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References
Lees, B., Squeglia, L. M., Breslin, F. J., Thompson, W. K., Tapert, S. F., & Paulus, M. P. (2020).
Screen media activity does not displace other recreational activities among 9-10 year-old
youth: a cross-sectional ABCD study®. BMC Public Health, 20(1), 1–11. https://lynn-
lang.student.lynn.edu:2092/10.1186/s12889-020-09894-w
Screen media activity does not displace other recreational activities among 9–10 year-
old youth: a cross-sectional ABCD study®
Background: Screen media is among the most common recreational activities engaged in
by children. The displacement hypothesis predicts that increased time spent on screen
media activity (SMA) may be at the expense of engagement with other recreational
activities, such as sport, music, and art. This study examined associations between non-
educational SMA and recreational activity endorsement in 9–10-year-olds, when accounting
for other individual (i.e., cognition, psychopathology), interpersonal (i.e., social
environment), and sociodemographic characteristics. Methods: Participants were 9254
youth from the Adolescent Brain Cognitive Development Study®. Latent factors reflecting
SMA, cognition, psychopathology, and social environment were entered as independent
variables into logistic mixed models. Sociodemographic covariates included age, sex,
race/ethnicity, education, marital status, and household income. Outcome variables included
any recreational activity endorsement (of 19 assessed), and specific sport (swimming,
soccer, baseball) and hobby (music, art) endorsements. Results: In unadjusted groupwise
comparisons, youth who spent more time engaging with SMA were less likely to engage
2/25/23, 8:42 PM
Page 1 of 18
with other recreational activities (ps <.001). However, when variance in cognition,
psychopathology, social environment, and sociodemographic covariates were accounted
for, most forms of SMA were no longer significantly associated with recreational activity
engagement (p >.05). Some marginal effects were observed: for every one SD increase in
time spent on games and movies over more social forms of media, youth were at lower
odds of engaging in recreational activities (adjusted odds ratio = 0·83, 95% CI 0·76–0·89).
Likewise, greater general SMA was associated with lower odds of endorsing group-based
sports, including soccer (0·93, 0·88–0·98) and baseball (0·92, 0·86–0·98). Model fit
comparisons indicated that sociodemographic characteristics, particularly socio-economic
status, explained more variance in rates of recreational activity engagement than SMA and
other latent factors. Notably, youth from higher socio-economic families were up to 5·63
(3·83–8·29) times more likely to engage in recreational activities than youth from lower
socio-economic backgrounds. Conclusions: Results did not suggest that SMA largely
displaces engagement in other recreational activities among 9–10-year-olds. Instead, socio-
economic factors greatly contribute to rates of engagement. These findings are important
considering recent shifts in time spent on SMA in childhood.
Keywords: Screen media; Social media; Sport; Physical activity; Recreational activities;
Hobbies; Displacement hypothesis; Children
Supplementary Information The online version contains supplementary material available at
https://lynn-lang.student.lynn.edu:2092/10.1186/s12889-020-09894-w.
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]].
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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]]. 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]]. 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,
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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.
Methods
Participants
This study used baseline cross-sectional data from participants aged 9 to 10 years included
in the Adolescent Brain Cognitive Development (ABCD) Data Release 2.0.1. The
Adolescent Brain Cognitive Development Study is the largest long-term study of child health
in the United States, with 21 research sites across the nation. A probability sample was
recruited through school systems with school selection informed by sex, race and ethnicity,
socio-economic status, and urbanicity [[20]]. Written informed consent and assent were
obtained from a parent or legal guardian and the child, respectively. All procedures were
approved by an Institutional Review Board. Of 11,875 participants enrolled, 9254 had
complete data on all relevant measures and were eligible to be included in the current study.
Outcome measure
Youth participation in a variety of organized recreational activities was assessed via The
Sports and Activities Involvement Questionnaire [[21]]. Parents reported on the frequency,
duration, and type of activity their child participates in, including physical activity, sports,
music, and hobbies. The questionnaire does not capture levels of physical activity outside of
these recreational activities. Data were positively skewed with little gradation, hence for the
current analyses, a binary variable was utilized which assessed any recreational activity
involvement (yes/no). Additionally, five binary variables for highly endorsed recreational
sports and hobbies were examined, including swimming, soccer, baseball, music, and art.
See Fig. 1 for endorsement rates of all 29 activities assessed.
Graph: Fig. 1 Parent-reported endorsement rates of 29 recreational activities
Explanatory measures
Screen media activity (youth report)
Non-educational SMA was assessed by asking youth to indicate how long (none, < 30 min,
30 min, 1 h, 2 h, 3 h, or 4h hours) they were engaged in the following activities during
weekdays and on the weekend: i) TV shows or movies; ii) videos; iii) video games on a
computer, console, phone or other device; iv) messaging on a cell phone, tablet, or
computer; v) social networking sites; and vi) video chat.
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Psychopathology (parent report)
Youth externalizing and internalizing psychopathology syndrome t-scores from the Child
Behavior Checklist were utilized in the analyses [[22]].
Cognition (youth performance)
The neurocognitive assessment included seven NIH Toolbox® tasks, the Rey Auditory
Verbal Learning Test, and the Weschsler Intelligence Scale for Children [[23]].
Social environment (youth/parent report)
The social environment domain was assessed using the youth and parent-reported
prosocial behavior subscale of Strengths and Difficulties Questionnaire, [[24]] the
acceptance subscale of the Children’s Reports of Parental Behavior for parent and
caregiver, [[25]] the Parent Monitoring Questionnaire, [[26]] and the conflict subscale of the
Family Environment Scale [[27]].
Covariates
The following sociodemographic variables were included in all statistical models and were
dummy coded: sex (M/F), race/ethnicity (White, Black, Hispanic, Asian, Other), parent
education (
status (single parent household, married/living together). Youth and parent age were
included as continuous variables.
Statistical analysis
Group comparisons
Initial groupwise comparisons on all explanatory measures were performed between youth
who did (n = 8308) and not did (n = 946) engage in at least one recreational activity. Using
R package ‘tableone’, one-way ANOVAs were conducted for continuous variables and chi-
square tests were conducted for categorical variables.
Group factor analysis
Next, Group Factor Analysis (GFA) [[28]] was conducted to generate a set of explanatory
factors that account for variance in SMA and other individual (i.e., cognition,
psychopathology) and interpersonal (i.e., social environment) factors. GFA is an
unsupervised learning technique that identifies latent variables across “groups” of variables.
This technique allows identification of factors that selectively load onto a construct (e.g.,
SMA) or across constructs (e.g., SMA and psychopathology). It then uses these factors to
determine whether the construct specifically is associated with outcomes of interest (e.g.,
recreational activity endorsement). For the current analyses, four variable groups were
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entered, including SMA, psychopathology, cognitive function, and social environment, using
the ‘GFA’ R package. The solution comprises a set of group factors (GFs) which load onto
the correlated groups of variables. GFA estimation was repeated 10 times with different
seeds of randomized numbers in order to identify robust GFs which were consistent across
sampling chains. The robust GFs were selected by two criteria. First, posterior means of GF
components obtained from the ten sampling chains were required to pass a Pearson
correlation threshold of 0·7 in order to be considered as the “same”. Second, a GF was
deemed robust if it was identified at least 70% of the time across the 10 replicates. The
robust GF scores were then averaged across the ten replicated analyses and utilized in the
mixed model analyses.
Mixed models
Subsequent association analyses were conducted within a generalized linear mixed models
(GLMMs) framework, using a logistic link to predict recreational activity involvement (R
package: ‘glmmTMB’). Parameters of the mixed model were estimated by the Restricted
Maximum Likelihood. Research site and siblings nested within site were entered as random
intercepts. In the first pass, a GLMM analysis of a base model was conducted where
sociodemographic variables (youth age, sex, and race/ethnicity, as well as parent age,
education, marital status, household income) were entered as independent variables
predicting involvement in any recreational activity (yes/no). In a second pass, a full model
was conducted where the robust SMA, psychopathology, cognitive function, and social
environment-related GFs were entered as additional independent variables, alongside the
sociodemographic measures (youth age, sex, and race/ethnicity, as well as parent age,
education, marital status, household income). Comparison between the base and full model
was conducted using the ANOVA F-test and Bayesian Information Criterion (BIC). The
nested models (i.e., base and full models) were then repeated for highly endorsed sports
and hobbies, including soccer, music, swimming, baseball, and art in five separate models.
Results
Group comparison
Recreational activity endorsement was high, with 89·8% of parents endorsing youth
involvement in at least one recreational activity, for whom the mean number of activities
endorsed was 3·4 and the maximum number was 23 (Fig. 1). Highly endorsed sports and
hobbies included soccer (40·2%), music (39·0%), swimming (31·5%), baseball (26·8%), and
art (19·3%). Sociodemographic group differences between those who did and did not
engage in at least one recreational activity are reported in Table 1. Group differences for
SMA, cognition, psychopathology, and social environment measures are reported in Suppl.
Table 1. In unadjusted groupwise comparisons, youth who engaged in recreational activities
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Page 6 of 18
spent less time engaging with SMA (all ps <.001), exhibited lower total psychopathology symptoms (p <.001), performed better on cognitive tasks (all ps <.001), experienced less family conflict (p <.001), greater parental acceptance (p <.001) and monitoring (p <.001), and exhibited greater prosocial behavior (p =.007) than youth who did not engage in recreational activities.
Table 1 Sociodemographic data on youth from the ABCD cohort
No activity endorsement N =
946
Activity endorsement N =
8308
p
Age (mean [SD]) 9.8 (0.6) 9.9 (0.6) <.001
Male (%) 477 (50.4) 4332 (52.1) .33
Race/Ethnicity (%) <.001
White 281 (29.7) 4925 (59.3)
Black 296 (31.3) 891 (10.7)
Hispanic 248 (26.2) 1461 (17.6)
Asian 13 (1.4) 179 (2.2)
Other 108 (11.4) 852 (10.3)
Household income (%) <.001
< $50 K 611 (64.6) 1899 (22.9)
$50-100 K 243 (25.7) 2406 (29.0)
> $100 K 92 (9.7) 4003 (48.2)
Parent education (%) <.001
≤ HS 336 (35.5) 719 (8.6)
College 392 (41.4) 1901 (22.9)
≥ Bachelor's degree 218 (23.0) 5688 (68.5)
Married/live together
(%)
561 (59.3) 6609 (79.5) <.001
Parent Age (mean
[SD])
37.2 (7.1) 40.5 (6.6) <.001
Group factor analysis
The GFA procedure yielded 15 robust GFs which occurred in at least 70% of the ten
replicated analyses and passed the Pearson correlation threshold of 0·7. These GFs
explained 33·7% of the total variance (suppl. Fig. 1), including 42·7% of SMA, 44·5% of
cognitive function, 13·7% of psychopathology, and 11·6% of social environment variance
(suppl. Fig. 2). Five GFs were strictly SMA-related, one was cognitive-related, two were
psychopathology-related, five were related to the social environment, one was
psychopathology- and SMA-related, and one was psychopathology- and cognitive-related.
These GFs were largely orthogonal (suppl. Fig. 3). All 15 GFs were extracted for GLMM
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Page 7 of 18
analysis and Figs. 2, 3, and 4 depict the three SMA-related GFs that showed some
association with recreational activity involvement, while all other GF figures are available in
the supplement (suppl. Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15).
Graph: Fig. 2 General media group factor
Graph: Fig. 3 Low media, high internalizing symptoms group factor
Graph: Fig. 4 Low social media, high other media (movies, videos, games) group factor
Mixed model findings
Engagement with any recreational activity
GLMM analysis showed that the full model (including 15 SMA, cognitive function, social
environment, and psychopathology GFs and sociodemographic independent variables) for
predicting involvement in any recreational activity (R = 0·35) did not significantly improve
the base model which comprised of age, sex, race/ethnicity, parental education, marital
status, household income, and parent age (R = 0·34, BIC = 5146·9, ∆BIC = 52·8, LRT =
84·2) (Fig. 5).
Graph: Fig. 5 Factors associated with any recreational activity endorsement (of 29 activities
assessed)
Of the six SMA-related GFs, two were significantly associated with recreational activity
endorsement when adjusting for sociodemographic, individual, and interpersonal factors in
the full model (Fig. 5). For every increase in one standard deviation (SD) on the ‘high
games/movies and low social media’ GF, youth were 0·83 (95% CI 0·76–0·89) times as
likely to endorse recreational engagement. For every one SD increase on the ‘low SMA and
high internalizing psychopathology’ GF, youth were 1·10 (1·02–1·18) times more likely to
endorse recreational engagement.
In terms of sociodemographic factors, compared to White youth, Black, Asian, and other
race/ethnicity youth were 0·68 (95% CI 0·54–0·85), 0·47 (0·25–0·87), and 0·75 (0��58–
0·97) times as likely to endorse any activity involvement, respectively. Compared to youth
where parents did not complete high school, youth of college attendees, Bachelor, or >
Bachelor graduates were 1·67 (1·26–2·22), 3·13 (2·24–4·37), and 5·63 (3·83–8·29) times
more likely to endorse recreational activity engagement, respectively. Similarly, youth from
middle ($50-100 K) and higher (>$100 K) income households were 1·42 (1·16–1·72) and
3·30 (2·47–4·42) times more likely to endorse engagement than youth from lower (<$50 K)
income households, respectively.
