Research
Original Investigation
Early Childhood Electronic Media Use as a Predictor of Poorer Well-being A Prospective Cohort Study Trina Hinkley, PhD; Vera Verbestel, MSc; Wolfgang Ahrens, PhD; Lauren Lissner, PhD; Dénes Molnár, PhD; Luis A. Moreno, PhD; Iris Pigeot, PhD; Hermann Pohlabeln, PhD; Lucia A. Reisch, PhD; Paola Russo, BSc; Toomas Veidebaum, PhD; Michael Tornaritis, PhD; Garrath Williams, PhD; Stefaan De Henauw, PhD; Ilse De Bourdeaudhuij, PhD; for the IDEFICS Consortium
IMPORTANCE Identifying associations between preschool-aged children’s electronic media
use and their later well-being is essential to supporting positive long-term outcomes. OBJECTIVE To investigate possible dose-response associations of young children’s electronic media use with their later well-being. DESIGN, SETTING, AND PARTICIPANTS The IDEFICS (Identification and Prevention of Dietaryand Lifestyle-Induced Health Effects in Children and Infants) study is a prospective cohort study with an intervention component. Data were collected at baseline from September 1, 2007, through June 30, 2008, and at follow-up from September 1, 2009, through May 31, 2010, in 8 European countries participating in the IDEFICS study. This investigation is based on 3604 children aged 2 to 6 years who participated in the longitudinal component of the IDEFICS study only and not in the intervention. EXPOSURE Early childhood electronic media use. MAIN OUTCOMES AND MEASURES The following 6 indicators of well-being from 2 validated instruments were used as outcomes at follow-up: Peer problems and Emotional problems subscales from the Strengths and Difficulties Questionnaire and Emotional well-being, Self-esteem, Family functioning, and Social networks subscales from the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version). Each scale was dichotomized to identify those children at risk for poorer outcomes. Indicators of electronic media use (weekday and weekend television and electronic game [e-game]/computer use) from baseline were used as predictors. RESULTS Associations varied between boys and girls; however, associations suggested that increased levels of electronic media use predicted poorer well-being outcomes. Television viewing on weekdays or weekends was more consistently associated with poorer outcomes than e-game/computer use. Across associations, the likelihood of adverse outcomes in children ranged from a 1.2- to 2.0-fold increase for emotional problems and poorer family functioning for each additional hour of television viewing or e-game/computer use depending on the outcome examined. CONCLUSIONS AND RELEVANCE Higher levels of early childhood electronic media use are associated with children being at risk for poorer outcomes with some indicators of well-being. Further research is required to identify potential mechanisms.
JAMA Pediatr. 2014;168(5):485-492. doi:10.1001/jamapediatrics.2014.94 Published online March 17, 2014.
Author Affiliations: Author affiliations are listed at the end of this article. Group Information The IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Consortium centers are listed at the end of this article. Corresponding Author: Stefaan De Henauw, PhD, Department of Public Health, Faculty of Medicine and Health Sciences, University of Ghent, De Pintelaan 185, Block A, Second Floor, B-9000 Ghent, Belgium ([email protected]).
485
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Research Original Investigation
Early Childhood Electronic Media Use
A
dverse health outcomes of sedentary behavior, which is characterized by a low energy expenditure while in a sitting or reclining posture,1 are increasingly acknowledged in children and adolescents.2-4 A growing body of evidence suggests that sedentary behaviors may be detrimental even at a very young age.5 Electronic media use incorporating television viewing and use of computers and electronic games (e-games) is one type of sedentary behavior. Evidence suggests that electronic media use (mainly in the form of television viewing, which is the most widely studied electronic media behavior) may be particularly detrimental to health outcomes during childhood and into adulthood.2,6 Psychological and social well-being (hereinafter referred to as well-being) as a potential outcome of young children’s contemporary lifestyle behaviors, particularly electronic media use, is not well investigated. A clear definition of well-being in the health behavior literature that reflects the multidimensional nature of this concept is lacking.7 Nonetheless, well-being can be reasonably conceptualized as constituting positive and adverse psychological and social attributes and behaviors, such as emotional symptoms, prosocial behavior, self-control, and externalizing problems. Poorer levels of well-being during early childhood are associated with later outcomes, such as depression and hostile and aggressive behavior.8-10 Conversely, good levels of well-being during early childhood may support positive behavioral, social, and academic outcomes during later childhood.11,12 Some evidence suggests that higher levels of electronic media use may be detrimental to well-being during early childhood.5 However, the evidence supporting these associations is extremely limited and largely inconclusive. A particular dearth of information on dose-response associations of electronic media use with well-being exists,5 and this information is necessary to inform targets for interventions, public health programs, and policy. Longitudinal studies are needed to identify such associations from early childhood to later childhood. The aim of this study was to investigate possible doseresponse associations of young children’s electronic media use with their well-being 2 years later.
Methods Participants This study used data from the European IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) study. The IDEFICS study is a European cohort study that investigated the causes of diet- and lifestyle-related diseases and disorders in children and developed and evaluated a primary prevention program focusing on childhood obesity. The design, sampling, and baseline participant characteristics have been described previously.13 A population-based sample of 16 864 children aged 2 to 9 years was recruited across 8 different European countries (Belgium [n = 2068], Cyprus [n = 2594], Estonia [n = 1777], Germany [n = 2132], Hungary [n = 2607], Italy [n = 2258], Spain [n = 1591], and Sweden [n = 1837]). In total, the families of 31 543 children aged 2 to 9 years were contacted, and 53.5% (range across countries, 41.4%-65.6%) of invited families consented 486
to participate.13 Data were collected simultaneously in all study centers at baseline (September 1, 2007, through June 30, 2008) and 24 months later at follow-up (September 1, 2009, through May 31, 2010). Subsequently, the families of 16 225 children (96.2% of those consenting) provided sufficient data (parental questionnaire, measured child height and weight) to be included in the IDEFICS database. Of the total sample, 7745 children (47.7%) resided in the control regions of participating countries. This study used data from participants in the control regions who were aged 2 to 6 years at baseline (n = 3604). Ethical committees at each of the 8 centers participating in the IDEFICS study provided appropriate approvals. Parents gave written informed consent, and children gave verbal assent for participation.
Measures and Data Management Data collection procedures were available in a central survey manual,14 and quality control checks performed at all study centers ensured standardized data collection across countries. We used a parental questionnaire that was tested for its comprehensibility, length, structure, and acceptability by parents to assess sociodemographic data and obtain parental reports of children’s electronic media use.14 Parents were requested to complete the questionnaire during study examinations or at home.
Predictor Variables We included 4 electronic media use variables—weekday television viewing, weekend television viewing, weekday e-game/ computer use, and weekend e-game/computer use—from the baseline survey in this study as predictors. Parents reported their child’s television viewing and e-game/computer use for weekdays and weekends separately. Response options for each of the predictor variables all used the following scale: 0 indicates not at all; 1, less than 30 min/d; 2, less than 1 h/d; 3, approximately 1 to 2 h/d; 4, approximately 2 to 3 h/d; and 5, more than 3 h/d. The questions were adapted from the Generation M-study,15 a nationally representative survey to assess children’s electronic media use in the United States. Test-retest reliability in the IDEFICS study (using 421 participants) demonstrated good reliability (measured by intercorrelation coefficient) for television viewing (weekdays, 0.71; weekends, 0.66) and e-game/computer use on weekends (0.74). Reliability bordered the generally accepted level of 0.5016,17 for e-game/computer use on weekdays (0.49). Electronic media use variables were transformed into an approximation of minutes per hour engaging in the behavior. Specifically, response categories 1 and 2, which combined represented less than 1 h/d, were transformed to 0.5 hours; response category 3 (1 to 2 h/d), to 1.5 hours; response category 4 (2-3 h/d), to 2.5 hours per day, and response category 5 (>3 h/d), to 3.5 h/d. This transformation was applied similarly for each of the 4 baseline electronic media use variables.18
Outcome Variables The IDEFICS parental questionnaire included a number of items assessing aspects of children’s well-being that were used to generate the outcomes at follow-up. Questions were drawn from
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Original Investigation Research
the Strengths and Difficulties Questionnaire.19,20 All items for the Emotional problems (eg, often worried, unhappy, depressed) and Peer problems (eg, rather solitary, picked on/ bullied) subscales were included and have been used in this study. In addition, 4 subscales—Self-esteem (eg, proud of self, pleased with self), Emotional well-being (eg, had fun, was scared), Family functioning (eg, felt fine at home, got on well with parents), and Social networks (eg, liked by other children, got on well with friends)—from the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version)21 were included in the parental questionnaire and used in this study. Items in the Peer problems and Emotional problems subscales of the Strengths and Difficulties Questionnaire were scored in accordance with published scoring instructions such that a higher score represents a less favorable outcome.20 Children’s responses can be categorized as normal, borderline, or abnormal for each of the subscales. For the purposes of analysis, each subscale was dichotomized into a healthy score (normal category) or an at-risk score (borderline and abnormal categories). Items in each of the 4 KINDLR subscales were scored according to the syntax provided on the instrument web site.22 Total scaled scores of 100 possible points were subsequently created, with higher scores representing more favorable indicators of well-being. In the absence of norms for children younger than 7 years, the 25th percentile of each scale was chosen to distinguish those children at risk of poorer well-being. That is, children with scores at or below the 25th percentile had poorer scores on each of the scales than children whose scores fell above that point. Scores were subsequently dichotomized around that threshold.
