], the absence of siblings [15], and a state of household chaos [38] were associated with increased screen time.

3) Environmental factors

Access and exposure: Easy access to digital media devices and predominantly home-based care are linked to increased screen time [17,39]. Conversely, access to green spaces nearby serves as a protective factor, helping reduce screen usage [40,41].

Physical activity and outdoor time: The lack of outdoor play equipment and fewer outings (fewer than 5 weekly) were correlated with higher screen time among children [37].

Discussion

Recognizing the rapidly evolving usage patterns of digital media is of the utmost importance when assessing the impact of digital media on children's lives. This initial stage is essential for understanding the influence of smart devices in this population. The factors that contribute to screen time among school-aged children and adolescents may not hold true for young children, as their early developmental stages represent distinct periods of life. Research on media use among preschool-aged children is limited. Establishing appropriate screen time habits in young children can significantly affect their overall health and well-being throughout life. Research on media usage among preschool children is critical, as establishing healthy screen time habits can profoundly impact their health and developmental trajectories [5].

This study conducted a thorough examination of 36 studies to systematically analyze the impact of individual, familial, and environmental factors on smart device use and screen time exposure among preschool-aged children. Given the notable recent increase in smart device usage among preschoolers, it is imperative that we assess the various risk and protective factors and provide expert recommendations. Several risk factors for excessive screen use in preschool children have been identified, and children’s screen time is positively correlated with parental screen time. This modeling behavior underscores the need for parents to set practical examples [11]. Parents with lower educational levels tend to have less restrictive media use policies that contribute to greater screen time among children [16]. Moreover, high levels of maternal depression are associated with increased screen use among children, likely due to less active engagement and supervision [40]. The amount and content of screen time vary significantly across different childcare settings, with less structured environments being correlated with increased screen use [39]. Conversely, several protective factors can help mitigate the risks associated with early screen exposure. Effective digital media parenting strategies, such as establishing screen time limits and a parental modeling of media use, are essential to mitigating screen time–associated risks [11,42-46]. Parents with higher educational levels are more likely to enforce strict screen time regulations, which can help limit children’s screen exposure and promote greater engagement in non-screen activities [32]. A broader environmental context, particularly the presence of green spaces, has emerged as a crucial element moderating the effects of screen time. Proximity to green spaces is inversely related to screen time, as these environments promote physical play and distract people from their digital devices [40]. In preschool-aged children, parental behaviors and the home media environment are pivotal in shaping screen use patterns [47,48]. Parental screen time and the establishment of media guidelines are consistently significant factors that influence children's media use. Stricter regulations for children younger than 2 years of age and more defined rules for older children are recommended to protect against excessive screen exposure.

Preschool-aged children are notably more influenced by media exposure within their families and households than other age groups. Parental screen time and the establishment of rules and boundaries for parental media exposure consistently emerged as important factors in most reviewed studies. Moreover, extensive media exposure significantly influences preschoolers' developmental outcomes, making them more susceptible to the adverse effects of excessive screen time than other age groups. Longitudinal studies have illustrated that excessive early screen time is linked to developmental delays, including language impairments and attention deficits, which can affect academic performance and social interactions as children grow [19,31]. Children with higher screen time tend to have poorer school performance, possibly due to reduced sleep quality and shorter attention spans [39]. Excessive screen time is associated with a higher incidence of obesity, sleep disorders, and behavioral problems. These conditions are often mediated by the content consumed as well as the context in which screen time occurs, such as during meals or immediately before bedtime [22,33].

Experts recommend stringent limits on screen time for children, particularly those younger than 2 years of age, to prevent overstimulation and developmental disruptions. For preschool-aged children, screen time quantity and quality should be carefully managed to maximize potential educational benefits while minimizing risks [49]. According to the American Academy of Pediatrics (AAP), children under 18 months of age should not use digital media except for video chatting, while those aged 18–24 months should only be exposed to high-quality programming and coviewing with a parent to ensure interaction and discussion, which enhance their understanding and development [10]. For children aged 2–5 years, the AAP recommends limiting screen use to 1 hour per day under parental guidance to ensure that the content is both educational and interactive. The American Academy of Child and Adolescent Psychiatry further emphasizes the importance of parental involvement. This suggests that media should not replace quality family, outdoor play, or sleep time and recommends that families engage with their children during media use to bridge the gap between screen content and real-world experience [50].

