Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: J Pediatr. 2021 Sep 2;240:213–220.e2. doi: 10.1016/j.jpeds.2021.08.077

Sociodemographic Correlates of Contemporary Screen Time Use among 9-10-Year-Old Children

Jason M Nagata 1, Kyle T Ganson 2, Puja Iyer 1, Jonathan Chu 1, Fiona C Baker 3,4, Kelley Pettee Gabriel 5, Andrea K Garber 1, Stuart B Murray 6, Kirsten Bibbins-Domingo 7
PMCID: PMC9107378  NIHMSID: NIHMS1797189  PMID: 34481807

Abstract

Objective:

To determine sociodemographic correlates of contemporary screen time use among a diverse population-based sample of 9-10-year-old children.

Study design:

In 2021, we analyzed cross-sectional baseline (2016-2018) data from the Adolescent Brain Cognitive Development (ABCD) Study (N=10,755). Multiple linear regression analyses were conducted to estimate associations between sociodemographic factors (sex, race/ethnicity, country of birth, household income, parental education) and six contemporary forms of screen time (television, videos [e.g., YouTube], video games, social networking, texting, and video chat).

Results:

On average, children reported 3.99 hours of screen time per day across six modalities, with the most time spent watching/streaming television shows/movies (1.31 hours), playing video games (1.06 hours), and watching/streaming videos (1.05 hours). On average, Black children reported 1.58 more hours of screen time per day and Asian children reported 0.35 less hours of screen time per day compared to White children (mean 3.46 hours per day), and these trends persisted across most modalities. Boys reported higher overall screen time (0.75 hours more) than girls, which was primarily attributed to video games and videos. Girls reported more time texting, social networking, and video chatting than boys. Higher income was associated with lower screen time usage across all modalities except video chat. However, in high-income households, Latinx children reported 0.65 more hours of screen time per day than White children.

Conclusions:

Given the sociodemographic differences in child screen use, guideline implementation strategies can focus on key populations, encourage targeted counseling by pediatricians, and adapt Family Media Use Plans for diverse backgrounds.

Keywords: Screen time, television, social media, smart phone, pediatrics, adolescents

The advancement, accessibility, and greater utility and entertainment value of technology has led to the rapid increase in use of screens by children and adolescents as a way to facilitate their interactions with the world [1–3]. Screen usage in young children has nearly tripled between 1997 and 2014 [4]. In addition, screen time utilization has increased by 1.3 hours over the span of five years among adolescents [5]. In 2019, children 8-12 years of age reported using screens for about 5 hours each day, not including time spent on school work [6]. Research exploring the effects of screen time on health in children and adolescents has grown in the last 10 years [7]. It has been shown that technology overuse predicts health and behavioral problems in children and early adolescents [8–11]. Furthermore, recent studies have specifically linked excessive screen time with adverse effects on children’s health, including depression, anxiety, inattention, poor sleep, and physical inactivity [2,7]. However, it is apparent that effects of screen time are nuanced, depending on factors such as level of engagement and interaction [12,13].

Several organizations have put forward recommendations for limiting screen time among youth and adolescence. The 5-2-1-0 community-based obesity prevention intervention, Australia’s 24-hour movement guidelines, and Canada’s 24-hour movement guidelines recommend no more than 2 hours of screen time per day for youth [14–16]. The American Academy of Pediatrics (AAP) formerly recommended less than 2 hours of screen time per day among children, and now advocates for a Family Media Use Plan rather than a one-size-fits-all approach [17].

Identifying the prevalence of screen usage and sociodemographic factors associated with children’s screen time are key to preventing adverse downstream effects like decreased physical activity, risk of overweight or obesity, and persistent psychological distress [18], particularly among higher risk groups. Minority children and children from lower socio-economic backgrounds tend to have higher levels of screen time when compared to their White peers [19,20]. Structural and systematic discrimination of minority populations in the U.S. can manifest in the built environment as inadequate access to safe parks and recreational areas, disinvestment in afterschool education programs promoting arts, music, or sports, food insecurity, and increased responsibilities on youth as family caretakers. Together, these factors may lead to the elevated screen time burden experienced by youth from these communities [21–25]. Children from lower socio-economic backgrounds are more likely to have screens (e.g. television, video games) in their bedrooms and decreased accessibility to opportunities for physical activity or sport than those from higher socioeconomic backgrounds, and these factors jointly increase their daily screen time [26,27]. Black and Hispanic adolescents report the highest levels of screen time [5]. In addition, Black, Asian, and Hispanic children and adolescents are more likely to exceed the two hours per day screen time recommendation than White children and adolescents [28]. These factors may be further nuanced by a child’s immigration status, with increased acculturation to the U.S. being associated with increased levels of screen time [29].

Prior evidence has suggested that screen time use increases the most among 11-14 year-olds [30]; however, there are few large-scale studies focusing on younger children [5]. Moreover, the screen time modalities available to youth have diversified, yet the majority of research is centered on television viewing. Additionally, prior work has not studied sociodemographic correlates of contemporary screen time modalities using a large national sample and an intersectional lens (e.g., interactions between race/ethnicity and socio-economic status).

Given these gaps, the objective of our study is to explore the usage of screen time in a demographically diverse and population-based sample of U.S. children aged 9-10-years-old, considering six different modalities: video gaming, social networking, texting, video chatting, video streaming, and television/movie viewing. We hypothesized that differences in screen time patterns would exist by sex, race/ethnicity, and socio-economic status.