Covariates Parents reported their child’s date of birth in the baseline survey, from which the child’s age was calculated. The socioeconomic position (SEP) of the family was assessed through parentreported educational level (highest educational level of both parents, classified according to the International Standard Classification of Education [ISCED]23), income, unemployment, dependence on social welfare, and migration background of parents.13 The original ISCED SEP levels 1 and 2 were categorized as low SEP; level 3, low to medium SEP; level 4, medium to high SEP; and levels 5 and 6, high SEP. Child weight was measured using an electronic scale (BC 420 SMA; Tanita Europe GmbH) to the nearest 0.1 kg, with all clothing except underwear and T-shirts removed. Height was measured using a telescopic stadiometer (Seca 225; Seca) to the nearest 0.1 cm. Body mass index was calculated as weight in kilograms divided by height in meters squared. Baseline measures of each of the 6 well-being indicators were managed in the same manner described above for those at follow-up.
Statistical Analysis Descriptive variables (mean [SD] and odds ratio [95% confidence interval]) and statistical tests (2-tailed paired or group mean comparison t tests, as appropriate, and χ2 test) were used to assess differences in predictor variables at baseline and outjamapediatrics.com
come variables at follow-up between boys and girls. Continuous and dichotomized scales of outcome variables were used for this purpose. Descriptive analysis was undertaken using commercially available software (Stata, version 8.0; StataCorp). Associations between each of the baseline electronic media use variables and each of the follow-up wellbeing variables were assessed using different statistical software (SPSS, version 20.0; SPSS, Inc) with generalized linear mixed models. Analyses investigated whether increased electronic media use at baseline predicted increased odds of children being categorized in the at-risk category of each of the 6 well-being subscales. All models controlled for center of recruitment as a random effect. As stated, only those children from the control region(s) in each country were included in this study. The fact that Italy recruited their children from more than 1 control region was accounted for in the analysis by including Italian regions as covariate dummies in all models. In addition, child baseline body mass index and age and family SEP were included as covariates in model A. A second set of models were analyzed for each outcome variable (model B), which included all variables from model A and the baseline equivalent of the follow-up well-being scale (ie, baseline peer problems when follow-up peer problems was the outcome). All analyses were undertaken separately for boys and girls. Data are expressed as mean (SD) or percentage.
Results The mean age of children in this sample was 4.3 (0.9) years at baseline and 6.3 (1.0) years at follow-up. Slightly more than half of the included sample (52.4%) was male. According to the criteria of Cole et al,24 most of the children (73.5%) were a healthy weight, 13.2% were underweight, and 13.3% were overweight or obese. Two-fifths of the sample (40.9%) was from high SEP families; 9.1%, from low SEP families; 33.4%, from medium to low SEP families; and 16.6%, from medium to high SEP families. Descriptive characteristics of the included children by country and sex are presented in Table 1.
Differences Between Boys and Girls in Predictor and Outcome Variables Boys spent more hours in each of the electronic media behaviors at baseline than girls (weekday television, 1.04 [0.75] vs 0.98 [0.70] h/d [P < .01]; weekend television, 1.62 [0.93] vs 1.53 [0.91] h/d [P < .01]; weekday e-games/computer, 0.19 [0.39] vs 0.11 [0.26] h/d [P < .001]; and weekend e-games/computer, 0.33 [0.56] vs 0.21 [0.40] h/d [P < .001]). In some cases, differences were minimal and may not have meaningful implications. Table 2 reports the mean scores for boys and girls for each of the 6 well-being subscales in this study and the percentage of boys and girls who were classified as at risk on each of the 6 subscales. Parents of boys reported slightly increased mean scores for Peer problems than parents of girls. We found no between-sex differences for the mean scores of other indicators of well-being or for the percentages of boys and girls classified as at risk by any of the subscales. JAMA Pediatrics May 2014 Volume 168, Number 5
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Table 1. Baseline Descriptive Characteristics of the Study Sample Stratified by Sexa Participants, % Age, y
Country
Socioeconomic Position
2 3 4 5 (n = 322) (n = 1033) (n = 1207) (n = 1042)
Medium to Low Low (n = 320) (n = 1167)
Medium to High (n = 582)
BMI Category High (n = 1429)
Underweight (n = 476)
Healthy Weight (n = 2647)
Overweight/ Obese (n = 481)
Belgium Boys
4.6
31.1
31.4
33.0
0.4
23.9
20.0
55.8
18.9
76.5
4.6
Girls
8.5
30.1
24.4
37.0
1.6
35.8
13.4
49.2
16.3
77.2
6.5
Boys
2.6
14.8
37.8
44.8
3.7
18.8
25.0
52.6
12.2
76.5
11.3
Girls
1.5
15.1
32.2
51.2
4.8
18.2
23.0
54.0
13.2
65.4
21.5
Boys
22.4
41.0
32.8
3.9
1.3
36.5
47.0
15.2
12.9
81.5
5.6
Girls
21.7
42.9
32.3
3.2
3.2
37.8
45.2
13.8
11.1
79.3
9.5
Boys
6.6
28.3
42.0
23.0
39.2
26.7
17.1
17.1
15.5
73.9
10.6
Girls
1.9
33.8
42.0
22.2
37.8
28.9
19.4
13.9
10.1
70.1
19.8
Boys
1.2
28.0
33.7
37.2
1.2
40.4
12.3
46.2
24.1
68.2
7.7
Girls
3.8
25.6
33.2
37.4
1.4
39.2
10.3
49.1
20.1
70.6
9.3
Boys
4.3
31.6
32.5
31.6
20.4
60.2
0
19.5
5.1
65.8
29.1
Girls
3.2
30.4
35.0
31.3
21.4
58.6
0
20.0
2.8
59.9
37.3
Boys
5.2
27.6
40.1
27.1
5.2
31.4
11.0
52.4
6.3
80.7
13.0
Girls
7.4
26.7
46.7
19.3
6.7
32.1
9.7
51.5
11.1
68.2
20.7
Cyprus
Estonia
Germany
Hungary
Italy
Spain
Sweden Boys
25.5
27.5
22.7
24.3
2.1
17.3
11.1
69.6
13.2
80.1
6.8
Girls
23.5
25.2
26.1
25.2
2.3
22.7
6.8
68.2
11.1
79.7
9.3
Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared). a
Includes 3604 children from the control regions of the IDEFICS (Identification
and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) study. Data on socioeconomic position were missing for 106 children.
Table 2. Mean Well-being Indicators and Risk for Poor Well-being Outcomes at Follow-up Boys Follow-up Well-being Indicators
Girls
Score, Mean (SD)
At Risk for Poor Outcome, %
Score, Mean (SD)
At Risk for Poor Outcome, %
1.5 (1.6)
11.9
1.5 (1.6)
12.3
17.6
1.1 (1.4)
15.2
a
SDQ subscales
Emotional problems Peer problems
b
1.3 (1.3)
KINDLR subscalesc Emotional well-being
81.9 (11.3)
33.7
82.3 (11.2)
31.8
Self-esteem
63.0 (10.6)
34.4
63.8 (10.6)
31.4
Family functioning
71.5 (10.2)
30.0
71.4 (10.2)
27.0
Social functioning
70.6 (10.2)
27.5
70.6 (9.7)
26.8
Associations Between Baseline Electronic Media Use and Follow-up Well-being Table 3 reports the odds ratios for associations between each of the baseline measures of electronic media use and boys and girls being classified as at risk on each of the Strengths and Difficulties Questionnaire and KINDLR subscale indicators of wellbeing 2 years later. Some associations identified in model A for each of the outcomes were attenuated when the relevant baseline well-being indicator was included, and only associations 488
a
Higher mean SDQ scores indicate less positive outcomes (range 1-10).
b
P = .04.
c
Higher mean scores on KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version) indicate more positive outcomes (range 1-100).
from model B analyses are discussed herein. Few associations were evident. Every additional hour of weekday e-game/ computer use was associated with a 2-fold increase in the likelihood of girls being at risk for emotional problems. Every additional hour of weekday television viewing was associated with a 1.3- to 1.2-fold increase in the likelihood of girls and boys, respectively, being at risk for poor family functioning. Similarly, every hour of weekend television viewing was associated with a 1.3-fold increase in the likelihood of girls being
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Abbreviation: SDQ, Strengths and Difficulties Questionnaire.