The World Health Organization also provides guidelines stressing the importance of active play over passive screen time. For children aged 2–5 years, screen time should be limited to 1 hour or less per day, and they should be encouraged to participate in more engaging activities that promote their physical and cognitive development [51]. Furthermore, practical recommendations suggest setting consistent limits on media content types and exposure durations to ensure that media use does not interfere with adequate sleep, physical activity, and other health-promoting behaviors. Parents are encouraged to co-view media with their children to facilitate discussions and provide a contextual understanding of the content being viewed, thereby enhancing its educational value. These guidelines highlight a critical approach to digital media use in early childhood by focusing on protective measures that can control and enrich young children’s media experiences. By adhering to these expert recommendations, parents and caregivers can help mitigate the risks associated with early and excessive screen time exposure [52].

Future research should use a longitudinal study design to elucidate the long-term effects of early screen exposure. Additionally, more culturally specific studies are needed to tailor recommendations to diverse contexts such as Korea, where familial structures and media use habits may differ significantly from those in Western countries [43].

The advent of newer portable smart devices has potentially augmented screen exposure access and duration among young children compared to older stationary devices. These devices are often interactive and can be used without adult supervision, which increases the risk of overuse. Touch-based smartphone and tablet interfaces are particularly appealing to young children and can lead to more intuitive and prolonged use.In contrast, traditional devices such as televisions and desktop computers typically require a fixed location and potentially more parental involvement in device setup and content selection. The implications of this shift are profound, as they influence screen exposure quantity and quality by moving from passive to more interactive forms. This review underscores the need for updated screen time guidelines to address the nuances of modern media consumption among preschoolers. By focusing on newer portable devices,this study increases our understanding of the current risks and protective factors for digital media exposure among preschool-aged children.

The current study has a few limitations. First, it relied on parental reports for the data collection, which may introduce bias regarding actual screen time and its effects. Second, the rapid evolution of digital media technology means that findings may quickly become outdated, necessitating continual research in this field. Third, future studies should longitudinally assess the impact of early exposure to portable devices on developmental outcomes. Moreover, studies should also explore culturally specific media use patterns to appropriately tailor interventions. Our findings highlight the critical need for experimental research to establish causal relationships between screen time and developmental metrics.

In conclusion, the increasing pervasiveness of smart devices among preschool children poses a significant public health challenge. The findings of this review underscore the necessity for comprehensive strategies, including parental education, environmental modifications, and public health policies, to effectively manage children's screen time in the digital age. As screen-based devices have become increasingly embedded in everyday life, the balance between their beneficial and harmful impacts remains a critical focus of ongoing research and policy.

Footnotes

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Funding

This research was supported by a grant of the R&D project, funded by the National Center for Mental Health (grant number: MHER22B03)

Author contribution

Conceptualization: SYM, LSH; Data curation: KDH, SYM; Formal analysis: LSH, SYM; Funding acquisition: SYM; Methodology: LSH, SYM; Project administration: SYM; Visualization: KDH; Writing - original draft: KDH, LSH; Writing - review & editing: SYM, LSH

Fig. 1.

Flowchart of the study selection.

Table 1.

Overview of studies examining individual, familial, and environmental factors related to preschoolers' screen time

