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Background: Excessive engagement with digital screens is harmful to children's health. However, new evidence suggests that exposure at moderate levels may not be harmful and may even provide benefit. Therefore, our objective was to determine if there are curvilinear relationships between different types of screen time and a diverse set of outcomes, including health and education. Methods: We address our objective using a repeated measures design. Children (N = 4013), initially aged 10-11 were assessed every 2 years between 2010 and 2014. Children's screen time behavior was measured using time-use diaries, and categorized into five types: social, passive, interactive, educational, or other. We used measures of children's physical health, health-related quality of life, socio-emotional outcomes, and school achievement. The analysis plan was pre-registered. Models were adjusted for gender, socio-economic status, ethnicity, number of siblings, and housing factors. Results: There were linear associations between total screen time and all outcomes, such that more screen time was associated with worse outcomes. However, there was variability when examined by screen time type. Passive screen time (e.g., TV) was associated with worse outcomes, educational screen time (e.g., computer for homework) was associated with positive educational outcomes and had no negative relations with other outcomes. Interactive screen time (e.g., video games) had positive associations with educational outcomes but negative associations with other outcomes. In all instances, these significant associations were small or very small, with standardised effects < 0.07. We found little evidence of curvilinear relationships. Conclusions: The small effects of screen time on children's outcomes appear to be moderated by the type of screen time. Policy makers, educators, and parents should consider the type of screen time when considering the benefits and harms of use.
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R E S E A R C H Open Access
Type of screen time moderates effects on
outcomes in 4013 children: evidence from
the Longitudinal Study of Australian
Taren Sanders
, Philip D. Parker
, Borja del Pozo-Cruz
, Michael Noetel
and Chris Lonsdale
Background: Excessive engagement with digital screens is harmful to childrens health. However, new evidence
suggests that exposure at moderate levels may not be harmful and may even provide benefit. Therefore, our
objective was to determine if there are curvilinear relationships between different types of screen time and a
diverse set of outcomes, including health and education.
Methods: We address our objective using a repeated measures design. Children (N= 4013), initially aged 1011
were assessed every 2 years between 2010 and 2014. Childrens screen time behavior was measured using time-use
diaries, and categorized into five types: social, passive, interactive, educational, or other. We used measures of
childrens physical health, health-related quality of life, socio-emotional outcomes, and school achievement. The
analysis plan was pre-registered. Models were adjusted for gender, socio-economic status, ethnicity, number of
siblings, and housing factors.
Results: There were linear associations between total screen time and all outcomes, such that more screen time
was associated with worse outcomes. However, there was variability when examined by screen time type. Passive
screen time (e.g., TV) was associated with worse outcomes, educational screen time (e.g., computer for homework)
was associated with positive educational outcomes and had no negative relations with other outcomes. Interactive
screen time (e.g., video games) had positive associations with educational outcomes but negative associations with
other outcomes. In all instances, these significant associations were small or very small, with standardised effects <
0.07. We found little evidence of curvilinear relationships.
Conclusions: The small effects of screen time on childrens outcomes appear to be moderated by the type of
screen time. Policy makers, educators, and parents should consider the type of screen time when considering the
benefits and harms of use.
Keywords: Screen time, Children, Health, Education
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* Correspondence:
Institute for Positive Psychology and Education, Australian Catholic
University, North Sydney, NSW, Australia
Full list of author information is available at the end of the article
Sanders et al. International Journal of Behavioral Nutrition and Physical Activity
(2019) 16:117
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High levels of engagement with digital screens (i.e.,
screen time) are harmful to childrens physical health
[1]. A body of evidence underpins guidelines that recom-
mend limiting childrens screen time exposure [2,3]. For
example, a recent review found that screen time is dele-
teriously associated with adiposity and cardiorespiratory
fitness [1]. There is also evidence that screen time is as-
sociated with negative psychological and educational
outcomes, such as greater depression [4] and lower aca-
demic achievement [5], respectively. As a result, guide-
lines [3,6] advise that lower levels of screen time are
associated with benefits for children. In our study, we
refer to this as the less-is-better hypothesis.
Evidence that moderate levels of screen time may have
benefits over abstinence or high use contradicts current
guidelines. For example, a review of the literacy develop-
ment literature revealed studies in which moderate
amounts of television was associated with better reading
than low or high amounts of viewing [7]. Curvilinear
relations have also been found with psychosocial out-
comes. For example, in an investigation of more than
120,000 adolescents, Przybylski and Weinstein found
moderate amounts of electronic screen time were associ-
ated with higher mental well-being compared to low or
high levels [8]. Similar curvilinear relationships for screen
time have also emerged in other studies related to chil-
drens health and well-being [912]. Some researchers
have labeled this the Goldilocks hypothesis [8].
When examining the Goldilocks screen hypothesis, pre-
vious studies have tended to focus on a single outcome, or
a narrow range of variables. For example, Przybylski and
Weinstein (2017) centred their investigation on screen
times association with adolescentswell-being, and did
not investigate other important outcomes, such as physical
health or educational achievement. It is possible that the
less-is-better hypothesis and the Goldilocks hypothesis
apply differentially to outcomes. For example, engaging
with moderate amounts of social media may benefit social
functioning, while high levels might displace face-to-face
contact, leading to poorer mental health (i.e., supporting
the Goldilocks hypothesis) [13]. In contrast, passive screen
time (e.g., television) would be unlikely to convey any
form of physical health benefit, and thus lower levels
would be expected to provide health benefits (i.e., support-
ing the less-is-better hypothesis). Studies that examine a
limited range of outcome variables [812]cannotexamine
this possibility.
In the current study, we aimed to investigate these two
competing hypotheses across different types of screen
time and different outcomes, including physical health,
psychological outcomes, and educational outcomes. We
further extended the Przybylski and Weinstein (2017)
study of adolescents by examining these hypotheses in a
large sample of children, and by examining if these rela-
tionships are stable as children age. As this is a concep-
tual replication of Przybylski and Weinsteins work, we
also examine differences by weekday and weekend.
Research Questions
1. Are there linear or curvilinear relations between
screen time and childrens physical health,
psychological outcomes, and educational outcomes?
And, if curvilinear relations exist, at what duration
of screen exposure do they become negative?
2. Are these relationships modified by age, screen time
type (e.g., device or content), and weekday vs.
weekend usage? If so, do these factors shift the
turning point?
Study design and sample
Data were drawn from Growing Up in Australia: The
Longitudinal Study of Australian Children (LSAC), a
population-based study which tracks two cohorts of chil-
dren aged 0/1 years (B-cohort) and 4/5 years (K-cohort)
every 2 years beginning in 2004. We used data from
Waves 46 of the K-cohort (20102014; ages 1015).
The overall response rate was 62% in the K-cohort (N=
4013) at baseline, with Wave 6 retention rates of 82%.
Other waves of the K-cohort could not be included be-
cause of significant changes in the design of the time-
use diary instrument used as our exposure measure [14].
We excluded the B-cohort because of the limited avail-
ability of time-use data during the ages of interest.
Further details on the LSAC methodology, including
sampling procedures, are available elsewhere [15].
Exposure variables
Screen time
Time spent engaging with screens was measured using
time-use diaries administered to the child. Children
recorded the activities they participated in during one
randomly allocated day on a paper diary. During a face-
to-face interview on the day following the diary, an
interviewer added additional contextual information
(e.g., where they were and who they were with). Partici-
pants nominated the primary activity they engaged in,
and the time of the activity (the activity window). The
child could also nominate additional secondary behav-
iors that occurred in parallel during the activity window.