2
2
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Engagement with highly endorsed sports and hobbies
The five full models (including 15 SMA, cognitive function, social environment, and
psychopathology GFs and sociodemographic independent variables) for predicting highly
endorsed recreational activities, including soccer (R = 0·20), swimming (R = 0·11),
baseball (R = 0·24), music (R = 0·23), and art (R = 0·09) did not significantly improve
base models which comprised of age, sex, race/ethnicity, parental education, marital status,
household income, and parent age (soccer [R = 0·20, BIC = 11,394·1, ∆BIC = 102·4, LRT =
34·6], swimming [R = 0·10, BIC = 11,153·5, ∆BIC = 90·0, LRT = 47·0], baseball [R = 0·23,
BIC = 9792·8, ∆BIC = 100·1, LRT = 36·8], music [R = 0·21, BIC = 11,142·5, ∆BIC = 64·1,
LRT = 201·0], art [R = 0·08, BIC = 8978·9, ∆BIC = 86·0, LRT = 51·0]) (see Suppl. Figs. 16,
17, 18, 19 and 20).
Of the six SMA-related GFs, three were significantly associated with various sports and
hobbies when adjusting for sociodemographic, individual, and interpersonal factors in full
models (Suppl. Figs. 16, 17, 18, 19 and 20). For every one SD increase on the ‘high
games/movies and low social media’ GF, youth were less likely to engage in swimming
(adjusted OR = 0·91, 95% CI 0·85–0·96), soccer (0·91, 0·86–0·96), baseball (0·91, 0·85–
0·97), and music (0·88, 0·83–0·94). For every one SD increase on the ‘high general SMA’
GF, youth were less likely to endorse soccer (0·93, 0·88–0·98) and baseball (0·92, 0·86–
0·98). For every one SD increase on the ‘low SMA use and high internalizing
psychopathology’ GF, youth were more likely to endorse swimming (1·07, 1·02–1·13) and
art (1·09, 1·03–1·16), and less likely to endorse baseball (0·92, 0·87–0·97).
For each activity, findings related to parent education and household incomes were mostly
consistent with the overall activity model described above. Compared to females, males
were less likely to endorse swimming (adjusted OR = 0·87, 95% CI = 0·79–0·95), music
(0·66, 0·60–0·72), and art activities (0·45, 0·40–0·51). In contrast, males were 1·96 (1·78–
2·16) and 3·57 (3·20–3·99) times more likely than females to endorse soccer and baseball
involvement, respectively. Compared to White youth, Black youth were less likely to endorse
soccer (0·39, 0·32–0·47) and baseball (0·37, 0·30–0·46), Asian youth were more likely to
endorse swimming (1·44, 1·06–1·95) and music (1·60, 1·15–2·23), and less likely to
endorse soccer (0·36, 0·26–0·51) and baseball (0·23, 0·14–0·36), and Hispanic youth were
less likely to endorse baseball (0·76, 0·65–0·91).
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,
2 2
2 2 2
2
2 2
2
2
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Page 9 of 18
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]] 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]]. 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], [18]]. 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
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recreational activities than youth from lower socio-economic backgrounds [[30]]. 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.
Conclusion
This study found that screen media activity does not appear to largely displace engagement
with other recreational activities, including sports and hobbies, in preadolescent youth.
Where associations between SMA and other activities were observed, effects were nuanced
and small at best. Considering the recent shift in leisure time spent on non-educational SMA
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Page 11 of 18
in childhood, it is encouraging that SMA does not appear to impede engagement with sports
and hobbies, which are potentially more beneficial for health and cognitive development [[
9]]. Importantly, the findings attribute much of the variance in recreational activity
endorsement to socio-economic factors. Longitudinal analysis of this cohort will provide
clarification on whether particular forms of screen media more greatly impacts engagement
with other activities when youth enter adolescence.
Acknowledgements
Not applicable.
Authors’ contributions
All authors are responsible for this reported research. MP conceptualized the study and
conducted the analyses. BL, LS, FB, WT, SF and MP interpreted the data. BL drafted the
manuscript. All authors critically reviewed and revised the manuscript and approved the
final version.
Funding
This work was supported by the Australian National Health and Medical Research Council
(GNT1169377 to BL). The ABCD Study is supported by the National Institutes of Health and
additional federal partners under award numbers U01DA041048, U01DA050989,
U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987,
U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134,
U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120,
U01DA051038, U01DA041148, U01DA041093, U01DA041089. A full list of supporters is
available at https://abcdstudy.org/federal-partners.html. The funding sources had no role in
the writing of the manuscript or the decision to submit for publication. This manuscript
reflects the views of the authors and may not reflect the opinions or views of the Australian
National Health and Medical Research Council, NIH, or ABCD consortium investigators.
Availability of data and materials
The datasets generated and/or analysed during the current study are available in the
National Institute of Mental Health Data Archive repository, https://lynn-
lang.student.lynn.edu:2323/abcd. Data used in the preparation of this article were obtained
from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org),
held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to
recruit more than 10,000 children age 9–10 and follow them over 10 years into early
adulthood. A listing of participating sites and a complete listing of the study investigators can
be found at https://abcdstudy.org/scientists/workgroups/. The ABCD data repository grows
and changes over time. The ABCD data used in this report came from https://lynn-
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lang.student.lynn.edu:2092/10.15154/1504431 (DOI). DOIs can be found at https://lynn-
lang.student.lynn.edu:2323/study.html?id=796.
Ethics approval and consent to participate
All procedures were approved by a central Institutional Review Board (IRB) at the University
of California, San Diego, and in some cases by individual site IRBs (e.g., Washington
University in St. Louis) [[33]]. Parents or guardians provided written informed consent after
the procedures had been fully explained and children assented before participation in the
study [[34]].
Consent for publication
Not applicable.
Competing interests
Dr. Paulus is an advisor to Spring Care, Inc., a behavioral health startup, he has received
royalties for an article about methamphetamine in UpToDate. All other authors declare that
they have no competing interests.
Supplementary Information
Graph: Additional file 1: Supplement Materials. Additional results are provided.
Graph: Additional file 2: STROBE Statement – Checklist of items that should be included in
reports of cross-sectional studies. The STROBE checklist has been used in conjunction with
this article. Page numbers of the manuscript are provided for relevant criteria/details.
Abbreviations
• ABCD
Adolescent Brain Cognitive Development
• BIC
Bayesian Information Criterion
• GF
Group factor
• GFA
Group factor analysis
2/25/23, 8:42 PM
Page 13 of 18
• GLMM
Generalized linear mixed models
• SMA
Screen media activity
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
References
1 Currie C, Nic Gabhainn S, Godeau E. The Health Behaviour in School-Aged Children:
WHO Collaborative Cross-National (HBSC) Study: Origins, Concept, History and
Development 1982–2008. Int J Public Health. 2009; 52: 131-139. 10.1007/s00038-009-
5404-x
2 Makela S, Aaltonen S, Korhonen T, Rose RJ, Kaprio J. Diversity of leisure-time sport
activities in adolescence as a predictor of leisure-time physical activity in adulthood. Scand
J Med Sci Sports. 2017; 27; 12: 1902-12. 1:STN:280:DC%2BC1c7ns1aktg%3D%3D.
10.1111/sms.12837
3 Hallal PC, Victora CG, Azevedo MR, Wells JC. Adolescent physical activity and health: a
systematic review. Sports Med. 2006; 36; 12: 1019-1030. 10.2165/00007256-200636120-
00003
4 Stiglic N, Viner RM. Effects of screentime on the health and well-being of children and
adolescents: a systematic review of reviews. BMJ Open. 2019; 9; 1: e023191.
10.1136/bmjopen-2018-023191
5 Twenge JM, Martin GN, Spitzberg BH. Trends in U.S. Adolescents’ media use, 1976–
2016: The rise of digital media, the decline of TV, and the (near) demise of print. 2019: US;
Educational Publishing Foundation: 329-345
6 Arundell L, Fletcher E, Salmon J, Veitch J, Hinkley T. A systematic review of the
prevalence of sedentary behavior during the after-school period among children aged 5-18
years. Int J Behav Nutri Physic Activity. 2016; 13: 93. 10.1186/s12966-016-0419-1
7 Sudan M, Olsen J, Sigsgaard T, Kheifets L. Trends in cell phone use among children in
the Danish national birth cohort at ages 7 and 11 years. J Exposure Sci Environ Epidemiol.
2016; 26: 606. 10.1038/jes.2016.17
2/25/23, 8:42 PM
Page 14 of 18
8 Rikkers W, Lawrence D, Hafekost J, Zubrick SR. Internet use and electronic gaming by
children and adolescents with emotional and behavioural problems in Australia–results from
the second child and adolescent survey of mental health and wellbeing. BMC Public Health.
2016; 16; 1: 399. 10.1186/s12889-016-3058-1
9 Mutz DC, Roberts DF, van Vuuren DP. Reconsidering the displacement hypotheiss:
Television’s influence on children’s use of time. Communic Res. 1993; 20; 1: 51-75.
10.1177/009365093020001003
Vella SA, Cliff DP, Okely AD, Scully ML, Morley BC. Associations between sports
participation, adiposity and obesity-related health behaviors in Australian adolescents. Int J
Behav Nutri Phys Activity. 2013; 10: 113. 10.1186/1479-5868-10-113
Romer D, Bagdasarov Z, More E. Older versus newer media and the well-being of United
States youth: results from a national longitudinal panel. J Adolescent Health. 2013; 52; 5:
613-619. 10.1016/j.jadohealth.2012.11.012
Spengler S, Mess F, Woll A. Do Media use and physical activity compete in adolescents?
Results of the MoMo study. PLoS One. 2015; 10; 12: e0142544.
10.1371/journal.pone.0142544
Auhuber L, Vogel M, Grafe N, Kiess W, Poulain T. Leisure activities of healthy children and
adolescents. Int J Environ Res Public Health. 2019; 16; 12: 2078. 10.3390/ijerph16122078
Ferrar K, Chang C, Li M, Olds TS. Adolescent time use clusters: a systematic review. J
Adolesc Health. 2013; 52; 3: 259-270. 10.1016/j.jadohealth.2012.06.015
Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with
screen-based sedentary behavior among adolescent girls: a systematic review. J
Adolescent Health. 2013; 52; 4: 382-392. 10.1016/j.jadohealth.2012.07.018
Melkevik O, Torsheim T, Iannotti RJ, Wold B. Is spending time in screen-based sedentary
behaviors associated with less physical activity: a cross national investigation. Int J Behav
Nutr Phys Act. 2010; 7; 1: 46. 10.1186/1479-5868-7-46
Pearson N, Braithwaite RE, Biddle SJH, van Sluijs EMF, Atkin AJ. Associations between
sedentary behaviour and physical activity in children and adolescents: a meta-analysis.
Obes Rev. 2014; 15; 8: 666-675. 1:STN:280:DC%2BC2cjks1Glsw%3D%3D.
10.1111/obr.12188
Ahn D, Shin D-H. Is the social use of media for seeking connectedness or for avoiding
2/25/23, 8:42 PM
Page 15 of 18
social isolation?Mechanisms underlying media use and subjective well-being. Comput
Human Behav. 2013; 29: 2453-2462. 10.1016/j.chb.2012.12.022
Liu M, Zhang J, Hu E, Yang H, Cheng C, Yao S. Combined patterns of physical activity and
screen-related sedentary behavior among Chinese adolescents and their correlations with
depression, anxiety and self-injurious behaviors. Psychol Res Behav Manag. 2019; 12:
1041-1050. 10.2147/PRBM.S220075
Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S. Recruiting the
ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018; 32: 16-
22. 1:STN:280:DC%2BC1MjnsFSquw%3D%3D. 10.1016/j.dcn.2018.04.004
Huppertz C, Bartels M, de Zeeuw EL, van Beijsterveldt CEM, Hudziak JJ, Willemsen G.
Individual differences in exercise behavior: stability and change in genetic and
environmental determinants from age 7 to 18. Behav Genet. 2016; 46; 5: 665-679.
10.1007/s10519-016-9799-x
Achenbach TM. The Achenbach system of empirically based assessment (ASEBA):
development, findings, theory, and applications: University of Vermont, research Center for
Children, youth, & families. 2009
Luciana M, Bjork JM, Nagel BJ, Barch DM, Gonzalez R, Nixon SJ. Adolescent
neurocognitive development and impacts of substance use: overview of the adolescent
brain cognitive development (ABCD) baseline neurocognition battery. Dev Cognitive
Neurosci. 2018; 32: 67-79. 1:STN:280:DC%2BC1MrpslGqsw%3D%3D.
10.1016/j.dcn.2018.02.006
Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol
Psychiatry. 1997; 38; 5: 581-586. 1:STN:280:DyaK2szpvFKjsA%3D%3D. 10.1111/j.1469-
7610.1997.tb01545.x
Barber BK, Olsen JE, Shagle SC. Associations between parental psychological and
behavioral control and youth internalized and externalized behaviors. Child Dev. 1994; 65;
4: 1120-1136. 1:STN:280:DyaK2M%2FksVGnug%3D%3D. 10.2307/1131309
Karoly HC, Callahan T, Schmiege SJ, Ewing SW. Evaluating the Hispanic paradox in the
context of adolescent risky sexual behavior: the role of parent monitoring. J Pediatr Psychol.