Factor	Study	Design	Subject age range, mean (SD)	No.	Country	Associated factors	Dependent variable	Statistical method	Result/main findings
Individual	Wijtzes et al. [18] (2013)	Cross-sectional	3 Mo–4 yr: 48.2 (47.8–50.6) yr	3,452	Netherlands	Children's ethnicity	TV viewing time	Multiple regression	Children from ethnic minority groups watched more TV compared to native Dutch children, regardless of family
	Birken et al. [22] (2011)	Cross-sectional	3 Yr	157	Australia	TV during meals	TV screen time	Simple linear regression	Eating lunch in front of the screen and 1 extra hour of parental screen time increased child weekend screen time by 78 and 12 min per day, respectively.
	Birken et al. [22] (2011)	Cross-sectional	3 Yr	157	Australia	Parental factors	TV screen time	Simple linear regression
	Del Pozo-Cruz et al. [15] (2019)	Longitudinal	4–5 Yr	3,974	Australia	Demographic factors	Screen time	Latent growth mixture model	Increased screen time may be associated with female, indigenous background, non-English-speaking households, non-biological parent, higher household affluence, absence of siblings, parents with poor mental health in children.
	Del Pozo-Cruz et al. [15] (2019)	Longitudinal	8–9 Yr	3,974	Australia	Demographic factors	Screen time	Latent growth mixture model
	Carson et al. [16] (2017)	Cross-sectional	1–3 Yr: 19.0 (1.9) 2–9.9 Yr	149	Canada	Demographic characteristics	Parent report on watching time, play games on weekday and weekend days	Multiple lnear regression	Female, lower income and ethnic minority groups may be important targets for screen time interventions.
	Poulain et al. [21] (2018)	Longitudinal	2–6 Yr: baseline 3.81 (0.89) 2–9.9 yr; follow-up 4.83 (0.90) 2–9.9 yr	527	Germany	Emotional and behavioral, peer relationship problems	Screen time	Multiple linear regression	At baseline, peer relationship problems increased the likelihood of computer/Internet and mobile phone use at follow-up.
	Poulain et al. [21] (2018)	Longitudinal			Germany	Emotional and behavioral, peer relationship problems	Screen time	Multiple logistic regression
	Chiu et al. [17] (2016)	Cohort	Baseline:18 mo	18,577	Taiwan	Demographic factor	TV viewing	Multinomial logistic regression	Boys, parental income, childcare arrangements, parental education, parental TV viewing time, and TV viewing restrictions for children are associated with children's TV viewing.
	Chiu et al. [17] (2016)	Cohort	Follow-up: 5 yr	18,577	Taiwan	Demographic factor	TV viewing	Multinomial logistic regression
Familial	Wijtzes et al. [24] (2012)	Cross-sectional	Pregnancy to 4 yr: 48.2 (47.8–50.2) yr	2,786	Netherlands	Maternal educational level	TV screen time	Logistic regression	Maternal education level, parental viewing time is associated with preschoolers' television viewing time.
	Chandra et al. [37] (2016)	Cross-sectional	18 Mo	500	Australia	Single-parenting Number of siblings	Screen time	Multivariable logistic regression	Significant associations were observed between over 2 hr of daily screen time and maternal single status, having 3 siblings, employed fathers, lack of outdoor equipment, and fewer than 5 weekly outings.
						Father's employment
						Ownership of outdoor activity equipment
						Frequency of outings per week
	Xu et al. [31] (2016)	Longitudinal	Pregnancy to 5 yr	667	Australia	Mother's screen time at baseline	Children's screen time across ages 2 to 5 yr	Mixed linear and logistic regression	Screen time during pregnancy and children's screen time at age 1 predict screen time from ages 2 to 5.
	Xu et al. [31] (2016)	Longitudinal	Pregnancy to 5 yr	667	Australia	Child's screen time at age 1	Children's screen time across ages 2 to 5 yr	Mixed linear and logistic regression
	Iguacel et al. [36] (2017)	Longitudinal	2 to <6 (43.5) Yr	8,482	Spain	Demographic factors	Screen time	Logistic mixed-effects analyses	Migrants and children with unemployed parents had longer screen time.
			6 to<12 (56.5) Yr
			2–9.9 Yr
	Chen et al. [19] (2019)	Longitudinal	Pregnancy to 5.5 yr: 5.5 (0.1) yr	547	Singapore	Ethnicity	Screen time	Simple/Multiple logistic regression	Chinese ethnicity, younger maternal age and lower maternal TV and sleep time were associated with greater screen viewing.
	Chen et al. [19] (2019)	Longitudinal	2–9.9 Yr	547	Singapore	Ethnicity	Screen time	Simple/Multiple logistic regression