The interviewer applied a coding framework to the chil-
drens activities to make the diaries comparable across
children [14]. We divided diary activities which involved
screens into five categories: social screen time (e.g., so-
cial media), passive screen time (e.g., television), inter-
active screen time (e.g., video games), educational screen
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time (e.g., computer use for homework), and other
screen time where the activity did not fit into any of the
To process the time-use diaries, we calculated the total
length of activities which represented screen time re-
gardless of whether they were primary or secondary
activities. To calculate total screen time, we added all ac-
tivities windows where any of the activities included
screen time. For example, if a child spent 15 min texting
(primary) while also watching TV (secondary), then we
calculated 15 min of both social screen timeand pas-
sive screen time, but only 15 min of total screen time
to avoid double-counting. Thus, it should be noted that
the individual categories of screen time variables will not
sum to total screen time. A list of items coded as screen
time is provided in Additional file 1: Table S1. Time-use
diaries have been successfully used in previous studies
investigating health behaviours in children [1620].
Physical health
Physical outcomes
Weight was measured to the nearest 50 g using glass
bathroom scales (Salter Australia, Springvale, VIC,
Australia; Code 79985) while children were in light
clothing. Height was measured twice, without shoes, to
the nearest 0.1 cm using a stadiometer (Invicta, Leices-
ter, UK; Code IPO955). Waist circumference was also
assessed twice to the nearest 0.1 cm. Body mass index
(BMI) was then calculated as kg/m
. The childs BMI z-
score for age was calculated based on Centre for Disease
Control growth charts [21,22]. All anthropometric mea-
sures were taken by the trained interviewer.
Global health
Parents were asked to report on their perception of their
childs overall health in a scale ranging from poorto ex-
cellent[23]. This scale has been previously validated for
Australian children [24]. Because there were fewer than
20 children with pooror fairhealth, global health was
dichotomized to excellentand less than excellent.
Psychological outcomes
Social and emotional functioning
Childrens socio-emotional outcomes were assessed using
the Strengths and Difficulties Questionnaire (SDQ), a vali-
dated, 25-item, parent-reported questionnaire [25]. We
used all five subscales (conduct problems, emotional prob-
lems, hyperactivity, peer problems, and prosocial behavior;
range: 010).
Childrens quality of life was assessed via the Paediatric
Quality of Life Inventory (PedsQL), a validated 23-item
parent-reported instrument [26]. We computed two sub-
scale scores (social and emotional functioning), which
ranged from 0 to 100. We chose not to include the
physical functioning subscale as the items were unlikely
to be related to screen time. A higher PedsQL score rep-
resents better quality of life. Parents were the respon-
dents for both the SDQ and PedsQL.
Temperament profile
Childrens temperament was assessed with the School-
Age Temperament Inventory, a 38-item parent-reported
questionnaire with four dimensions: negative reactivity
(intensity and frequency of negative affect), task persist-
ence (the self-direction that a child exhibits in fulfilling
tasks), approach/withdrawal (response to new people and
situations), and activity (moves quickly to get where he/
she wants to go) [27]. In the context of this study, only
negative reactivity and task persistence were included be-
cause of their plausibility as outcomes of screen time.
Higher scores indicate that the child is higher in negative
reactivity and task persistence.
Educational outcomes
School achievement
Estimates of both numeracy and literacy ability were taken
from government administration records of the National
Assessment Program - Literacy and Numeracy (NAPLAN, The NAPLAN data are
linked to child data by the LSAC organisers via a unique
identifier. The NAPLAN tests are given to all eligible chil-
dren in Australia in Grades 3 (age 8), 5 (age 10), 7 (age
12), and 9 (age 14). We used scores from Grades 59. The
tests are scaled so they are comparable across age cohort
and across grade. Scores have an overall mean of 500 and
a standard deviation of 100. Numeracy was measured
using a single test and literacy was measured using four
tests covering reading, writing, spelling, and grammar. We
conducted principal component analysis on the the four
literacy scores and formed a single factor score to repre-
sent literacy.
Adjustment variables
To provide an all-else-being-equal estimate of the effect of
screen time, we adjusted results for: child gender, Indigen-
ous status, language-other-than-English status, childs
country of birth (Australia vs. elsewhere), and a composite
measure of family socioeconomic status provided by the
LSAC organizers [28], which is calculated using parents
occupational prestige, income and education. We also
used a measure of the average socioeconomic status of the
childspostcode[29]. To adjust for opportunity to engage
in activities other than screen time, we further adjusted
for home type (detached house vs other), number of sib-
lings of the study child, and a parent-reported index of
neighborhood livability (including parks and safety), as
neighborhood factors have previously been linked to
screen time [17].
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To minimise potential bias we pre-registered our ana-
lysis plan prior to commencing the study, including
specifying what analyses would be included and our
criteria for including variables in the analysis [30]. Any
deviations from the pre-registered plan are noted below.
Analysis was based on Przybylski and Weinsteins[8]
study of screen time and well-being that provided sup-
port for the Goldilocks hypothesis. We fitted screen time
as both linear and quadratic effects. If the quadratic ef-
fect was significant, we calculated the turning point (i.e.,
the point at which more screen time moved from having
a beneficial to negative influence) using the equation:
xmax ¼
2βscreen:time:quadratic . We also calculated the point at
which increases in screen time led to poorer outcomes
than no screen time calculated as twice the turning
point, which we refer to as the zero point.
The LSAC data comes from a complex sampling de-
sign with postcode as the primary sampling unit. In
addition, we combined data from different waves, mean-
ing that each participant had multiple waves of data. To
account for these factors, we used multilevel models
with observations nested within individuals and individ-
uals nested within postcodes. Our repeated measures
design takes advantage of the multiple waves of data, but
we do not test for longitudinal associations. We accounted
for attrition by using all available information for each
participant and using sample attrition weights provided by
the survey organizers to ensure that the data remained
representative of the population at each wave. We handled
unit non-response missing data using multiple imputa-
tions, combining effects across 10 imputations [31]. We
reverse coded variables such that increases could be con-
sistently interpreted as improvements in these outcomes.
We tested unadjusted models and adjusted models. As
we were interested in whether the effects differed by age
or weekday versus weekend, all models included terms
for age and weekday/weekend. Note that our pre-
registered analysis plan [30] mistakenly included gender
as both an interaction term and a control variable, and
we chose to only include it as a control variable.
Our analysis included 4013 children in the LSAC study.
Of those analyzed, 51.2% were male, 96.1% were non-
indigenous, 85.7% spoke English as their primary lan-
guage, and 95.9% were born in Australia. Most children
lived in a detached house (88.2%), and the study children
had a mean of 1.7 siblings (SD = 1.2). At age 10 there
were 4013 participants. This declined to 3682 by age 12
and 3276 by age 14. There was a notable increase in
childrens educational and social screen time between
the ages of 12 and 14. This increase may be due to the
participants transitioning from primary to secondary
schooling. Australian children typically begin high
school at age 13, and this transition may increase their
autonomy or change the amount of technology they use
at school. Further description of the sample is found in
Table 1. For unit non-response, the most missing data
was for the time-use diaries (21%) and NAPLAN scores
(16%). All other variables had less than 5% missing data
(see Additional file 4: Figure S1).