2016; 41; 4: 429-440. 10.1093/jpepsy/jsv039
Moos RH. Conceptual and empirical approaches to developing family-based assessment
2/25/23, 8:42 PM
Page 16 of 18
procedures: resolving the case of the family environment scale. Fam Process. 1990; 29; 2:
199-208. 1:STN:280:DyaK3czitlagsA%3D%3D. 10.1111/j.1545-
5300.1990.00199.xdiscussion 9-11
Klami A, Virtanen S, Leppaaho E, Kaski S. Group factor analysis. IEEE Transactions Neural
Networks Learning Syst. 2015; 26; 9: 2136-2147. 10.1109/TNNLS.2014.2376974
Parker KE, Salmon J, Costigan SA, Villanueva K, Brown HL, Timperio A. Activity-related
behavior typologies in youth: a systematic review. Int J Behav Nutr Phys Act. 2019; 16; 1:
44. 10.1186/s12966-019-0804-7
Leech RM, McNaughton SA, Timperio A. The clustering of diet, physical activity and
sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act. 2014;
11; 1: 4. 10.1186/1479-5868-11-4
Armstrong S, Wong CA, Perrin E, Page S, Sibley L, Skinner A. Association of Physical
Activity with Income, race/ethnicity, and sex among adolescents and young adults in the
United States: findings from the National Health and nutrition examination survey, 2007-
2016. JAMA Pediatr. 2018; 172; 8: 732-740. 10.1001/jamapediatrics.2018.1273
Gordon-Larsen P, Nelson MC, Page P, Popkin BM. Inequality in the built environment
underlies key health disparities in physical activity and obesity. Pediatrics. 2006; 117; 2:
417-424. 10.1542/peds.2005-0058
Auchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA, Tapert
SF, Dowling GJ. A description of the ABCD organizational structure and communication
framework. Dev Cognitive Neurosci. 2018; 32: 8-15. 10.1016/j.dcn.2018.04.003
Clark DB, Fisher CB, Bookheimer S, Brown SA, Evans JH, Hopfer C, Hudziak J, Montoya I,
Murray M, Pfefferbaum A, Yurgelun-Todd D. Biomedical ethics and clinical oversight in
multisite observational neuroimaging studies with children and adolescents. Dev Cognitive
Neurosci. 2018; 32: 143-154. 10.1016/j.dcn.2017.06.005
~~~~~~~~
By Briana Lees; Lindsay M. Squeglia; Florence J. Breslin; Wesley K. Thompson; Susan F.
Tapert and Martin P. Paulus
Reported by Author; Author; Author; Author; Author; Author
BioMed Central publishes under the Creative Commons Attribution License (CCAL). Under
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Page 18 of 18
Emerging Digital Generations? Impacts of Child
Digital Use on Mental and Socioemotional Well-Being
across Two Cohorts in Ireland, 2007–2018
Melissa Bohnert1 & Pablo Gracia1
Accepted: 15 August 2020 /
# The Author(s) 2020
Abstract
Despite the growing body of literature on how digital technologies impact child well-
being, previous research has provided little evidence on recent digital trends. This paper
examines the patterns and effects of digital use on child socioemotional well-being
across two cohorts of children grown up ten years apart during the ‘digital
age’: the 1998 cohort (interviewed in 2007/08) and the 2008 cohort
(interviewed in 2017/18). Multivariate linear regression models were conducted
for these two cohorts from the Growing Up in Ireland (GUI) study, a multi-
cohort longitudinal study with rich comparable data on a large sample of 9-year
olds (N = 13,203). Results show that (i) in 2017/18 children were more active in
digital devices and social media, while in 2007/2008 children spent more tim
e
watching TV and adopted less diversified forms of media engagement; (ii)
spending more than 3 daily hours on TV/digital activities was associated with
significant declines in child socioemotional well-being, while such effects were
stronger in 2017/18 than in 2007/08; (iii) media engagement (but not other
forms of digital engagement) was associated with moderate declines in
socioemotional well-being, both in 2007/08 and in 2017/18; (iv) while chil-
dren’s media and digital engagement differed by the child gender and socio-
economic background, none of these variables moderated the effects of digital
use on children’s socioemotional well-being, neither in 2007/08 nor in 2017/18.
Overall, the study reveals persistence, but also some important changes, in
recent trends on children’s digital use and its impact on socioemotional well-
being in Ireland.
Keywords Digital use .Media . Child socioemotional wellbeing . Cohort effects . Ireland
https://doi.org/10.1007/s12187-020-09767-z
* Melissa Bohnert
bohnertm@tcd.ie
1 Department of Sociology, School of Social Sciences and Philosophy, Trinity College Dublin,
College Green 1, Dublin Dublin 02, Ireland
Child Indicators Research (2021) 14:629–659
Published online: 31 August 2020
http://crossmark.crossref.org/dialog/?doi=10.1007/s12187-020-09767-z&domain=pdf
http://orcid.org/0000-0002-8077-4297
mailto:bohnertm@tcd.ie
1 Introduction
Today’s youth have unprecedented access to digital media and technologies, having
grown up as ‘digital natives.’ Digital devices and activities, and how children
engage with them, are constantly evolving. These recent rapid transformations
in digitalization suggest that today’s youth do not form a single coherent digital
generation, with children’s ‘new’ digital contexts differing remarkably from
those of children in previous cohorts (Livingstone and Helsper 2007;
Livingstone et al. 2018). Consequently, studying how recent digital changes
can affect the well-being of children from the youngest generations is critical to
understand childhood in our digital age.
Previous studies suggest that children born after 2008, so-called ‘digitods’, have
patterns of digital engagement that differ significantly from those of children born a
decade earlier (Holloway et al. 2015; Leathers et al. 2013). Digitods are the first cohort
to grow up entirely after the launch of smartphones to the popular market, with most
having grown up in households with access to portable touch-screen devices with
enhanced computing power and mobility (Livingstone and Helsper 2007; Kucirnova
and Sakr 2015). The new cohort of ‘digitods’ have parents who tend to be experienced
digital users themselves and who often adopt ‘new’ digital parental mediation strategies
to promote children’s safe media use and digital literacy (Bennett et al. 2008; Brito
et al. 2018). Yet, younger children are increasingly exposed to a multitude of digital
technologies at a very early age (Mascheroni and Olafsson 2016), which may lead to
potential well-being problems associated with excessive screen-time or non-
developmentally appropriate media use. To date, however, very little is known on
whether current children’s digital engagement is affecting their well-being differently
compared to previous generations. Our study addresses this critical question to
further understand children’s lives and well-being in contemporary societies.
This paper investigates whether children’s patterns of digital use, and the effects of
this usage on socioemotional well-being, differs across cohorts. Cohorts present a
fundamental way to ‘demarcate groups of individuals who experience historical periods
that are suspected to produce unique outcomes not present in individuals from different
periods of time’ (Campbell and Pearlman 2013: 1698). By comparing different cohorts,
scholars can ideally examine whether the association between specific variables (i.e.,
child digital use and well-being) differs between individuals who have been socialized
at different socio-historical moments (Mannheim 1952; Yang 2008). Therefore, our
cohort approach provides a nuanced understanding of temporal trends on how digita-
lization can affect child well-being (see Campbell et al. 2015).
We use data from a representative large-scale multi-cohort longitudinal survey from
Ireland: the Growing Up in Ireland (GUI) study. The GUI allows us to ideally measure
the role technology plays in children’s lives by using comparable measures of chil-
dren’s digital use and socioemotional well-being at the age of 9 by contrasting two birth
cohorts born ten years apart: the 1998 Cohort and 2008 Cohort. Our focus on middle
childhood (9 years old) is relevant to research on child development, as this is a life-
course stage in which children increasingly start to develop genuine and independent
media and technology styles with key well-being consequences (Valkenburg and
Piotrowski 2017). Our cohort approach on 9-year old children permits us to trace a
sociohistorical account of 10-years difference across two cohorts.
M. Bohnert, P. Gracia630
The paper makes two key contributions to the literature. First, our study is – to our
knowledge – the first analysis of cohort changes on the effect of child digital use on
well-being outcomes. Two studies with data from the US Panel Study of Income
Dynamics addressed cohort changes in the associations between children’s digital
behaviors and other daily activities (Fomby et al. 2019; Goode et al. 2019). Using data
from two cohorts of children aged 2–11 sampled in 1997 (born 1986–1995) and 2014–
2016 (born 2003–2014), Goode et al. (2019) found more varied usage of digital
technology and stronger negative associations between digital use and physical
activity in the youngest cohort, compared to the older cohort. Likewise, Fomby et al.
(2019) found increased digital time use and further displacement of physical activity
across cohorts, while also finding significant associations of digital time use with play
(in early childhood) and increased sleep (in middle childhood) in the younger cohort of
11 to 17-year-olds (born 1985–1991), compared to the older cohort (born 1997–2003).
Yet, previous cohort studies did not assess the impact of digital use on specific well-
being outcomes. Our study makes an important contribution by providing evidence on
the persistence (or lack of it) in the impact of digital use on child socioemotional
outcomes across two recent cohorts.
Second, our paper examines whether digital engagement has affected children’s
well-being differently across time by examining the moderating role of socioeconomic
status (SES) and gender. Low-SES children were found to spend more time on screen-
based activities, with or without parents, compared to high-SES children, while more
privileged children tend to be socialized in family digital contexts that are likely to
maximize their well-being opportunities and minimize developmental risks (Gracia and
Garcia-Roman 2018; Nikken and Opree 2018). Also, boys were found to spend more
time than girls on screen-based activities across different countries, while the effect of
child digital engagement on well-being is gendered, with studies suggesting that boys’
and girls’ well-being might be harmed differently by the usage of digital devices
(Barker 2009; Booker et al. 2015, 2018; Gracia et al. 2020; Jackson et al. 2008; Liff
and Sheperd 2004; The Children’s Society 2015). In our context of rapid digital
innovation, changing gender roles and increasing socioeconomic inequalities, it is
relevant to examine whether SES and gender are playing a role in moderating the
impact of digital activities on child well-being across recent cohorts. In this sense, our
study contributes to debates on the heterogeneous impact of digital technologies on
child well-being across socioeconomic and demographic groups.
2 Analytical Framework
2.1 Children’s Digitalization in Ireland
We start the analytical background by briefly situating our national case of study:
Ireland. While our study does not compare Ireland with other countries, we can better
contextualize our empirical study within the literature on digitalization and child well-
being by situating the Irish case internationally.
Ireland presents an interesting case of study. In Ireland, internet and smartphone
penetration is growing, but it has not reached the highest saturation rates of many
Continental and Northern European countries. Data from the EU Kids Online Survey
Emerging Digital Generations? Impacts of Child Digital Use on… 631
(2011) reported that 93% of Irish children aged 9–16 have regular access to the Internet.
The Net Children Go Mobile (2015) survey shows that 63% of Irish children aged 9–16
use the Internet at least once a day, with 46% accessing the Internet primarily through their
own smartphone (O’Neill and Dinh 2015). Both surveys found daily Internet use and
average time spent online by Irish young people somewhat below average compared to
other European countries (O’Neill et al. 2011; O’Neill and Dinh 2015).
The two Irish cohorts we are comparing in our study (one born in 1998; another born
in 2008), not only differ in terms of their relative access to forms of digitalization, but
also in how the 2008 economic recession has affected them (Cantillon et al. 2017).
Children born in the late 1990s (1998 cohort) experienced a booming economy in
Ireland during their early years, while the children of the Great recession (2008 cohort)
have experienced higher risks of poverty and particularly pervasive levels of economic
uncertainty (Reinhard et al. 2018).
The two Irish cohorts, in addition to these differing economic circumstances, are also
characterized by differing social and cultural conditions. Social media in particular has
facilitated changing social dynamics in the way children interact and network with others.
For ‘digitods’, children’s technology use shifted from focusing on education and passive
entertainment to being intertwined with identities, social networks, and everyday experi-
ences (Fomby et al. 2019). Additionally, this increased connectivity, networking, and
potential for identity formation has coincided with a time of international political and
cultural upheaval (e.g. 2016 US presidential election, Brexit) that has potentially generated
new forms of media engagement that may be affecting the cultural and media socialisation
of children from the younger cohort, compared to the older cohort.
To date, few studies have examined how child and adolescent digital media usage
affects well-being in Ireland. The few studies in this field focus on academic outcomes,
with findings being mixed (Casey et al. 2012; Dempsey et al. 2019). Additionally,
while the EU Kids Online (2011) and Net Children Go Mobile (2015) have provided
insights into how Irish children spend their time online, the data published are largely
descriptive in nature, without showing how these patterns of digital use may be
impacting key developmental outcomes like mental and socioemotional well-being.
No study has, to the best of our knowledge, compared recent historical trends in the
effects of digital engagement on child well-being. These important gaps strongly
motivate our study with Irish data.
2.2 Impacts of Children Digital Use on Socioemotional Well-Being
Concerns over the potential effects of children’s and adolescents’ digital technology
use on their well-being have been emerging since the 1990s. Early studies were
important first glimpses of how emerging digital technologies can affect child well-
being outcomes. Yet, these studies were conducted at a time in which children’s digital
and online engagement was ‘generally still marginal and impacts were more difficult to
discern’ (Vilhelmson et al. 2018: 2901), leading to limited samples and highly biased
findings (George et al. 2018). As access to digital technologies has widened dramat-
ically and an ever-increasing majority of children found their way online, new research
in this field has appeared.
Recent studies on child digital engagement and well-being have provided results that
are often mixed and inconclusive (Castellacci and Tveito 2018; Orben and Przybylski
M. Bohnert, P. Gracia632
2019a, b; Stiglic and Viner 2019). Considerable evidence has suggested a negative
relationship between digital technology usage and child and adolescent well-being
(Fioravanti et al. 2012; Hwang et al. 2009; Kelly et al. 2018; Kross et al. 2013;
Pantic et al. 2012; Parkes et al. 2013; Liu et al. 2016; Twenge et al. 2018; van den
Eijnden et al. 2008). These studies indicated that children’s increased usage of digital
technologies is associated with increased depressive symptoms, increased difficulties in
psychosocial adjustment, reduced self-esteem/self-concept, and increased social isola-
tion through displacement of in-person socializing activities. Such studies reporting
negative associations have tended to capture large amounts of public and political
attention on children’s media use. However, other studies find a positive relationship
between child well-being and time spent on digital technologies (Berryman et al. 2018;
Davis 2012; Gross 2009; Selfhout et al. 2009). These studies tend to indicate that
children’s use of digital technologies foster increased social support, widened social
networks, reduced social anxiety, and reduced social isolation (Best et al. 2014). And
still, many studies have found no significant association at all (Babic et al. 2017; Baker
and White 2010; Bruggeman et al. 2019; Leung 2014; Williams and Merten 2008).
