Trajectories of screen use during early childhood: Predictors and associated behavior and learning outcomes

https://doi.org/10.1016/j.chb.2020.106501Get rights and content

Highlights

  • Screen use patterns are formed early in childhood.

  • Many factors in the family environment relate to screen use patterns.

  • High screen users across early childhood have poorer developmental outcomes.

  • Families are encouraged to engage with technology in an age-appropriate way.

Abstract

Little is known about the development of early screen use patterns. Using data from 1949 families in Calgary, Alberta, drawn from the All Our Families cohort, this study examined patterns of screen use across 3 time points (24, 36, 60 months) to identify trajectories of screen use, socio-demographic factors that predict trajectory membership, and whether high use trajectories relate to suboptimal child behavior and learning outcomes. Mothers reported on children's screen use (hours per day), children's behavior problems (Behavior Assessment System for Children) and developmental milestones (Ages and Stages Questionnaire). Using latent class growth modeling, two trajectories were identified: low to moderate screen use (90.5% of sample; 2.16, 3.28, and 1.34 h/day, respectively) and high-persistent screen use (9.5% of sample; 3.91, 5.26, and 3.31 h/day, respectively). A number of socio-demographic factors were associated with the high-persistent screen use trajectory. Additionally, the high-persistent screen use trajectory was associated with higher levels of externalizing behavior (e.g., inattention, aggression), poor adaptive skills (e.g., social, life skills), and less likelihood of achievement of developmental milestones (e.g., language, motor skills), at 60 months of age. Findings support that screen use patterns are formed early in childhood, emphasizing the need for prevention and early intervention.

Introduction

The American Academy of Pediatrics recommends no more than 1 hour of screen use per day for 2–5 year olds (i.e., time spent with a tablet, smartphone, TV, computer, video game or wearable technology). However, the majority of preschoolers are failing to meet screen use guidelines. A recent report suggests as many as 80% of 2 year olds and 95% of 3 year olds (Madigan, McArthur, Anhorn, Eirich, & Christakis, 2020) are exceeding the pediatric guidelines (American Academy of Pediatrics Council on Communications and Media, 2016; Tremblay et al., 2017; World Health Organization, 2019). While there is likely some high-quality programming that could be considered beneficial (Madigan, Racine, & Tough, 2020; Rice, Huston, Truglio, & Wright, 1990), according to the displacement hypothesis, when children are watching screens they spend less time practicing skill development via interactions with, and exploration of, their environment (Christakis, 2009). As such, there is growing concern among educators, researchers, health professionals, and parents about the impact of screen use on children's development (Rideout, 2017).

Only two studies (Chiu, Li, Wu, & Chiang, 2017; Trinh et al., 2020) have explicitly examined trajectories of screen use in preschool-aged children. One study focused solely on television (TV) viewing across 3 timepoints (18, 36, and 66 months) identifying three developmental trajectories among a representative Taiwanese sample (N = 18577): a consistently low trajectory (20% of sample), an increasing trajectory (46.5% of sample), and a consistently high trajectory (33.5% of sample; Chiu et al., 2017). The other study examined a composite of TV, movie and computer game use across 5 timepoints (12, 18, 24, 30, and 36 months), identifying two developmental trajectories of screen use among a representative US sample (N = 1045): a consistently low trajectory (73.3% of sample) and an increasing trajectory (26.7% of sample; Trinh et al., 2020). Both studies provide evidence that screen use patterns are forming early in development. Although these studies provide insight into early screen use trajectories for young children, an examination of whether trajectory membership is associated with children's behavior and learning outcomes has not been examined.

Theoretically, children who are exposed to more persistent screen use may be missing out on learning opportunities, such as practicing language, communication, and literacy skills (Christakis, 2009; Tamis-LeMonda, Luo, McFadden, Bandel, & Vallotton, 2019), as well as parent and peer interactions that have been shown to build interpersonal skills and foster well-being (Radesky, Peacock-Chambers, Zuckeman, & Silverstein, 2016; Twenge & Campbell, 2018). Past research supports that children exposed to a higher duration of screen use tend to have associated behavior or social-emotional problems (Hinkley et al., 2014; Tamana et al., 2019), and are less likely to meet age appropriate developmental milestones (Madigan, Browne, Racine, Mori, & Tough, 2019). However, this research lacks a developmental perspective with most studies measuring children's screen use cross-sectionally (Browne, Thompson, & Madigan, 2020Odgers & Jensen, 2020). To our knowledge, the association between trajectories of screen use and children's behavior and learning outcomes has not been examined. Thus, studies examining the heterogeneous patterns of screen use development are needed to better understand the normative and persistent patterns of screen use that may emerge early in development, and how these patterns relate to child behavior and learning outcomes.

Individual, family, and environmental factors impact a child's screen use trajectory and offer avenues for additional prevention efforts. A number of socio-demographic factors have been associated with higher durations of screen use; including, household income (Hoyos Cillero & Jago, 2010; Rideout & Hamel, 2006), parental education level (Rideout & Hamel, 2006), the presence of an older sibling (Duch, Fisher, Ensari, & Harrington, 2013), ethnic minority status (Duch et al., 2013; Hoyos Cillero & Jago, 2010; Rideout & Hamel, 2006), child sex (Rideout & Hamel, 2006), maternal age (Duch et al., 2013), home-based versus center-based child care (Madigan, Racine, & Tough, 2020), maternal depression (Hoyos Cillero & Jago, 2010), and maternal screen use (Madigan, Racine, & Tough, 2020). Given the limited longitudinal studies for preschool aged children (Chiu et al., 2017; Trinh et al., 2020), to improve our ability to identify children at risk, research is needed to determine the socio-demographic factors that predict a child's screen use trajectory.

