Television viewing and television in bedroom associated with overweight risk among low-income preschool children
Barbara A. Dennison
Over the past 20 to 30 years, the prevalence of obesity has dramatically increased among adults and children in the United States. (1-3) Fifty-five percent of adults are overweight with a body mass index (BMI) >25 kg/[m.sup.2], (1) while 25% of 6-to 17-year-old children have a BMI >85th percentile, (2) and 22% of low-income children under 5 years of age have a weight for height >85th percentile. (3) Longitudinal studies indicate that the overweight child is at significant risk of remaining overweight into adulthood. (4,5) Although genetic factors predispose an individual to develop obesity and its comorbidities, the increasing prevalence of obesity among children and adults is more likely attributable to changes in environmental and lifestyle behaviors.
Among school-aged children, cross-sectional studies reveal a dose-response relationship between the amount of time viewing television (TV), (6-10) time viewing TV/video, (11) or frequency of viewing TV (12) and the prevalence of obesity. Several recent studies in children and adults report that the relationship between TV viewing and obesity is independent of physical activity or fitness level. (13-15) Among teenagers, the amount of time spent watching TV 4 to 6 years previously predicted incidence or remission rates of obesity. (6)
Elementary school-based interventions to reduce TV/video viewing have been associated with reductions in adiposity. In one Study, reductions in TV viewing were associated with lower obesity rates among girls but not among boys. (16) A more recent study found that reducing TV/video viewing and decreasing the number of meals eaten in front of the TV yielded significant reductions in BMI and in skin-fold thicknesses among both girls and boys. (17) In a clinic-based obesity treatment program for children aged 8 through 12 years, reduction in sedentary activities (including watching TV and videos and playing computer and board games) was just as effective in reducing obesity measures as a program that increased physical activity levels. (18)
Although there has been recent concern about the effects of TV and video viewing by preschool-aged children, we found only 1 study evaluating TV viewing and adiposity among preschool children. DuRant and colleagues (19) estimated the amount of time the children spent watching TV by directly observing 3- and 4- year-old children in their homes for up to 4 days (mean = 2.4 days). Children who watched more hours per day of TV and those who watched TV for longer periods of time were less likely to engage in physical activity. Unlike most previous studies in which parents estimated their child’s usual TV viewing, DuRant et al did not find a relationship between the amount of observed TV viewing and the child’s adiposity. Whether children’s observed TV viewing represents their “usual” TV viewing habits or are similar to their TV viewing habits when not being observed is not known. Parental report of their child’s “usual” TV viewing have been shown to correlate with both videotaped observations of the child’s TV viewing (20) and with parental diaries of their child’s TV/video viewing. (20,21)
Children from families with lower educational attainment spend more time viewing TV than children from more highly educated families. (22-24) Minority children watch more hours per week of TV than white children. (7,11,25) The prevalence of overweight children is also higher among non-Caucasians. (2,3) Therefore, it is possible that TV viewing and child obesity might be confounded by race/ethnicity and/or parental educational attainment. Recent studies have also raised concerns about the increasing presence of TV sets in children’s bedrooms. (17,26,27) Thus, the purpose of this study was to determine whether the amount of time preschool-aged children view TV/video is related to their overweight status, independent of race/ethnicity and parental educational attainment, and to determine whether a TV set in the child’s bedroom is related to the child’s weight status.
METHODS
Adults with children aged [greater than or equal to] 1 through <5 years, participating in the Supplemental Nutrition Program for Women, Infants, and Children (WIC) at 1 of the 49 WIC agencies in New York State (NYS) were eligible to be surveyed. The 49 WIC agencies were selected because they had been surveyed in 1998, as part of a racially and geographically balanced sample of NYS WIC agencies (N = 15), or because agency staff were participating in a counseling skills training program (N = 41, 7 of which were also 1 of the 15 agencies). In 1999 and again in 2000, clients attending 1 of the 49 WIC agencies were surveyed.