Studies increasingly indicate a small (when any) association between children’s time
spent on digital technologies and socioemotional well-being. When significant (positive
or negative) associations are found, they tend to be small. For example, Orben and
Przybylski’s (2019b) study with three large-scale datasets found statistically significant
associations between children’s digital technology use and well-being problems, but
effects were smaller than those observed for apparently neutral activities (i.e., regularly
eating potatoes) (Orben and Przybylski 2019b). This evidence calls for research on the
specific type and context of digital activities to understand the mechanisms by which
child digital engagement can impact socioemotional well-being.
Previous studies on the impact of technologies on child well-being tended to omit a
clear distinction between quantity and quality of digital engagement (Blair et al. 2015).
As digital media and technologies have become more complex and ubiquitous, there is
an ever-increasing number of disparate activities that children can participate in through
digital technologies. For example, today children can watch TV, access huge numbers
of games and apps or socialize with friends, which are likely to have differing impacts
on well-being. Therefore, rather than studying digital use as a ‘singular black box’,
researchers are increasingly utilizing more nuanced measures that can account for both
quality and quantity of digital use (Livingstone et al. 2018). Our paper follows this
approach by including both the quantity (amount of time) and quality (specific activ-
ities) of children’s digital use and its impact on socioemotional well-being.
Drawing on the time displacement hypothesis, we argue that high quantities of
digital use replace more enriching face-to-face activities, especially at extreme levels
of screen-time (Kross et al. 2013; George et al. 2018). Following Przybylski and
Weinstein (2017), we do not hypothesize a linear negative relationship across all levels
of digital use. Thus, we expect negative effects of screen-based time (digital or TV) on
socioemotional well-being only for children reporting the highest levels of digital
screen-time, compared to their peers. As for the quality of digital engagement, some
recent evidence suggests that informational and educational activities increase
socioemotional and psychosocial well-being, as they are relatively low-risk and high-
opportunity digital activities (Camerini et al. 2018; Livingstone et al. 2018). By
contrast, social, entertainment, and media activities might be more high-risk and low-
Emerging Digital Generations? Impacts of Child Digital Use on… 633
opportunity activities when it comes to child socioemotional well-being, as they have
been found to be associated with distraction (Mascheroni and Olafsson 2016), poor
self-concept (Kelly et al. 2018; van der Aa et al. 2009; Verduyn et al. 2017), cyber-
bullying (Fahy et al. 2016), and depressive symptoms (Lemola et al. 2015; George et al.
2018).
Hypothesis 1a: High levels of children’s screen-time engagement (digital or TV)
are associated with higher socioemotional problems.
Hypothesis 1b: Child engagement in social and entertainment media digital
activities is associated with higher socioemotional problems, while informational
and educational digital activities are associated with lower socioemotional
problems.
2.3 Heterogeneity by Socioeconomic Status and Gender
Research shows that demographic and socioeconomic factors influence children’s
digital usage and their associated outcomes. High-SES parents are able to impart digital
skills and competencies to their children that allow them to stay ahead (Nikken and
Opree 2018). Across countries, children in high-SES backgrounds are socialized in
ways that lead them to reduce their amounts of time in screen-based activities,
compared to low-SES children (Gracia et al. 2019). Thus, high-SES children grow
up in families that grant privileged social, cultural and digital capitals to wield in
mitigating risks and maximizing opportunities intrinsic to technology use (Livingstone
and Helsper 2010). Hence, the negative effects of child digital use on socioemotional
well-being might be weaker for high-SES children than for low-SES children.
Gender is another key moderator considered in this literature. Boys spend more time
than girls on digital activities, focusing especially on gaming, while girls engage
relatively more on social digital activities (Ortega et al. 2010; Wight et al. 2009;
Brooks et al. 2016; Gracia et al. 2020). Boys, particularly at high levels of digital
usage, were found to show greater risks of aggressive behavior and depressive symp-
toms than girls (Ortega et al. 2010; Booker et al. 2018). If boys are at higher risk of
experiencing psychosocial problems associated with digital engagement (i.e., hyperac-
tivity, aggressiveness), and are more likely to spend ‘too much’ time in front of screens,
compared to girls, one could expect that boys’ socioemotional well-being is
disproportionally harmed by digital engagement.
Hypothesis 2a: The hypothesized association between child digital use and higher
socioemotional problems affects boys more than girls.
Hypothesis 2b: The hypothesized association between child digital use and higher
socioemotional problems affects low-SES families more than high-SES families.
2.4 Digital Generations and Cohort Effects
Birth cohorts are made up of individuals born in the same year, with those within the
cohort moving through the life course simultaneously and encountering the same socio-
M. Bohnert, P. Gracia634
historical events at the same ages (Gentile et al. 2013; Yang 2008). We conceptualize
cohorts as ‘age cohorts that come to have social significance by virtue of
constituting itself within a social, cultural, and political identity’ (Fortunati
et al. 2019). Increasingly, many researchers have questioned the cultural and
academic tendency to group all of today’s youth into a single coherent digital
generation (e.g. ‘Gen Z’) (see Livingstone and Helsper 2007). Our study
compares the 1998 cohort with the 2008 cohort in Ireland by acknowledging
the important differences in the digital, economic and cultural experiences
between these two cohorts.
To compare our two Irish cohorts of study, one born 1998 and the other in 2008, we
need to recall the rapid digital changes that have occurred in the world in the last
decade. The early 2010s marked a shift in technological innovation with the invention
and rapid adoption of smartphones, with other highly portable and interactive technol-
ogies like tablets following soon after. Compared to children born in the late 1990s,
children born around 2010 have been socialized since birth with the power, portability
and ubiquity of newer ‘digi-tod’ technologies and digital platforms (Kucirnova
and Sakr 2015; Livingstone and Helsper 2007; Mascheroni and Cuman 2014).
Children born around 2010, compared to those born one decade earlier, are also
more likely to be socialized in digitally rich family contexts, where parents are
experienced digital users who adopt active digital parenting techniques (Bennett
et al. 2008; Brito et al. 2018). However, the existing body of research has – to
our knowledge – not provided evidence on whether the effect of digital use on
child well-being outcomes has changed over the last decade.
Our hypotheses on cohort changes in digital use and socioemotional outcomes are
threefold. First, following previous studies, the younger cohort of 9-year olds would
spend more time on digital technologies, especially on newer digital media and
entertainment activities, and less time on TV, compared to the older cohort (Fomby
et al. 2019; Goode et al. 2019). Second, younger cohorts (i.e. 2008 cohort) have a
greater chance of using potentially harmful mobile digital technologies with higher
frequency, which may increase socioemotional problems associated with digital use
compared to older cohorts (i.e. 1998 cohort). Despite parents’ efforts to supervise their
children’s digital time (Brito et al. 2018), young children today are more able to use
smartphones and tablets to engage in digital activity at all hours of the day, potentially
leading the young 2008 cohort to engage more frequently and deeply in risky digital
behavior, with negative effects on their socio-emotional well-being compared to the
older 1998 cohort. Third, considering the rising SES inequalities during a decade of
strong austerity and recession in Ireland (Cantillon et al. 2017), SES inequalities in the
impact of child digital engagement on socioemotional well-being might be larger for
the younger generation of 9-year olds (born in 2008, a recession period) than for the
older generation of 9-year olds (born in 1998, a booming economy period). By contrast,
as changes in children’s gender roles and attitudes tend to take several decades to
emerge (Shu and Meagher 2018), we expect stability in gendered patterns of how
children’s technology use impacts their socioemotional well-being.
Hypothesis 3a: The younger 2008 cohort spends more time on digital technolo-
gies, especially in digital media and entertainment activities, and less on TV,
compared to the older 1998 cohort.
Emerging Digital Generations? Impacts of Child Digital Use on… 635
Hypothesis 3b: The hypothesized effects of digital technology use on higher
socioemotional problems are stronger for the younger 2008 cohort than for the
older 1998 cohort.
Hypothesis 3c: The effect of child digital use on socioemotional problems is
moderated by SES to a higher extent for the youngest 2008 cohort than for the
older 1998 cohort, but gender differences in how digital use impacts child
socioemotional problems remain stable across cohorts.
3 Data and Methods
3.1 Sample
We use two comparable cohorts of theGrowing Up in Ireland (GUI) study, a nationally
representative multi-cohort longitudinal study of children living in Ireland (Williams
et al. 2009). We use Wave 1 of the ‘1998 Child Cohort’, which consists of a
representative sample of 8568 children aged 9 who were born between 1st
November 1997 and 31st October 1998. This group of children was first interviewed
between August 2007 and May 2008. We also utilize Wave 5 of the ‘2008 Infant
Cohort’, which contains 8032 children aged 9 who were born between December 2007
and June 2008. Data collection for this Wave 5 was conducted between June 2017 and
February 2018. While the current GUI only allows us to compare these two cohorts at
9 years old, the existing measures for these two cohorts provide an ideal comparative
framework. Both cohorts gathered both parent completed and child completed indica-
tors. Parents assessed their child’s socioemotional problems, as measured by the SDQ,
and average daily TV and digital screen-time, while children reported what digital
activities they engaged in, their mobile phone ownership, and supervision on digital
devices by adults. While questions for digital use changed between cohorts, the Wave 5
child questionnaire of the 2008 cohort was adapted so as to establish solid comparisons
with Wave 1 of the 1998 cohort (i.e., by making the child questionnaire shorter) (see
Quail et al. 2019).
Table 1 describes the cases missing from the two cohorts on the main variables of
analysis. Overall, among the 16,600 observations within the two cohorts, approximate-
ly 20% of cases had missing responses on at least one of the variables of interest.
Missing data affected the 1998 cohort slightly more (22.64%) than the 2008 cohort
(18.14%). The majority of these differential missing cases came from the binary
activity variables (Gaming, Media, Socializing, Education, Other) and the adult super-
vision indicator. Missingness along the child-reported variables was significantly lower
in the 2008 cohort (~10%), than in the 1998 cohort (~18%). The missingness across the
variables might be due to the fact that child-reported measures are often subject to
increased missingness, due to factors like comprehension or boredom (Livingstone
et al. 2018).
We conducted a number of robustness checks to examine missingness across the
two cohorts. Little’s MCAR tests (not shown) indicated that all six child-reported
measures are completely missing at random and the missingness is independent of
both the observed and unobserved data; this poses no significant bias to the analytic
sample (Li 2013). Also, while missingness in SES is slightly higher for the youngest
M. Bohnert, P. Gracia636
cohort, additional analyses (not shown) reveal similar probabilities of missing data
across all SES groups between the two cohorts. We decided to exclude all cases with
missing values on at least one of the variables of interest (n = 3397, 20.46%) for a final
analytic sample of n = 13,397 (79.54% of the original sample).
3.2 Dependent Variables
Our main analyses used a continuous dependent variable to measure socioemotional
well-being outcomes: the Strengths and Difficulties Questionnaire Total Difficulties
Score (TDS). SDQ Total Difficulties Score (TDS) is regarded as ‘a concise and well-
validated tool’ used to measure socioemotional well-being of 3 to 16-year-olds
(Goodman and Goodman 2011). Throughout the literature discussed previously,
well-being is largely defined as ‘an abstract and wholly individualized concept whose
meaning appears to be in constant flux’ (Best et al. 2014). For our analyses, we utilize
an objective conception of well-being that specifies positive well-being as healthy,
congruent and vital functioning, rather than mere subjective experiences and percep-
tions of happiness (Castellacci and Tveito 2018). In utilizing the SDQ, we
Table 1 Sample selection by cohort
Variables Overall Sample
(n = 16,600)
1998 Child Cohort
(n = 8568)
2008 Infant Cohort
(n = 8032)
n Missing % Missing n Missing % Missing n Missing % Missing
Child Gender 6 0.04 0 0.00 6 0.07
Parent Age 6 0.04 0 0.00 6 0.07
Parental Employment Status 0 0.00 0 0.00 0 0.00
Parental Education 34 0.02 0 0.00 34 0.42
Household Social Class 1010 0.20* 458 5.35 552 6.87
Region 55 6.08 19 0.22 36 0.45
Single Parent 6 0.33 0 0.00 6 0.07
TV Screen-time 6 0.04 0 0.00 6 0.07
Digital Screen-time 15 0.09 4 0.05 11 0.14
Games 2428 14.63* 1605 18.73 823 10.25
Media 2438 14.69* 1607 18.76 831 10.35
Socializing 2439 14.69* 1610 18.79 829 10.32
Education 2430 14.64* 1610 18.79 820 10.21
Personal Development 2435 14.67* 1608 18.77 827 10.30
Mobile Phone Ownership 174 1.05 52 0.61 122 1.52
Adult Supervision 2535 15.27* 1613 18.83 922 11.48
Overall Sample 16,600 100 8.568 100 8032 100
Missing Cases 3397 20.46 1940 22.64 1457 18.14
Analytic Sample 13,203 49.54 6628 77.36 6575 81.86
Sample selection for the analytic sample construction, indicating when the missingness across variables
between the 1998 and 2008 cohort is statistically significant at the level of p < 0.05 (*)
Emerging Digital Generations? Impacts of Child Digital Use on… 637
operationalize socioemotional problems to mean the prescience of emotional, conduct,
hyperactivity, and peer relationship difficulties and socioemotional well-being as the
absence of these problems.