Preliminary analysis
Initial analyses showed that the screen time variables
were positively skewed, especially for the less popular
screen time types (e.g., social screen time) where there
were high numbers of participants with zero screen time
(Fig. 1). Therefore, we log transformed the screen time
variables for imputation and translated back to the ori-
ginal scale for analysis. Despite evidence of skew in both
exposure and some outcome variables, assumption
checking revealed few problems in the models.
As per our pre-registered protocol [30], we checked
that outcomes were independent using zero-order corre-
lations and planned to remove variables if any were cor-
related above r= 0.70. The literacy and numeracy
outcomes were correlated at r= 0.71 and we therefore
created a composite score that was the unweighted mean
of the first principal component of each of the two
scores. We refer to this composite score as school
achievement. We noted that PedsQL emotional subscale
and SDQ emotional subscale were correlated at r=
0.67 and BMI and waist circumference at r= 0.69 (see
Additional file 2: Table S2 for other correlations). While
these were below our a priori cutpoint, to minimize
spurious associations we chose to keep only the variables
with the least missing data (SDQ emotional subscale and
waist circumference). We also checked for missing data
prior to imputation, and planned to remove variables
where missing data was > 60% [30]. No variables were
removed on this basis.
Linear effects
To examine the less-is-better hypothesis, we first exam-
ined linear models without quadratic terms. These re-
sults are presented in Fig. 2as adjusted linear effects,
standardised for each outcome (β). These effects sizes
are typically interpreted as small effect: β= 0.1; medium
effect: β= 0.3; large effect: β= 0.5. All linear results were
β< 0.07; that is, very small in size.
Total screen time was associated with worse educa-
tional outcomes, but this result was fully attenuated in
adjusted models (see Additional file 3: Table S3). In both
unadjusted and adjusted models, total screen time was
linearly associated with unfavorable temperament
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Fig. 1 Density Plots for Components of Total Screen Time
Table 1 Sample descriptive statistics
Wave 4 (Age 1011) Wave 5 (Age 1213) Wave 6 (Age 1415)
Total Screen Time (min/day) 76.6 (35.88) 76.77 (35.85) 97.44 (36.19)
Social Screen Time (min/day) 0.59 (4.03) 0.56 (3.97) 22.3 (34.23)
Educational Screen Time (min/day) 2.56 (10.3) 2.61 (10.36) 11.18 (23.79)
Passive Screen Time (min/day) 61.37 (35.36) 61.35 (35.3) 65.27 (40.76)
Interactive Screen Time (min/day) 26.43 (32.66) 26.8 (32.84) 28.7 (40.19)
Other Screen Time (min/day) 1.24 (6.39) 1.25 (6.41) 12.11 (25.74)
Emotional Functioning (PedsQL) 74.01 (16.49) 75.64 (17.1) 74.97 (18.23)
Social Functioning (PedsQL) 80.24 (18.58) 82.61 (17.86) 80.54 (18.32)
Prosocial Behaviour (SDQ) 8.5 (1.66) 8.3 (1.74) 8.06 (1.86)
Peer Relationship Problems (SDQ) 1.5 (1.71) 1.43 (1.65) 1.54 (1.67)
Emotional Symptoms (SDQ) 1.9 (1.95) 1.94 (1.95) 1.89 (1.98)
Hyperactivity (SDQ) 3.16 (2.36) 2.93 (2.32) 2.66 (2.23)
Conduct Problems (SDQ) 1.33 (1.48) 1.06 (1.42) 0.97 (1.41)
Reactivity (SATI) 2.3 (0.81) 2.38 (0.78) 2.32 (0.81)
Task Persistence 3.5 (0.89) 3.57 (0.85) 3.65 (0.86)
Body Mass Index z-score 0.37 (1.03) 0.35 (1.03) 0.37 (1.13)
Waist Circumference (cm) 66.76 (9.4) 71.89 (10.26) 75.14 (10.19)
Global Health 0.44 (0.5) 0.46 (0.5) 0.48 (0.5)
Literacy 0.02 (1) 0.05 (0.99) 0.05 (0.97)
Numeracy 0.01 (1) 0.04 (1) 0.05 (0.99)
All data presented as means (standard deviations). PedsQL Pediatric Quality of Life Inventory, SDQ Strengths and Difficulties Questionnaire, SATI School-Age
Temperament Inventory
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outcomes, worse socio-emotional outcomes, lower
health-related quality of life, and poorer health out-
comes. However, there was substantial variability when
results were examined by screen time type.
Educational screen time (e.g., homework on electronic
devices) showed the most benefits in unadjusted and ad-
justed models, with positive effects on childrens persist-
ence and educational outcomes, and no significant
Fig. 2 Adjusted Standardised Linear Effects for Each Combination of Screen Time Exposure and Outcome
Sanders et al. International Journal of Behavioral Nutrition and Physical Activity (2019) 16:117 Page 6 of 10
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effects on psychological or health outcomes. Interactive
screen time (e.g., video games) showed similar trends as
total screen time. However, unlike total screen time,
interactive screen time was associated with positive edu-
cational outcomes. Passive screen time (e.g., TV) was
associated with worse psychological outcomes, poorer
health outcomes, and lower educational outcomes in un-
adjusted and adjusted models. Poorer prosocial behavior
and lower persistence were also associated with higher
passive screen time, but only in unadjusted models. So-
cial screen time was linearly associated with poorer
health-related quality of life, higher reactivity, and worse
socio-emotional outcomes for the prosocial, emotional,
and conduct subscales of the SDQ, with no influence on
the peer or hyperactivity subscales of the SDQ, nor chil-
drens persistence, health, or educational outcomes. Fi-
nally, other screen time showed minimal associations
with outcomes, with negative effects on only the pro-
social and peer SDQ subscales.
Quadratic effects
We next examined if any relationships were better rep-
resented by a quadratic function. In unadjusted models,
there were non-linear relationships between total screen
time and the hyperactivity SDQ subscale, social screen
time and the peer SDQ subscale, interactive screen time
and the hyperactivity and prosocial SDQ subscales and
persistence, educational screen time and persistence, and
other screen time and persistence. After adjustment for
covariates, only the total screen time and hyperactivity
SDQ subscale (β
= 0.028 [0.0130.043]; β
0.001 [0.002 0.000]; turning point: 12.29 [6.44
18.14] hours; zero point: 24.59 [12.9036.28] hours), and
the social screen time and peer SDQ subscale (β
0.096 [0.1590.034]; β
= 0.011 [0.0030.019];
turning point: 4.48 [3.425.53] hours, zero point: 8.96
[6.8511.06] hours) quadratic associations remained sig-
nificant (Additional file 7Table S3). We note that, owing
to the very small quadratic effect, the zero point for the
total screen time and hyperactivity SDQ association is
outside the range of plausible values. Scatterplots of all
associations and the quadratic results are available in
Additional file 5: Figure S2.
Interactions with age and weekday
To determine the extent to which these relationships
changed as the children aged, we tested an interaction
between screen time and sample wave (as an indicator
for age). There were very few significant interactions (6
of 132 for the linear effects and 3 of 132 for the quad-
ratic effects in the adjusted models with p< .05), sug-
gesting that these associations are stable between the
ages of 10 and 15. All interaction results are available in
Additional file 7: Table S3.
For the linear models, all six interactions related to
age. Three interactions were present for waist circumfer-
ence, and one each for prosociality, social PedsQL, and
reactivity. All indicated that increased screen time had a
more detrimental association with these outcomes at
ages 10 and 12 than at age 14. No significant linear in-
teractions were found for weekday vs weekend.