2. Methods

Cross-sectional baseline data are analyzed from the Adolescent Brain Cognitive Development (ABCD) Study (3.0 release; 2016-2018, ages 9-10 years) to address the study objectives. The ABCD Study is a longitudinal study of brain development and health in 11,875 children recruited from 21 sites around the U.S. Further descriptions of the study sample, recruitment, procedures, and measures have been described previously [31]. Data were taken from the baseline (Year 0) ABCD assessment. Participants with missing data for sociodemographic or screen time variables were excluded (Table 1; online only), leaving a total of 10,755 in the cohort. Participants with missing data were more likely to be racial/ethnic minorities, born outside the U.S., and have parents with lower education and lower incomes. Centralized institutional review board (IRB) approval was obtained from the University of California, San Diego. Study sites obtained approval from their local IRBs. Caregivers provided written informed consent and each child provided written assent.

Table 1.

Comparison of sociodemographic characteristics of adolescents in the Adolescent Brain Cognitive Development (ABCD) Study who were included versus excluded, May 2020 (n=11,875)

Included
n=10,755		 Excluded
n=1,120
% pa
Sex 0.251
 Female 48.0% 46.2%
 Male 52.0% 53.8%
Race/ethnicity <0.001
 White 54.5% 29.5%
 Latino / Hispanic 16.1% 27.6%
 Black 19.1% 31.7%
 Asian 6.0% 5.7%
 Native American 3.4% 3.8%
 Other 0.9% 1.7%
Country of birth <0.001
 US 97.2% 95.2%
 Outside of US 2.8% 4.8%
Highest parent education <0.001
 College education or more 95.2% 62.3%
 High school education or less 15.1% 37.7%
Household income <0.001
 $75,000 and greater 56.7% 35.3%
 Less than $75,000 43.3% 64.7%
Open in a new tab

Participants were excluded due to nonresponse or missing screen time data.

a

Pearson’s chi square test

Measures

Dependent Variable: Screen Time

Self-reported screen time was determined from the ABCD Youth Screen Time Survey, based on a previously validated measure [32–35]. Participants reported typical hours per day spent on six different screen modalities (viewing/streaming television shows or movies, watching/streaming videos [e.g., YouTube], playing video games, texting, video chatting [e.g., Skype, Facetime], and social networking [e.g., Facebook, Instagram, Twitter]) separately for weekdays and weekend days [32–35]. Similar to a previous study looking at screen time exposure in the ABCD study, we performed a weighted average calculation of the participants’ typical weekday and weekend screen time consumption to obtain a typical week measure ((weekday average × 5) + (weekend average × 2))/7 [9]. This single measure allowed us to incorporate an estimated average daily screen use over a week in order to allow for a single screen time outcome. After obtaining this total for each screen time modality used by participants, we reported the weighted total screen time average as a continuous variable.

Independent Variables:

Sex (male or female), race/ethnicity (White, Latinx/Hispanic, Black, Asian, Native American, other), and country of birth (U.S. or outside U.S.) were based on parent’s report.

Household income and highest parent education were based on parents’ report. For household income, parents were asked, “What is your TOTAL COMBINED FAMILY INCOME for the past 12 months? This should include income (before taxes and deductions) from all sources, wages, rent from properties, social security, disability and/or veteran’s benefits, unemployment benefits, workman’s compensation, help from relative (include child payments and alimony), and so on.” Household income was categorized into the following categories: less than $75,000 and $75,000 and greater, as this approximated the median household income in the U.S. [36]. For education, parents were asked, “What is the highest grade or level of school you have completed or the highest degree you have received?” and “What is the highest grade or level of school your partner completed or highest degree they received?” Highest parent education was defined as the highest grade or level of school received by the parent or their partner and categorized into high school or less versus college education or more.

Statistical Analyses

Data analysis was performed in 2021 using Stata 15.1 (StataCorp). Multiple linear regression analyses were conducted to estimate associations between sociodemographic factors (sex, race/ethnicity, country of birth, household income, parental education, BMI) and six contemporary forms of screen time (television, videos [e.g., YouTube], video games, social networking, texting, and video chat), adjusting for site. We tested for effect modification by income in the association between race/ethnicity and screen time as well as sex and screen time, and presented income-stratified results given evidence of significant effect modification (p<0.05). Some children within the sample were twins or siblings. Sensitivity analyses were conducted including only one sibling per family and findings did not differ; therefore, we present results from the full sample. Propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the U.S. Census [37].

Results

Table 2 describes sociodemographic characteristics of the 10,755 participants included. The analytic sample was approximately matched by sex (48.8% female) and racially and ethnically diverse (47.8% racial/ethnic minority). On average, at baseline, youth reported 3.99 hours of screen time per day, with the most time spent watching/streaming television shows/movies (1.31 hours), playing video games (1.06 hours), and watching/streaming videos (e.g. YouTube, 1.05 hours).

Table 2.

Sociodemographic and screen time characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants (N=10,755)

Sociodemographic characteristics (baseline) Mean (SD) / %
 Age (years) 9.9 (0.6)
 Sex (%)
  Female 48.8%
  Male 51.2%
 Race/ethnicity (%)
  White 52.2%
  Latino / Hispanic 20.0%
  Black 17.3%
  Asian 5.5%
  Native American 3.2%
  Other 1.9%
 Country of birth (%)
  USA 96.2%
  Other country 3.8%
 Household income (%)
  Less than $25,000 18.7%
  $25,000 through $49,999 20.4%
 $50,000 through $74,999 17.5%
  $75,000 through $99,999 13.4%
  $100,000 through $199,999 22.6%
  $200,000 and greater 7.4%
 Parent with college education or more (%) 79.7%
Screen time variables (baseline, hours per day)
 Total screen time 3.99 (3.16)
 Social media (social networking, video chat, texting) 0.58 (1.21)
 Television shows/movies 1.31 (1.31)
 Videos (e.g. YouTube) 1.05 (1.18)
 Video games 1.06 (1.13)
 Texting 0.24 (0.56)
 Video chat 0.21 (0.52)
 Social networking 0.13 (0.45)
Open in a new tab