The parent is asked to assess the applicability of 25 statements to their
child’s behavior with three response options: ‘Not true’, ‘Somewhat true’ or
‘Certainly true’. These 25 responses generate scores on five subscales: (1)
emotional symptoms; (2) conduct problems; (3) hyperactivity; (4) peer relation-
ship problems; and (5) prosocial behavior. Subscales 1 through 4 are combined
to generate a Total Difficulties Score (TDS) and assess overall psychosocial
functioning and adjustment, with a minimum score of 0 (indicating the lowest
socioemotional problems) and a maximum score of 40 (indicating the highest
socioemotional problems). In additional analyses we subdivide the categories of
TDS, and further add the SDQ Prosocial Score. This measure comes from the
Prosocial subscale and calculates children’s pro-social behavior on a scale from
0 to 10. For the sake of comparability, we inverted the sign of the prosocial
measure and named it ‘anti-social behavior’ (see Table 5).
3.3 Independent Variables
We used two measures for the quantity of screen-time: (i) time spent watching TV per
average weekday and (ii) time spent on digital devices per average weekday (i.e.,
mobile phone, computer, tablet, and e-reader). These variables were measured by four
categories: ‘none’, ‘less than an hour’, ‘1 to 3 hours’, and ‘3 hours or more’. Although
this will assess only digital technology time use on weekdays, ‘measurements of
weekday and weekend digital technology use have been shown to be highly correlated’
(Orben et al. 2019).
We further employed five measures of the quality of digital engagement,
used as binary dummy variables. These five categories separate children be-
tween those who engage in these activities on an average day (1 = yes) and
those wo do not (0 = no): (1) ‘gaming’; (2) leisure (i.e. media); (3) information
(i.e. education); (4) digital social interaction; (5) digital personal development.
Table 6 provides details on the exact examples of activities that were included
in each of these digital activities. The GUI child questionnaire designed ques-
tions in which children were given a list of digital activities and were asked
which activities they currently did or did not engage in. Some activities
included in the questionnaire changed and expanded from the 1998 to the
2008 cohort, responding to changes in the use of technologies during this
decade difference. To address this inconsistency, digital activities were
reclassified into a modified version of Van Deursen and Van Dijk’s (2014)
measure of ‘Internet activity types’ by excluding the ‘commercial transaction’
and ‘news’ activity types for use with child data.
Finally, we included two additional binary variables capturing digital access and
technology family contexts: (1) ‘mobile phone ownership’ split the sample between
children who own a smartphone (1 = yes) and those who do not own a smartphone (0 =
no); (2) ‘adult supervision’ was a variable differentiating between children with an
adult that frequently or always supervises children’s digital activities (1 = yes) and
otherwise (0 = no).
M. Bohnert, P. Gracia638
3.4 Moderating and Control Variables
Several sociodemographic, household, and parental characteristics were selected as
covariates for the models, guided by previous research (Parkes et al. 2013; Hope et al.
2014; Orben and Przybylski 2019a). These measures came primarily from the GUI
Primary Caregiver (PCG) Questionnaire, with the PCG being the caregiver within the
household that provided the most care to the Study Child and knew most about him/
her. In 98% of cases, the Primary Caregiver was the child’s mother.
We used two moderators and several controls. SES was coded into four
categories: ‘low-skilled routine’, ‘skilled routine’, ‘non-routine intermediate’ and
‘professional or managerial’ (reference). ‘All others gainfully occupied and
unknown’ were counted as missing (N = 1002), as these are cases where ‘no
precise allocation is possible’ (CSO 2015; 105). This measure of SES utilized
in the GUI is similar to the commonly used Erikson-Goldthorpe-Portocarero
(EGP) occupational class schema. Socioeconomic status (SES) was generated
from both the PCG and the Secondary Caregiver Questionnaires, with overall
household SES taken as the highest SES of both partners in the household (as
relevant). Due to sample size reasons, in some analyses we separated our
sample by parental SES using a binary measure, differentiating between (i)
professional and managerial class and (ii) otherwise. Gender was as a dummy
variable of the gender attributed to the child: boy (0) or girl (1).
We included the following control variables: parental age (PCG, continuous);
parental employment (PCG, binary); single parenthood (binary); Urban versus
Rural locality (binary). On top of using SES, we included parental education as
a control variable, as family SES and parental education play different roles in
children’s screen-based activities with well-being implications (Gracia 2015).
Parental education had three categories on the highest level of education of the
PCG: ‘low secondary or lower’, ‘high secondary or vocational’, and ‘college
education’ (reference).
3.5 Empirical Strategy
Our analyses followed various steps. First, OLS linear regression models were
conducted, which allowed comparison of coefficients to assess the magnitude
and significance of the various independent and control variables on SDQ
scores. Second, we examined the digital effects on well-being outcomes sepa-
rately by gender and SES. Third, we run additional robustness checks with
quantile regression models to examine how the impact of digital use varies
across different SDQ scores. Quantile regressions are an extension of classical
least squares estimation of conditional mean functions (in OLS regressions) to
the estimation of conditional quantile functions. Quantile regressions allow for
the estimation of heterogeneous effects of covariates at different levels of a
dependent variable, without the inherent subsample selection biases of
split sample analyses (Koenker and Hallock 2001). Fourth, we run additional
analyses for sub-scales included in the SDQ scores, including anti-social be-
havior, to examine how child digital engagement effects differ across specific
well-being measures.
Emerging Digital Generations? Impacts of Child Digital Use on… 639
4 Results
4.1 Descriptive Analyses
Table 2 shows the descriptive statistics with means and standard deviations for all our
variables of study, both overall and across the two cohorts. Parents in the 2008 cohort
were older than in the 1998 cohort, with an increase of 8% in parents aged 40–49.
Additionally, parents in the 2008 sample showed higher employment rates, in part
reflecting the timing of the 1998 cohort’s Age 9 wave during the 2007/08 economic
recession. Parents in the 2008 cohort, compared to the 1998 cohort, had very few
respondents with Lower Secondary education and more respondents with higher
education, while the proportion of children with parents in professional and managerial
classes was higher in the 2008 cohort than in the 1998 cohort.
Table 2 also shows relevant differences in the quantity of digital technology
engagement. We observe that TV screen-time was slightly lower and digital screen-
time higher in the 2008 cohort, compared to the 1998 cohort. For example, in the 1998
cohort sample 73% of children spent over an hour watching TV, while this number
dropped to 48% in the 2008 cohort sample; digital screen-time over an hour rose from
13% to 28% between the 1998 and the 2008 cohorts, with the proportion of non-users
dropping by 9%. These findings indicate that children are moving away from tradi-
tional TV screen-time and supplanting it with time spent on digital technologies (e.g.
mobile phones, iPads, Tablets). This figure is line with the increase in mobile phone
ownership at age 9 from 61% in the 1998 cohort to 78% in the 2008 cohort.
In Table 2 we further observe differences in the quality of digital engagement of 9-
year-olds across cohorts. We see small increases in both social and personal develop-
ment digital engagement. There was also a marked decrease in child-reported educa-
tional use of digital technologies from 56% in the 1998 cohort to only 17% in the 2008
cohort. By contrast, children reporting engaging in media activities (e.g. watching
YouTube, downloading or streaming movies/music/apps) jumped from only 28% in
1998 to 89% in 2008. This significant increase in digital media use may be explained
by the proliferation of music and video streaming platforms (e.g. Netflix, YouTube,
Spotify) over the last decade.
4.2 Digital Use Effects on Child Socioemotional Wellbeing
Table 3 presents the multivariate OLS models for SDQ Total Difficulties Scores (TDS).
Analyses are presented for (i) the overall sample, adding both the 1998 and 2008 cohort
(Column 1), (ii) the 1998 cohort only (Column 2) and (iii) the 2008 cohort only
(Column 3). In this section we only discuss the results for the overall sample
(Column 1).
In Table 3 (see Column 1), positive associations of the covariates with the dependent
variable indicate an increase in socioemotional problems, and negative values a decline
in socioemotional problems. Results are in line with H-1a regarding screen-time. In the
overall sample, TV Screen-time was only significantly associated with an increase in
children’s socioemotional problems at 3+ hours of daily engagement (B = 1.527;
p < 0.001), when compared to children who did not watch any TV. Digital screen-
time presents significant negative associations at 1 to 3 h (B = 0.472; p < 0.001) as well
M. Bohnert, P. Gracia640
Table 2 Summary statistics of study variables
Overall Sample 1998 Child Cohort 2008 Infant Cohort
Variables Mean SD Mean SD Mean SD
SDQ Total Difficulties Score 6.998 4.986 7.024 4.788 6.972 5.178
SDQ Emotional Subscale 1.907 1.938 1.914 1.916 1.902 1.961
SDQ Conduct Subscale 1.127 1.331 1.180 1.360 1.075 1.301
SDQ Hyperactivity Subscale 2.931 2.447 2.858 2.365 3.005 2.525
SDQ Peer Subscale 1.031 1.390 1.072 1.369 0.990 1.411
SDQ Prosocial Subscale 8.928 1.429 8.886 1.420 8.971 1.438
TV Screen-time
None 0.04 0.03 0.06
Less than an hour 0.35 0.24 0.45
1 to 3 h 0.55 0.65 0.46
3 + hours 0.06 0.08 0.03
Digital Screen-time
None 0.23 0.22 0.23
Less than an hour 0.56 0.63 0.49
1 to 3 h 0.20 0.14 0.26
3 + hours 0.01 0.01 0.02
Activities
Games 0.85 0.86 0.84
Media 0.58 0.28 0.88
Socializing 0.25 0.22 0.29
Education 0.37 0.56 0.17
Personal development 0.52 0.48 0.56
Mobile phone owner 0.70 0.61 0.78
Adult supervision 0.60 0.67 0.54
Child Gender
Boy 0.49 0.48 0.51
Girl 0.51 0.52 0.49
Parent Age
20–39 0.38 0.41 0.34
40–49 0.59 0.56 0.62
50 or older 0.03 0.03 0.04
Parental employment status
Employed 0.67 0.62 0.73
Not in employment 0.33 0.38 0.27
Parental education
Lower secondary or less 0.10 0.14 0.05
Upper secondary/non-degree 0.56 0.57 0.56
College education 0.34 0.29 0.39
SES level
Professional and managerial class 0.59 0.58 0.61
Emerging Digital Generations? Impacts of Child Digital Use on… 641
as 3+ hours (B = 1.708; p < 0.001), indicating an increasing magnitude and stronger effects of digital screen-time on child socioemotional problems as screen-time increased.
Additionally, Column 1 of Table 3 shows that media use was the only digital activity
with a significant (positive) association with socioemotional problems among 9-year-
old children, (B = 1.527; p < 0.001). The coefficients for all the other digital activities,
including Gaming, Socializing, Education, and Personal Development, were found to
be non-significant. These results are partially in line with our H-1b. While media/
entertainment digital activities were associated with an increase in socioemotional
problems, the positive effects of digital socializing and gaming on socioemotional
problems, and negative effects of involvement in digital educational activities, were
small and not statistically significant.
Finally, Column 1 in Table 3 presents some other relevant findings. Interestingly,
mobile phone ownership was associated with less socioemotional problems (B =
−0.231; p < 0.05) and adult digital supervision with more socioemotional problems
(B = 0.211; p < 0.05). This might suggest that (i) not owing a mobile phone limits some
positive elements for children’s social capital and connectedness (Verduyn et al. 2017)
and (ii) that parents active digital supervision is partly a response of children’s intrinsic
socioemotional problems (Valkenburg and Piotrowski 2017). As for gender, consistent
with previous literature (Ortega et al. 2010; Booker et al. 2018), boys had higher risks
of showing socioemotional problems than girls (B = −0.859; p < 0.001). Regarding
parental education, children of parents with low secondary or lower education (B =
1.549; p < 0.001) and with higher secondary or vocational education (B = 0.557;
p < 0.001), reported lower socioemotional problems than children with parents having
higher education. As for parental SES, children of both low-skilled routine workers
(B = 0.359; p < 0.05) and skilled routine workers (B = 0.519; p < 0.001) had higher
levels of socioemotional problems, compared to children with parents having profes-
sional and managerial occupations. These findings are consistent with previous litera-
ture indicating that socioeconomic inequalities have negative impacts on
Table 2 (continued)
Overall Sample 1998 Child Cohort 2008 Infant Cohort
Variables Mean SD Mean SD Mean SD
Non-routine intermediate class 0.19 0.20 0.17
Skilled routine class 0.13 0.14 0.12
Low-skilled routine class 0.09 0.08 0.10
Region
Urban 0.43 0.46 0.41
Rural 0.57 0.54 0.59
Single Parents 0.07 0.07 0.08
N 13,203 6628 6575
Means and Standard Deviations
Source: Growing Up in Ireland (GUI) Survey
M. Bohnert, P. Gracia642
socioemotional well-being (Davis et al. 2010; Bøe et al. 2012, Klanšček et al. 2014;
Piotrowska et al. 2015).