All three of the significant interactions for the adjusted
quadratic relationships related to weekend vs weekday. The
interactions were present for a) conduct problems, b) emo-
tional problems, and c) reactivity as predicted by interactive
screen time. All significant quadratic interactions indicated
a Goldilocks effect for weekends, with turning points at ap-
proximately two to 3 h (see Additional file 6:FiguresS3
and Additional file 7: Figure S4), and no quadratic effects
on weekdays. No significant quadratic interactions were
found for age.
In this study, we compared competing hypotheses for
screen time effects on childrens physical health, psycho-
logical outcomes, and educational outcomes. We found
evidence that screen time was associated with childrens
physical health, health-related quality of life, socio-
emotional outcomes, and school achievement, with sub-
stantial variation based on the type of screen time. In
moderation analyses, these results appeared to remain
stable for screen time on weekdays versus weekends.
While there were some significant interactions, none
were meaningful in terms of practical significance. There
was little evidence to support the Goldilocks hypothesis
in our data. Instead, our findings lend qualified support
to the less-is-better hypothesisqualified because educa-
tional screen time was associated with positive educa-
tional outcomes and higher persistence, with no negative
consequences for other outcomes. Educational screen
time, therefore, appears beneficial and would not fit the
less-is-better or Goldilocks hypotheses. However, the
magnitude of the effects observed in our study were con-
sistently very small, with almost all less than 0.05 of a
standard deviation per hour of additional screen time.
This finding is consistent with meta-analytic results,
where effect sizes for physical health [32] and socio-
emotional and behavioral outcomes [33] have been small
[34]. Yet, screen time has become a major concern that
parents have about their childrens health [35]. Our re-
sults suggest that detrimental effects may be domain-
specific and, as such, some of the concern around screen
time may be unjustified.
Our results also demonstrate a need for future guide-
lines to embrace the complexity of screen time. We
found that interactive screen time can be simultaneously
harmful and beneficial, in that it negatively impacts most
outcomes but is positively associated with educational
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outcomes. Most current guidelines [2,3] focus on redu-
cing harm and largely ignore the potential benefits some
types of screen time can provide. Future evidence-based
guidelines should focus on providing parents, and pro-
fessionals who advise parents and children (e.g., doctors,
teachers), with information that allows them to balance
the risks and benefits of screen time. It is likely useful
for parents to know that duration is not the only screen
exposure variable to consider content also matters.
For example, our analyses show there are unlikely to be
negative educational consequences, and there may even
be some small benefits, when children engage in educa-
tional types of screen time such as using a computer for
Our findings are in contrast to previous research that
found non-linear relationships between screen time and
mental well-being [8], socio-emotional outcomes [10], sleep
[11], and other health outcomes [12]. One explanation is
differences in sample sizes. For instance, Przybylski and
Weinstein [8] investigated associations between different
types of screen time and mental well-being in 120,000 ado-
lescents. They found significant results, with standardized
effect sizes for the quadratic terms between 0.03 and 0.13.
Its possible that even though our data included more than
10,000 data points, it was insufficient to detect these weak
effects. If this is the case, we would question the clinical
significance of such small effects.
Strengths and limitations
We used a nationally-representative, longitudinal data-
set, which provided time-use diary estimates of behavior,
as opposed to simple recall questions. We preregistered
our analysis plan prior to analyzing the data, and used
methods to address the complex survey method and
missing data. Finally, we examined a broad range of
screen time exposures, including educational, interactive
and passive forms of screen time. We also examined di-
verse outcomes, including physical health, psychological,
and educational variables. In addition, we conditioned
on a much broader range of potential covariates than
previous research.
Despite these strengths, our study has several import-
ant limitations. As with the vast majority of screen time
research [36], our study relied on subjectively-reported
screen time. Currently, there are limited options for ob-
jectively measuring screen exposure. More precise meas-
urement devices (e.g., wearable cameras) may yield more
accurate determinations not only of screen exposure
duration, but also the specific content being viewed.
These measurement improvements may have less noise,
and provide a clearer indication of the effects [37]. Des-
pite using longitudinal data we would be reluctant to
draw causal conclusions. The data used covers the
period 20102014 and it is plausible that screen time
behaviour has changed since these data were collected.
As such, it is possible that the results presented here are
not generalizable to contemporary children of the target
ages. While we adjusted for important confounders there
is still a risk of unmeasured variable bias influencing the
findings (e.g., parenting style or companion) and we can-
not rule out the possibility of reverse causation.
Previous studies suggested that, compared with very low
or very high amounts of screen time, moderate amounts
of screen media use may benefit childrens mental well-
being. Our findings contradict that research, with little
support for the Goldilocks hypothesis across a wide
range of physical health, psychological and educational
outcomes. Indeed, we observed only very small effect
sizes on the outcomes we measured and across the dif-
ferent types of screen time. We observed that what small
effects do exist seem to be moderated by the type of
screen time, with passive screen time (e.g., TV) having
mostly detrimental effects, while educational screen time
could confer slight benefits in school achievement and
persistence. These results suggest that policymakers,
professionals, and parents should consider the type of
childrens screen time rather than just duration. How-
ever, our overall findings indicate that the high levels of
concern about their childrens screen time exhibited by
parents may be unwarranted.
Supplementary information
Supplementary information accompanies this paper at
Additional file 1: Table S1. Time Use Diary Categories.
Additional file 2: Table S2. Correlations Matrix.
Additional file 3: Table S3. Full Results.
Additional file 4: Figure S1. Missing Data.
Additional file 5: Figure S2. Scatterplots.
Additional file 6: Figure S3. Interactions and Quadratics.
Additional file 7: Figure S4. Example plot of unadjusted linear and
quadratic effects: social quality of life predicted by social screen time with
original scale (left) and modified scale (right).
BMI: Body Mass Index; LSAC: Longitudinal Study of Australian Children;
NAPLAN: National Assessment Program - Literacy and Numeracy;
PedsQL: Paediatric Quality of Life Inventory; SATI: School-Age Temperament
Inventory; SDQ: Strengths and Difficulties Questionnaire; TV: Television
This paper uses unit record data from Growing Up in Australia: the
Longitudinal Study of Australian Children. The study is conducted in
partnership between the Department of Social Services (DSS), the Australian
Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS).
The findings and views reported in this paper are those of the authors and
should not be attributed to DSS, AIFS or the ABS.
Sanders et al. International Journal of Behavioral Nutrition and Physical Activity (2019) 16:117 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
TS conceptualized the study, processed the time-use diaries, drafted parts of
the initial manuscript, and reviewed and revised the manuscript. PP and BdP-
C conceptualized the study, analyzed the data, drafted parts of the initial
manuscript, and reviewed and revised the manuscript. MN conceptualized
the study, produced the figures, drafted parts of the initial manuscript, and
reviewed and revised the manuscript. CL conceptualized the study, drafted
parts of the initial manuscript, and reviewed and revised the manuscript. All
authors approved the final manuscript as submitted and agree to be
accountable for all aspects of the work.
No funding was provided for this research. Publication fees were provided
by an Australian Catholic University Faculty of Health Sciences Open Access
Publishing Support Grant.
Availability of data and materials
The LSAC dataset is available from the National Centre for Longitudinal Data
(see The authors do not have
permission to share this data without endorsement from the Australian
Institute of Family Studies. Materials for this study, including analysis files and
preregistered analysis plans, are available through the Open Science
Framework (
Ethics approval and consent to participate
The Australian Institute of Family Studies Ethics Committee provided ethics
approval for the LSAC, and all participants provided written informed
Consent for publication
Not applicable no individual data presented.