	Hinkley et al. [32] (2013)	Cross-sectional	4 Yr: 4.54 (0.70) yr	935	Australia	Social variables	Physical environment	Compliance rate with screen time guidelines	Multinomial logistic regression
	Hinkley et al. [32] (2013)	Cross-sectional	2–9.9 yr	935	Australia	Social variables	Physical environment	Compliance rate with screen time guidelines	Multinomial logistic regression
	Loprinzi et al. [29] (2012)	Cross-sectional	2–5 Yr: 4.0 (0.1) yr	164	USA	Demographic factors	Screen time guideline adherence rate	Chi-square analysis	Children with higher parental education levels have a higher compliance rate with weekend electronic media use guidelines.
	Loprinzi et al. [29] (2012)	Cross-sectional	2–5 Yr: 4.0 (0.1) yr	164	USA	Demographic factors	Screen time guideline adherence rate	One-way ANOVA
	Kılıç et al. [25] (2019)	Cross-sectional	1–60 Mo	422	Turkey	Mobile device type, child's age, household income, parental education	Frequency of mobile media exposure, ownership	Descriptive statistics	Mothers' education levels are inversely related to children's tablet use.
	Park et al. [35] (2018)	Cross-sectional	2–5 Yr	400	South Korea	Maternal depression	Children's TV overuse	Logistic regression	Maternal depression is linked to children's TV viewing time, while a family income is associated with children's tablet PC usage time.
	Yang-Huang et al. [26] (2017)	Chi-square 1-way ANOVA	2–9 Yr: 2 yr: 24.4 (1.1) mo; 3 yr: 36.5 (1.1) mo; 4 yr: 48.5 (1.0) mo; 6 yr: 71.8 (4.8) mo; 9yr : 116.2 (3.4) mo	3,561	Netherlands	Mother's education level	Whether using TV for more than 1 hour per day	Generalized logistic mixed models	Lower maternal education levels associated with more screen time.
	Yang-Huang et al. [26] (2017)	Chi-square 1-way ANOVA		3,561	Netherlands	Family income	Whether using TV for more than 1 hour per day	Generalized logistic mixed models
	Emond et al. [38] (2018)	Cross-sectional	2–5 Yr: 3.3 (1.1) yr	385	USA	Household chaos	Weekly screen use, use of screens	Adjusted linear and Poisson regression	Greater household chaos is associated with increased total screen use.
	Lee et al. [43] (2018)	Cross-sectional	1.6 Yr: 1.6 (0.2) yr	193	Canada	Parents and their interactions with the home environment	Screen time on weekdays and weekend days	Bayesian estimation in structural equation modeling	Higher parental screen time limiting practices was associated with lower screen time among toddlers.
	Tang et al. [44] (2018)	Cross-sectional	1.5–5 Yr: 3.65 (1.36)	62	USA	Monitoring and limiting screen time	Children’s total recreational screen time on weekday and weekend days	Linear regression using generalized estimating equations	Parents’ media parenting practices were associated with children’s screen time.
	Tang et al. [44] (2018)	Cross-sectional	2–9.9 Yr	62	USA	Monitoring and limiting screen time		Linear regression using generalized estimating equations
	Pearson et al. [23] (2018)	Cross-sectional	5–6 Yr: 5.58 (0.73)	126	UK	Parental screen time, parental income	Screen time	Multinomial logistic regression analyses	Correlates associated with clusters included parental income, eating meals at the TV, parental screen time and energy-dense snack food consumption, and home availability of snack foods.
	Pearson et al. [23] (2018)	Cross-sectional	2–9.9 Yr	126	UK	Parental screen time, parental income	Screen time	Multinomial logistic regression analyses
	Barber et al. [1] (2017)	Longitudinal, birth cohort	0–3 Yr: 27.5 (5.74)	1558	UK	Parental factors	child’s TV time	Linear longitudinal multi-level models	Mothers’ own TV time, mothers’ attitudes toward child TV time and the time the TV is on in the home are modifiable factors.
	Barber et al. [1] (2017)	Longitudinal, birth cohort	2–9.9 Yr	1558	UK	Parental factors	child’s TV time	Linear longitudinal multi-level models
	Domoff et al. [14] (2017)	Longitudinal	4–11 Yr: 7 (2) 2–9.9 yr	220	USA	Early parenting disciplinary, child emotionality	Background Mealtime TV viewing (3 typical dinnertime meals were video recorded by mothers over 1 week)	Regression analyses	Managing child negative emotionality using positive parenting strategies may reduce later child TV engagement.