ABCD propensity weights were applied to yield nationally representative estimates based on the American Community Survey from the US Census. SD = standard deviation

Table 3 shows linear regression analyses examining sociodemographic associations with contemporary screen time usage. On average, Black children reported 1.58 (95% CI 1.35-1.79) more hours of screen time per day and Asian children reported 0.35 (95% CI 0.09-0.62) less hours of screen time per day compared to White children, and these trends persisted across most screen time modalities. Latinx/a children reported 0.09 (95% CI 0.01-0.18) more hours of videos per day than White children. Boys reported higher overall screen time (0.75, 95% CI 0.63-0.89 hours) than girls, which was driven by more time spent on video games and videos. Girls reported more time texting, social networking, and video chat than boys. Lower income was associated with higher screen time usage across modalities except video chat. Lower parental education was associated with higher total screen time, videos, and texting.

Table 3.

Sociodemographic associations with screen time in the Adolescent Brain Cognitive Development (ABCD) Study (N=10,755)

Total screen time Television Videos (YouTube) Video games Social networking Texting Video chat
Sociodemographic characteristics B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p
Sex
 Female reference reference reference reference reference reference reference
 Male 0.75 (0.63 - 0.89) <0.001 0.04 (−0.01 - 0.08) 0.119 0.19 (0.14 - 0.24) <0.001 0.68 (0.64 - 0.73) <0.001 −0.05 (−0.07 - −0.03) <0.001 −0.07 (−0.10 - −0.05) <0.001 −0.04 (−0.06 - −0.02) <0.001
Race/ethnicity
 White reference reference reference reference reference reference reference
 Latinx / Hispanic 0.22 (−0.02-0.45) 0.080 0.06 (−0.02-0.14) 0.154 0.09 (0.01-0.18) 0.035 0.01 (−0.07-0.10) 0.718 −0.01 (−0.04 - 0.03) 0.712 0.03 (−0.02 - 0.07) 0.198 0.03 (−0.01-0.07) 0.155
 Black 1.58 (1.35 - 1.79) <0.001 0.36 (0.28-0.43) <0.001 0.39 (0.31-0.47) <0.001 0.28 (0.20-0.35) <0.001 0.13 (0.09-0.17) <0.001 0.22 (0.18-0.27) <0.001 0.17 (0.13-0.22) <0.001
 Asian −0.35 (−0.62 - −0.09) 0.008 −0.15 (−0.26- −0.05) <0.005 −0.05 (−0.16-0.06) 0.357 −0.10 (−0.19-0.00) 0.051 −0.03 (−0.06 - −0.01 ) <0.005 −0.03 (−0.06 - 0.01) 0.101 0.00 (−0.04-0.04) 0.999
 Native American 0.37 (0.00-0.74) 0.052 0.01 (−0.14-0.15) 0.944 0.15 (−0.02-0.31) 0.093 0.13 (−0.03-0.29) 0.112 0.02 (−0.05-0.08) 0.599 0.03 (−0.03-0.10) 0.311 0.04 (−0.02-0.09) 0.194
 Other 0.24 (−0.70-1.19) 0.615 −0.01 (−0.28-0.26) 0.952 −0.08 (−0.34-0.17) 0.525 −0.02 (−0.31-0.27) 0.884 0.14 (−0.05-0.33) 0.153 0.13 (−0.03-0.29) 0.123 0.08 (−0.07-0.22) 0.288
Country of birth
 USA reference reference reference reference reference reference reference
 Other country −0.07 (−0.45-0.31) 0.724 −0.10 (−0.23-0.31) 0.135 0.03 (−0.11-0.17) 0.653 −0.67 (−0.20-0.07) 0.331 0.04 (−0.01-0.10) 0.174 0.04 (−0.04-0.12) 0.361 0.02 (−0.06-0.09) 0.623
Household income
 $75,000 and greater reference reference reference reference reference reference reference
 Less than $75,000 0.91 (0.77-1.05) <0.001 0.24 (0.19-0.30) <0.001 0.31 (0.25-0.37) <0.001 0.25 (0.19-0.30) <0.001 0.06 (0.04-0.08) <0.001 0.04 (0.01-0.06) 0.004 0.02 (−0.01-0.03) 0.220
Parent’s highest education
 College education or more reference reference reference reference reference reference reference
 High school education or less 0.28 (0.06-0.49) 0.012 −0.04 (−0.11-0.03) 0.311 0.13 (0.04-0.21) 0.003 0.09 (0.01-0.17) 0.019 0.03 (−0.01-0.06) 0.107 0.06 (0.02-0.10) 0.007 0.03 (−0.01-0.07) 0.139
Open in a new tab

Bold indicates p<0.05. Propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the US Census.

All models include sex, race/ethnicity, country of birth, household income, parent education, and site.

Table 4 shows linear regression analyses examining sociodemographic associations with contemporary screen time usage stratified by income given evidence of significant effect modification by income (p<0.05). There were some notable differences by race/ethnicity and income level. In high-income households, Latinx children reported 0.64 more hours (95% CI, 0.36-0.91) of screen time per day compared to White children, and these trends were true of television, videos, texting, and video chat. However, in low-income households, there were no differences in total hours of screen time between Latinx and White children. Differences in the association of parent education and screen time were also observed by income. In high-income households, having a parent with a high school education or less was associated with 1.05 more hours (95% CI 0.55-1.56) of screen time per day than having a college-educated parent. In low-income households, there were no significant differences in screen time by parents’ education level. While overall associations with sex and total screen time remained similar by income level, there was evidence of effect modification by income for video games and social networking (both p for interaction <0.05). Among low-income households, boys had higher video game usage and lower social networking usage than girls, compared to those in high-income households.