Competing interests
The authors declare that they have no competing interests.
Author details
Institute for Positive Psychology and Education, Australian Catholic
University, North Sydney, NSW, Australia.
School of Behavioural and Health
Sciences, Australian Catholic University, Brisbane, QLD, Australia.
Received: 13 May 2019 Accepted: 12 November 2019
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... We also found that passive screen media was relatively popular, especially cartoon/ animation programs and live-action programs. Sanders et al. found that the specific domain of screen use moderated educational and health effects [66]. For example, they found that educational screen time was associated with positive educational outcomes and higher persistence [66]. ...
... Sanders et al. found that the specific domain of screen use moderated educational and health effects [66]. For example, they found that educational screen time was associated with positive educational outcomes and higher persistence [66]. Meanwhile, interactive screen use (e.g. ...
... Meanwhile, interactive screen use (e.g. video games), had a positive correlation with educational outcomes but a negative correlation with health and socioemotional outcomes [66]. Moreover, passive screen use was found to have a negative association with educational, health, and socio-emotional outcomes [66]. ...
Full-text available
Background Previous physical activity and sedentary behaviour studies during the pandemic have largely utilized online surveys, with known limitations including recall bias. Employing both device-based and self-reported measurements may provide a more comprehensive picture of both behaviours. Physical activity and sedentary behaviour research in adolescents is still limited in low- and middle-income countries (LMICs), including Indonesia. Male adolescents had been identified as more active than females but have had a greater decrease in physical activity during the pandemic. The present study aimed to investigate the quantity, temporal patterns, contexts, and biopsychosocial factors of physical activity and sedentary behaviour during the COVID-19 pandemic in a small group of male Indonesian adolescents. Methods Male adolescents (n = 5; 14–15 years old) from Yogyakarta wore accelerometers and automated wearable cameras for four days, and completed diaries and interviews in November 2020. Results Participants’ activity was dominated by light intensity (67% of all physical activity). Sedentary behaviour was high; accelerometer, school days: 456 ± 145 min (78 ± 10% of wear time), non-school days: 344 ± 160 min (79 ± 17% of wear time); camera, school days: 176 ± 101 min (81 ± 46% of wear time), non-school days: 210 ± 165 min (86 ± 67% of wear time). Sedentary behaviour was mainly done during school hours on school days and from late afternoon to evening on non-school days. Screen time was largely for leisure purposes and action games were most favoured. Smartphones were the most used device, mainly used in a solitary context in the bedroom. Non-screen-based sedentary behaviour was consistently low. Interviews suggested that during the pandemic, supporting factors for physical activity are: self-determination, enjoyment, parental support, and physical education; meanwhile, factors influencing screen time are: educational demands, device and internet availability, screen time opportunities, parental control, social facilitators, phone notifications, and emotional state. Conclusions Most participants were not able to stay active during the pandemic. Using digital platforms may be beneficial to shift some screen-based sedentary behaviour to ‘screen-based’ or ‘screen-prompted’ physical activity.
... Excessive and uncontrolled use of gadgets or smartphones and TV watching are on an upward trend not only among adults but also under-five children (Roza, Kamayani and Gunawan, 2018;Rahmalah et al., 2019;Ludyanti and Ishariani, 2020). Screen time is defined as time or hours per day spent in front of screens such as a television, computer, smartphone, tablet, and video games/digital video discs (Sanders et al., 2019;Webster, Martin and Staiano, 2019). Under-five children are at a period where they grow rapidly and undergo changes in their daily habit patterns (Harahap, Sandjaja and Nur Cahyo, 2013). ...
... Principally, this behavior itself, known as screen time, especially featuring gadgets and smartphones, entails a multitude of impacts, both positive or negative ones. According to (Cliff et al., 2017;Sanders et al., 2019;Webster, Martin and Staiano, 2019), since screen time involves technology such as a television, tablet and smartphone, it has positive impacts for children such as providing learning opportunities, entertainment for children, and allowing parents to enjoy free time. On the other hand, some of the negative impacts include difficulty to limit children's time in using the technology and children's disappointment and anger when their time limit is up. ...
... In other words, high screen time in children greatly reduces the rate of physical activities. Screen time is defined as time or hours spent every day in front of screen media such as a television, computer, smartphone, tablet, and videogames/digital video discs (Sanders et al., 2019;(Janssen et al., 2020). The American Academy Pediatrics recommends a screen time no more than an hour a day for children aged 3-6 years (Bingham et al., 2016;Cliff et al., 2017). ...
Full-text available
Introduction: High screen time is defined as activities in front of a screen for more than 60 minutes in 24 hours. These activities can lead to a decreased rate of physical activities in under-five children and pose a risk of turning into gaming addiction or gaming disorder. This study aimed to identify the relationship between screen time and physical activities in under-five children at PAUD Al Azhaar Tulungagung.Method: This study was designed as a correlational study with a cross-sectional approach with a purposive sampling technique. The population in this study was all mothers and children aged 24-60 months at PAUD Al Azhaar Tulungagung totaling 35 people. The number of samples was 31 respondents. The study was conducted at one of PAUD in Tulungagung area. Instruments for collecting data in this study were modified screen time and PAQ-C questionnaires to measure physical activities.Results: The results show that nearly all (83.9%) respondents had high screen time and the majority of them (51.6%) displayed decreased physical activities. An analysis with Spearman’s Rank statistical test obtained a p-value of 0.00 <α 0.05, meaning that there is a relationship between screen time and physical activities in under-five children at PAUD Al Azhaar Tulungagung. A correlation coefficient of -0.701 shows that there is a negative (inverse) and strong relationship.Conclusions: Parents play a significant role in directing positive screen time for children and encouraging them to be physically active instead of spending time in front of a screen.
... Stiglic y Viner (2019) encontraron asociaciones, de intensidad baja a moderada, entre el tiempo de pantallas y el desarrollo cognitivo de los niños en edades prescolares. En cambio, otros trabajos han informado de asociaciones positivas, nulas o inconsistentes entre el tiempo de pantallas y los resultados académicos Hu et al., 2020;Sanders et al., 2019) que cuestionan el significado práctico de las recomendaciones formuladas para el tiempo de uso de dispositivos tecnológicos en los jóvenes (Odgers & Jensen, 2020). Además, estas relaciones podrían estar influenciadas por el valor educativo de los contenidos, el medio utilizado, el género, la edad, el estilo de crianza, la fijación de límites en la exposición a los medios tecnológicos y el autocontrol de los jóvenes (Lauricella et al., 2015;Schulz van Endert, 2021). ...
... Aunque se aprecia un menor nivel de aprendizaje entre niños caracterizados por un elevado tiempo de pantallas, el efecto de esta variable sobre el rendimiento presenta un tamaño limitado. En este sentido, cabe considerar las aportaciones de trabajos anteriores (Amez & Baert, 2020;Poulain et al., 2018;Sharif & Sargent, 2006;Stiglic & Viner, 2019;Zapata-Lamana et al., 2021) en los que se ha señalado la existencia de una relación negativa entre tiempo de pantallas y rendimiento escolar, si bien matizando que esta relación estaría mediada por el valor educativo de los contenidos, el control parental o rasgos de personalidad del individuo, entre otros factores (Adelantado-Renau et al., 2019;Sanders et al., 2019). ...
