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From the Division of General and Community Pediatrics, Children’s Hospital Medical Center, Cincinnati, Ohio
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Methods. Data from the National Longitudinal Survey of Youth, 1990 to 1998, were used to analyze reported television viewing at 0 to 35 months of age and to follow the trajectory of a child’s viewing from infancy through age 6. Logistic regression models were used to determine risk factors associated with greater television viewing at 0 to 35 months and the association of early viewing habits with school-age viewing.
Results. Seventeen percent of 0- to 11-month-olds, 48% of 12- to 23-month-olds, and 41% of 24- to 35-month-olds were reported to watch more television than the AAP recommends. Compared with college graduates, less-educated women were more likely to report that their children watched more television than recommended. Children who watched >2 hours per day at age 2 were more likely to watch >2 hours per day at age 6 (odds ratio: 2.7; 95% confidence interval: 1.8–3.9), controlling for maternal education, race, marital status and employment, household income, and birth order.
Conclusions. A substantial number of children begin watching television at an earlier age and in greater amounts than the AAP recommends. Furthermore, these early viewing patterns persist into childhood. Preventive intervention research on television viewing should consider targeting infants and toddlers and their families.
Key Words: television • infant • children • longitudinal survey • socioeconomic factors
Abbreviations: AAP, American Academy of Pediatrics • NLSY, National Longitudinal Survey of Youth • HOME, Home Observation for Measurement of the Environment • OR, odds ratio • CI, confidence interval
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Delineating patterns of early childhood television viewing may be important for several reasons. First, it may provide insight into the earliest behavioral antecedents of obesity and other health outcomes linked to excessive television viewing. Second, a description of social differences in early childhood television viewing may illuminate the mechanisms by which social disparities in these health outcomes emerge. If school-age television habits begin to develop early in life, then the roots of social disparities in obesity and violence may be explained in part by these early experiences. Finally, if indeed very young children watch substantial amounts of television, then the findings raise important questions about the constraints that parents may face in choosing alternative activities for their children.
The present study had 2 objectives. First, we described the prevalence and correlates of television viewing that exceeded the AAP guidelines for a national sample of 0- to 35-month-olds. Second, we examined the trajectory of a child’s viewing over time. Data came from the National Longitudinal Survey of Youth (NLSY).
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Cross-Sectional Design
For determining the prevalence and correlates of television viewing that exceeded the AAP guidelines, a cross-sectional design was used. Our sample for this objective (the "Cross-Sectional Sample") consisted of children who were 0 to 35 months of age in any survey year from 1990 to 1998 (N = 3556). Because television viewing is strongly correlated with age, this sample was divided into 3 subgroups: 0- to 11-month-olds ("Youngest"), 12- to 23-month-olds ("Middle"), and 24- to 35-month-olds ("Oldest"). Sibling pairs existed within these subgroups, so we randomly chose 1 child for each mother. Five percent of children were missing outcome data. Children who were excluded because of missing data did not differ significantly from those who were included with respect to maternal education level or survey year. The final sample sizes were 1084 for the Youngest, 1254 for the Middle, and 1247 for the Oldest. These are not mutually exclusive samples; for example, a child who was 5 months old at the 1992 survey and 30 months old at the 1994 survey was included in both the Youngest sample and the Oldest sample.
Longitudinal Design
For examining the trajectory of a child’s viewing over time, a longitudinal design was used. The "Longitudinal Sample" was a subset of the Cross-Sectional Sample. Children who were 0 to 23 months of age in 1990 or 1992 were followed for 6 years (until 1996 or 1998); 5% were lost to follow-up. As above, this sample was divided by age into subgroups: children 0 to 11 months of age at baseline (n = 554) and children 12 to 23 months of age at baseline (n = 666).