Table 4.

Sociodemographic associations with screen time in the Adolescent Brain Cognitive Development (ABCD) Study, stratified by income

Total screen time Television Videos (YouTube) Video games Social networking Texting Video chat
Income less than $75,000 B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p
 Sex
  Female reference reference reference reference reference reference reference
  Male 0.83 (0.63 - 1.02) <0.001 0.05 (−0.02 - 0.12) 0.185 0.20 (0.13- 0.30) <0.001 0.78 (0.71-0.85) <0.001 −0.07 (−0.10- −0.04) <0.001 −0.09 (−0.13 - −0.05) <0.001 −0.06 (−0.09 - −0.02) <0.001
 Race/ethnicity
  White reference reference reference reference reference reference reference
  Latinx / Hispanic 0.03 (−0.31 - 0.38) 0.853 −0.02 (−0.09 - 0.14) 0.676 0.05 (−0.08- 0.17) 0.47 −0.04 (−0.15 - 0.08) 0.534 −0.01 (−0.06 - 0.04) 0.587 0.00 (−0.06- 0.07) 0.959 0.02 (−0.04 - 0.08) 0.58
  Black 1.47 (1.19 - 1.76) <0.001 0.32 (0.23 - 0.42) 0.676 0.35 (0.25 - 0.46) <0.001 0.24 (0.15 - 0.34) <0.001 0.13 (0.08 - 0.18) <0.001 0.23 (0.17 - 0.29) <0.001 0.17 (0.12 - 0.22) <0.001
  Asian −0.44 (−0.97 - 0.09) 0.106 −0.17 (−0.39 - 0.04) 0.112 −0.10 (−0.29 - 0.10) 0.33 −0.03 (−0.23 - 0.17) 0.761 −0.08 (−0.12 - −0.04) <0.001 −0.06 (−0.12 - −0.01) 0.035 −0.01 (−0.08 - 0.07) 0.858
  Native American 0.28 (−0.20 - 0.76) 0.256 −0.03 (−0.21 - 0.15) 0.73 0.10 (−0.11 - 0.31) 0.151 0.13 (−0.07 - 0.33) 0.247 0.02 (−0.06 - 0.10) 0.679 0.01 (−0.07 - 0.10) 0.699 0.04 (−0.03 - 0.11) 0.296
  Other 0.04 (−1.32 - 1.40) 0.950 −0.13 (−0.49- 0.24) 0.501 −0.10 (−0.47 - 0.28) 0.63 −0.18 (−0.54 - 0.19) 0.352 0.19 (−0.10 - 0.48) 0.197 0.13 (−0.04 - 0.29) 0.129 0.09 (−0.13 - 0.30) 0.416
 Country of birth
  USA reference reference reference reference reference reference reference
  Other country 0.05 (−0.51-0.61) 0.862 −0.10 (−0.29-0.09) 0.298 0.12 (−0.09-0.33) 0.266 −0.72 (−0.27-0.13) 0.482 0.042 (−0.04-0.12) 0.306 0.10 (−0.03-0.22) 0.136 0.02 (−0.09-0.14) 0.698
 Parent’s highest education
  College education or more reference reference reference reference reference reference reference
  High school education or less 0.21 (−0.03-0.45) 0.083 −0.07 (−0.15 - 0.01) 0.008 0.11 (0.02-0.20) 0.021 0.08 (0.00 - 0.17) 0.059 0.02 (−0.02-0.06) 0.262 0.06 (0.01-0.11) 0.015 0.03 (−0.1-0.07) 0.131
Total screen time Television Videos (YouTube) Video games Social networking Texting Video chat
Income $75,000 and greater B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p B (95% CI) p
 Sex
  Female reference reference reference reference reference reference reference
  Male 0.65 (0.52 - 0.78) <0.001 0.02 (−0.03 - 0.07) 0.45 0.17 (0.12 - 0.23) <0.001 0.56 (0.51-0.60) <0.001 −0.03 (−0.04- −0.01) <0.001 −0.05 (−0.08- −0.03) <0.001 −0.02 (−0.04 - 0.00) 0.033
 Race/ethnicity
  White reference reference reference reference reference reference reference
  Latinx / Hispanic 0.64 (0.36 - 0.91) <0.001 0.14 (0.04-0.24) 0.005 0.20 (0.09-0.30) <0.001 0.12 (0.03-0.22) 0.012 0.02 (−0.01-0.05) 0.213 0.09 (0.04-0.14) <0.001 0.07 (0.02-0.12) 0.009
  Black 1.65 (1.31-1.98) <0.001 0.42 (0.30-0.54) <0.001 0.50 (0.36-0.62) <0.001 0.36 (0.24-0.48) <0.001 0.07 (0.03-0.11) <0.001 0.15 (0.08-0.22) <0.001 0.15 (0.09-0.22) <0.001
  Asian −0.29 (−0.57- −0.02) 0.039 −0.14 (−0.25 - −0.03) 0.017 −0.01 (−0.13-0.11) 0.881 −0.12 (−0.22- −0.03) 0.011 −0.014 (−0.03-0.10) 0.246 −0.01 (−0.05-0.03) 0.668 0.00 (−0.03-0.03) 0.897
  Native American 0.44 (−0.33-0.92) 0.068 0.08 (−0.15-0.31) 0.493 0.20 (−0.41-0.45) 0.103 0.08 (−0.11-0.26) 0.415 −0.01 (−0.04-0.04) 0.978 0.06 (0.00-0.12) 0.052 0.02 (−0.05-0.09) 0.593
  Other 0.41 (−0.55-1.36) 0.402 0.15 (−0.20-0.51) 0.389 −0.14 (−0.40-0.11) 0.277 0.21 (−0.24-0.67) 0.363 0.03 (−0.06-0.12) 0.48 0.10 (−0.76-0.28) 0.262 0.05 (−0.07-0.17) 0.416
 Country of birth
  USA reference reference reference reference reference reference reference
  Other country −0.17 (−0.64-0.29) 0.467 −0.10 (−0.26-0.71) 0.262 −0.73 (−0.24-0.09) 0.382 −0.07 (−0.23-0.09) 0.385 0.06 (−0.02-0.14) 0.165 −0.02 (−0.09-0.04) 0.531 0.02 (−0.04-0.09) 0.481
 Parent’s highest education
  College education or more reference reference reference reference reference reference reference
  High school education or less 1.05 (0.55-1.56) <0.001 0.27 (0.09-0.45) 0.003 0.35 (0.16-0.54) <0.001 0.24 (0.05-0.43) 0.012 0.07 (0.02-0.13) 0.005 0.10 (0.01-0.20) 0.029 0.01 (−0.05-0.08) 0.673
Open in a new tab