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Early Childhood Electronic Media Use
Original Investigation Research
Table 3. Associations Between Baseline Electronic Media Use Behaviors and Risk for Poorer Well-being at Follow-upa Risk for Poorer Well-being, OR (95% CI) Well-being Outcome Measures by Individual Electronic Media Behaviorsb
Girls
Boys
Model A
Model B
Model A
Model B
Weekday television
1.2 (0.9-1.5)
1.1 (0.9-1.5)
1.1 (0.9-1.3)
1.0 (0.8-1.3)
Weekend television
1.3 (1.1-1.6)
1.2 (1.0-1.5)
1.1 (0.9-1.2)
1.0 (0.9-1.2)
Weekday PC
1.9 (1.0-3.6)
1.9 (1.0-3.8)
0.9 (0.6-1.3)
0.8 (0.5-1.3)
Weekend PC
1.3 (0.8-1.9)
1.2 (0.8-1.8)
0.9 (0.7-1.2)
0.9 (0.6-1.2)
Weekday television
1.4 (1.1-1.9)
1.3 (1.0-1.7)
1.2 (1.0-1.6)
1.2 (0.9-1.5)
Weekend television
1.3 (1.1-1.6)
1.3 (1.0-1.6)
1.1 (0.9-1.4)
1.0 (0.8-1.3)
Weekday PC
1.9 (1.0-3.6)
2.0 (1.0-4.0)
1.2 (0.7-1.9)
1.3 (0.8-2.1)
Weekend PC
1.1 (0.7-1.8)
1.1 (0.7-1.8)
1.0 (0.7-1.4)
1.0 (0.7-1.4)
Weekday television
1.0 (0.9-1.3)
1.0 (0.8-1.3)
1.2 (1.0-1.4)
1.1 (0.9-1.3)
Weekend television
1.0 (0.8-1.1)
1.0 (0.8-1.1)
1.1 (1.0-1.3)
1.1 (0.9-1.3)
Weekday PC
0.9 (0.5-1.6)
1.0 (0.6-1.9)
0.9 (0.6-1.2)
1.0 (0.7-1.4)
Weekend PC
0.8 (0.6-1.2)
1.0 (0.7-1.4)
1.0 (0.8-1.3)
1.1 (0.8-1.4)
1.1 (0.9-1.3)
1.1 (0.9-1.4)
1.2 (1.0-1.5)
1.2 (1.0-1.4)
SDQ peer problems
SDQ emotional problems
KINDLR self-esteem
KINDLR emotional well-being Weekday television Weekend television
1.1 (0.9-1.3)
1.1 (0.9-1.3)
1.2 (1.0-1.3)
1.1 (1.0-1.3)
Weekday PC
1.3 (0.8-2.2)
1.2 (0.7-2.0)
1.2 (0.9-1.7)
1.1 (0.8-1.5)
Weekend PC
1.1 (0.8-1.5)
0.9 (0.7-1.3)
1.1 (0.9-1.4)
1.0 (0.8-1.3)
Weekday television
1.2 (1.0-1.5)
1.3 (1.0-1.6)
1.4 (1.1-1.6)
1.2 (1.0-1.5)
Weekend television
1.3 (1.1-1.5)
1.3 (1.0-1.5)
1.2 (1.0-1.3)
1.1 (0.9-1.3)
Weekday PC
0.9 (0.5-1.6)
0.9 (0.5-1.7)
1.0 (0.7-1.4)
1.0 (0.6-1.4)
Weekend PC
1.0 (0.7-1.5)
1.0 (0.7-1.5)
0.8 (0.6-1.0)
0.8 (0.6-1.0)
Weekday television
1.0 (0.8-1.2)
0.9 (0.7-1.1)
1.2 (1.0-1.4)
1.2 (1.0-1.4)
Weekend television
1.1 (1.0-1.3)
1.1 (0.9-1.3)
1.0 (0.9-1.2)
1.0 (0.9-1.2)
Weekday PC
0.8 (0.5-1.5)
0.8 (0.5-1.5)
0.8 (0.5-1.1)
0.7 (0.5-1.1)
Weekend PC
0.8 (0.4-1.2)
0.8 (0.6-1.2)
0.9 (0.7-1.1)
0.9 (0.7-1.1)
Abbreviations: OR, odds ratio; PC, personal computer; SDQ, Strengths and Difficulties Questionnaire. a
Model A controls for region, age, socioeconomic position, body mass index, and clustering by center of recruitment; model B additionally controls for baseline levels of the SDQ or the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version) outcome variable under investigation. Boldface indicates those associations that were significant at the level of P < .05.
b
PC use includes electronic games and computer use. Behaviors are measured in hours of use per day.
KINDLR family functioning
KINDLR social functioning
at risk for poor family functioning. We found no associations for girls or boys on the Peer problems, Self-esteem, Emotional well-being, or Social network scales of well-being.
Discussion This study has investigated possible dose-response associations between electronic media use during early childhood and the increased risk of poorer well-being 2 years later. Where associations were identified, they suggest that increased television/e-game/computer use was associated with a greater likelihood of being in the at-risk category for poorer well-being. The differences in associations between models while controlling and not controlling for baseline well-being enable us to identify causal pathways. These findings suggest that children with higher levels of television viewing at baseline are at increased risk for poor family functioning and that girls with higher levels of e-game/computer use are at increased risk for jamapediatrics.com
emotional problems. The consistency of associations between television viewing and being at risk for poor family functioning in the fully adjusted models suggests that families who view more television during their child’s early years do not support children’s well-being as well as other families. This lack of support may result from a lack of appropriate relationships within the family or a failure to develop them. Investigation of associations between electronic media use and indicators of well-being during early childhood is an emerging area. Those studies that investigate such associations have used a range of instruments to capture well-being, with mixed findings. Previous studies have reported dose-response associations suggesting that electronic media use is associated with poorer outcomes for aggression,25 attention problems,26,27 externalizing behavior,28 classroom engagement,29 and emotional problems.30 Findings from the present study therefore reinforce the adverse influence of electronic media use on children’s well-being. However, previous studies have focused solely on television viewing5 and have neglected to investigate associations with the other forms of electronic media use. JAMA Pediatrics May 2014 Volume 168, Number 5
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This study is therefore unique in its investigation of associations between e-game/computer use and the risk for poorer well-being. This study found null associations with several indicators of poor well-being. Previous studies27,30-33 have also reported null associations. Well-being indicators in young children may be more homogeneous than those in their older counterparts, therefore precluding some possibility of identifying contributory factors. As an alternative, greater sensitivity in existing well-being instruments may be required to detect subtle but potentially meaningful differences in wellbeing in children. With respect to electronic media use, viewing time may not be the only detrimental variable. Previous studies have found that other electronic media use characteristics, such as violent content34 or background television,25 are associated with children’s well-being outcomes. Future research may wish to simultaneously investigate associations among viewing time, content, constancy (background television), and other characteristics of the family electronic media environment, including parental electronic media use practices such as coviewing or rules.35,36 Differences in findings between boys and girls have rarely been investigated, and a recent review5 noted this as a limitation to the current literature. However, when this comparison has been undertaken, some differences are noticeable,30,34 as in the present study. Such differences may be owing to socialization processes within the family that have previously been shown to be evident even in young children’s behaviors.37 However, further exploration is necessary to discern the potential mechanisms of these differences. Several possible mechanisms may explain the identified associations, but little research has investigated these mechanisms. The available research focuses primarily on the adult population and outcomes such as depression. One potential mechanism that may be appropriate to investigations of the early childhood population could be associated with the minimization of social interaction. For instance, the social withdrawal hypothesis suggests that increased television viewing leads to less social interaction, which may have subsequent detrimental effects on positive well-being. 38,39 However, because such research has not been undertaken in the early childhood population, the potential for decreased social interaction or other factors to explain the identified associations is unclear. Further, parents or siblings may participate in television viewing and other electronic media use with the young child. 35,36 If coviewing occurs and discussion and interaction centered
ARTICLE INFORMATION Accepted for Publication: January 8, 2014. Published Online: March 17, 2014. doi:10.1001/jamapediatrics.2014.94. Author Affiliations: Centre for Physical Activity and Nutrition Research, Deakin University, Melbourne, Australia (Hinkley); Department of Movement and Sport Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium (Verbestel, De Henauw, De Bourdeaudhuij); Bremen Institute for Prevention 490
around the content ensues, the social withdrawal hypothesis may not be applicable. Such interaction may explain the lack of association of electronic media use with peer problems and social networks, whereas the social withdrawal hypothesis may explain identified associations with other outcomes in this study. Further studies in this area are warranted. Investigation of factors such as parental coviewing as a potential mediating factor is also necessary. Strengths and limitations of the present study must be acknowledged. The study includes a large socioeconomically diverse sample, which allows for investigation of associations separately for boys and girls. The study includes only parental reports of predictor and outcome variables, and therefore some bias may exist. An objective measure of electronic media use or inclusion of teacher or child report of well-being may lead to different findings. Nonetheless, this study includes follow-up measures of well-being, allowing for investigation of associations across time. The context in which children’s participation in electronic media use occurs, such as with parents, siblings, or peers, may mediate associations between predictor and outcome variables; however, it was beyond the scope of this study to undertake such analyses. In addition, parent report of children’s electronic media use is unlikely to have captured any use undertaken while the child was cared for by other adults (eg, in daycare). Future research may wish to test the published findings from cohort studies such as this in interventions that target a reduction in electronic media use behaviors and monitor potential changes in a range of well-being indicators. Such programs would ideally be delivered to large, diverse samples so as to identify potential differences in influences on wellbeing through behaviors when the same strategies and opportunities are provided to children. Ideally, changes in behaviors and outcomes (such as well-being) would be monitored for longer rather than shorter periods.