Table 3 Linear regression models on child’s socioemotional problems (SDQ)
Overall sample 1998 Cohort 2008 Cohort
Variables b SE b SE b SE
TV screen-time
None (Ref)
Less than an hour −0.279 (0.219) −0.628 (0.370) −0.119 (0.277)
1 to 3 h 0.254 (0.216) −0.123 (0.360) 0.464 (0.277)
3+ hours 1.527*** (0.278) 1.042* (0.410) 2.080*** (0.460)
Digital screen-time
None (Ref)
Less than an hour 0.063 (0.106) −0.002 (0.143) 0.141 (0.158)
1 to 3 h 0.472*** (0.134) 0.536** (0.203) 0.393* (0.185)
3+ hours 1.708*** (0.371) 0.820 (0.650) 1.944*** (0.471)
Gaming 0.079 (0.119) −0.162 (0.168) 0.217 (0.177)
Media 0.460*** (0.092) 0.408** (0.130) 0.498* (0.204)
Social 0.144 (0.102) 0.141 (0.146) 0.188 (0.144)
Education −0.123 (0.092) −0.111 (0.119) −0.087 (0.167)
Personal development −0.023 (0.088) 0.083 (0.122) −0.085 (0.130)
Mobile phone owner −0.231* (0.097) −0.234 (0.123) −0.166 (0.157)
Adult supervision 0.211* (0.090) 0.336** (0.128) 0.090 (0.127)
Girl −0.859*** (0.085) −0.547*** (0.116) −1.169*** (0.126)
Parent age −0.809*** (0.080) −0.930*** (0.109) −0.689*** (0.118)
Parent employed −0.156 (0.098) 0.023 (0.128) −0.371* (0.152)
Single parent 1.399*** (0.167) 1.531*** (0.240) 1.279*** (0.234)
Parent education
College education (Ref)
Low secondary or lower 1.549*** (0.152) 1.591*** (0.175) 1.745*** (0.302)
High secondary or vocational 0.557*** (0.170) 0.711*** (0.206) 0.420*** (0.319)
SES level
Professional or managerial (Ref)
Non-routine intermediate class 0.217 (0.118) 0.187 (0.156) 0.251 (0.178)
Skilled routine 0.519*** (0.140) 0.525** (0.186) 0.533* (0.212)
Semi−/un-skilled routine 0.359* (0.163) 0.195 (0.229) 0.504* (0.234)
Rural −0.191* (0.087) 0.136 (0.118) −0.535*** (0.128)
Constant 9.781*** (0.490) 9.502*** (0.698) 10.49*** (0.764)
N 13,203 6628 6575
adj. R2 0.058 0.061 0.060
Standard errors in parentheses
Source: Growing Up in Ireland (GUI) Survey
* p < 0.05, ** p < 0.01, *** p < 0.001
Emerging Digital Generations? Impacts of Child Digital Use on… 643
4.3 Cohort Digital Effects on Child Socioemotional Wellbeing
Table 3 (see Column 2 and Column 3) shows that the effects of screen-time on
socioemotional outcomes increased in the last decade. Spending 3+ hours of TV time
was associated with an increase of socioemotional problems for both the 1998 and
2008 cohort. But this association was higher in size and stronger in statistical signif-
icance for the 2008 cohort (B = 2.080; p < 0.001) than for the 1998 cohort (B = 1.042;
p < 0.05). As for digital engagement, the association between 3+ hours of digital
screen-time and socioemotional problems was again larger and stronger in terms of
statistically significance for the 2008 cohort (B = 1.944; p < 0.001) than for the 1998
cohort (B = 0.820). Spending ‘1 to 3 hours’ of digital screen-time, compared to
spending none in digital activities, was quite similarly associated with higher
socioemotional problems in the cohort 1998 (B = 0.536; p < 0.01) than in the 2008
cohort (B = 0.393; p < 0.05).
Table 3 shows stability in the effects of the types of digital use on socioemotional
problems between the 1998 cohort (Column 2) and the 2008 cohort (Column 3). The
positive association between children’s engagement in media activities and
socioemotional problems was very similar for the 1998 cohort (B = 0.408; p < 0.05)
and the 2008 cohort (B = 0.498; p < 0.01). The effects of the other digital activities of
our study were small and statistically insignificant. Except for gaming, where the
coefficient was negative for the 1998 cohort and positive for the 2008 cohort, the
direction of associations between digital activity types and child socioemotional well-
being went in the same direction. See Fig. 4 in the Appendix for a complementary
graphical representation of the main cohort effects shown in Column 2 and Column 3
of Table 3.
Overall, the analyses of our cohort effects were partly in line with H-3b. While the
positive associations with socioemotional problems were stronger in the younger 2008
cohort than in the 1998 cohort (as expected), the types of digital activities in which 9-
years old participated had persistent effects on socioemotional problems across both
cohorts where we expected stronger effects for the 2008 cohort than for the 1998
cohort.
4.4 The Role of Gender and SES
Figure 1 presents the main results of the multivariate linear regression models on SDQ
Total Difficulties scores, separately by gender (left) and SES (right). Gender and SES
played a minor role in moderating the impact of child digital engagement on
socioemotional problems. We only found some exceptions in this regard. Educational
digital activities were associated with a decline in socioemotional problems for boys
(p < 0.05), having neutral effects for girls. By contrast, owning a mobile phone was
associated with lower socioemotional problems among girls (p < 0.05), with neutral
effects for boys. As for SES, gaming had neutral effects for high-SES children and was
quite clearly associated with higher socioemotional problems for low-SES children
(p < 0.05). In short, globally, gender and SES did not moderate the effect of child
digital usage on socioemotional problems.
Figure 2 shows the multivariate linear regressions results on SDQ Total Difficulties
scores, split by gender and cohort. Most of the minor gender differences observed in
M. Bohnert, P. Gracia644
Fig. 1 remained stable across cohorts. There were only some exceptions. For the 1998
cohort, spending 3+ hours of digital screen-time led to high socioemotional problems
among girls (p < 0.05), having neutral effects among boys. By contrast, for the 2008
cohort, 3+ hours of digital screen-time led to high socioemotional problems for both
girls and boys (p < 0.05). Personal development activities were associated with higher
socioemotional problems among girls in the 1998 cohort (p < 0.05), with neutral effects
on girls’ socioemotional problems in the 2008 cohort. Overall, despite some excep-
tions, the effects of child digital engagement on socioemotional problems were quite
similar between boys and girls across both cohorts.
Figure 3 shows the main multivariate linear regressions on SDQ Total Difficulties
scores by SES and cohort. SES differences were small in both cohorts, with very few
exceptions. Both low-SES and high-SES children increased their socioemotional
problems associated with gaming over these ten years, low-SES children always more
Fig. 1 Linear Regression Models. Child Socioemotional Problems (SDQ) by Gender and Socioeconomic
Status (SES) in Overall Sample. Note: Regression coefficients for selected variables separately by gender and
SES from the overall sample (N = 13,203) with Confidence Intervals at the 95% level, adjusting for all the
study control variables
Fig. 2 Linear Regression Models. Child Socioemotional Problems (SDQ) by Gender and Cohort. Note:
Regression coefficients for selected variables separately by gender, differentiating between the 1998 cohort
(N = 6628) and 2008 cohort (N = 6575), with Confidence Intervals at the 95% level, adjusting for all the study
control variables
Emerging Digital Generations? Impacts of Child Digital Use on… 645
affected by gaming. Media usage was associated with higher socioemotional problems
among high-SES children in both the 1998 and 2008 cohort (p < 0.05), while such
association for low-SES children became neutral for the 2008 cohort. Despite some
exceptions, the moderating role of SES in the impact of digital use on child
socioemotional problems was small and insignificant across both cohorts.
Overall, the moderating role of gender and SES was minor and similar across
cohorts. We found no general support for H-2a and H-2b. Gender and SES tended to
not moderate the association between child digital use and socioemotional problems.
We found mixed support for H-3c. Across cohorts, gender gaps were similarly low (as
expected), as well as SES gaps (we expected higher SES gaps for the 2008 cohort than
for the 1998 cohort).
4.5 Robustness Checks: Quantile Regressions, SDQ Subscales and Heterogeneous
Digital Use
We conducted several robustness checks. First, we run quantile regression analyses of
children’s digital use on socioemotional problems (Table 4). 3+ hours of digital screen-
time was associated with higher socioemotional problems across nearly all TDS score
levels. Yet, the size of the effects increased across the conditional distribution, with
children with the lowest socioemotional problems being largely unaffected by their
digital engagement, and children with highest socioemotional problems being dispro-
portionately affected by digital engagement. For digital screen-time, 3+ hours daily
screen-time was particularly salient in the 90th percentile (highest socioemotional
problems) (B = 3.575; p < 0.001), and smaller and insignificant at the 10th percentile
(lowest socioemotional problems) (B = 0.607). Likewise, 1 to 3 h of daily screen-time
only reached significant associations with higher socioemotional problems in the 75th
percentile (B = 0.568; p < 0.05) and 90th percentiles (B = 1.173; p < 0.001). Similarly,
engagement in digital media began to form an increasing association with
socioemotional problems in the 25th percentile (B = 0.282; p < 0.01) up to the 90th
percentile (B = 0.705; p < 0.01), with a small and insignificant effect for the 10th
Fig. 3 Linear Regression Models. Child Socioemotional Problems (SDQ) by Socioeconomic Status (SES)
and Cohort. Note: Regression coefficients for selected variables separately by SES, differentiating between the
1998 cohort (N = 6628) and 2008 cohort (N = 6575), with Confidence Intervals at the 95% level, and adjusting
for all the study control variables
M. Bohnert, P. Gracia646
percentile (B = 0.182). Additional quantile regression analyses (not shown) revealed
that digital screen-time was more strongly associated with socioemotional problems
across the conditional distribution in the 1998 cohort, while for the 2008 cohort there
were more similar digital effects across SDQ levels.
Second, we conducted analyses on the effect of digital use on children’s five specific
socioemotional outcomes (Table 5). For digital screen time, 3+ hours of daily use had
significant associations with higher socioemotional problems, except that less than an
hour of TV screen-time (compared to not watching TV at all) was associated with
declines in socioemotional problems for peer and for the anti-social subscales. Effects
of digital time were generally stable over the five subscales. By contrast, media usage
was associated with a substantive increase of socioemotional problems in two subscales
(conduct, hyperactivity) but it was weakly associated with other three outcomes
(emotional, peer and anti-social). Additional analyses (not shown) revealed a similar
direction of the most relevant effects across cohorts, yet with slightly stronger effects
for the 2008 cohort, compared to the 1998 cohort.
Third, we examined gender and SES differences in digital engagement (Table 7;
Appendix). We found that boys were more active than girls in digital activities, especially
for the 2008 cohort. Girls were more involved in socializing digital activities and were more
digitally supervised by adults, while boys were more involved in digital personal develop-
ment activities. We found that low-SES children spent more time watching TV and on
digital activities than high-SES children, while SES gaps in digital screen-time clearly
increased for the 2008 cohort. In both cohorts, high-SES children were overrepresented
among those owning a mobile phone. For the 1998 cohort, high-SES children were less
supervised by adults and engaged more with socializing and educational activities, while
such patterns reversed for the 2008 cohort. These results show that, even if gender and SES
were not found to moderate the effect of children’s digital engagement on socioemotional
problems, there were relevant gender and SES differences in children’s digital engagement
across the two cohorts, especially for the 2008 cohort.
5 Discussion
This study is – to our knowledge – the first cohort comparison of the impact of digital
engagement on socioemotional outcomes. Using unique cohort data from Ireland on 9-year
old children born a decade apart (in 1998 and2008),we foundboth change and persistence in
children’s digital usage and its impact onwell-being.While some hypotheseswere generally
confirmed (H-1,H-3a,H-3b), otherhypotheseswere rejected (H-2a,H-2b), andsomeyielded
mixed findings (H-3c).We globally discuss ourmixed results in this section.
Four main findings can be highlighted. First, we found increased and slightly more
diversified digital use in the birth cohort of 1998 than in the birth cohort of 2008. In line
with findings of TV screen-time being functionally displaced by digital screen-time
(Gershuny 2003; De Waal and Schoenbach 2010; Vilhelmson et al. 2018), TV screen-
time decreased between cohorts, while digital screen-time increased. This increase in
digital screen-time is consistent with previous research, which has found moderate but
consistent increases in digital screen-time between child cohorts since the 1990s
(Vilhelmson et al. 2018; Goode et al. 2019; Fomby et al. 2019). Although there were
modest increases to engagement in socializing and personal development activities, the
Emerging Digital Generations? Impacts of Child Digital Use on… 647
Ta
bl
e
4
Q
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es
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48
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)
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s
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*
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)
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)
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(0
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)
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(0
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(0
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(0
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rs
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(0
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Pa
re
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uc
at
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n
M. Bohnert, P. Gracia648
Ta
bl
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4
(c
on
tin
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Emerging Digital Generations? Impacts of Child Digital Use on… 649
Ta
bl
e
5
L
in
ea
r
re
gr
es
si
on
s
on
su
bs
ca
le
m
ea
su
re
s
of
ch
ild
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ci
oe
m
ot
io
na
l
pr
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le
m
s
(S
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)
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m
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io
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l
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yp
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tiv
ity
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nt
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so
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e
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)
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es
s
th
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Pa
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at
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ol
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ge
ed
uc
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(R
ef
)
M. Bohnert, P. Gracia650
Ta
bl
e
5
(c
on
tin
ue
d)
T
D
S
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Emerging Digital Generations? Impacts of Child Digital Use on… 651
far greatest change in child digital activity usage comes with media/leisure activities
(e.g. watching Youtube videos, listening to music, streaming TV/movies), which went
from 28% of users in the 1998 cohort to 88% of users among the 2008 cohort. Indeed,
with the ever-increasing capabilities of digital devices and emergence of video and
streaming platforms (e.g. Youtube, Spotify, Netflix, etc.) ‘digitod’ children have new
access to digital media and leisure activities that were unavailable to children born a
decade earlier.