Several research gaps remain to adequately understand how screen use trajectories develop during the preschool years, the socio-demographic factors that may interrupt or expedite these patterns, and how these trajectories relate to child behavior and learning outcomes. The aims of this study were 3-fold. The first was to use a rigorous data analysis approach, Latent Class Growth Modeling (LCGM; Nagin, 2005), to identify homogeneous trajectories of screen use between the ages of 24 and 60 months, among a Canadian sample. The second was to identify the socio-demographic factors associated with trajectory membership. The third was to examine the association of patterns of screen use with child behavior and learning outcomes. This study is the first to validate the identified trajectories by examining the association between trajectory membership and behavior (i.e., internalizing, externalizing, and adaptive behavior) and learning (i.e., achievement of developmental milestones) outcomes at 60 months of age.

Section snippets

Study design and population

Participants included individuals recruited to participate in the All Our Families cohort, an ongoing pregnancy cohort of mothers and children from Calgary, Canada (Tough et al., 2017). Women were recruited between August 2008 and December 2010 through primary health care offices, community advertising, and laboratories. Inclusion criteria were: (1) ≥ 18 years, (2) fluent in English, (3) gestational age < 25 weeks, and (4) receiving community-based prenatal care. Mothers and children have been

Results

Based on screen use measured at 24, 36, and 60 months of age, the best fitting LCGM was the 2-trajectory quadratic model (Table 2, Fig. 1). The low to moderate screen use trajectory (n = 1764 [90.5%]) was composed of children who exhibited a lower level of screen use across all time points (2.14[1.37], 3.28[1.47], 1.35[0.43]; M[SD] hours/day, respectively). The high-persistent screen use trajectory (n = 185 [9.5%]) was composed of children who exhibited higher levels of screen use across all

Discussion

The emerging media options now available to young children has resulted in increased screen use among this demographic (Rideout, 2017). Indeed, screen use is now considered ubiquitous in the lives of young children, with approximately 98% of children under age 8 living in a home with an internet-connected device, and, on average 5 devices per household (Common Sense Media, 2018). However, currently, little empirically supported information can be provided to practitioners and parents about the

Limitations

Using a large longitudinal cohort, this study sheds light on important factors that may be useful in identifying children at risk for patterns of high screen use exposure. However, the findings must be interpreted with the following limitations in mind. First, this study would be strengthened if replicated with objective measures of child behavior, learning outcomes, and screen use. Second, the current sample was socio-demographically homogeneous. While this is representative of the region of

Conclusions

In response to the evolving digital landscape and rapid increase in accessibility and exposure to screen media, there is a need for longitudinal approaches to better understand the long-term impact of screen use for young children (Browne et al., 2020). In the current study, we identified two unique patterns of screen use – a high-persistent screen use trajectory and a low to moderate screen use trajectory, suggesting that early screen use patterns may persist over time. Moreover, a number of

CRediT authorship contribution statement

Brae Anne McArthur: Conceptualization, Formal analysis, Writing - original draft. Dillon Browne: Formal analysis, Writing - review & editing. Suzanne Tough: Data curation, Methodology, Funding acquisition, Writing - review & editing. Sheri Madigan: Conceptualization, Formal analysis, Writing - original draft, Supervision.

Declaration of competing interest

None.

References (36)

  • J. Cohen

    Statistical power analysis for the behvaioral sciences

    (1988)
  • H. Duch et al.

    Screen time use in children under 3 years old: A systematic review of correlates

    International Journal of Behavioral Nutrition and Physical Activity

    (2013)
  • J.W. Graham

    Missing data analysis: Making it work in the real world

    Annual Review of Psychology

    (2009)
  • T. Hinkley et al.

    Early childhood electronic media use as a predictor of poorer well-being

    JAMA Pediatrics

    (2014)
  • R.W. Kamphaus

    Behavior assessment system for children, second edition (BASC-2)

  • P.M. Lewinsohn et al.

    Center for Epidemiological Studies-Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults

    Psychology and Aging

    (1997)
  • S. Madigan et al.

    Association between screen time and children's performance on a developmental screening test

    JAMA Pediatrics

    (2019)
  • S. Madigan et al.

    Associations between screen use and child language skills: A systematic review and meta-analysis

    JAMA Pediatrics

    (2020)
  • Cited by (26)

    • Screen use and early child development: Risks and benefits

      2023, Encyclopedia of Child and Adolescent Health, First Edition
    • Screen media are associated with fine motor skill development in preschool children

      2022, Early Childhood Research Quarterly
      Citation Excerpt :

      Specifically, dominant sensory input in the visual and auditory modalities can be expected (Suggate & Martzog, 2021b), whereas purposeful motor activity involving complex actions and manipulations of objects in 3-dimensional space can be reduced to a minimum —perhaps depending on media type (Hinkley, Salmon, Okely, Crawford, & Hesketh, 2012). Although research indicates that gross motor skill is negatively associated with high screen media use (McArthur, Browne, Tough, & Madigan, 2020; True et al., 2017), it has been argued that newer media tend to encourage fine manual actions stimulating Fine motor skills (FMS) (Bedford, Saez de Urabain, Cheung, Karmiloff-Smith, & Smith, 2016). Conversely, it may be that movements such as button pressing are insufficiently varied, lacking differentiated proprioceptive and haptic feedback, and 3-dimensionality (Latash, Turvey, & Bernshteĭn, 1996; Pesce et al., 2016) to support motor development (Hadders-Algra, 2010).

    View all citing articles on Scopus
    1

    shared senior authorship.

    View full text