Bold indicates p<0.05. ABCD propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the US Census.

All models include sex, race/ethnicity, country of birth, parent education, and site.

Discussion

In this population-based, demographically diverse sample of 9-10-year-old children in the U.S., we found several notable sociodemographic factors associated with contemporary screen time usage. We found that Black children had higher total screen time usage than White children, while Asian children had lower total screen time usage. Lower income was associated with higher usage of all forms of contemporary screen time except for video chat. We found that income modified screen time differences by race/ethnicity. In low-income households, differences by race/ethnicity were attenuated. In high-income households, Latinx children had higher screen time usage than White children. Lower parental education was associated with higher total screen time usage. Although boys overall had higher total screen time usage than girls, girls had higher daily usage of social networking, texting, and video chatting.

We found that Black children reported higher levels of all contemporary screen time types. This finding is in accord with prior research showing higher levels of TV viewing and video games in Black children [38–40], but broadens these findings to texting, video chat, social networking, and internet videos. Racial differences in screen time usage may be related to neighborhood environments, including fewer opportunities for outdoor physical activity in predominantly Black neighborhoods [41]. Prior studies have shown that lower perceptions of neighborhood safety are associated with lower physical activity and more screen time [42,43]. In addition, children may turn to technology’s vast entertainment and social networking features as an accessible way to cope with everyday stressors [44].

We found that Asian children reported lower levels of all types of screen time. Prior studies have had mixed findings regarding Asian children’s screen usage [28,45]. It is possible that lower screen time usage reflects lower representation and content marketing for Asian American children and thus less relatable content for this population [46].

Overall, children in lower-income families had higher engagement in nearly all forms of screen time except for video chatting. In families from low socioeconomic backgrounds, higher screen use is associated with reduced parent-child interactions, such as screen-free conversations or field trips [47]. Heavy parent screen use predicts child screen use and may lead to distracted parenting, or a phenomenon referred to as “technology interference” or “technoference” [48]. Lower parental education was associated with higher total screen time usage, videos, and texting, expanding on prior findings for television and computer screen time usage [27]. These findings may reflect the influence of the neighborhood environment in low-income neighborhoods, with prior studies showing that neighborhood-level factors such as poorer perceived aesthetics [49], higher social neighborhood disorder [50], and inadequate access to outdoor activities [51] are associated with higher adolescent screen time. In low-income households, differences by race/ethnicity were attenuated, indicating that the association of socio-economic status on screen time may cut across race/ethnicity. Low socio-economic status may be a prominent driver of higher screen time regardless of race/ethnicity. Among high-income households, we found more pronounced racial and ethnic screen time differences especially for Black and Latinx children, similar to prior studies examining the relationship between screen time and income in Black youth [45]. Overall, we found that Latinx children reported higher usage of videos (e.g., YouTube), but not other contemporary forms of screen time, than White children. Among high-income households, Latinx children had higher total screen time, television, videos, video chat, and texting. Prior literature on Latinx populations has mostly shown higher levels of TV viewing compared to non-Latinx populations, with Latinx families reporting that they use TV time as a way to help keep their child engaged, help them fall asleep, and allow for other televisions in the home to be more widely available [52]. Among first generation Spanish speaking youth, parents often turn to American television to reinforce English language development with their youth [52].

Boys on average report higher total daily screen time, which is driven by more time playing video games and watching/streaming videos, similar to findings of most other studies [38,53]. However, girls spend more time than boys on social networking, texting, and video chat. While the amount of time that 9-10-year-old children spent on social networking, texting, and video chat was much less overall than on television viewing, as these children enter adolescence and young adulthood, time spent on these contemporary screen modalities may increase. It is also noteworthy that sex differences in video games and social networking were greater in low-income households compared to high-income households.