Second, we found high levels of daily TV/digital screen-time (i.e. 3+ hours) to be
associated with significant declines in child socioemotional well-being. The size of
such effects was about twice as large for the 2008 cohort, compared to the 1998 cohort.
Most digital activities (i.e., gaming, educational, socializing) had small and insignifi-
cant effects on children’s socioemotional well-being. Yet, we found engagement in
digital media activities to lead to increasing child socioemotional problems, with
similar effects for the 1998 and 2008 cohort. Our results generally support recent
research showing significant negative effects of digital screen-time on child well-being
(e.g., Twenge et al. 2018). These findings link to literature implying that examining
both the quantity and quality of digital engagement is crucial for the child well-being
literature (Livingstone et al. 2018), while indicating increasing negative effects of high
screen-based time on child socioemotional well-being in contemporary Ireland.
Third, we found the effects of digital use on socioemotional well-being to be quite
similar by gender and SES in both cohorts. This finding is relevant, especially
considering that additional analyses showed gender and SES differences in child digital
engagement, in line with previous scholarship (Brooks et al. 2016; Gracia et al. 2019;
O’Neill and Dinh 2015; Ortega et al. 2010). These findings with regard to SES are
particularly important due to the marked differences in the economic circumstances of
the two cohorts. Girls were more active in socializing digital activities, while boys
engaged more in personal development digital activities, with boys being overrepre-
sented among mobile phone owners. Low-SES children, despite having lower rates of
mobile phone ownership than high-SES children, spent more time on screen-based
activities. Also, some of these gaps in digital activities were clearly stronger for the
younger 2008 cohort, compared to the older 1998 cohort. And yet, despite significant
cohort changes in digital use, parental characteristics (e.g. employment, education,
age), and economic circumstances, we found similar effects of digital engagement on
child socioemotional well-being by gender and SES and across cohorts. This might
indicate that gender and SES inequalities in socioemotional well-being are not driven
by distinct forms of digital engagement. Future research should further examine the role
of gender and SES in influencing child digital engagement and well-being.
Fourth, in additional analyses we further identified at which levels of socioemotional
well-being children are most affected by digital engagement. Digital screen-time and
media engagement had the strongest detrimental effects on well-being among children
who were already at high levels of risk (i.e. high socioemotional problems), but more
modest effects among those at the lowest risk levels. Further, we found that child media
engagement is associated with conduct and hyperactivity, but not with emotional, peer,
and anti-social subscales. These findings support the claims of Valkenburg and
Piotrowski (2017) in suggesting that the effects of child digital engagement on well-
being differ across risks levels and depending on the well-being indicator that we
examine. Further analyses (not shown) did not give conclusive evidence of cohort
M. Bohnert, P. Gracia652
variations on how children’s digital engagement impacts their socioemotional risks and
specific psychological measures. We hope these analyses will inspire future studies
looking at the (changing) effects of digital engagement on child well-being measures,
beyond looking at average digital effects.
Ourstudyhas someshortcomings thatweneed tostress.First,ourdata,whilespanning two
cohorts, is cross-sectional in nature.Unfortunately, at this stage, there is only comparable data
for 9-years old through the GUI, given that age 9 is the most recent currently available wave
from the 2008 cohort. We hope that in the years to come our study will guide new cohort
comparisons benefiting fromhigh-quality longitudinal data. Second, our data had a relatively
small number (N = 190) of participants who were ‘high users’ (3+ hours daily) of digital
technology. This led to large confidence intervals in some analyses and meant that caution
needs to be taken in drawing conclusions from this category. Still, our results use a large
representative sample of Irish children, indicating that the observed effect sizes are generally
reliableandaccurate.Third, theTVanddigital timeusedataandSDQscores fromtheGUIare
parent-reported, rather thanchild-reported,whichaddschallenges to the reliabilityof some the
data (Noonan et al. 2018).While theGUI corrected for this in variousways (i.e., adjusting the
timeanddifficultyofthechildquestionnaires),weneedtostressthislimitation,callingforfuture
comparisons. Fourth, while we used reliable and comparable digital time-use measures,
scholars have recommended the use 24-hour time-diary measures as reliable indicators to
assess children’s activities and their related well-being outcomes (Ben-Arieh and Ofir 2002;
Gracia 2020). Unfortunately, such time-diary data were not available in our data when we
conducted our study. Future research will hopefully further address these caveats by using
related high-quality data.
To conclude, our study – despite having some limitations – is the first examination of how
child digital usage impacts socioemotional well-being and how these impacts have changed
across recentcohorts.Wefindbothchangeandpersistence inhow9-yearsoldchildrenengage
in digital technologies and how this digital involvement affects their socioemotional well-
being. A decade of difference between two cohorts in Ireland (the 1998 cohort and the 2008
cohort) has led to significant changes in the nature of digital engagement.While the negative
effects of the quantity of child screen-based time on socioemotional well-being have become
more pervasive in the younger generation of children, the relative effect of the quality of this
digital engagement (forms of digital and screen engagement) on child well-being has
remained quite stable over the last decade in Ireland. We hope that our study will inspire
new observational and experimental research on the changing nature of child digital engage-
ment and its impacts on well-being.
Authors’ Contributions Bohnert was the main leading author of this study. The idea was developed by
Bohnert and Gracia. The analyses were conducted by Bohnert. The first draft was written by Bohnert and,
subsequently, both Bohnert and Gracia contributed to the writing of the study.
Funding This study is integrated within the DIGYMATEX project (Grant agreement ID: 870578), funded
by the European Commission H2020 within the call ‘DT-TRANSFORMATIONS-07-2019 – The impact of
technological transformations on children and youth.’ Project link: http://www.digymatex.eu.
Data Availability The data used in this study were obtained from the Growing Up in Ireland (GUI)
study, following all the ethical and formal criteria within the Irish Social Science Data Archive (ISSDA)
and according to EU data protection policies.
Emerging Digital Generations? Impacts of Child Digital Use on… 653
Compliance with Ethical Standards
Conflict of Interest This study has no conflict of interest of any sort.
Code Availability Analyses were conducted with the statistical software Stata 16. Data coding will be made
accessible to the scientific community.
Appendix
Table 6 Digital activities definition
Activity Types 1998 Cohort 2008 Cohort
Gaming Playing games Play games on your own
Play games with other people
Media Watching movies and
downloading music
Watch videos on YouTube
Watch TV or movies on the Internet
Downloading apps
Download or stream music or films
Educational
Doing homework
Surfing the internet for school projects
Doing homework
Socializing Chatrooms
Instant messaging
E-mailing
Visit a social media site
Instant messaging
Share photos, videos or music
with people other than your family
Personal Development Surfing the internet for things
that interest you
Search for information on things
that interest you
Source: Growing Up in Ireland Survey (GUI) Survey. Child Questionnaires
Table 7 Children’s digital engagement by gender and SES
GENDER PARENTAL SES
Variables 1998 Cohort 2008 Cohort 1998 Cohort 2008 Cohort
Boys Girls Boys Girls Low High Low High
TV screen-time
None 0.03 0.02 0.06 0.05 0.02 0.03 b 0.05 0.06 b
Less than an hour 0.23 0.25 0.44 0.47 0.20 0.27 b 0.42 0.48 b
1 to 3 h 0.66 0.64 0.46 0.45 0.68 0.62 b 0.49 0.44 b
3 + hours 0.08 0.08 0.03 0.03 0.10 0.07 b 0.04 0.02 b
Digital screen-time
None 0.24 0.21a 0.23 0.24 a 0.23 0.22 0.20 0.25 b
Less than an hour 0.61 0.64 a 0.45 0.52 a 0.61 0.64 0.47 0.50 b
1 to 3 h 0.14 0.14 a 0.29 0.14 a 0.22 0.13 0.30 0.23 b
3 + hours 0.01 0.01 a 0.01 0.02 a 0.02 0.01 0.03 0.01 b
M. Bohnert, P. Gracia654
Table 7 (continued)
GENDER PARENTAL SES
Variables 1998 Cohort 2008 Cohort 1998 Cohort 2008 Cohort
Activities
Gaming 0.83 0.89 a 0.88 0.80 a 0.87 0.86 0.85 0.83 b
Media 0.28 0.27 0.89 0.87 a 0.27 0.28 0.90 0.87 b
Socializing 0.19 0.24 a 0.26 0.32 a 0.20 0.22 b 0.32 0.27 b
Education 0.53 0.59 a 0.17 0.18 0.53 0.58 b 0.18 0.16 b
Personal development 0.51 0.46 a 0.58 0.52 a 0.46 0.50 b 0.55 0.55
Mobile phone ownership 0.65 0.58 a 0.79 0.77 0.53 0.68 b 0.70 0.83 b
Adult supervision 0.65 0.69 a 0.52 0.55 a 0.71 0.64 b 0.52 0.55 b
N 3195 3443 3195 3443 2785 3843 2851 3994
Source: Growing Up in Ireland (GUI) Survey
a Indicates significant gender differences at the 95% level for the specific activity within the same cohort
b Indicates significant SES differences at the 95% level for the specific activity within the same cohort
Fig. 4 Linear Regression Models. Child Socioemotional Problems (SDQ) by Cohort. Note: Regression
coefficients from models of Table 3 (Column 2 and Column 3) for selected variables differentiating between
the 1998 cohort (N = 6628) and 2008 cohort (N = 6575), including Confidence Intervals at the 95% level, and
adjusting for all the study control variables
Emerging Digital Generations? Impacts of Child Digital Use on… 655
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References
Babic, M., Smith, J., Morgan, P., Eather, N., Plotnikoff, R., & Lubans, D. (2017). Longitudinal associations
between changes in screen-time and mental health outcomes in adolescents.Mental Health and Physical
Activity, 12, 124–131.
Baker, R., & White, K. (2010). In their own words: Why teenagers don’t use social networking sites.
Cyberpsychology, Behavior and Social Networking, 14(6), 395–398.
Barker, V. (2009). Older adolescents’ motivations for social network site use: The influence of gender, group
identity, and collective self-esteem. Cyberpsychology & Behavior, 12(2), 209–213.
Ben-Arieh, A., & Ofir, A. (2002). Opinion, dialogue, review: Time for (more) time-use studies: Studying the
daily activities of children. Childhood, 9(2), 225–248.
Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence.
British Journal of Educational Technology, 39(5), 775–786.
Berryman, C., Ferguson, C. J., & Negy, C. (2018). Social media use and mental health among young adults.
Psychiatric Quarterly, 89, 307–314.
Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing:
A systematic narrative review. Children and Youth Services Review, 41, 27–36.
Blair, S., Claster, P., & Claster, S. (2015). Technology and youth: Growing up in a digital world. Bingley:
Emerald Group Publishing.
Bøe, T., Øverland, S., Lundervold, A., & Hysing, M. (2012). Socioeconomic status and children’s mental
health: Results from the Bergen child study. Social Psychiatry Psychiatric Epidemiology, 47, 1557–1566.
Booker, C., Skew, A., Kelly, Y., & Sacker, A. (2015). Media use, sports participation, and well-being in
adolescence: Cross-sectional findings from the UK household longitudinal study. American Journal of
Public Health, 105(1), 173–179.
Booker, C., Kelly, Y., & Sacker, A. (2018). Gender differences in the associations between age trends of social
media interaction and well-being among 10-15 year olds in the UK. BMC Public Health, 18, 321.
Brito, R., Dias, P., & Oliveira, G. (2018). Young children, digital media and smart toys: How perceptions
shape adoption and domestication. British Journal of Educational Technology, 49(5), 807–820.
Brooks, F., Chester, K., Smeeton, N., & Spencer, N. (2016). Video gaming in adolescence: Factors associated
with leisure time use. Journal of Youth Studies, 19(1), 36–54.
Bruggeman, H., Van Hiel, A., Van Hal, G., & Van Dongen, S. (2019). Does the use of digital media affect
psychological well-being? An empirical test among children aged 9 to 12. Computers in Human
Behavior, 101, 104–113.
Camerini, A., Schulz, P. J., & Jeannet, A. (2018). The social inequalities of Internet access, its use, and the
impact on children’s academic performance: Evidence from a longitudinal study in Switzerland. New
Media & Society, 20(7), 2489–2508.
Campbell, C., & Pearlman, J. (2013). Period effects, cohort effects, and the narrowing gender wage gap. Social
Science Research, 42(6), 1693–1711.
Campbell, W. K., Campbell, S. M., Siedor, L. E., & Twenge, J. M. (2015). Generational differences are real
and useful. Industrial and Organizational Psychology, 8(3), 324–408.
Cantillon, B., Chzhen, Y., Handa, S., & Nolan, N. (Eds.). (2017). Children of austerity: Impact of the great
recession on child poverty in rich countries. Oxford: Oxford University Press.
Casey, A., Layte, R., Lyons, S., & Silles, M. (2012). Home computer use and academic performance of nine-
year-olds. Oxford Review of Education, 38(5), 617–634.
Castellacci, F., & Tveito, V. (2018). Internet use and well-being: A survey and a theoretical framework.
Research Policy, 47, 308–325.
M. Bohnert, P. Gracia656
https://doi.org/
Central Statistics Office. (2015). Census 2002: Principal socio-economic results. Dublin: Stationary Office.
Davis, K. (2012). Friendship 2.0: Adolescents’ experiences of belonging and self-disclosure online. Journal of
Adolescence, 35(6), 1527–1536.
Davis, E., Sawyer, M., Kai Lo, S., Priest, N., & Wake, M. (2010). Socioeconomic risk factors for mental
health problems in 4–5-year-old children: Australian population study. Academic Pediatrics, 10(1), 41–
47.