... Stiglic and Viner (2019) found low to moderate associations between screen time and preschool children's cognitive development. In contrast, other work has reported positive, no or inconsistent associations between screen time and academic outcomes Hu et al., 2020;Sanders et al., 2019) that question the practical significance of recommendations made for youth screen time (Odgers & Jensen, 2020). Furthermore, these relationships might be influenced by the educational value of the content, the medium used, gender, age, parenting style, limit-setting in technological media exposure and young people's self-control (Lauricella et al., 2015;Schulz van Endert, 2021). ...
Full-text available
El actual desarrollo de las TIC ha generalizado el uso de pantallas. Los riesgos de un tiempo excesivo de exposición a pantallas son especialmente relevantes para los niños, desde edades tempranas. Este trabajo analiza la relación entre tiempo de pantallas y otras variables personales y contextuales en la infancia. Participan 94092 niños de aproximadamente 8 años, escolarizados en centros educativos de Andalucía (España). Los datos fueron generados en una evaluación a gran escala promovida por la Administración educativa regional, que supuso la aplicación de pruebas para medir aprendizajes y de cuestionarios de contexto familiar. Los análisis se basan en las pruebas t y chi-cuadrado, para comprobar las diferencias entre los grupos, y en la construcción de un modelo de regresión logística binaria para valorar el peso de las variables en la explicación del tiempo de pantallas. Los resultados indican que un elevado tiempo de pantallas se asocia a menor rendimiento en comunicación lingüística y razonamiento matemático. El género, la hora de acostarse y el nivel socioeconómico familiar son variables que contribuyen a explicar el tiempo de pantallas. A partir de estos resultados se formulan recomendaciones de cara a la intervención preventiva por parte de las familias y otros agentes educativos.
... But even if one hypothesises that media use can cause impaired mental well-being, it is unclear how the links between media use and impaired psychological well-being should be described. The associations may be linear in that the total screen time is directly proportional to outcomes; less time for media is associated with better outcomes, "the less-is-better hypothesis" (Sanders et al., 2019;Twenge & Campbell, 2018), or "the displacement hypothesis" (Neuman, 1988). In short, the displacement hypothesis states that the more time someone spends on the media, the less time remains for other activities. ...
... Different media activities have various outcomes (e.g. Sanders et al., 2019;Twenge & Farley, 2020) and outcomes are moderated by personal, psychological characteristics and abilities (e.g. Beyens et al., 2020;Tandoc et al., 2015) and by contextual characteristics, such as cultural norms and beliefs that inform the historical period within which the individual has grown up and been socialised (Castellacci & Tveito, 2018). ...
... Research has showed that how social media is used, actively or passively, matters for the outcomes and effects on psychological well-being (Sanders et al. 2019, Verduyn et al., 2015. ...
Conference Paper
Full-text available
This paper describes Swedish adolescents’ self-reported psychosomatic symptoms during 2018 and 2020, and how the correlations between adolescents’ self-reported psychosomatic symptoms and how much time they spend on different media have changed between these years. Second, it describes the correlations between psychological well-being and how adolescents answer questions concerning how their own media use impact on other things they should do, their sleep, and if they think that they spend ‘too much time’, ‘just enough’ or ‘too little time’ on their mobile phone and social media. In short, the results show that adolescents with impaired psychological well-being are more discontent with how much time they spend with media and the impact media use has on other things in their life.
... In contrast, boys were more likely to be in clusters characterized by large amounts of time using the computer and playing videogames [15-17, 24, 25, 36, 41], consistent with literature [15,46,49]. Studies have shown that different SB components have different effects on youths physical and mental health [50,51]. For example, TV viewing was associated with worse physical health, quality of life and emotional problems, whereas interactive screen time (e.g. ...
... For example, TV viewing was associated with worse physical health, quality of life and emotional problems, whereas interactive screen time (e.g. video game, social media and internet) showed negative psychological effects [50,51]. These results suggest that policymakers, professionals, and parents should consider the type of youths' screen time rather than only use-time. ...
Full-text available
Identifying the clustering and correlates of physical activity (PA) and sedentary behavior (SB) is very important for developing appropriate lifestyle interventions for children and adolescents. This systematic review (Prospero CRD42018094826) aimed to identify PA and SB cluster patterns and their correlates among boys and girls (0-19 years). The search was carried out in five electronic databases. Cluster characteristics were extracted in accordance with authors' descriptions by two independent reviewers and a third resolved any disagreements. Seventeen studies met the inclusion criteria and the population age ranged from six to 18 years old. Nine, twelve, and ten cluster types were identified for mixed-sex samples, boys, and girls, respectively. While girls were in clusters characterized by "Low PA Low SB" and "Low PA High SB", the majority of boys were in clusters defined by "High PA High SB" and "High PA Low SB". Few associations were found between sociodemographic variables and all cluster types. Boys and girls in "High PA High SB" clusters had higher BMI and obesity in most of the tested associations. In contrast, those in the "High PA Low SB" clusters presented lower BMI, waist circumference, and overweight and obesity. Different cluster patterns of PA and SB were observed in boys and girls. However, in both sexes, a better adiposity profile was found among children and adolescents in "High PA Low SB" clusters. Our results suggest that it is not enough to increase PA to manage the adiposity correlates, it is also necessary to reduce SB in this population.
... However, recent studies have further scrutinized screen time use by how adolescents are interacting with screens, including what they contribute and gain from screen time use (Odgers et al., 2020). As such, different forms of screen time use, such as active, passive, educational, or social use, have been examined in the literature, specifically their effect on adolescent wellbeing and health behaviours (Sanders et al., 2019;. With the growing presence and use of social media, there is concern surrounding its influence on adolescent well-being, health, and development (Uhls et al., 2017). ...
Full-text available
Objective: To determine the association between social media use (SMU) and physical activity (PA) among Canadian adolescents. Methods: We used data from 12,358 participants in grades 6 to 10 who responded to the Canadian component of the 2017/2018 Health Behaviour in School-aged Children (HBSC) survey. Social media intensity and problematic SMU were assessed using a 4-point mutually exclusive scale that contained three categories based on intensity (non-active, active, and intense SMU) and one category based on the presence of addiction-like symptoms irrespective of intensity (problematic SMU). PA was assessed for five domains (i.e., school curriculum, organized sport, exercise, outdoor play, and active transport) and dichotomized using the first quartile to represent high PA engagement in each domain. Meeting PA recommendation of 60 min per day of moderate-to-vigorous PA was calculated using the sum of the five domains. Logistic regression models were used to assess the association between SMU and PA, with active SMU used as the reference group for all models. Results: Non-active SMU was associated with lower odds of meeting the daily PA recommendations and of high engagement in all five domains of PA when compared to active SMU. Intense SMU was associated with higher odds of meeting the daily PA recommendations. Problematic SMU was not associated with meeting daily PA recommendations, but it was significantly associated with lower odds of high PA engagement in the exercise domain. Conclusion: The findings of this study suggest that non-active SMU was significantly associated with lower PA levels. Problematic SMU was only significantly associated with lower PA levels in the exercise domain. Intense SMU was associated with higher odds of meeting the PA recommendation.
... Another study with 4,013 children confirmed the effect of different types of ST on educational outcomes. Passive ST, like watching TV, was associated with worse outcomes, but educational ST, like using a computer for homework and interactive video games, had a positive effect on educational outcomes (Sanders et al., 2019). ...