	Downing et al. [33] (2017)	Cohort	3–5 Yr: 4.5 (0.7) yr	1002	Australia	Individual, social and physical environment correlates	Parent report on screen time on weekdays and weekend days	Multivariable linear regression analyses	Parental self-efficacy to limit screen time and screen time rules are inversely associated with screen time.
	Downing et al. [33] (2017)	Cohort	3–5 Yr: 4.5 (0.7) yr	1002	Australia	Individual, social and physical environment correlates	Parent report on screen time on weekdays and weekend days	Multivariable linear regression analyses	Maternal television viewing is positively associated with boys' screen time.
	Määttä et al. [30] (2017)	Cross-sectional	3–6 Yr	864	Finland	Parental norms and attitudes, parental education	screen time	The mediator models	Parents with higher education tend to limit children's screen time due to factors descriptive norms, reduced parental screen use, and stronger commitment to restricting screen time.
	Määttä et al. [30] (2017)	Cross-sectional	4.7 (0.89) yr	864	Finland	Parental norms and attitudes, parental education	screen time	The mediator models
	Matarma et al. [27] (2016)	Longitudinal	1–3 Yr	1,829	Finland	Father’s sitting time, maternal education	Screen time	Linear and logistic regression analyses	Mother’s screen time habits and education seem to be associated with their children’s screen time.
	Sanders et al. [45] (2016)	Cross-sectional	3–7 Yr	615	USA	General and technology-related parenting	Screen time	Structural equation modeling	Technology-related parenting strategies was related to the child screen time.
	Sanders et al. [52] (2016)	Cross-sectional	3–7 Yr	615	USA	Parental perceptions of technology	Screen time	Structural equation modeling	Parental self-efficacy with technology is associated with children's screen time.
	Sigmundová et al. [34] (2016)	Cross-sectional	4–7 Yr: male: 5.58 (0.84) yr; female: 5.63 (0.86) yr	194	Czech Republic	Parents’ screen time	Child’s and parent’s screen time	Bivariate Pearson correlation	The screen time of parents and children showed a strong correlation.
	Jago et al. [46] (2015)	Cross-sectional	5–6 Yr: 6.0 (0.4) yr	954	UK	Parental self-efficacy	TV viewing, computer	Four-step regression approach	Parental self-efficacy to limit screen time mediated associations between parental control and screen viewing.
	Miguel-Berges et al. [47] (2019)	Longitudinal	3.5–5.5 Yr: baseline 4.74 (0.4) 2–9.9 yr; follow-up 5.72 (0.4) 2–9.9 yr	4,836	EU	Parental perception, attitudes, and knowledge	Screen time	Multilevel logistic regression model	Preschool children whose caregivers stated rules limiting their sedentary screen time were less likely to spend total screen time.
	Certain and Kahn [28] (2002)	Cohort	0–35 Mo	3,556	USA	Demographic factor	Hours of television per week-day		Maternal low education level is associated with children's television viewing time.
Environmental	Feng et al. [49] (2011)	Cross-sectional	5–9 Yr: 6.61 (0.889) Yr	597	USA	TV in the child’s bedroom	Daily watching TV/DVD	t-test, logistic regression	Children with TV in bedroom spent 93 hr more daily watching TV/DVD.
	Tandon et al. [39] (2011)	Cross-sectional	9 Month, 2 yr age 4 or away 4.37 (0.01) yr	8,950	USA	Childcare Environment	Screen time	Multiple regression	Children in centers had the lowest screen time (3.2 hr) compared to children in parental care only (4.4 hr), home-based care (5.5 hr), and head start (4.2 hr).
	Sanders et al. [40] (2015)	Longitudinal	4–5 Yr	4,983	Australia	Presence of green Spaces near residence	Physical activity, TV Screen time	Multiple linear regression	For boys, a 10% increase in nearby green space is associated with a 2.3% reduction in weekend TV viewing time.
			Wave 1: 4.2 (0.4) 2–9.9 Yr					Multiple logistic regression
			Wave 5: 12.4 (0.5) 2–9.9 yr					Multiple logistic regression
	Aggio et al. [41] (2015)	Cross-sectional	5.9 Yr: 70.2 (0.5) yr	3,586	UK	Green space	Average weekly TV viewing time, use of computers	General linear models	Mother’s perceived distance from home to green/open spaces was associated with child’s TV time.
	Sijtsma et al. [48] (2015)	Cohort	3.4–4.4 Yr: 3.9 (0.1) 2–9.9 yr	759	German	TV in the bedroom	Total screen time on weekdays and weekend days, number of TV at home	Mediation model	A TV in the bedroom or more TV at home gave a higher screen time.

ANOVA, analysis of variance.

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