De Waal, E., & Schoenbach, K. (2010). News sites’ position in the mediascape: Uses, evaluations and media
displacement effects over time. New Media & Society, 12(3), 477–496.
Dempsey, S., Lyons, S., & McCoy, S. (2019). Later is better: Mobile phone ownership and child academic
development, evidence from a longitudinal study. Economics of Innovation and New Technology, 28,
798–815.
Fahy, A., Stansfeld, S., Smuk, M., et al. (2016). Longitudinal associations between cyberbullying involvement
and adolescent mental health. Journal of Adolescent Health, 59(5), 502–509.
Fioravanti, G., Dèttore, D., & Casale, S. (2012). Adolescent internet addiction: Testing the association
between self-esteem, the perception of internet attributes, and preference for online social interactions.
Cyberpsychology, Behavior and Social Networking, 15(6), 318–323.
Fomby, P., Goode, J. A., Truong-Vu, K. P., & Mollborn, S. (2019). Adolescent technology, sleep, and
physical activity time in two U.S. cohorts. Youth and Society, 1–25.
Fortunati, L., Taipale, S., & de Luca, F. (2019). Digital generations, but not as we know them. Convergence,
25(1), 95–112.
Gentile, D., Coyne, S., & Bricolo, F. (2013). Pathological technology addictions: What is scientifically known
and what remains to be learned. In K.E. Dill (Ed.),Oxford library of psychology. The Oxford handbook of
media psychology (pp 382–402). New York City: Oxford University Press.
George, M., Russell, M., Piontak, J., & Odgers, C. (2018). Concurrent and subsequent associations between
daily digital technology use and high-risk adolescents’ mental health symptoms. Child Development,
89(1), 78–88.
Gershuny, J. (2003). Web use and net nerds: A neofunctionalist analysis of the impact of information
Technology in the Home. Social Forces, 82(1), 141–168.
Goode, J. A., Fomby, P., Mollborn, S., & Limburg, A. (2019). Children’s technology time in two US cohorts.
Child Indicators Research, 13, 1107-1132.
Goodman, A., & Goodman, R. (2011). Population mean scores predict child mental disorder rate: Validating
SDQ prevalence estimators in Britain. The Journal of Child Psychology and Psychiatry, 52(1), 100–108.
Gracia, P. (2015). Parent-child leisure activities and cultural capital in the United Kingdom: The gendered
effects of education and social class. Social Science Research, 52(4), 290–302.
Gracia, P. (2020). Children’s time use. In D. T. Cook (Ed.), The SAGE encyclopedia of children and childhood
studies (pp. 504–506). London: Sage.
Gracia, P., & Garcia-Roman, J. (2018). Child and adolescent developmental activities and time use in Spain:
The gendered role of parents’ work schedules and education levels. European Sociological Review.,
34(5), 518–538.
Gracia, P., Garcia-Roman, J., Oinas, T., & Anttila, T. (2019). Child and adolescent time use: A cross-national
study. Journal of Marriage and Family., 82, 1304–1325. https://doi.org/10.1111/jomf.12626.
Gracia, P., Garcia-Roman, J., Oinas, T., & Anttila, T. (2020). Do boys’ and girls’ daily activities differ? Cross-
country evidence. SocArXiv Papers. https://doi.org/10.31235/osf.io/nruq7.
Gross, E. F. (2009). Logging on, bouncing back: An experiential investigation of online communication
following social exclusion. Developmental Psychology, 45, 1787–1793.
Holloway, D., Green, L., & Stevenson, K. (2015). Digitods: Toddlers, touch screens and Australian family
life. M/C Journal, 18(5).
Hope, S., Pearce, A., Whitehead, M., & Law, C. (2014). Family employment and child socioemotional
behaviour: Longitudinal findings from the UK millennium cohort study. Journal of Epidemiology and
Community Health, 68(10), 950–957.
Hwang, J. M., Cheong, P. H., & Feeley, T. H. (2009). Being young and feeling blue in Taiwan: Examining
adolescent depressive mood and online and offline activities. New Media &Society, 11(7), 1101–1121.
Jackson, L., FitzGerald, H., Zhao, Y., Kolenic, A., von Eye, A., & Harold, R. (2008). Information Technology
(IT) use and children’s psychological well-being. Cyberpsychology & Behavior, 11(6), 755–757.
Kelly, Y., Zilanawala, A., Booker, C., & Sacker, A. (2018). Social media use and adolescent mental health:
Findings from the UK millennium cohort study. EClinicalMedicine, 6, 59–68.
Klanšček, H. J., Žiberna, J., Korošec, A., Zurc, J., & Albeht, T. (2014). Mental health inequalities in Slovenian
15-year-old adolescents explained by personal social position and family socioeconomic status.
International Journal for Equity in Health, 13(26), 26.
Emerging Digital Generations? Impacts of Child Digital Use on… 657
https://doi.org/10.1111/jomf.12626
https://doi.org/10.31235/osf.io/nruq7
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143–
156.
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O.
(2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8(8), 1–6.
Kucirnova, N., & Sakr, M. (2015). Child-father creative text-making at home with crayons, iPad collage and
PC. Thinking Skills and Creativity, 17, 59–63.
Leathers, H., Summers, S., & Desollar, A. (2013). Toddlers on technology: A Parents’ guide. Bloomington:
AuthorHouse.
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J., & Grob, A. (2015). Adolescents’
electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age.
Journal of Youth & Adolescence, 44, 405–418.
Leung, L. (2014). Predicting internet risks: A longitudinal panel study of gratifications-sought, internet
addiction symptoms, and social media use among children and adolescents. Health Psychology and
Behavioral Medicine: An Open Access Journal, 2(1), 424–439.
Li, C. (2013). Little’s test of missing completely at random. The Stata Journal, 13(4), 795–809.
Liff, S., and Shepherd, A. (2004). An evolving gender digital divide? OII Internet Issue Brief, 2, 1–10.
Liu, M., Ming, Q., Yi, J., Wang, X., & Yao, S. (2016). Screen time on school days and risks for psychiatric
symptoms and self-harm in mainland Chinese adolescents. Frontiers in Psychology, 7(1), 1–11.
Livingstone, S., & Helsper, E. (2007). Gradations in digital inclusion: Children, young people and the digital
divide. New Media and Society, 9(4), 671–696.
Livingstone, S., & Helsper, E. (2010). Balancing opportunities and risks in teenagers’ use of the internet: The
role of online skills and internet self-efficacy. New Media & Society, 12(2), 309-329.
Livingstone, S., Mascheroni, G., & Staksrud, E. (2018). European research on children’s internet use:
Assessing the past and anticipating the future. New Media and Society, 20(3), 1103–1122.
Mannheim, K. (1952). The sociological problem of generations. Essays in the Sociology of Knowledge, 276–
322.
Mascheroni, G., & Cuman, A. (2014). Net Children Go Mobile: Final report. Deliverables D6.4 & D5.2.
Milano: Educatt.
Mascheroni, G., & Olafsson, K. (2016). The mobile internet: Access, use, opportunities and divides among
European children. New Media & Society, 18(8), 1657–1679.
Nikken, P., & Opree, S. J. (2018). Guiding young children’s digital media use: SES-differences in mediation
concerns and competence. Journal of Child and Family Studies, 27, 1844–1857. https://doi.org/10.1007
/s10826-018-1018-3.
Noonan, K., Burns, R., & Violato, M. (2018). Family income, maternal psychological distress and child socio-
emotional behaviour: Longitudinal findings from the UK millennium cohort study. SSM – Population
Health, 4, 280–290.
O’Neill, B., & Dinh, T. (2015). Net children go Mobile: Full findings from Ireland. Dublin: Dublin Institute of
Technology.
O’Neill, B., Grehan, S., & Ólafsson, K. (2011). Risks and safety for children on the internet: The Ireland
report. LSE, London: EU Kids Online.
Orben, A., & Przybylski, A. (2019a). The association between well-being and digital technology use. Nature
Human Behaviour, 3(February), 173–182.
Orben, A., & Przybylski, A. (2019b). Screens, teens, and psychological well-being: Evidence from three time-
use-diary studies. Psychological Science, 30, 682–696.
Orben, A., Dienlin, T., & Przybylski, A. (2019). Reply to Foster and Jackson: Open scientific practices are the
way forward for social media effects research. PNAS, 116(31), 15334-15335.
Ortega, F. B., Ruiz, J. R., Martinez-Gómez, D., De Henauw, S., et al. (2010). Sedentary patterns and media
availability in European adolescents: The HELENA study. Preventive Medicine, 51(1), 50–55.
Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., Ristic, S., & Pantic, S. (2012).
Association between online social networking and depression in high school students: Behavioral
physiology viewpoint. Psychiatria Danubina, 24(1), 90–93.
Parkes, A., Sweeting, H., Wight, D., & Henderson, M. (2013). Do television and electronic games predict
children’s psychosocial adjustment? Longitudinal research using the UK millennium cohort study.
Archives of Disease in Childhood, 98(5), 341–348.
Piotrowska, P., Stride, C., Croft, S., & Rowe, R. (2015). Socioeconomic status and antisocial behaviour
among children and adolescents: A systematic review and meta-analysis. Clinical Psychology Review, 35,
47–55.
M. Bohnert, P. Gracia658
https://doi.org/10.1007/s10826-018-1018-3
https://doi.org/10.1007/s10826-018-1018-3
Przybylski, A. K., & Weinstein, N. (2017). A large-scale test of the goldilocks hypothesis: Quantifying the
relations between digital screen use and the mental well-being of adolescents. Psychological Science,
28(2), 204–215.
Quail, A., O’Reilly, C., Watson, D., McNamara, E., O’Mahony, D., and Murray, A. (2019) Growing up in
Ireland: A summary guide to cohort ‘08 at 9 years. Available at: https://www.growingup.
ie/pubs/Summary-Guide-Infant-Cohort-Wave-5 .
Reinhard, E., Layte, R., McCrory, C., Panico, L., & Avendano, M. (2018). The great recession and the health
of young children: A fixed-effects analysis in Ireland. American Journal of Epidemiology, 187(7), 1438–
1448.
Selfhout, M., Branje, S., Delsing, M., ter Bogt, T., & Meeus, W. (2009). Different types of internet use,
depression, and social anxiety: The role of perceived friendship quality. Journal of Adolescence, 32(4),
819–833.
Shu, X., & Meagher, K. (2018). Beyond the Stalled Gender Revolution: Historical and Cohort Dynamics in
Gender Attitudes from 1977 to 2016. Social Forces, 96(3), 1243–1274.
Stiglic, N., & Viner, R. (2019). Effects of screentime on the health and well-being of children and adolescents:
A systematic review of reviews. BMJ Open, 9(1), 1–15.
The Children’s Society. (2015). The good childhood report 2015. London: The Children’s Society.
Twenge, J., Joiner, T., Rogers, M., & Martin, G. (2018). Increases in depressive symptoms, suicide-related
outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen
time. Clinical Psychological Science, 6(1), 3–17.
Valkenburg, P. M., & Piotrowski, J. T. (2017). Plugged in: How media attract and affect youth. New Haven:
Yale University Press.
van den Eijnden, R., Meerkerk, G., Vermulst, A., Spijkerman, R., & Engels, R. (2008). Online communica-
tion, compulsive internet use, and psychosocial well- being among adolescents: A longitudinal study.
Developmental Psychology, 44(3), 655–665.
van der Aa, N., Overbeek, G., Engels, R., Scholte, R., Meerkerk, G., & van den Eijnden, R. (2009). Daily and
compulsive internet use and well-being in adolescence: A diathesis-stress model based on big five
personality traits. Journal of Youth and Adolescence, 38(765).
van Deursen, A., & van Dijk, J. (2014). The digital divide shifts to differences in usage. NewMedia & Society,
16(3), 507–526.
Verduyn, P., Jonides, J., & Kross, E. (2017). Do social network sites enhance or undermine subjective well-
being? A Critical Review. Social Issues and Policy Review, 11(1), 274–302.
Vilhelmson, B., Elldér, E., & Thulin, E. (2018). What did we do when the internet wasn’t around? Variation in
free-time activities among three young-adult cohorts from 1990/1991, 2000/2001, and 2010/2011. New
Media and Society, 20(8), 2898–2916.
Wight, V., Price, J., Bianchi, S., & Hunt, B. (2009). The time use of teenagers. Social Science Research, 38(4),
792–809.
Williams, A., &Merten, M. (2008). A review of online social networking profiles by adolescents. Implications
for future research and intervention. Adolescence, 32(170), 253–273.
Williams, J., Greene, S., Doyle, E., Harris, E., Layte, R., McCoy, S., … & Thornton, M. (2009).Growing up in
Ireland national longitudinal study of children. The lives of 9 year olds. Dublin: The Stationary Office.
Yang, Y. (2008). Social inequalities in happiness in the United States, 1972 to 2004: An age- period-cohort
analysis. American Sociological Review, 73, 204–226.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
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Abstract
Introduction
Analytical Framework
Children’s Digitalization in Ireland
Impacts of Children Digital Use on Socioemotional Well-Being
Heterogeneity by Socioeconomic Status and Gender
Digital Generations and Cohort Effects
Data and Methods
Sample
Dependent Variables
Independent Variables
Moderating and Control Variables
Empirical Strategy
Results
Descriptive Analyses
Digital Use Effects on Child Socioemotional Wellbeing
Cohort Digital Effects on Child Socioemotional Wellbeing
The Role of Gender and SES
Robustness Checks: Quantile Regressions, SDQ Subscales and Heterogeneous Digital Use
Discussion
Appendix
References
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