At each agency, the WIC Participant Survey in English or Spanish was self-administered, although clients could ask for assistance in completing the survey. One child per family was included, so that each parent/guardian was surveyed only once during each year. The 1999 sample and the 2000 sample were independent, thus a participant could, by chance, have been surveyed in both years. In 1999, surveys were collected between July 12 and December 9. In 2000, surveys were collected between May 27 and December 15. Although the study design was to survey 30 consecutive eligible adults at each WIC agency, logistic constraints at the WIC centers, at times, necessitated some variations in this protocol. The exact details of these deviations were not documented by staff at the WIC centers, nor was information collected regarding participant refusal rates or whether assistance was given in computing the survey.
Data collected from the WIC Participant Survey is determined by WIC staff and reflects the interests of the WIC program and/or the NYS Department of Health. In 1999 and 2000, the surveys assessed child dietary behaviors, such as fruit and vegetable intakes and consumption of low-fat milk, child TV/video viewing habits, and demographic data. Each parent/guardian self-reported his/her race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, Asian, Native American, multicultural, or other) and the last grade of school he/she completed (10th, 11th, high school degree, general equivalency diploma, 2 years of college but <4 years of college, 4-year college graduate, Master's degree, or Doctoral degree). The parent/guardian also reported (in hours per day, with fractions entered as such) the amount of TV/videotapes that his/her child "usually watched" on weekdays, on Saturday, and on Sunday. For the year 2000 survey, each parent/guardian also reported his/her own weight and height, and whether or not the child has a TV set in his/her bedroom. All questions were pilot-tested but not validated.
The child’s most recent height or length, weight, and date of measurement were abstracted from the WIC chart. At the time of this study, parents and children participating in NYS WIC visited the clinic every 2 months for nutrition counseling and to receive food vouchers. Children are required to have their height and weight measured every 6 months as part of the recertification process to ensure eligibility for program participation. Each child’s height and weight was measured by trained WIC staff using standard protocols. (28) The child’s height and weight was frequently measured on the same day the survey was completed (43%). If not, the child’s most recent height, weight, and measurement date (within 4 months of the survey date) was abstracted from the WIC chart. Weight was measured using a standard balance beam scale. Height was measured for children >36 inches tall or >2 years of age using a wall-mounted stadiometer. Otherwise, child length was measured using a recumbent board; child length was converted to child height by subtracting 0.5 cm. For both child and parent, BMI was computed as weight in kilograms divided by the square of the height in meters (kg/[m.sup.2]).
The institutional review boards of both the Mary Imogene Bassett Hospital and the NYS Department of Health approved the study. As NYS WIC staff have a program mandate to collect information annually from participants in the WIC program, consent of individuals completing these surveys was not deemed necessary and was not obtained. Before data entry and analysis, all identifying information was removed from the surveys by WIC staff.
Statistical Analysis
Data were analyzed using the Statistical Analysis Software (SAS, Version 8.0; Cary, NC). All statistical tests were 2-sided; P values >.05 were considered nonsignificant. Children were classified into 2 groups: those with a BMI for age [less than or equal to] 85th sex-specific percentile and those with a BMI for age >85th sex-specific percentile using the BMI cutpoints of Frisancho. (29) The child’s age on the date of measurement was used to determine the sex-specific BMI for age percentile. Thus, the group of children with a BMI >85th percentile includes both children “at risk for overweight” (85th 95th percentile). (30)
For children 2 years of age and older, we compared the classification into BMI categories (BMI for age [less than or equal to] 85th and BMI for age >85th sex-specific percentile) using the cutpoints of Frisancho (29) and the Centers for Disease Control and Prevention (CDC) (31) using Kappa statistics. The Frisancho growth charts and the revised CDC growth charts are based on data from the first and second National Health and Nutrition Examination Surveys; NHANES I (1971-1974) and NHANES II (1976-1980). The revised CDC growth charts for children <6 years, however, also include data from NHANES III (1988-1994). (31) For mothers who completed a survey in 2000 and self-reported their height and weight, BMI was computed. Mothers with BMI <25 kg/[m.sup.2] were classified as not overweight and overweight categories of 25 [less than or equal to] BMI < 30 kg/[m.sup.2], 30 [less than or equal to] BMI < 35 kg/[m.sup.2], and BMI [greater than or equal to] 35 kg/[m.sup.2] were created. (1)
Parental educational attainment was categorized as [less than or equal to] 11th grade, high school degree or general equivalency diploma, and college (any attendance). Race/ethnicity was collapsed into Hispanic, non-Hispanic black, non-Hispanic white, and Other (Asian, Native American, multicultural). For some analyses, child age group strata were also created as 1 through <2 years, 2 through <3 years, 3 through <4 years, and 4 through <5 years of age.