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Academic achievement is an important factor that plays a role in shaping a person’s outlook on life, future plans and subjective well-being (Steinmayr et al., 2016; Bücker et al., 2018). Also, it is related to both personal and social outcomes by predicting higher self- efficacy, lower stress (Zajacova et al., 2005), positive health behavior (Eide et al., 2010) and national economic growth (Cheung and Chan, 2008). Therefore, determining the negative factors affecting academic achievement in adolescents and young adults has been an important research topic for many years. One of the important risk factors for academic achievement is PIU. With the Covid-19 pandemic, education switched from face-to-face to online learning in an unexpected way; therefore, the relationship between PIU and academic success gained a different dimension. In this opinion paper, we collate the available empirical evidence to gain more insight into the relationship between academic achievement and PIU.
Purpose/aim of the study: The purpose of this study was to analyse the relationship between digital media use and expressive language skills in the semantic and morphosyntactic domains, of pre-school-aged children (3 years-and-0 months to 5 years-and-11 months). Materials and methods: Verbal oral expression (VOE) tasks of the Pre-school Assessment of Language Test (Teste de Linguagem-Avaliação da Linguagem Pré-Escolar) were administered to 237 pre-school children with no previous identified neurological or developmental conditions associated with language disorders to assess expressive language skills in the semantic and morphosyntactic domains. Parents completed a questionnaire about their children's medical conditions, development (using the milestones of the Survey of Well-being of Young Children and the Pre-school Paediatric Symptom Checklist), and exposure to screens (using ScreenQ). Correlations between VOE and continuous variables such as ScreenQ were computed and a regression model incorporating all variables significantly associated with total language verbal expression was created. Results: ScreenQ revealed a negative and significant correlation with children's verbal oral expression as well as significance in the regression model. Parents' education was the most significant predictor in this regression model. Conclusions: This study emphasizes the importance of parents establishing limits for digital media use and promote good practices such as co-viewing.
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Annotation. The mode of a modern schoolboy-teenager is characterized by insufficient duration of night sleep, prolongation of homework, non-observance of diet, reduction of daily physical activity, excessive screen time, which under constant exposure can provoke a number of non-infectious and psychosomatic disorders. The purpose of the study is to evaluate the effectiveness of optimizing the daily routine of schoolchildren using the method of modeling structural equations. The study involved 175 students aged 10–14 years. Health status was assessed according to preventive medical examinations and screening-questionnaires with the calculation of the level of ill health and the coefficient of determination. Statistical data processing was performed using the licensed package SPSS Statistic v. 20 using one-way analysis of variance, correlation analysis, Chi-square test according to the Friedman method, Student’s t-test, as well as the method of modeling by structural equations (SEM) in the statistical package SEPATH Statistica. Using a normalized coefficient of determination, it was determined that for students in the experimental group were the most common signs of cardiorheumatological and allergic pathological conditions. Based on the pre-determined significance of the elements of the daily routine, the primary measures of primary prevention of pathological conditions were selected and implemented. The application of the multivariate SEM method using block data structures grouped by 11 variables (according to disease classes) and 15 blocks-elements of the daily routine allowed to evaluate the effectiveness of the prevention program. Pathology of the nervous (χ2=19.54; p<0.0001), respiratory (χ2=13.47; p=0.001) and cardiovascular systems (χ2=9.88; р=0.007) was determined to be the most “sensitive” to the impact of preventive measures. Thus, the proposed model allows to systematize the blocks that characterize both the prevention program and its effectiveness at different stages of implementation, which was determined by the level of morbidity of schoolchildren. The use of targeted preventive measures reduced the incidence rate by 10% in the experimental group against the background of an increase in pathological lesions in the control group by 22.9%.
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Within the past decade, parents, scientists, and policy makers have sought to understand how digital technology engagement may exacerbate or ameliorate young people’s mental health symptoms, a concern that has intensified amidst the COVID-19 pandemic. Previous research has been far from conclusive, and a lack of research consensus may stem in part from widely varying measurement strategies (including subjective and objective measurement) around digital technology engagement. In a cross-sectional study of 323 university students, the present study seeks to understand the ways in which youth engagement with digital technology – across subjective and objective measurements, weekday and weekend distinctions, and social and non-social uses – is associated with mental health (as measured by depression, loneliness, and multidimensional mood and anxiety). The present study also tested a differential susceptibility hypothesis to examine whether COVID-19 related social isolation might exacerbate the potential harms or helps of digital technology engagement. Results yielded few observed associations between digital technology engagement and mental health, with little evidence of detrimental effects of observed or perceived time spent on digital technology. Rather, those significant findings which did emerge underscore potential protections conferred by social connections with friends (both online and offline), and that the loneliest students may be the most likely to be reaching out for these types of connections. It is important that the field move beyond crude (largely self-reported) measures of screen time to instead understand how and to what effect youth are using digital technologies, especially during the social corridor of emerging adulthood.
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Introduction: The influence of screens and technology on adolescent well-being is controversial and there is a need to improve methods to measure these behaviors. This study examines the feasibility and acceptability of using automated wearable cameras to measure evening screen use in adolescents. Methods: A convenience sample of adolescents (aged 13-17 years, n=15) wore an automated camera for 3 evenings from 5:00pm to bedtime. The camera (Brinno TLC120) captured an image every 15 seconds. Fieldwork was completed between October and December 2017, and data analyzed in 2018. Feasibility was examined by quality of the captured images, wear time, and whether images could be coded in relation to contextual factors (e.g., type of screen and where screen use occurred). Acceptability was examined by participant compliance to the protocol and from an exit interview. Results: Data from 39 evenings were analyzed (41,734 images), with a median of 268 minutes per evening. The camera was worn for 78% of the evening on Day 1, declining to 51% on Day 3. Nearly half of the images contained a screen in active use (46%), most commonly phones (13.7%), TV (12.6%), and laptops (8.2%). Multiple screen use was evident in 5% of images. Within the exit interview, participants raised no major concerns about wearing the camera, and data loss because of deletions or privacy concerns was minimal (mean, 14 minutes, 6%). Conclusions: Automated cameras offer a feasible, acceptable method of measuring prebedtime screen behavior, including environmental context and aspects of media multitasking in adolescents.
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Background: Understanding the early roots of physical activity and sedentary behaviors is critical to developing intervention programs that promote healthy lifestyle habits in infants and children. There is, however, no evidence on how these behaviors cluster and develop together during early childhood. The aim of this study was to identify single and joint longitudinal trajectories in physical activity and screen time amongst children aged 0 to 9 years, their social-demographic predictors and their prospective health-related quality-of-life and socio-emotional outcomes. Methods: Three waves of data from The Longitudinal Study of Australian Children, a national study tracking two cohorts every 2 years (B-cohort, 0-5 years, n = 4,164; K-cohort, 4-9 years, n = 3,974) were analysed. Growth mixture modelling was applied to longitudinal time-use diary data to identify joint trajectories in children's physical activity and screen time over Waves 1-3. Key socio-demographic variables measured at Wave 1 were used to predict membership in different trajectories. The prospective consequences (at Wave 3) of time-use trajectories on health-related quality-of-life and socio-emotional outcomes were assessed. Results: Three physical-activity/screen-time trajectories were identified for both cohorts: Cluster-A-children who maintained low levels of physical activity and screen time (∽50% of the sample), Cluster-B-children who progressively increased physical activity and maintained low screen-time levels (∽25%), and Cluster-C-children who maintained low physical-activity levels and increased screen time (∽25%). Children in Cluster-B experienced the best health-related quality-of-life and socio-emotional outcomes, while those in Cluster-C experienced the worst. Children who were female, Indigenous, from non-English-speaking backgrounds, not living with two biological parents, in more affluent households and neighbourhoods, without siblings and with parents with poor mental health were at greater risk of falling into Cluster-A or Cluster-C. Conclusion: Our findings identified which children are most at-risk of falling into time-use trajectories that lead to poor health-related quality-of-life and socio-emotional outcomes later in life, increasing our ability to monitor, detect and prevent these suboptimal behaviours prior to their onset.
