Adherence to Screen Time Guidelines Among Families in Australia With Children of Different Ages

Screens are an integral part of life, and many countries have screen time guidelines for children that are informed by the World Health Organization’s recommendations.¹ Australia has screen time guidelines with age-based limits: none for children younger than 2 years, 1 hour per day for children aged 2 to 4 years, and 2 hours per day for children aged 5 to 12 years.² Although these guidelines are clear, parenting resources^3,4 often do not address the challenge of managing screen time in families with children of different ages who have different recommended screen time limits. We aimed to understand adherence to screen time guidelines in families whose children spanned different age-based screen time categories.

In this cross-sectional study, data from the Mothers and Their Children’s Health (MatCH) Study, a substudy of the Australian Longitudinal Study on Women’s Health,⁵ were primarily used along with some maternal demographic characteristics and health-related data of 1993 women in the 1973 to 1978 cohort who completed the 2015 Australian Longitudinal Study on Women’s Health survey. The Australian Longitudinal Study on Women’s Health survey collected data on race and ethnicity by asking the participants of the 1973 to 1978 cohort their country of birth; however, given the sample size analyzed for the current study, such data are not reported here to protect participant privacy. The MatCH Study, which was conducted between 2016 and 2017, recruited mothers’ 3 youngest children aged younger than 13 years (n = 4543 children).⁶ Data were analyzed from April 28 to August 17, 2021. The Human Research Ethics Committees at The University of Newcastle and The University of Queensland approved this cross-sectional study. Mothers gave informed written consent for themselves and their children. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

In the MatCH Study, mothers were asked, “Over the past month, about how much screen time has your child had per day on weekdays and weekends?” Answers were recorded as mean hours and minutes to the nearest 15 minutes. Screen time was defined as “time spent watching/using any screen-based equipment, such as television, computers, tablets, mobile phones and electronic games.” Mothers reported screen time on weekdays and weekends for school and nonschool (or recreational) purposes. Total (weekday and weekend) recreational screen time was analyzed. Initially, descriptive analyses were conducted. Multinomial logistic regression was used to assess for associations between having children in different age-based screen time categories (yes or no) and whether all children met age-based screen time guidelines, all children exceeded guidelines, or some children met and some exceeded guidelines. Covariates were the mother’s educational level, ability to manage on income, passive leisure time per week, and the presence of electronic screen equipment in children’s bedrooms. Analyses were conducted using SAS software, version 9.4 (SAS Institute Inc).

The Table shows the demographic characteristics and age combinations of 1993 mothers (mean [SD] age, 40.27 [1.47] years) and their 4543 children (mean [SD] age, 6.94 [3.10] years; 2162 [47.6%] girls and 2379 [52.4%] boys). The age-based screen guidelines were met by all of the children in 855 of 1993 families (42.9%) and exceeded by all of the children in 683 families (34.2%). In 455 families (22.8%), some children met and some exceeded the age-based screen time guidelines. Families with children aged 2 to 4 years had the highest percentage of children exceeding guidelines (55 of 66 [83.3%]). When presented by age-based screen time categories, 169 of 725 families (23.3%) with children in different age-based screen time categories met the guidelines, compared with 686 of 1268 families (54.1%) with children in the same age-based screen time category (Figure, A). Families with children in different age-based screen time categories had the highest percentage of children who either all exceeded the guidelines (30.1% [n = 218 of 725]) or had some children who met and some who exceeded the guidelines (46.6% [n = 338 of 725]). In the adjusted multinomial logistic regression analysis, families with children in different age-based screen time categories had 2.21 (95% CI, 1.73-2.82) higher odds of all children exceeding screen time guidelines and 13.36 (95% CI, 10.08-17.70) higher odds of some children meeting and some exceeding guidelines compared with families with children who were all in the same age-based screen time category.

Of 4543 children, 1098 (24.2%) were from families in which some children met and some exceeded guidelines and, of those, 824 children (75.0%) were in different age-based screen time categories within the family. In this group, 303 of 330 children (91.8%) aged 2 to 4 years exceeded screen time guidelines (Figure, B).

Although many screen time guidelines have moved past quantity to quality (eg, coviewing, enriching content), difficulties exist for families when children have different age-based screen time categories. In these families, younger children may exceed guidelines by matching the screen time of their older siblings. Screen time guidelines need to accommodate families with multiple children, and more policies and resources could help provide practical tips and strategies for families with children of different ages.

Limitations of this study included that screen time was self-reported by mothers. The screen time measure was also based on the mother’s reported total time rather than inclusive of different types. Only the 3 youngest children aged younger than 13 years were included in the MatCH Study, so the implications of older age groups could not be assessed. And the mothers who participated in the MatCH Study were aged between 25 and 43 years, which may introduce bias, as characteristics of women who give birth at younger ages may be different than those who are older.

Accepted for Publication: November 28, 2021.

Published Online: February 14, 2022. doi:10.1001/jamapediatrics.2021.6382

Corresponding Author: Leigh R. Tooth, PhD, School of Public Health, The University of Queensland, Herston Road, Herston, QLD 4006, Australia (l.tooth@uq.edu.au).

Author Contributions: Drs Tooth and Moss had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tooth.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Moss, Mishra.

Obtained funding: Tooth, Mishra.

Administrative, technical, or material support: Tooth.

Supervision: Tooth.

Conflict of Interest Disclosures: Prof Mishra reported receiving grants from the Australian National Health and Medical Research Council. No other disclosures were reported.

Funding/Support: This work was supported by the Australian Government Department of Health, which funds the Australian Longitudinal Study on Women’s Health; grant App1121844 from the National Health and Medical Research Council of Australia Principal Research Fellowship (Prof Mishra); and grant APP1059550 for the MatCH Study from the National Health and Medical Research Council of Australia project grant.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: The research on which this article is based was conducted as part of the Australian Longitudinal Study on Women’s Health by The University of Queensland and The University of Newcastle. The authors thank the Australian Government Department of Health and the women who provided the survey data and acknowledge the important contribution of Colleen Loos, MMedSci, senior project coordinator, School of Public Health, Faculty of Medicine, The University of Queensland, in managing the MatCH Study.