Several limitations and strengths of the study should be noted. Given the cross-sectional analysis, we were unable to make causal claims. Although we adjusted for several potential confounders, there is the possibility of residual confounding. Measures were based on self-report, which could be subject to recall and reporting bias. Screen time categories of “TV shows and movies” and “videos (such as YouTube)” may not be mutually exclusive, although YouTube videos may include more home-made videos and short clips [54]. It is important to note that the effect sizes of some of the less-used screen time modalities were small. There was a possibility of selection bias given that participants with missing data were more likely to be racial/ethnic minorities, born outside the U.S., and from lower socio-economic backgrounds. Strengths of the study include a large, diverse, population-based sample. Further, the measures captured diverse and contemporary screen time modalities.

Our findings have important clinical, policy, and public health implications, particularly to inform the implementation and adaptation of existing screen time guidelines. This research can further inform more targeted guidance for specific populations. For instance, given limited time during primary care well visits, counseling for both caregivers and children from pediatricians could include more targeted discussion about the potential risks and benefits of video games for boys and social networking for girls. Targeted screening and counseling could align with a precision medicine or precision public health-based approach. Given our finding that children from lower socio-economic backgrounds are more likely to have higher screen use, the AAP family media use plan could be adapted for families with fewer resources. For instance, designating the child’s bedroom as a screen-free zone or recharging devices overnight outside the child’s bedroom may not be possible for families living in a single room or bedroom. Other alternatives could be turning off or placing devices in “do not disturb” mode overnight if multiple rooms are not available, as well as informing and educating parents on technology features that allow for monitoring and limiting their child’s screen usage (e.g., app limits). Furthermore, community and school-level implementation efforts to engage families of color may involve building community coalitions, mobilizing social networks, and tailoring culturally specific messages [55]. For example, schools and communities may choose to invest resources in developing safer and more accessible recreational centers for children and their families to encourage alternative modes of engagement. They may also work with community centers and organizations to provide educational guidance for parents who wish to learn more about effective ways to tailor their own Family Media Use Plan.

Policies and guidelines for screen time use should consider the unique differences found in this study to inform individualized counseling and implementation efforts. Increased knowledge on current differences in screen time among children 9-10-year-olds can also inform and strengthen future child-facing interventions across various technological platforms while tailoring approaches for the needs of children in this age group. Understanding differences in screen time usage is important especially given unprecedented levels of screen time during the COVID-19 pandemic [56]. Future research can examine what factors are associated with contemporary screen time usage in older adolescents and young adults.

Acknowledgements

The authors thank Catherine A. Cortez for analytic support and Samuel E. Benabou and Ananya Rupanagunta for editorial assistance. They report no industry relation, funding, or conflicts of interest.

Funding/Support:

J.M.N. was supported by the American Heart Association Career Development Award (CDA34760281) and the National Institutes of Health (K08HL159350). S.B.M. was supported by the National Institutes of Health (K23 MH115184). K.B.D. is supported by the National Institutes of Health (K24DK103992). The ABCD Study was supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/nihcollaborators. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report.

Role of Funder Sponsor:

The funders had no role in the study analysis, decision to publish the study, or the preparation of the manuscript.

Abbreviations

AAP

American Academy of Pediatrics

ABCD

Adolescent Brain Cognitive Development

BMI

body mass index

TV

television

Footnotes

Conflicts of Interest Disclosures (includes financial disclosures): The authors have no conflict to declare