Outcome Measure
For both of the designs, the outcome variable was "hours of television per weekday," assessed by maternal response to the following question: "How much time would you say your child spends watching television on a typical weekday (either in your home or elsewhere)?" A separate question with parallel wording assessed television on a "typical weekend day." Mothers reported the hours of viewing in whole numbers; watching <1 hour of television per day was considered equivalent to watching no television. Following the AAP guidelines, we dichotomized the outcome variable as 0 versus 
1 hour per day for children 0 to 23 months of age and 0 to 2 versus 3 hours per day for children 24 months and older. The Spearman correlation between weekday and weekend viewing was 0.7 for each age group, and the average amount of television on a typical weekend day was the same as on a typical weekday for all ages. Therefore, only the results for weekday viewing are shown. There was no question specific to video viewing, so mothers may have included videos in their estimate of television viewing.
Independent Variables
Because of the limited information available on television viewing among infants and toddlers, we chose predictors based on other studies of parenting style and home environment.31–34 We calculated the average annual household income across the 5 surveys and divided the resulting value into quintiles. Children whose household income was missing in 3 or more of the survey years (11%) were given a missing value. Mothers who reported working, going to school, or serving in the active armed forces were considered to be employed "out of the home"; women who were on leave from their jobs, unemployed, out of the labor force, or "keeping house" were considered "in the home"; all others were considered "unknown." For the paternal variables, we used the data for the person whom the mother identified as both a member of her household and a spouse or partner. The quality of the home environment was determined by the Short Form of the Home Observation for Measurement of the Environment (HOME-SF).35 Both the total standardized score and the 2 standardized subscale scores (cognitive stimulation and emotional support) were analyzed as continuous variables.
Additional Variables
Data on child care, maternal depression, and neighborhood quality were available only in selected years; therefore, these variables were analyzed separate from the main analysis.
Child Care
Because child care information was collected retrospectively, complete data were available for children in 1990 and 1992 only. Mothers were asked about any regular child care during their child’s first, second, and third years of life. We classified child care responses as "none (besides maternal)," "in a (private) home," and "in a center/preschool."
Maternal Depressive Symptoms
Maternal depressive symptoms were measured only in 1992 and 1994. In 1992, the NLSY included the full Center for Epidemiologic Studies Depression Scale, a 20-item self-report instrument.36–38 In 1994, the survey included only 7 questions from the Center for Epidemiologic Studies Depression Scale. The scores for each year were divided into quintiles for analysis. Within the 1992 data, the Spearman correlation between the full 20 questions and the 7 questions was 0.9.
Neighborhood Quality
Neighborhood quality was assessed in 2 ways, beginning in 1992. First, mothers were asked the following question: "How would you rate your neighborhood as a place to raise children? Would you say it is excellent, very good, good, fair, or poor?" In addition, mothers rated the following problems in their neighborhood: lack of respect for rules and laws; crime and violence; abandoned or run-down buildings; not enough police protection; not enough public transportation; too many unsupervised children; people keep to themselves, don’t care about the neighborhood; and lots of people who can’t find jobs. Mothers rated these problems on a 3-point scale, and the answers were summed into a composite score.
Analysis
Cross-Sectional
The 
2 test and t test were used in the cross-sectional analysis to examine the associations between the independent variables and television viewing. Any predictors that were significantly (P < .05) correlated with television viewing in the bivariate analyses were included in multivariate logistic regression models that examined the odds of viewing 1 hour/d for the Youngest and Middle groups and 3 hours/d for the Oldest group. In the longitudinal analyses, a logistic regression model was used to examine the odds of watching 3 hours of television per day at age 6. The predictor of interest was television viewing as an infant or toddler, but models also controlled for maternal education, marital status, employment, race, household income, child age and birth order.
Weighting and Design Effects
As recommended when combining data across survey years,30 we did not use sample weights to make our results nationally representative of children born to mothers who were 14 to 22 years of age in 1979.30 However, an exploratory comparison of weighted results to our unweighted results for any given individual year showed no difference in the amount of television viewed at each age.