Conclusions Higher levels of early childhood electronic media use are associated with children being at risk for poorer outcomes with some indicators of well-being. In particular, the risk for adverse outcomes in children ranged from a 1.2- to 2.0-fold increase for emotional problems and poorer family functioning for each additional hour of television viewing or e-game/ computer use. Further research is required to identify potential mechanisms of this association.
Research and Social Medicine, University of Bremen, Bremen, Germany (Ahrens); Department of Public Health and Community Medicine, University of Göteborg, Göteborg, Sweden (Lissner); Department of Paediatrics, University of Pécs, Pécs, Hungary (Molnár); Faculty of Science, University of Zaragoza, Zaragoza, Spain (Moreno); Leibniz Institute for Prevention Research and Epidemiology–BIPS, Bremen, Germany (Pigeot, Pohlabeln); Department of Mathematics/Computer Science, University of Bremen, Bremen, Germany (Pigeot); Department of Intercultural
Communication and Management–DEN, Consumer Sciences, Copenhagen Business School, Frederiksberg, Denmark (Reisch); Unit of Epidemiology and Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italy (Russo); National Institute for Health Development, Tervise Arengu Instituut, Tallinn, Estonia (Veidebaum); Research and Education Institute of Child Health, Strovolos, Cyprus (Tornaritis); Department of Politics, Philosophy and Religion, Lancaster University, Lancaster, England (Williams); Department of Public Health, Faculty of
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Medicine and Health Sciences, University of Ghent, Ghent, Belgium (De Henauw). Author Contributions: Dr Hinkley had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Hinkley, Verbestel, Ahrens, Lissner, Moreno, Reisch, Veidebaum, De Henauw, De Bourdeaudhuij. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Hinkley. Critical revision of the manuscript for important intellectual content: Verbestel, Ahrens, Lissner, Molnár, Moreno, Pigeot, Pohlabeln, Reisch, Russo, Veidebaum, Tornaritis, Williams, De Henauw, De Bourdeaudhuij. Statistical analysis: Hinkley, Verbestel, Pigeot, Pohlabeln. Obtained funding: Ahrens, Lissner, Moreno, Pigeot, Reisch, Veidebaum, De Henauw, De Bourdeaudhuij. Administrative, technical, or material support: Verbestel, Ahrens, Lissner, Reisch, Tornaritis. Study supervision: Ahrens, Pigeot, Pohlabeln, De Henauw, De Bourdeaudhuij. Conflict of Interest Disclosures: None reported. Funding/Support: This study was supported by contract 016181 (FOOD) from the European Community within the Sixth RTD Framework Programme as part of the IDEFICS Study (http://www.idefics.eu/Idefics) and by an Alfred Deakin Postdoctoral Fellowship (Dr Hinkley). Role of the Sponsors: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Group Information: Study centers participating in the IDEFICS Consortium are as follows: Departments of Public Health and Movement and Sport Sciences, Faculty of Medicine and Health Sciences, University of Ghent, Ghent, Belgium; Research and Education Institute of Child Health, Strovolos, Cyprus; Department of Intercultural Communication and Management–DEN, Consumer Sciences, Copenhagen Business School, Frederiksberg, Denmark; National Institute for Health Development, Tervise Arengu Instituut, Tallinn, Estonia; Laboratory of Nutrition, Ageing and Cardiovascular Diseases, University Joseph Fourier, Grenoble, France; Bremen Institute for Prevention Research and Social Medicine, University of Bremen, Bremen, Germany; Sensory Laboratory, Technologie-Transfer-Zentrum Bremerhaven, Bremerhaven, Germany; Institute for Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland; Department of Politics, Philosophy and Religion, Lancaster University, Lancaster, England; Department of Paediatrics, University of Pécs, Pécs, Hungary; Centre for High Technology Research and Education in Biomedical Sciences, Universita Cattolica del Sacro Cuore, Campobasso, Italy; Institute of Food Sciences, Unit of Epidemiology and Population Genetics, National Research Council, Avellino, Italy; Nutritional Epidemiology Unit, National Cancer Institute, Milan, Italy; Department of Pharmacological Sciences, University of Milan, Milan, Italy; Growth, Exercise, Nutrition and Development Research Group,
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University of Zaragoza, Zaragoza, Spain; Laboratory of Molecular Biology, Nutrition and Biotechnology, University Illes Balears, Palma de Mallorca, Spain; and Department of Paediatrics, Queen Silvia Children’s Hospital, University of Göteborg, Göteborg, Sweden. Disclaimer: The information in this report reflects the views of the authors and is provided as is on behalf of the European Consortium of the IDEFICS Project (http://www.idefics.eu/Idefics). Additional Contributions: We thank the participating school boards, headmasters, and communities. REFERENCES 1. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab. 2012;37(3):540-542. 2. Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. J Adolesc Health. 2013;52(4):382-392. 3. Chinapaw MJ, Proper KI, Brug J, van Mechelen W, Singh AS. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev. 2011;12(7):e621-e632. doi:10.1111/j.1467-789X.2011.00865.x. 4. Tremblay MS, LeBlanc AG, Kho ME, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98. doi:10.1186/1479-5868-8-98. 5. LeBlanc AG, Spence JC, Carson V, et al. Systematic review of sedentary behaviour and health indicators in the early years (aged 0-4 years). Appl Physiol Nutr Metab. 2012;37(4):753-772. 6. Hancox RJ, Milne BJ, Poulton R. Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet. 2004;364(9430):257-262. 7. Guérin E. Disentangling vitality, well-being, and quality of life: a conceptual examination emphasizing their similarities and differences with special application in the physical activity domain. J Phys Act Health. 2012;9(6):896-908. 8. Jones SM, Brown JL, Lawrence Aber J. Two-year impacts of a universal school-based social-emotional and literacy intervention: an experiment in translational developmental research. Child Dev. 2011;82(2):533-554. 9. Meagher SM, Arnold DH, Doctoroff GL, Dobbs J, Fisher PH. Social-emotional problems in early childhood and the development of depressive symptoms in school-age children. Early Educ Dev. 2009;20(1):1-24. 10. Toumbourou JW, Williams I, Letcher P, Sanson A, Smart D. Developmental trajectories of internalising behaviour in the prediction of adolescent depressive symptoms. Aust J Psychol. 2011;63(4):214-223. 11. Sanson A, Letcher P, Smart D, Prior M, Toumbourou JW, Oberklaid F. Associations between early childhood temperament clusters and later psychosocial adjustment. Merrill-Palmer Q. 2009;55(1):26-54.
12. McCabe PC, Altamura M. Empirically valid strategies to improve social and emotional competence of preschool children. Psychol Sch. 2011;48(5):513-540. 13. Ahrens W, Bammann K, Siani A, et al; IDEFICS Consortium. The IDEFICS cohort: design, characteristics and participation in the baseline survey. Int J Obes (Lond). 2011;35(suppl 1):S3-S15. 14. Suling M, Hebestreit A, Peplies J, et al; IDEFICS Consortium. Design and results of the pretest of the IDEFICS study. Int J Obes (Lond). 2011;35(suppl 1):S30-S44. 15. Rideout V, Roberts DF, Foehr UG. Generation M: media in the lives of 8-18 year olds. Kaiser Family Foundation; March 2005. http://www .outdoorfoundation.org/pdf/ExecutiveSummaryGenerationM.pdf. Accessed September 25, 2013. 16. Sim J, Wright C. Research in Health Care: Concepts, Designs and Methods. Cheltenham, Australia: Stanley Thornes Ltd; 2000. 17. Veitch J, Salmon J, Ball K. The validity and reliability of an instrument to assess children’s outdoor play in various locations. J Sci Med Sport. 2009;12(5):579-582. 18. Santaliestra-Pasías AM, Mouratidou T, Verbestel V, et al. Physical activity and sedentary behaviour in European children: the IDEFICS study [published online October 8, 2013]. Public Health Nutr. doi:10.1017/S1368980013002486. 19. Goodman R. The Strengths and Difficulties Questionnaire: a research note. J Child Psychol Psychiatry. 1997;38(5):581-586. 20. Youth in Mind. Information for researchers and professionals about the Strengths and Difficulties Questionnaire. 2009. http://www.sdqinfo.org. Accessed June 12, 2013. 21. Ravens-Sieberer U, Bullinger M. KINDLR: Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version: manual [in German]. 2000. http://kindl.org/cms/wp-content/uploads/2009/11 /ManEnglish.pdf. Accessed June 5, 2013. 22. University Medical Center HamburgEppendorf. KINDL. ND. http://kindl.org/english /analysis/. Accessed June 12, 2013. 23. UNESCO. International Standard Classification of Education: ISCED 1997. http://www.unesco.org /education/information/nfsunesco/doc/isced _1997.htm. Published November 1997. Accessed September 25, 2013. 24. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240-1243. 25. Manganello JA, Taylor CA. Television exposure as a risk factor for aggressive behavior among 3-year-old children. Arch Pediatr Adolesc Med. 2009;163(11):1037-1045. 26. Christakis DA, Zimmerman FJ, DiGiuseppe DL, McCarty CA. Early television exposure and subsequent attentional problems in children. Pediatrics. 2004;113(4):708-713. 27. Foster EM, Watkins S. The value of reanalysis: TV viewing and attention problems. Child Dev. 2010;81(1):368-375. 28. Tomopoulos S, Dreyer BP, Valdez P, et al. Media content and externalizing behaviors in Latino toddlers. Ambul Pediatr. 2007;7(3):232-238.