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The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
There is little empirical understanding of how young children's screen engagement links to their well-being. Data from 19,957 telephone interviews with parents of 2- to 5-year-olds assessed their children's digital screen use and psychological well-being in terms of caregiver attachment, resilience, curiosity, and positive affect in the past month. Evidence did not support implementing limits (< 1 or < 2 hr/day) as recommended by the American Academy of Pediatrics, once variability in child ethnicity, age, gender, household income, and caregiver educational attainment were considered. Yet, small parabolic functions linked screen time to attachment and positive affect. Results suggest a critical cost–benefit analysis is needed to determine whether setting firm limits constitutes a judicious use of caregiver and professional resources.
The authors regret that in the above article a misprint appears in table two presenting the evidence synthesis stratified by main type of sedentary behaviour and overall sedentary time: Not all high quality studies were printed in bold letter type. The correct Table is shown on page 2. 2 Evidence synthesis stratified by main type of sedentary behaviour and overall sedentary time (Table presented.) Bold indicates a high-quality study. *Note that the amount of studies under the stratified evidence synthesis do not count up in het combined evidence synthesis, due to two reasons. First, some studies examined types of sedentary behaviour that could not be classified in one of the four main types (e.g. subjective sitting time). As these additional types were only examined in its relationship with one health indicator, they were not considered as an additional main type of sedentary behaviour. Second, studies reporting relationships of more than one measurement type were counted once in the combined evidence synthesis, and were considered to add evidence when consistent findings were reported (i.e. ≥75% of the relationships showing results in the same direction). +, study adding evidence for a positive relationship; − study adding evidence for an inverse relationship; 0 study indicating no evidence for a relationship; BMI, body mass index; CRF, cardiorespiratory fitness; DBP, diastolic blood pressure; FMI, fat mass index; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; SBP, systolic blood pressure; SSF, sum of skinfolds; SLJ, standing long jump; TC/HDL-c, ratio of total cholesterol to high-density lipoprotein cholesterol; TG, triglycerides; TV, television; WC, waist circumference. Reference 1. van Ekris E, Altenburg TM, Singh AS, Proper KI, Heymans MW, Chinapaw MJM. An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysis. Obes Rev 2016; 17: 833–849.
Although the time adolescents spend with digital technologies has sparked widespread concerns that their use might be negatively associated with mental well-being, these potential deleterious influences have not been rigorously studied. Using a preregistered plan for analyzing data collected from a representative sample of English adolescents ( n = 120,115), we obtained evidence that the links between digital-screen time and mental well-being are described by quadratic functions. Further, our results showed that these links vary as a function of when digital technologies are used (i.e., weekday vs. weekend), suggesting that a full understanding of the impact of these recreational activities will require examining their functionality among other daily pursuits. Overall, the evidence indicated that moderate use of digital technology is not intrinsically harmful and may be advantageous in a connected world. The findings inform recommendations for limiting adolescents' technology use and provide a template for conducting rigorous investigations into the relations between digital technology and children's and adolescents' health.
Despite accumulating evidence linking screen-based sedentary behaviours (i.e. screen time) with poorer health outcomes among children and youth <18 years of age, the prevalence of these behaviours continues to increase, with roughly half of children and youth exceeding the public health screen time recommendation of 2 h per day or less. The purpose of this article is to provide an overview of key research initiatives aimed at understanding the associations between screen time and health indicators including physical health, quality of life and psychosocial health. Available evidence suggests that screen time is deleteriously associated with numerous health indicators in child and youth populations, including adiposity, aerobic fitness, quality of life, self-esteem, pro-social behaviour, academic achievement, depression and anxiety. However, few longitudinal or intervention studies have been conducted, with most of these studies focusing on physical health indicators. While most studies have used self-reported assessments of screen time, the availability of more objective assessment methods presents important opportunities (e.g. more accurate and precise assessment of sedentary time and screen time) and challenges (e.g. privacy and participant burden). Novel statistical approaches such as isotemporal substitution modelling and compositional analysis, as well as studies using longitudinal and experimental methodologies, are needed to better understand the health impact of excessive screen time, and to develop strategies to minimise or reverse the negative impacts of these behaviours. The evidence to date suggests a clear need for policy aimed at minimising the hazardous health consequences associated with screen time among children and youth.
This policy statement focuses on children and adolescents 5 through 18 years of age. Research suggests both benefits and risks of media use for the health of children and teenagers. Benefits include exposure to new ideas and knowledge acquisition, increased opportunities for social contact and support, and new opportunities to access health-promotion messages and information. Risks include negative health effects on weight and sleep; exposure to inaccurate, inappropriate, or unsafe content and contacts; and compromised privacy and confidentiality. Parents face challenges in monitoring their children's and their own media use and in serving as positive role models. In this new era, evidence regarding healthy media use does not support a one-size-fits-all approach. Parents and pediatricians can work together to develop a Family Media Use Plan (www.healthychildren. org/MediaUsePlan) that considers their children's developmental stages to individualize an appropriate balance for media time and consistent rules about media use, to mentor their children, to set boundaries for accessing content and displaying personal information, and to implement open family communication about media.
Objectives: The aim of this study is to use national Australian time-diary data to examine both (1) cross-sectionally and (2) longitudinally whether being late versus early to sleep or wake is associated with poorer child behavior, quality of life, learning, cognition and weight status, and parental mental health. Methods: Design/setting: Data from the first three waves of the Longitudinal Study of Australian Children were taken. Participants: A national representative sample of 4983 4-5-year-olds, recruited in 2004 from the Australian Medicare database and followed up biennially, was taken; 3631 had analyzable sleep information and a concurrent measure of health and well-being for at least one wave. Measures: Exposure: Parents completed 24-h child time-use diaries for one week and one weekend day at each wave. Using median splits, sleep timing was categorized into early-to-sleep/early-to-wake (EE), early-to-sleep/late-to-wake (EL), late-to-sleep/early-to-wake (LE), and late-to-sleep/late-to-wake (LL) at each wave. Outcomes: The outcomes included parent-reported child behavior, health-related quality of life, maternal/paternal mental health, teacher-reported child language, literacy, mathematical thinking, and approach to learning. The study assessed child body mass index and girth. Results: (1) Using EE as the comparator, linear regression analyses revealed that being late-to-sleep was associated with poorer child quality of life from 6 to 9 years and maternal mental health at 6-7 years. There was inconsistent or no evidence for associations between sleep timing and all other outcomes. (2) Using the count of the number of times (waves) at which a child was categorized as late-to-sleep (range 0-3), longitudinal analyses demonstrated that there was a cumulative effect of late-to-sleep profiles on poorer child and maternal outcomes at the child age of 8-9 years. Conclusions: Examined cross-sectionally, sleep timing is a driver of children's quality of life and maternal depression. Examined longitudinally, there appears to be cumulative and adverse relationships between late-to-sleep profiles and poorer child and maternal outcomes at the child age of 8-9 years. Understanding how other parameters - such as scheduling consistency, sleep efficiency and hygiene - are also related to child and parent outcomes will help health professionals better target sleep management advice to families.