The data were collected through a multistage stratified cluster random sampling procedure, and consequently the standard errors may be underestimated. The information required to adjust for such design effects is not in the public use data; however, respondent movement out of their original sampling units has reduced such design effects.30 We present significance at the 95% confidence level, but a conservative approach is to focus on findings significant at P < .01.39 All analyses were conducted using SAS 8.1 for Windows 95 (SAS Institute, Inc, Cary, NC).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Of the variables that were available only in selected years, none were consistent correlates of television viewing. Child care was significantly associated with television viewing for the Oldest sample only; children in center-based child care were the least likely to watch more than the AAP recommends (P = .02). Similarly, increased maternal depressive symptoms were significantly associated with increased television viewing for the Oldest sample only (P = .02). Poor neighborhood quality was significantly associated with increased television viewing in the Youngest and Oldest samples (P < .05 for both neighborhood assessments).
Multivariate Results
In logistic regression models, maternal race, maternal education, and child age were consistent predictors of high television viewing (Table 3). A black mother was twice as likely as a white/other mother to report that her 2-year-old watched at least 3 hours of television per day (odds ratio [OR]: 2.0; 95% confidence interval [CI]: 1.4–2.8). A woman who had not graduated from high school was almost 4 times as likely as a woman who had graduated from college to report that her 0- to 11-month-old watched at least 1 hour of television per day (OR: 3.7; 95% CI: 1.7–7.7). Survey year was also a significant predictor of child television viewing, particularly for the Youngest sample. Infants in 1998 were more likely to watch television than infants in preceding years. The HOME score was significant for the Youngest children only; higher total and subscale scores were associated with increased viewing.
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To determine whether neighborhood quality confounded the relationship between television viewing and race or education, we looked for changes in the ß-coefficients for race and education on entering neighborhood quality in the model. Including neighborhood quality did not substantially change the coefficients of either, suggesting that perceived neighborhood quality was not a confounder of these associations.
Longitudinal Results
The trajectory of television viewing with age is shown in Fig 2. Daily television viewing increased by roughly 1 hour per year during the first 3 years of life, then leveled off. Differences between children of less-educated mothers and children of well-educated mothers were significant at an early age and became more pronounced as the children got older. By age 4, children of less-educated mothers were watching an additional 2 hours of television per day, on average, a nearly 2-fold difference.
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3 hours per day) at age 6 (OR: 2.3; 95% CI: 1.4–3.9; P = .002). |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Children in our study were reported to watch slightly more than those in a national cross-sectional study, in which the average 1-year-old watched 6 hours per week and the average 3-year-old watched 13 hours per week.6 Children in our study watched slightly less than children surveyed in 2 pediatric clinics.40 These differences are probably attributable to differences in sample composition, data collection methods, and the inclusion of videos. For example, the national study used diaries to determine the amount of time spent with television, and the clinic study separated television from video viewing.
Children of black and less-educated mothers watched more television at all ages. These social gradients in television viewing were our most persistent findings and were consistent with studies of older children1,18 and adults.29 Because it is unlikely that race and education directly influence television viewing, there must be other contributing factors. For example, it has been suggested that concerns about safety might present a barrier to children’s going outside to play and that blacks are more likely to live in neighborhoods perceived as unsafe.18 Surprisingly, adding perceived neighborhood quality to our regression models did not change the estimates for race or maternal education, perhaps because the questions in the NLSY did not capture the relevant neighborhood characteristics.41,42 In addition to neighborhood quality, other factors potentially related both to television viewing and to race or education need to be considered, such as residential stability,42 wealth,43 and the accessibility of playgrounds, museums, and libraries. Our results can speak only indirectly to the many constraints that disadvantaged families and their children may face in pursuing beneficial alternatives to television.