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29. Pagani LS, Fitzpatrick C, Barnett TA, Dubow E. Prospective associations between early childhood television exposure and academic, psychosocial, and physical well-being by middle childhood. Arch Pediatr Adolesc Med. 2010;164(5):425-431. 30. Griffiths LJ, Dowda M, Dezateux C, Pate R. Associations between sport and screenentertainment with mental health problems in 5-year-old children. Int J Behav Nutr Phys Act. 2010;7:30. doi:10.1186/1479-5868-7-30. 31. Cheng S, Maeda T, Yoichi S, Yamagata Z, Tomiwa K; Japan Children’s Study Group. Early television exposure and children’s behavioral and social outcomes at age 30 months. J Epidemiol. 2010;20(suppl 2):S482-S489. 32. Mistry KB, Minkovitz CS, Strobino DM, Borzekowski DLG. Children’s television exposure and behavioral and social outcomes at 5.5 years:
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parenting: report of a working group. Child Obes. 2013;9(suppl):S110-S118.
33. Conners-Burrow NA, McKelvey LM, Fussell JJ. Social outcomes associated with media viewing habits of low-income preschool children. Early Educ Dev. 2011;22(2):256-273.
37. Morrongiello BA, Dawber T. Parental influences on toddlers’ injury-risk behaviors: are sons and daughters socialized differently? J Appl Dev Psychol. 1999;20(2):227-251.
34. Christakis DA, Zimmerman FJ. Violent television viewing during preschool is associated with antisocial behavior during school age. Pediatrics. 2007;120(5):993-999.
38. Lewinsohn P. A Behavioral Approach to Depression. Washington, DC: Hemisphere Publishing; 1974.
35. Jago R, Edwards MJ, Urbanski CR, Sebire SJ. General and specific approaches to media parenting: a systematic review of current measures, associations with screen-viewing, and measurement implications. Child Obes. 2013;9(suppl):S51-S72.
39. Varni JW, Magnus B, Stucky BD, et al. Psychometric properties of the PROMIS® pediatric scales: precision, stability, and comparison of different scoring and administration options [published online October 2, 2013]. Qual Life Res. doi:10.1007/s11136-013-0544-0.
36. O’Connor TM, Hingle M, Chuang RJ, et al. Conceptual understanding of screen media
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Early Childhood Electronic Media Use as a Predictor of Poorer Well-being A Prospective Cohort Study Trina Hinkley, PhD; Vera Verbestel, MSc; Wolfgang Ahrens, PhD; Lauren Lissner, PhD; Dénes Molnár, PhD; Luis A. Moreno, PhD; Iris Pigeot, PhD; Hermann Pohlabeln, PhD; Lucia A. Reisch, PhD; Paola Russo, BSc; Toomas Veidebaum, PhD; Michael Tornaritis, PhD; Garrath Williams, PhD; Stefaan De Henauw, PhD; Ilse De Bourdeaudhuij, PhD; for the IDEFICS Consortium
IMPORTANCE Identifying associations between preschool-aged children’s electronic media
use and their later well-being is essential to supporting positive long-term outcomes. OBJECTIVE To investigate possible dose-response associations of young children’s electronic media use with their later well-being. DESIGN, SETTING, AND PARTICIPANTS The IDEFICS (Identification and Prevention of Dietaryand Lifestyle-Induced Health Effects in Children and Infants) study is a prospective cohort study with an intervention component. Data were collected at baseline from September 1, 2007, through June 30, 2008, and at follow-up from September 1, 2009, through May 31, 2010, in 8 European countries participating in the IDEFICS study. This investigation is based on 3604 children aged 2 to 6 years who participated in the longitudinal component of the IDEFICS study only and not in the intervention. EXPOSURE Early childhood electronic media use. MAIN OUTCOMES AND MEASURES The following 6 indicators of well-being from 2 validated instruments were used as outcomes at follow-up: Peer problems and Emotional problems subscales from the Strengths and Difficulties Questionnaire and Emotional well-being, Self-esteem, Family functioning, and Social networks subscales from the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version). Each scale was dichotomized to identify those children at risk for poorer outcomes. Indicators of electronic media use (weekday and weekend television and electronic game [e-game]/computer use) from baseline were used as predictors. RESULTS Associations varied between boys and girls; however, associations suggested that increased levels of electronic media use predicted poorer well-being outcomes. Television viewing on weekdays or weekends was more consistently associated with poorer outcomes than e-game/computer use. Across associations, the likelihood of adverse outcomes in children ranged from a 1.2- to 2.0-fold increase for emotional problems and poorer family functioning for each additional hour of television viewing or e-game/computer use depending on the outcome examined. CONCLUSIONS AND RELEVANCE Higher levels of early childhood electronic media use are associated with children being at risk for poorer outcomes with some indicators of well-being. Further research is required to identify potential mechanisms.
JAMA Pediatr. 2014;168(5):485-492. doi:10.1001/jamapediatrics.2014.94 Published online March 17, 2014.
Author Affiliations: Author affiliations are listed at the end of this article. Group Information The IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Consortium centers are listed at the end of this article. Corresponding Author: Stefaan De Henauw, PhD, Department of Public Health, Faculty of Medicine and Health Sciences, University of Ghent, De Pintelaan 185, Block A, Second Floor, B-9000 Ghent, Belgium ([email protected]).
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A
dverse health outcomes of sedentary behavior, which is characterized by a low energy expenditure while in a sitting or reclining posture,1 are increasingly acknowledged in children and adolescents.2-4 A growing body of evidence suggests that sedentary behaviors may be detrimental even at a very young age.5 Electronic media use incorporating television viewing and use of computers and electronic games (e-games) is one type of sedentary behavior. Evidence suggests that electronic media use (mainly in the form of television viewing, which is the most widely studied electronic media behavior) may be particularly detrimental to health outcomes during childhood and into adulthood.2,6 Psychological and social well-being (hereinafter referred to as well-being) as a potential outcome of young children’s contemporary lifestyle behaviors, particularly electronic media use, is not well investigated. A clear definition of well-being in the health behavior literature that reflects the multidimensional nature of this concept is lacking.7 Nonetheless, well-being can be reasonably conceptualized as constituting positive and adverse psychological and social attributes and behaviors, such as emotional symptoms, prosocial behavior, self-control, and externalizing problems. Poorer levels of well-being during early childhood are associated with later outcomes, such as depression and hostile and aggressive behavior.8-10 Conversely, good levels of well-being during early childhood may support positive behavioral, social, and academic outcomes during later childhood.11,12 Some evidence suggests that higher levels of electronic media use may be detrimental to well-being during early childhood.5 However, the evidence supporting these associations is extremely limited and largely inconclusive. A particular dearth of information on dose-response associations of electronic media use with well-being exists,5 and this information is necessary to inform targets for interventions, public health programs, and policy. Longitudinal studies are needed to identify such associations from early childhood to later childhood. The aim of this study was to investigate possible doseresponse associations of young children’s electronic media use with their well-being 2 years later.
Methods Participants This study used data from the European IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) study. The IDEFICS study is a European cohort study that investigated the causes of diet- and lifestyle-related diseases and disorders in children and developed and evaluated a primary prevention program focusing on childhood obesity. The design, sampling, and baseline participant characteristics have been described previously.13 A population-based sample of 16 864 children aged 2 to 9 years was recruited across 8 different European countries (Belgium [n = 2068], Cyprus [n = 2594], Estonia [n = 1777], Germany [n = 2132], Hungary [n = 2607], Italy [n = 2258], Spain [n = 1591], and Sweden [n = 1837]). In total, the families of 31 543 children aged 2 to 9 years were contacted, and 53.5% (range across countries, 41.4%-65.6%) of invited families consented 486
to participate.13 Data were collected simultaneously in all study centers at baseline (September 1, 2007, through June 30, 2008) and 24 months later at follow-up (September 1, 2009, through May 31, 2010). Subsequently, the families of 16 225 children (96.2% of those consenting) provided sufficient data (parental questionnaire, measured child height and weight) to be included in the IDEFICS database. Of the total sample, 7745 children (47.7%) resided in the control regions of participating countries. This study used data from participants in the control regions who were aged 2 to 6 years at baseline (n = 3604). Ethical committees at each of the 8 centers participating in the IDEFICS study provided appropriate approvals. Parents gave written informed consent, and children gave verbal assent for participation.
Measures and Data Management Data collection procedures were available in a central survey manual,14 and quality control checks performed at all study centers ensured standardized data collection across countries. We used a parental questionnaire that was tested for its comprehensibility, length, structure, and acceptability by parents to assess sociodemographic data and obtain parental reports of children’s electronic media use.14 Parents were requested to complete the questionnaire during study examinations or at home.