References

  • [1].Hill D, Ameenuddin N, Chassiakos YR, Cross C, Radesky J, Hutchinson J, et al. Media and young minds. Pediatrics 2016;138. 10.1542/peds.2016-2591. [DOI] [PubMed] [Google Scholar]
  • [2].Viner RM, Davie M, Firth A. The health impacts of screen time: a guide for clinicians and parents. Edinburgh, Scotland: 2019. [Google Scholar]
  • [3].De’ R, Pandey N, Pal A. Impact of digital surge during Covid-19 pandemic: A viewpoint on research and practice. Int J Inf Manage 2020;55:102171. 10.1016/j.ijinfomgt.2020.102171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Chen W, Adler JL. Assessment of Screen Exposure in Young Children, 1997 to 2014. JAMA Pediatr 2019;173:391–3. 10.1001/JAMAPEDIATRICS.2018.5546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Rideout VJ, Foehr UG, Roberts DF. GENERATION M2 Media in the Lives of 8- to 18-Year-Olds. Menlo Park: 2010. [Google Scholar]
  • [6].Rideout VJ, Robb MB. The Common Sense Census: Media use by tweens and teens. 2019. San Francisco, CA: Common Sense Media [Google Scholar]
  • [7].Lissak G. Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study. Environ Res 2018;164:149–57. 10.1016/j.envres.2018.01.015. [DOI] [PubMed] [Google Scholar]
  • [8].Rosen LD, Lim AF, Felt J, Carrier LM, Cheever NA, Lara-Ruiz JM, et al. Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits. Comput Human Behav 2014;35:364–75. 10.1016/j.chb.2014.01.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Guerrero MD, Barnes JD, Chaput JP, Tremblay MS. Screen time and problem behaviors in children: Exploring the mediating role of sleep duration. Int J Behav Nutr Phys Act 2019;16. 10.1186/s12966-019-0862-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].Nagata JM, Iyer P, Chu J, Pettee Gabriel K, Baker FC, Garber AK, et al. Contemporary screen time modalities among children 9 – 10 years old and binge-eating disorder at one-year follow-up : A prospective cohort study. Int J Eat Disord 2021;54:887–92. 10.1002/eat.23489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Nagata JM, Iyer P, Chu J, Baker FC, Gabriel KP, Garber AK, et al. Contemporary screen time usage among children 9-10-years-old is associated with higher body mass index percentile at 1-year follow-up: A prospective cohort study. Pediatr Obes 2021:e12827. 10.1111/IJPO.12827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Orben A, Przybylski AK. Screens, Teens, and Psychological Well-Being: Evidence From Three Time-Use-Diary Studies. Psychol Sci 2019;30:682–96. 10.1177/0956797619830329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Przybylski AK, Orben A, Weinstein N. How Much Is Too Much? Examining the Relationship Between Digital Screen Engagement and Psychosocial Functioning in a Confirmatory Cohort Study. J Am Acad Child Adolesc Psychiatry 2020;59:1080–8. 10.1016/j.jaac.2019.06.017. [DOI] [PubMed] [Google Scholar]
  • [14].Australian Government Department of Health. For children and young people (5 to 17 years) https://www.health.gov.au/health-topics/physical-activity-and-exercise/physical-activity-and-exercise-guidelines-for-all-australians/for-children-and-young-people-5-to-17-yearss , 28July2021.
  • [15].Tremblay MS, Carson V, Chaput JP, Connor Gorber S, Dinh T, Duggan M, et al. Canadian 24-hour movement guidelines for children and youth: An integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab 2016;41:S311–27. 10.1139/apnm-2016-0151. [DOI] [PubMed] [Google Scholar]
  • [16].Rogers VW, Hart PH, Motyka E, Rines EN, Vine J, Deatrick DA. Impact of let’s go! 5-2-1-0: A community-based, multisetting childhood obesity prevention program. J Pediatr Psychol 2013;38:1010–20. 10.1093/jpepsy/jst057. [DOI] [PubMed] [Google Scholar]
  • [17].American Academy of Pediatrics. New research on media use informs discussion on digital recommendations. AAP News; 2015;36:30–30. [Google Scholar]
  • [18].Fiechtner L, Fonte ML, Castro I, Gerber M, Horan C, Sharifi M, et al. Determinants of Binge Eating Symptoms in Children with Overweight/Obesity. Child Obes 2018;14:510–7. 10.1089/chi.2017.0311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Anderson SE, Economos CD, Must A. Active play and screen time in US children aged 4 to 11 years in relation to sociodemographic and weight status characteristics: A nationally representative cross-sectional analysis. BMC Public Health 2008;8. 10.1186/1471-2458-8-366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Hoyos Cillero I, Jago R. Systematic review of correlates of screen-viewing among young children. Prev Med (Baltim) 2010;51:3–10. 10.1016/j.ypmed.2010.04.012. [DOI] [PubMed] [Google Scholar]
  • [21].Wen M, Zhang X, Harris CD, Holt JB, Croft JB. Spatial disparities in the distribution of parks and green spaces in the USA. Ann Behav Med 2013;45:18. 10.1007/s12160-012-9426-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Hankonen N, Heino MTJ, Kujala E, Hynynen ST, Absetz P, Araújo-Soares V, et al. What explains the socioeconomic status gap in activity? Educational differences in determinants of physical activity and screentime. BMC Public Health 2017;17:1–15. 10.1186/s12889-016-3880-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Galvez MP, McGovern K, Knuff C, Resnick S, Brenner B, Teitelbaum SL, et al. Associations between neighborhood resources and physical activity in inner-city minority children. Acad Pediatr 2013;13:20–6. 10.1016/j.acap.2012.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Brodersen NH, Steptoe A, Boniface DR, Wardle J. Trends in physical activity and sedentary behaviour in adolescence: Ethnic and socioeconomic differences. Br J Sports Med 2007;41:140–4. 10.1136/bjsm.2006.031138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Boen CE, Kozlowski K, Tyson KD. “Toxic” schools? How school exposures during adolescence influence trajectories of health through young adulthood. SSM Popul Heal 2020;11:100623. 10.1016/j.ssmph.2020.100623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Tandon PS, Zhou C, Sallis JF, Cain KL, Frank LD, Saelens BE. Home environment relationships with children’s physical activity, sedentary time, and screen time by socioeconomic status. Int J Behav Nutr Phys Act 2012;9:88. 10.1186/1479-5868-9-88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Atkin AJ, Sharp SJ, Corder K, Van Sluijs EMF. Prevalence and correlates of screen time in youth: An international perspective. Am J Prev Med 2014;47:803–7. 10.1016/j.amepre.2014.07.043. [DOI] [PubMed] [Google Scholar]