Our longitudinal analysis indicates that greater television viewing in early childhood is associated with greater viewing at school age. The persistence of this behavior pattern may reflect continuing environmental influences, the development of child preferences or habits, or, most likely, an interaction between the 2. Regardless, prevention research directed at much younger children and their families is warranted, especially because successful preventive intervention studies by Robinson and colleagues15,19 and Gortmaker et al23,44 strongly suggest a causal relationship between school-age viewing and obesity and aggression. Our analyses also indicate that, like television viewing in general, the social gradients in television viewing emerge at an early age, increase, and persist into childhood. These viewing gradients may be among the earliest antecedents of social disparities in health, offering potential insight into "predisease pathways"45 and suggesting earlier opportunities to address these disparities.
Surprisingly, child care, maternal employment, and marital status were not among the strongest independent predictors of increased television viewing, which runs counter to the notion that television often serves as a "babysitter" for busy parents trying to juggle jobs, children, and taking care of a home. Similarly, one would expect a measure of the home environment to capture parenting style and therefore to correlate with the amount of television watched. However, the HOME score was not a strong predictor of television viewing. This could be attributable to 2 factors: first, the HOME-SF may be a less accurate measure of parenting style for children younger than 346; second, overall parenting style may not correlate with parental approaches to child television viewing. If a parent believes that television is beneficial, then letting an infant or toddler watch television may reflect a desire to do what is best for the child. The surprising positive association between HOME scores and viewing in the Youngest group may be reasonably explained by such parental beliefs. Indeed, 1 study found that the majority of parents of 0- to 35-month-olds believed that television could improve a child’s vocabulary,40 highlighting the need to study parental knowledge about and attitudes toward television. Future studies should focus on untangling the interactive effects of the home environment, parenting style, and child preference on child television viewing.
An important limitation of this study is the reliance on maternal response to single questions regarding weekday and weekend day television viewing. Studies comparing parental estimates with diaries or with direct observation (video) suggest that parents may overestimate their children’s time with television.47–49 However, when we analyzed children 2 to 11 years of age in 1998 and compared our (weighted) findings to data collected using the Nielsen People Meter in 1999,29 NLSY mothers reported only a half-hour more per day, on average, a difference that may be explained by NLSY mothers’ inclusion of videos. In addition, parental estimates of television viewing in older children were used in the randomized, controlled trials that linked television with obesity,19 indicating that parental estimates have some predictive validity.
Another limitation is that we do not know how mothers defined "watching television" for their infants and toddlers. This is particularly an issue for the Youngest sample. Although 6-month-olds will attend to television roughly half the time that it is on,50 qualitative observation of infants in a small study suggested that they look at television continuously for only short periods of time.51 By 15 months, children can imitate what they see,52,53 and by 18 months attention to television can last as long as 30 minutes,51 but more information on the early development of television viewing is needed. The maternal estimates are further limited by the fact that viewing was reported in whole numbers. Any child who views <1 hour a day was reported as watching no television, and older children who watch television between 2 and 3 hours per day may have been reported as 2 hours per day. Both could result in some underestimation relative to the AAP viewing guidelines. Additional work, based on both detailed diaries and direct observation, is clearly needed to confirm our results.
A final limitation is that the NLSY sample design makes it difficult to generalize the findings to all young children. The survey oversampled disadvantaged groups, and the requirements for our sample meant that no mothers were younger than 22 years at delivery, and most were older than 30. It is not clear whether or in what direction these sample characteristics might bias the results.
This study could not address the content of children’s viewing. The relative consumption by and effects on infants and toddlers of educational versus entertainment television are not known. A study of the relationships between preschool television viewing and adolescent achievement, behavior, and attitudes found that the effects of television depended on the content of the programs viewed.54 Studies of elementary school children, however, have shown positive effects of reducing television without reference to the quality of programs viewed.15,19,23
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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FOOTNOTES |
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Reprint requests to (R.S.K.) Division of General and Community Pediatrics, TCHRF 6549, Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229. E-mail: robert.kahn{at}chmcc.org
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