Predictor Variables We included 4 electronic media use variables—weekday television viewing, weekend television viewing, weekday e-game/ computer use, and weekend e-game/computer use—from the baseline survey in this study as predictors. Parents reported their child’s television viewing and e-game/computer use for weekdays and weekends separately. Response options for each of the predictor variables all used the following scale: 0 indicates not at all; 1, less than 30 min/d; 2, less than 1 h/d; 3, approximately 1 to 2 h/d; 4, approximately 2 to 3 h/d; and 5, more than 3 h/d. The questions were adapted from the Generation M-study,15 a nationally representative survey to assess children’s electronic media use in the United States. Test-retest reliability in the IDEFICS study (using 421 participants) demonstrated good reliability (measured by intercorrelation coefficient) for television viewing (weekdays, 0.71; weekends, 0.66) and e-game/computer use on weekends (0.74). Reliability bordered the generally accepted level of 0.5016,17 for e-game/computer use on weekdays (0.49). Electronic media use variables were transformed into an approximation of minutes per hour engaging in the behavior. Specifically, response categories 1 and 2, which combined represented less than 1 h/d, were transformed to 0.5 hours; response category 3 (1 to 2 h/d), to 1.5 hours; response category 4 (2-3 h/d), to 2.5 hours per day, and response category 5 (>3 h/d), to 3.5 h/d. This transformation was applied similarly for each of the 4 baseline electronic media use variables.18
Outcome Variables The IDEFICS parental questionnaire included a number of items assessing aspects of children’s well-being that were used to generate the outcomes at follow-up. Questions were drawn from
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the Strengths and Difficulties Questionnaire.19,20 All items for the Emotional problems (eg, often worried, unhappy, depressed) and Peer problems (eg, rather solitary, picked on/ bullied) subscales were included and have been used in this study. In addition, 4 subscales—Self-esteem (eg, proud of self, pleased with self), Emotional well-being (eg, had fun, was scared), Family functioning (eg, felt fine at home, got on well with parents), and Social networks (eg, liked by other children, got on well with friends)—from the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version)21 were included in the parental questionnaire and used in this study. Items in the Peer problems and Emotional problems subscales of the Strengths and Difficulties Questionnaire were scored in accordance with published scoring instructions such that a higher score represents a less favorable outcome.20 Children’s responses can be categorized as normal, borderline, or abnormal for each of the subscales. For the purposes of analysis, each subscale was dichotomized into a healthy score (normal category) or an at-risk score (borderline and abnormal categories). Items in each of the 4 KINDLR subscales were scored according to the syntax provided on the instrument web site.22 Total scaled scores of 100 possible points were subsequently created, with higher scores representing more favorable indicators of well-being. In the absence of norms for children younger than 7 years, the 25th percentile of each scale was chosen to distinguish those children at risk of poorer well-being. That is, children with scores at or below the 25th percentile had poorer scores on each of the scales than children whose scores fell above that point. Scores were subsequently dichotomized around that threshold.
Covariates Parents reported their child’s date of birth in the baseline survey, from which the child’s age was calculated. The socioeconomic position (SEP) of the family was assessed through parentreported educational level (highest educational level of both parents, classified according to the International Standard Classification of Education [ISCED]23), income, unemployment, dependence on social welfare, and migration background of parents.13 The original ISCED SEP levels 1 and 2 were categorized as low SEP; level 3, low to medium SEP; level 4, medium to high SEP; and levels 5 and 6, high SEP. Child weight was measured using an electronic scale (BC 420 SMA; Tanita Europe GmbH) to the nearest 0.1 kg, with all clothing except underwear and T-shirts removed. Height was measured using a telescopic stadiometer (Seca 225; Seca) to the nearest 0.1 cm. Body mass index was calculated as weight in kilograms divided by height in meters squared. Baseline measures of each of the 6 well-being indicators were managed in the same manner described above for those at follow-up.
Statistical Analysis Descriptive variables (mean [SD] and odds ratio [95% confidence interval]) and statistical tests (2-tailed paired or group mean comparison t tests, as appropriate, and χ2 test) were used to assess differences in predictor variables at baseline and outjamapediatrics.com
come variables at follow-up between boys and girls. Continuous and dichotomized scales of outcome variables were used for this purpose. Descriptive analysis was undertaken using commercially available software (Stata, version 8.0; StataCorp). Associations between each of the baseline electronic media use variables and each of the follow-up wellbeing variables were assessed using different statistical software (SPSS, version 20.0; SPSS, Inc) with generalized linear mixed models. Analyses investigated whether increased electronic media use at baseline predicted increased odds of children being categorized in the at-risk category of each of the 6 well-being subscales. All models controlled for center of recruitment as a random effect. As stated, only those children from the control region(s) in each country were included in this study. The fact that Italy recruited their children from more than 1 control region was accounted for in the analysis by including Italian regions as covariate dummies in all models. In addition, child baseline body mass index and age and family SEP were included as covariates in model A. A second set of models were analyzed for each outcome variable (model B), which included all variables from model A and the baseline equivalent of the follow-up well-being scale (ie, baseline peer problems when follow-up peer problems was the outcome). All analyses were undertaken separately for boys and girls. Data are expressed as mean (SD) or percentage.
Results The mean age of children in this sample was 4.3 (0.9) years at baseline and 6.3 (1.0) years at follow-up. Slightly more than half of the included sample (52.4%) was male. According to the criteria of Cole et al,24 most of the children (73.5%) were a healthy weight, 13.2% were underweight, and 13.3% were overweight or obese. Two-fifths of the sample (40.9%) was from high SEP families; 9.1%, from low SEP families; 33.4%, from medium to low SEP families; and 16.6%, from medium to high SEP families. Descriptive characteristics of the included children by country and sex are presented in Table 1.
Differences Between Boys and Girls in Predictor and Outcome Variables Boys spent more hours in each of the electronic media behaviors at baseline than girls (weekday television, 1.04 [0.75] vs 0.98 [0.70] h/d [P < .01]; weekend television, 1.62 [0.93] vs 1.53 [0.91] h/d [P < .01]; weekday e-games/computer, 0.19 [0.39] vs 0.11 [0.26] h/d [P < .001]; and weekend e-games/computer, 0.33 [0.56] vs 0.21 [0.40] h/d [P < .001]). In some cases, differences were minimal and may not have meaningful implications. Table 2 reports the mean scores for boys and girls for each of the 6 well-being subscales in this study and the percentage of boys and girls who were classified as at risk on each of the 6 subscales. Parents of boys reported slightly increased mean scores for Peer problems than parents of girls. We found no between-sex differences for the mean scores of other indicators of well-being or for the percentages of boys and girls classified as at risk by any of the subscales. JAMA Pediatrics May 2014 Volume 168, Number 5
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Table 1. Baseline Descriptive Characteristics of the Study Sample Stratified by Sexa Participants, % Age, y
Country
Socioeconomic Position
2 3 4 5 (n = 322) (n = 1033) (n = 1207) (n = 1042)
Medium to Low Low (n = 320) (n = 1167)
Medium to High (n = 582)
BMI Category High (n = 1429)
Underweight (n = 476)
Healthy Weight (n = 2647)
Overweight/ Obese (n = 481)
Belgium Boys
4.6
31.1
31.4
33.0
0.4
23.9
20.0
55.8
18.9
76.5
4.6
Girls
8.5
30.1
24.4
37.0
1.6
35.8
13.4
49.2
16.3
77.2
6.5
Boys
2.6
14.8
37.8
44.8
3.7
18.8
25.0
52.6
12.2
76.5
11.3
Girls
1.5
15.1
32.2
51.2
4.8
18.2
23.0
54.0
13.2
65.4
21.5
Boys
22.4
41.0
32.8
3.9
1.3
36.5
47.0
15.2
12.9
81.5
5.6
Girls
21.7
42.9
32.3
3.2
3.2
37.8
45.2
13.8
11.1
79.3
9.5
Boys
6.6
28.3
42.0
23.0
39.2
26.7
17.1
17.1
15.5
73.9
10.6
Girls
1.9
33.8
42.0
22.2
37.8
28.9
19.4
13.9
10.1
70.1
19.8
Boys
1.2
28.0
33.7
37.2
1.2
40.4
12.3
46.2
24.1
68.2
7.7
Girls
3.8
25.6
33.2
37.4
1.4
39.2
10.3
49.1
20.1
70.6
9.3
Boys
4.3
31.6
32.5
31.6
20.4
60.2
0
19.5
5.1
65.8
29.1
Girls
3.2
30.4
35.0
31.3
21.4
58.6
0
20.0
2.8
59.9
37.3
Boys
5.2
27.6
40.1
27.1
5.2
31.4
11.0
52.4
6.3
80.7
13.0
Girls
7.4
26.7
46.7
19.3
6.7
32.1
9.7
51.5
11.1
68.2
20.7
Cyprus
Estonia
Germany
Hungary
Italy
Spain
Sweden Boys
25.5
27.5
22.7
24.3
2.1
17.3
11.1
69.6
13.2
80.1
6.8
Girls
23.5
25.2
26.1
25.2
2.3
22.7
6.8
68.2
11.1
79.7
9.3
Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared). a
Includes 3604 children from the control regions of the IDEFICS (Identification
and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) study. Data on socioeconomic position were missing for 106 children.