  • [28].Haughton CF, Wang ML, Lemon SC. Racial/Ethnic Disparities in Meeting 5-2-1-0 Recommendations among Children and Adolescents in the United States. J. Pediatr, vol. 175, Mosby Inc.; 2016, p. 188–194.e1. 10.1016/j.jpeds.2016.03.055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Singh GK, Yu SM, Siahpush M, Kogan MD. High levels of physical inactivity and sedentary behaviors among US immigrant children and adolescents. Arch Pediatr Adolesc Med 2008;162:756–63. 10.1001/archpedi.162.8.756. [DOI] [PubMed] [Google Scholar]
  • [30].Parent J, Sanders W, Forehand R. Youth screen time and behavioral health problems: The role of sleep duration and disturbances. J Dev Behav Pediatr 2016;37:277–84. 10.1097/DBP.0000000000000272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Barch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev Cogn Neurosci 2018;32:55–66. 10.1016/j.dcn.2017.10.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Paulus MP, Squeglia LM, Bagot K, Jacobus J, Kuplicki R, Breslin FJ, et al. Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. Neuroimage 2019;185:140–53. 10.1016/j.neuroimage.2018.10.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Gray JC, Schvey NA, Tanofsky-Kraff M. Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study. Psychol Med 2019;50:1539–47. 10.1017/S0033291719001545. [DOI] [PubMed] [Google Scholar]
  • [34].Bagot KS, Matthews SA, Mason M, Squeglia LM, Fowler J, Gray K, et al. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Dev Cogn Neurosci 2018;32:121–9. 10.1016/j.dcn.2018.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Sharif I, Wills TA, Sargent JD. Effect of visual media use on school performance: A prospective study. J Adolesc Heal 2010;46:52–61. 10.1016/j.jadohealth.2009.05.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Semega J, Kollar M, Creamer J, Mohanty A. Income and Poverty in the United States: 2018. 2019. [Google Scholar]
  • [37].Heeringa S, Berglund P. A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data. BioRxiv 2020:2020.02.10.942011. 10.1101/2020.02.10.942011. [DOI] [Google Scholar]
  • [38].Abdel Magid HS, Milliren CE, Pettee Gabriel K, Nagata JM. Disentangling individual, school, and neighborhood effects on screen time among adolescents and young adults in the United States. Prev Med (Baltim) 2021;142. 10.1016/j.ypmed.2020.106357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Carson V, Staiano AE, Katzmarzyk PT. Physical activity, screen time, and sitting among U.S. adolescents. Pediatr Exerc Sci 2015;27:151–9. 10.1123/pes.2014-0022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].de Sousa GR, Silva DAS. Comportamento sedentário baseado em tempo de tela: Prevalência e fatores sociodemográficos associados em adolescentes. Cienc e Saude Coletiva 2017;22:4061–72. 10.1590/1413-812320172212.00472016. [DOI] [PubMed] [Google Scholar]
  • [41].Whitaker KM, Gabriel KP, Buman MP, Pereira MA, Jacobs DR, Reis JP, et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: The CARDIA study. J Am Heart Assoc 2019;8. 10.1161/JAHA.118.010212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Datar A, Nicosia N, Shier V. Parent perceptions of neighborhood safety and children’s physical activity, sedentary behavior, and obesity: Evidence from a national longitudinal study. Am J Epidemiol 2013;177:1065–73. 10.1093/aje/kws353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Lenhart CM, Wiemken A, Hanlon A, Perkett M, Patterson F. Perceived neighborhood safety related to physical activity but not recreational screen-based sedentary behavior in adolescents. BMC Public Health 2017;17. 10.1186/s12889-017-4756-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Khalili-Mahani N, Smyrnova A, Kakinami L. To each stress its own screen: A cross-sectional survey of the patterns of stress and various screen uses in relation to self-admitted screen addiction. J Med Internet Res 2019;21:e11485. 10.2196/11485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Assari S. American children’s screen time: Diminished returns of household income in black families. Inf 2020;11:1–10. 10.3390/info11110538. [DOI] [PubMed] [Google Scholar]
  • [46].Peruta A, Powers J. Look Who’s Talking To Our Kids: Representations of Race and Gender In TV Commercials On Nickelodeon | Peruta | International Journal of Communication. Int J Commun 2017;11:1133–48. [Google Scholar]
  • [47].Wong RS, Tung KTS, Rao N, Leung C, Hui ANN, Tso WWY, et al. Parent Technology Use, Parent–Child Interaction, Child Screen Time, and Child Psychosocial Problems among Disadvantaged Families. J Pediatr 2020;226:258–65. 10.1016/J.JPEDS.2020.07.006. [DOI] [PubMed] [Google Scholar]
  • [48].McDaniel BT, Radesky JS. Technoference: longitudinal associations between parent technology use, parenting stress, and child behavior problems. Pediatr Res 2018 842 2018;84:210–8. 10.1038/s41390-018-0052-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [49].Bejarano CM, Carlson JA, Cushing CC, Kerr J, Saelens BE, Frank LD, et al. Neighborhood built environment associations with adolescents’ location-specific sedentary and screen time. Heal Place 2019;56:147–54. 10.1016/j.healthplace.2019.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Carson V, Janssen I. Neighborhood disorder and screen time among 10-16 year old Canadian youth: A cross-sectional study. Int J Behav Nutr Phys Act 2012;9:66. 10.1186/1479-5868-9-66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [51].Minges KE, Owen N, Salmon J, Chao A, Dunstan DW, Whittemore R. Reducing youth screen time: Qualitative metasynthesis of findings on barriers and facilitators. Heal Psychol 2015;34:381–97. 10.1037/hea0000172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [52].Ochoa A, Berge JM. Home Environmental Influences on Childhood Obesity in the Latino Population: A Decade Review of Literature. J Immigr Minor Heal 2017;19:430–47. 10.1007/s10903-016-0539-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [53].Bounova A, Michalopoulou M, Agelousis N, Kourtessis T, Gourgoulis V. Home and Neighborhood Environment Predictors of Adolescents’ Screen Viewing. J Phys Act Heal 2016;13:1310–6. 10.1123/jpah.2015-0508. [DOI] [PubMed] [Google Scholar]
  • [54].van Dijck J. YouTube beyond technology and cultural form. After Break, Amsterdam University Press; 2018, p. 147–60. 10.1515/9789048518678-010/HTML. [DOI] [Google Scholar]
  • [55].Yancey AK, Kumanyika SK, Ponce NA, McCarthy WJ, Fielding JE, Leslie JP, et al. Peer Reviewed: Population-based Interventions Engaging Communities of Color in Healthy Eating and Active Living: A Review. Prev Chronic Dis 2004;1. [PMC free article] [PubMed] [Google Scholar]
  • [56].Nagata JM, Abdel Magid HS, Gabriel KP. Screen time for children and adolescents during the Coronavirus Disease 2019 pandemic. Obesity 2020;28:1582–3. 10.1002/oby.22917. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES