Introduction
Childhood is a critical period for the development and establishment of behaviors and attitudes that continue into adult life [
1]. Children and adolescents who have partaken in a variety of physical and recreational activities are much more active as adults [
2], and a lifestyle that includes regular physical and social activity has been associated with numerous immediate and long-term health benefits. These include lower risk of mental health issues, obesity, and cardiovascular disease risk factors [
3]. Conversely, sedentary behavior is predictive of poor metabolic and physical health, and social wellbeing in childhood [
4]. Children and adolescents report a multitude of sedentary behaviors, some of which are necessary and/or should not be discouraged (e.g., homework, hobbies). However, much of their sedentary time involves non-educational screen media activity (e.g., television watching, computer gaming, social media engagement). The amount of leisure time spent by children and adolescents online has doubled in the past decade [
5]. Children spend up to 50% of their time after school on screens, including cell phones, tablets, computers, gaming consoles, and televisions [
6]. Over 94% of children aged 11 years use a cell phone [
7] and approximately 85% engage in electronic gaming [
8]. Therefore, it is important that research examine the associated outcomes of this shift in leisure time spent on screen media activity (SMA) in childhood.
The displacement hypothesis predicts that SMA and other activities compete for leisure time, where screen time might be at the expense of other recreational activity involvement such as sport and other hobbies, which are potentially more beneficial for health and cognitive development [
9]. For the most part, previous studies investigating this hypothesis have focused on the impacts of SMA on physical activity and have reported inconsistent findings. Some studies have reported moderate inverse relationships between SMA and physical activity in adolescents, where greater SMA use has been associated with lower activity [
10‐
13]. Conversely, two systematic reviews including samples of up to 31,022 youth have found a common “technoactive” cluster of young people who engage in high levels of sports and SMA [
14,
15]. However, a cross-national study from 39 countries with a very large sample size (
n = 200,615) reported no consistent association between SMA time and physical activity in youth aged 11, 13, and 15 years [
16]. Likewise, a recent systematic review of reviews [
4] and a meta-analysis of 163 studies [
17] have found very little empirical evidence to suggest that playing digital games, using a computer, and watching television competes with physical activity involvement in children and adolescents. Overall, results on the interdependence of SMA and recreational physical activity involvement in childhood are inconsistent.
Exploring different types of recreational activities (e.g., sports, music, art) and different forms of media (e.g., television viewing, electronic gaming, cell phones, tablets, computers, social media-related SMA), using data-driven techniques which group and characterize similar patterns of behavior, may be useful [
4]. Associations may differ for various forms of SMA and recreational activities. Additionally, other individual, interpersonal, and sociodemographic factors are likely to play a role in these relations. For instance, when compared to high levels of social media messaging, greater television viewing or gaming may be associated with social isolation, depression, anxiety, and self-injurious behavior in children and adolescents [
11,
18,
19]. In turn, this may decrease interest and involvement in group-based sports and clubs, or vice versa [
11,
19]. Yet, analysis of these different activity settings and types of SMA use, as well as sociodemographic, cognitive, social, and psychopathology factors likely impacting these associations, are uncommon. Moreover, many children also spend their leisure time engaging with hobbies other than physical activity, such as music and art. In contrast to research on associations between SMA and physical activity, studies on other hobbies are particularly sparse.
In light of this, the current research aimed to examine unique associations between various data-driven forms of non-educational SMA use and recreational activities including sports, music, and art, when accounting for other individual (i.e., cognition, psychopathology), interpersonal (i.e., social environment), and sociodemographic factors. Cross-sectional data were utilized from a large participant sample of children aged 9 to 10 years, collected in 2016 and 2017.
Discussion
This study used a large dataset of 9–10-year-old youth to isolate the relationship between screen media activity and youth recreational activity involvement, when accounting for other sociodemographic, cognitive, psychopathology, and social environment factors. Overall, GF-augmented models did not provide a significantly better fit to the data than base models, indicating that sociodemographic factors, particularly socio-economic status, explain more variance in rates of recreational activity engagement than other factors, such as SMA. While greater SMA was related to activity displacement in unadjusted group comparisons, most forms of SMA were no longer significantly associated with recreational activity engagement when accounting for confounding factors. The SMA effects that were observed in adjusted models were small, showing only marginal associations with some activities. Taken together, and contrary to the displacement hypothesis, this study did not find strong evidence that non-educational SMA was at the expense of other recreational activity engagement in 9–10-year-old youth, when accounting for other individual, interpersonal, and sociodemographic factors.
The current findings are in agreement with some previous research which shows SMA does not compete with other activities [
4,
16,
17] and is in disagreement with other studies which conclude SMA displaces physical and outdoor activities in youth and adolescence [
10‐
13]. Consistent with other data, exploration of different types of recreational activities and different forms of media show that where relations do exist, they are nuanced [
14,
15]. For example, the current study provided some indication that “technoactive” (i.e., high social SMA, high recreational engagement) and “socially isolated SMA” (i.e., high general SMA, low group-based recreational engagement) clusters of youth exist. Although, prior studies of adolescents have identified stronger associations [
11,
14,
15,
18,
19]. These inconsistencies may be due to the relatively early developmental period under study and suggest that patterns of behavior may continue to diverge throughout adolescence.
There are several noteworthy aspects of the current study which may account for some of the observed differences. Firstly, this is the first large-scale study of a preadolescent population and the impact of SMA on youth recreational activity involvement may change as a function of age. Accordingly, stronger associations between high SMA and low physical activity engagement have been previously observed in older adolescents [
16]. To date, most studies in this field have reported on cross-sectional data. Longitudinal analysis of this large cohort will provide further clarification on possible clusters of youth and the relative interdependence of SMA use and recreational activity involvement throughout adolescence. Secondly, studies examining associations between SMA and other outcome variables are complicated by the fact that these activities strongly correlate with other factors, such as sociodemographic characteristics [
29]. Using a mixed model analytic approach, the current study demonstrated that associations between SMA, sports, and other hobbies are minimal when confounding factors are appropriately taken into account.
Further to this point, and consistent with other data, the most robust finding from the current study was that youth from higher socio-economic families were more likely to engage in recreational activities than youth from lower socio-economic backgrounds [
30,
31]. Previous studies have demonstrated that lower socio-economic status and high-minority areas have reduced access to recreational activity facilities, bike trails, gym equipment, and perceived safe outdoor spaces [
32]. Similarly, associations between poverty and recreational inactivity have been observed across the life span [
32]. Therefore, greater availability of free recreational resources and programs could be beneficial to families with limited resources. Of note, many of the recreational activities examined in the current study require some form of registration and paid membership. Associations between socio-economic status and endorsement of free leisure activities may differ to those observed here. Further exploratory work examining causes of non-participation is warranted.
Key strengths of this study include utilization of data-driven techniques (i.e., GFA) to distinguish clusters of youth who share similar patterns of behavior or characteristics. Identifying unique patterns of SMA engagement, cognition, social environment, and psychopathology allowed for complex patterns of behavior to be adequately characterized. Furthermore, using a mixed model analytic approach allowed for appropriate adjustment of the complexity of factors that influence youth behaviors. This provided more robust conclusions than reported in some previous association studies. This study also has several limitations. First, this is a cross-sectional assessment, which enabled establishment of associations but does not address causation or directionality. The longitudinal component of ABCD will be essential to begin to delineate causal pathways. Second, unmeasured confounding factors may be contributing to the observed associations. Third, the initial ABCD assessments of media activity are limited to self-report, which may introduce a number of biases and could be improved by more direct assessments of SMA. Fourth, recreational activity involvement was examined as a binary outcome variable due to positively skewed data with little gradation. Therefore, associations between SMA and other factors on the level of activity involvement could not be explored. Fifth, the ABCD cohort are a probability sample which is not necessarily representative of the US population. Finally, the present study was limited to examination of youth aged 9–10 years, which inhibited exploration of age as a moderating factor between SMA and recreational activity displacement. Although, it should be noted that examination of this younger cohort is unique to the existing evidence base, where previous studies have focused on associations in adolescents.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.