Table 2. Mean Well-being Indicators and Risk for Poor Well-being Outcomes at Follow-up Boys Follow-up Well-being Indicators
Girls
Score, Mean (SD)
At Risk for Poor Outcome, %
Score, Mean (SD)
At Risk for Poor Outcome, %
1.5 (1.6)
11.9
1.5 (1.6)
12.3
17.6
1.1 (1.4)
15.2
a
SDQ subscales
Emotional problems Peer problems
b
1.3 (1.3)
KINDLR subscalesc Emotional well-being
81.9 (11.3)
33.7
82.3 (11.2)
31.8
Self-esteem
63.0 (10.6)
34.4
63.8 (10.6)
31.4
Family functioning
71.5 (10.2)
30.0
71.4 (10.2)
27.0
Social functioning
70.6 (10.2)
27.5
70.6 (9.7)
26.8
Associations Between Baseline Electronic Media Use and Follow-up Well-being Table 3 reports the odds ratios for associations between each of the baseline measures of electronic media use and boys and girls being classified as at risk on each of the Strengths and Difficulties Questionnaire and KINDLR subscale indicators of wellbeing 2 years later. Some associations identified in model A for each of the outcomes were attenuated when the relevant baseline well-being indicator was included, and only associations 488
a
Higher mean SDQ scores indicate less positive outcomes (range 1-10).
b
P = .04.
c
Higher mean scores on KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version) indicate more positive outcomes (range 1-100).
from model B analyses are discussed herein. Few associations were evident. Every additional hour of weekday e-game/ computer use was associated with a 2-fold increase in the likelihood of girls being at risk for emotional problems. Every additional hour of weekday television viewing was associated with a 1.3- to 1.2-fold increase in the likelihood of girls and boys, respectively, being at risk for poor family functioning. Similarly, every hour of weekend television viewing was associated with a 1.3-fold increase in the likelihood of girls being
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Abbreviation: SDQ, Strengths and Difficulties Questionnaire.
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Original Investigation Research
Table 3. Associations Between Baseline Electronic Media Use Behaviors and Risk for Poorer Well-being at Follow-upa Risk for Poorer Well-being, OR (95% CI) Well-being Outcome Measures by Individual Electronic Media Behaviorsb
Girls
Boys
Model A
Model B
Model A
Model B
Weekday television
1.2 (0.9-1.5)
1.1 (0.9-1.5)
1.1 (0.9-1.3)
1.0 (0.8-1.3)
Weekend television
1.3 (1.1-1.6)
1.2 (1.0-1.5)
1.1 (0.9-1.2)
1.0 (0.9-1.2)
Weekday PC
1.9 (1.0-3.6)
1.9 (1.0-3.8)
0.9 (0.6-1.3)
0.8 (0.5-1.3)
Weekend PC
1.3 (0.8-1.9)
1.2 (0.8-1.8)
0.9 (0.7-1.2)
0.9 (0.6-1.2)
Weekday television
1.4 (1.1-1.9)
1.3 (1.0-1.7)
1.2 (1.0-1.6)
1.2 (0.9-1.5)
Weekend television
1.3 (1.1-1.6)
1.3 (1.0-1.6)
1.1 (0.9-1.4)
1.0 (0.8-1.3)
Weekday PC
1.9 (1.0-3.6)
2.0 (1.0-4.0)
1.2 (0.7-1.9)
1.3 (0.8-2.1)
Weekend PC
1.1 (0.7-1.8)
1.1 (0.7-1.8)
1.0 (0.7-1.4)
1.0 (0.7-1.4)
Weekday television
1.0 (0.9-1.3)
1.0 (0.8-1.3)
1.2 (1.0-1.4)
1.1 (0.9-1.3)
Weekend television
1.0 (0.8-1.1)
1.0 (0.8-1.1)
1.1 (1.0-1.3)
1.1 (0.9-1.3)
Weekday PC
0.9 (0.5-1.6)
1.0 (0.6-1.9)
0.9 (0.6-1.2)
1.0 (0.7-1.4)
Weekend PC
0.8 (0.6-1.2)
1.0 (0.7-1.4)
1.0 (0.8-1.3)
1.1 (0.8-1.4)
1.1 (0.9-1.3)
1.1 (0.9-1.4)
1.2 (1.0-1.5)
1.2 (1.0-1.4)
SDQ peer problems
SDQ emotional problems
KINDLR self-esteem
KINDLR emotional well-being Weekday television Weekend television
1.1 (0.9-1.3)
1.1 (0.9-1.3)
1.2 (1.0-1.3)
1.1 (1.0-1.3)
Weekday PC
1.3 (0.8-2.2)
1.2 (0.7-2.0)
1.2 (0.9-1.7)
1.1 (0.8-1.5)
Weekend PC
1.1 (0.8-1.5)
0.9 (0.7-1.3)
1.1 (0.9-1.4)
1.0 (0.8-1.3)
Weekday television
1.2 (1.0-1.5)
1.3 (1.0-1.6)
1.4 (1.1-1.6)
1.2 (1.0-1.5)
Weekend television
1.3 (1.1-1.5)
1.3 (1.0-1.5)
1.2 (1.0-1.3)
1.1 (0.9-1.3)
Weekday PC
0.9 (0.5-1.6)
0.9 (0.5-1.7)
1.0 (0.7-1.4)
1.0 (0.6-1.4)
Weekend PC
1.0 (0.7-1.5)
1.0 (0.7-1.5)
0.8 (0.6-1.0)
0.8 (0.6-1.0)
Weekday television
1.0 (0.8-1.2)
0.9 (0.7-1.1)
1.2 (1.0-1.4)
1.2 (1.0-1.4)
Weekend television
1.1 (1.0-1.3)
1.1 (0.9-1.3)
1.0 (0.9-1.2)
1.0 (0.9-1.2)
Weekday PC
0.8 (0.5-1.5)
0.8 (0.5-1.5)
0.8 (0.5-1.1)
0.7 (0.5-1.1)
Weekend PC
0.8 (0.4-1.2)
0.8 (0.6-1.2)
0.9 (0.7-1.1)
0.9 (0.7-1.1)
Abbreviations: OR, odds ratio; PC, personal computer; SDQ, Strengths and Difficulties Questionnaire. a
Model A controls for region, age, socioeconomic position, body mass index, and clustering by center of recruitment; model B additionally controls for baseline levels of the SDQ or the KINDLR (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents–Revised Version) outcome variable under investigation. Boldface indicates those associations that were significant at the level of P < .05.
b
PC use includes electronic games and computer use. Behaviors are measured in hours of use per day.
KINDLR family functioning
KINDLR social functioning
at risk for poor family functioning. We found no associations for girls or boys on the Peer problems, Self-esteem, Emotional well-being, or Social network scales of well-being.
Discussion This study has investigated possible dose-response associations between electronic media use during early childhood and the increased risk of poorer well-being 2 years later. Where associations were identified, they suggest that increased television/e-game/computer use was associated with a greater likelihood of being in the at-risk category for poorer well-being. The differences in associations between models while controlling and not controlling for baseline well-being enable us to identify causal pathways. These findings suggest that children with higher levels of television viewing at baseline are at increased risk for poor family functioning and that girls with higher levels of e-game/computer use are at increased risk for jamapediatrics.com
emotional problems. The consistency of associations between television viewing and being at risk for poor family functioning in the fully adjusted models suggests that families who view more television during their child’s early years do not support children’s well-being as well as other families. This lack of support may result from a lack of appropriate relationships within the family or a failure to develop them. Investigation of associations between electronic media use and indicators of well-being during early childhood is an emerging area. Those studies that investigate such associations have used a range of instruments to capture well-being, with mixed findings. Previous studies have reported dose-response associations suggesting that electronic media use is associated with poorer outcomes for aggression,25 attention problems,26,27 externalizing behavior,28 classroom engagement,29 and emotional problems.30 Findings from the present study therefore reinforce the adverse influence of electronic media use on children’s well-being. However, previous studies have focused solely on television viewing5 and have neglected to investigate associations with the other forms of electronic media use. JAMA Pediatrics May 2014 Volume 168, Number 5
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This study is therefore unique in its investigation of associations between e-game/computer use and the risk for poorer well-being. This study found null associations with several indicators of poor well-being. Previous studies27,30-33 have also reported null associations. Well-being indicators in young children may be more homogeneous than those in their older counterparts, therefore precluding some possibility of identifying contributory factors. As an alternative, greater sensitivity in existing well-being instruments may be required to detect subtle but potentially meaningful differences in wellbeing in children. With respect to electronic media use, viewing time may not be the only detrimental variable. Previous studies have found that other electronic media use characteristics, such as violent content34 or background television,25 are associated with children’s well-being outcomes. Future research may wish to simultaneously investigate associations among viewing time, content, constancy (background television), and other characteristics of the family electronic media environment, including parental electronic media use practices such as coviewing or rules.35,36 Differences in findings between boys and girls have rarely been investigated, and a recent review5 noted this as a limitation to the current literature. However, when this comparison has been undertaken, some differences are noticeable,30,34 as in the present study. Such differences may be owing to socialization processes within the family that have previously been shown to be evident even in young children’s behaviors.37 However, further exploration is necessary to discern the potential mechanisms of these differences. Several possible mechanisms may explain the identified associations, but little research has investigated these mechanisms. The available research focuses primarily on the adult population and outcomes such as depression. One potential mechanism that may be appropriate to investigations of the early childhood population could be associated with the minimization of social interaction. For instance, the social withdrawal hypothesis suggests that increased television viewing leads to less social interaction, which may have subsequent detrimental effects on positive well-being. 38,39 However, because such research has not been undertaken in the early childhood population, the potential for decreased social interaction or other factors to explain the identified associations is unclear. Further, parents or siblings may participate in television viewing and other electronic media use with the young child. 35,36 If coviewing occurs and discussion and interaction centered
ARTICLE INFORMATION Accepted for Publication: January 8, 2014. Published Online: March 17, 2014. doi:10.1001/jamapediatrics.2014.94. Author Affiliations: Centre for Physical Activity and Nutrition Research, Deakin University, Melbourne, Australia (Hinkley); Department of Movement and Sport Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium (Verbestel, De Henauw, De Bourdeaudhuij); Bremen Institute for Prevention 490
around the content ensues, the social withdrawal hypothesis may not be applicable. Such interaction may explain the lack of association of electronic media use with peer problems and social networks, whereas the social withdrawal hypothesis may explain identified associations with other outcomes in this study. Further studies in this area are warranted. Investigation of factors such as parental coviewing as a potential mediating factor is also necessary. Strengths and limitations of the present study must be acknowledged. The study includes a large socioeconomically diverse sample, which allows for investigation of associations separately for boys and girls. The study includes only parental reports of predictor and outcome variables, and therefore some bias may exist. An objective measure of electronic media use or inclusion of teacher or child report of well-being may lead to different findings. Nonetheless, this study includes follow-up measures of well-being, allowing for investigation of associations across time. The context in which children’s participation in electronic media use occurs, such as with parents, siblings, or peers, may mediate associations between predictor and outcome variables; however, it was beyond the scope of this study to undertake such analyses. In addition, parent report of children’s electronic media use is unlikely to have captured any use undertaken while the child was cared for by other adults (eg, in daycare). Future research may wish to test the published findings from cohort studies such as this in interventions that target a reduction in electronic media use behaviors and monitor potential changes in a range of well-being indicators. Such programs would ideally be delivered to large, diverse samples so as to identify potential differences in influences on wellbeing through behaviors when the same strategies and opportunities are provided to children. Ideally, changes in behaviors and outcomes (such as well-being) would be monitored for longer rather than shorter periods.
Conclusions Higher levels of early childhood electronic media use are associated with children being at risk for poorer outcomes with some indicators of well-being. In particular, the risk for adverse outcomes in children ranged from a 1.2- to 2.0-fold increase for emotional problems and poorer family functioning for each additional hour of television viewing or e-game/ computer use. Further research is required to identify potential mechanisms of this association.
Research and Social Medicine, University of Bremen, Bremen, Germany (Ahrens); Department of Public Health and Community Medicine, University of Göteborg, Göteborg, Sweden (Lissner); Department of Paediatrics, University of Pécs, Pécs, Hungary (Molnár); Faculty of Science, University of Zaragoza, Zaragoza, Spain (Moreno); Leibniz Institute for Prevention Research and Epidemiology–BIPS, Bremen, Germany (Pigeot, Pohlabeln); Department of Mathematics/Computer Science, University of Bremen, Bremen, Germany (Pigeot); Department of Intercultural
Communication and Management–DEN, Consumer Sciences, Copenhagen Business School, Frederiksberg, Denmark (Reisch); Unit of Epidemiology and Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italy (Russo); National Institute for Health Development, Tervise Arengu Instituut, Tallinn, Estonia (Veidebaum); Research and Education Institute of Child Health, Strovolos, Cyprus (Tornaritis); Department of Politics, Philosophy and Religion, Lancaster University, Lancaster, England (Williams); Department of Public Health, Faculty of
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Medicine and Health Sciences, University of Ghent, Ghent, Belgium (De Henauw). Author Contributions: Dr Hinkley had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Hinkley, Verbestel, Ahrens, Lissner, Moreno, Reisch, Veidebaum, De Henauw, De Bourdeaudhuij. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Hinkley. Critical revision of the manuscript for important intellectual content: Verbestel, Ahrens, Lissner, Molnár, Moreno, Pigeot, Pohlabeln, Reisch, Russo, Veidebaum, Tornaritis, Williams, De Henauw, De Bourdeaudhuij. Statistical analysis: Hinkley, Verbestel, Pigeot, Pohlabeln. Obtained funding: Ahrens, Lissner, Moreno, Pigeot, Reisch, Veidebaum, De Henauw, De Bourdeaudhuij. Administrative, technical, or material support: Verbestel, Ahrens, Lissner, Reisch, Tornaritis. Study supervision: Ahrens, Pigeot, Pohlabeln, De Henauw, De Bourdeaudhuij. Conflict of Interest Disclosures: None reported. Funding/Support: This study was supported by contract 016181 (FOOD) from the European Community within the Sixth RTD Framework Programme as part of the IDEFICS Study (http://www.idefics.eu/Idefics) and by an Alfred Deakin Postdoctoral Fellowship (Dr Hinkley). Role of the Sponsors: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Group Information: Study centers participating in the IDEFICS Consortium are as follows: Departments of Public Health and Movement and Sport Sciences, Faculty of Medicine and Health Sciences, University of Ghent, Ghent, Belgium; Research and Education Institute of Child Health, Strovolos, Cyprus; Department of Intercultural Communication and Management–DEN, Consumer Sciences, Copenhagen Business School, Frederiksberg, Denmark; National Institute for Health Development, Tervise Arengu Instituut, Tallinn, Estonia; Laboratory of Nutrition, Ageing and Cardiovascular Diseases, University Joseph Fourier, Grenoble, France; Bremen Institute for Prevention Research and Social Medicine, University of Bremen, Bremen, Germany; Sensory Laboratory, Technologie-Transfer-Zentrum Bremerhaven, Bremerhaven, Germany; Institute for Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland; Department of Politics, Philosophy and Religion, Lancaster University, Lancaster, England; Department of Paediatrics, University of Pécs, Pécs, Hungary; Centre for High Technology Research and Education in Biomedical Sciences, Universita Cattolica del Sacro Cuore, Campobasso, Italy; Institute of Food Sciences, Unit of Epidemiology and Population Genetics, National Research Council, Avellino, Italy; Nutritional Epidemiology Unit, National Cancer Institute, Milan, Italy; Department of Pharmacological Sciences, University of Milan, Milan, Italy; Growth, Exercise, Nutrition and Development Research Group,
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University of Zaragoza, Zaragoza, Spain; Laboratory of Molecular Biology, Nutrition and Biotechnology, University Illes Balears, Palma de Mallorca, Spain; and Department of Paediatrics, Queen Silvia Children’s Hospital, University of Göteborg, Göteborg, Sweden. Disclaimer: The information in this report reflects the views of the authors and is provided as is on behalf of the European Consortium of the IDEFICS Project (http://www.idefics.eu/Idefics). Additional Contributions: We thank the participating school boards, headmasters, and communities. REFERENCES 1. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab. 2012;37(3):540-542. 2. Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. J Adolesc Health. 2013;52(4):382-392. 3. Chinapaw MJ, Proper KI, Brug J, van Mechelen W, Singh AS. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev. 2011;12(7):e621-e632. doi:10.1111/j.1467-789X.2011.00865.x. 4. Tremblay MS, LeBlanc AG, Kho ME, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98. doi:10.1186/1479-5868-8-98. 5. LeBlanc AG, Spence JC, Carson V, et al. Systematic review of sedentary behaviour and health indicators in the early years (aged 0-4 years). Appl Physiol Nutr Metab. 2012;37(4):753-772. 6. Hancox RJ, Milne BJ, Poulton R. Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet. 2004;364(9430):257-262. 7. Guérin E. Disentangling vitality, well-being, and quality of life: a conceptual examination emphasizing their similarities and differences with special application in the physical activity domain. J Phys Act Health. 2012;9(6):896-908. 8. Jones SM, Brown JL, Lawrence Aber J. Two-year impacts of a universal school-based social-emotional and literacy intervention: an experiment in translational developmental research. Child Dev. 2011;82(2):533-554. 9. Meagher SM, Arnold DH, Doctoroff GL, Dobbs J, Fisher PH. Social-emotional problems in early childhood and the development of depressive symptoms in school-age children. Early Educ Dev. 2009;20(1):1-24. 10. Toumbourou JW, Williams I, Letcher P, Sanson A, Smart D. Developmental trajectories of internalising behaviour in the prediction of adolescent depressive symptoms. Aust J Psychol. 2011;63(4):214-223. 11. Sanson A, Letcher P, Smart D, Prior M, Toumbourou JW, Oberklaid F. Associations between early childhood temperament clusters and later psychosocial adjustment. Merrill-Palmer Q. 2009;55(1):26-54.
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