Each child’s total weekly hours spent viewing TV/video was estimated by multiplying by 5 the amount of time the child was reported to “usually” watch TV/video on weekdays, and adding the time he/she “usually” watched TV/video on Saturday and on Sunday. Because this TV/video viewing variable was found to have strong right skew, subsequent parametric analyses, such as analysis of variance (analysis of variance), were performed using both the natural log of the variable (after adding 0.1 hours per week to each value to eliminate zeros) and the untransformed variable. As there were no significant differences, the results presented are for the untransformed weekly TV/video viewing time variable. Based on the total weekly hours, the children were dichotomized into a group that “usually” watches TV/video (weekly TV/video time not equal to zero) or into a group that does not “usually” watch TV/video (weekly TV/video time equal to zero).
Differences in the continuous TV/video viewing time variable between the sexes, study years (1990 vs 2000), and BMI groups were examined by t tests. Differences in TV/video viewing time by race/ethnicity and by parental educational attainment were analyzed by analysis of variance. Scheffe posthoc test was used to further explain the nature of any significant overall F tests. Differences in the percentages of children viewing any TV/videos and the percentages of children viewing >2 hours per day (>14 hours per week) were compared by race/ethnicity with [chi square] tests, and by age group with Mantel-Haenszel [chi square] tests.
The relationships of dichotomous variables (such as child sex or child BMI group) with ordered variables (such as child age group or parental educational attainment) or other nonordered categorical variables were explored with Mantel-Haenszel [chi square] tests, or [chi square] tests, respectively. Demographic and other variables found to be related to child BMI group at the univariate level were then entered into a multiple logistic regression equation to identify independent predictors of children with a BMI >85th percentile.
Among year 2000 WIC Survey participants, the percentage of children reported to have a TV set in their bedroom was compared by race/ethnicity and by BMI group, using [chi square] tests. The mean TV/video viewing times of children with and without a TV set in his/her bedroom were compared using t tests. Demographic variables, including maternal BMI (based on self-reported height and weight), and other variables found to be related to child BMI at the univariate level were entered into a multiple logistic regression equation to identify independent predictors of children with a BMI >85th percentile.
RESULTS
Surveys were completed by 2761 adults (1364 in 1999 and 1397 in 2000); 90% of them were the children’s mother (Table 1). The age distribution of children in the study sample mirrors that seen among those enrolled in the WIC program, with a preponderance of children in the younger age strata ([chi square] = 59; P < .0001). By self-report, the race/ethnicity of the study population included more whites (34.7% vs 26.8%) but fewer Hispanics (33.4% vs 34.5%) and fewer blacks (22.7% vs 29.2%) than the total WIC population at the 49 surveyed WIC agencies ([chi square] = 42 [df = 31; P < .0001).
The self-reported parental educational attainment differed by race/ethnicity (Mantel-Haenszel [chi square] = 100 [df = 1]; P < .0001). For children 2 years of age and older, BMI classification (85th age- and sex-specific percentile) based on Frisancho (29) and the CDC (31) were very similar (kappa = 0.94). Based on the Frisancho cutpoints, 37.1% (742/1999) of children had a BMI >85th percentile, whereas the CDC cutpoints classified 37.2% (744/1999) as being over this threshold. Because the classification schemes are so similar and the CDC tables do not list BMI percentiles for 1-year-old children, the Frisancho cutpoints were used in all subsequent analyses.
The prevalence of children with a BMI >85th percentile did not vary with child sex or child age. However, this phenomenon was related to race/ethnicity. Specifically, Hispanic children were found to have the highest prevalence (42.8%), compared with whites (32.7%), blacks (34.6%), or other race children (29.9%) ([chi square] = 27 [df = 3]; P 95th percentile did not differ by race/ethnicity.
The percentage of children with a BMI >85th percentile decreased with higher parental educational attainment (38% if [less than or equal to] 11th grade education, 37% if high school degree, 34% if education beyond high school, and 27% if college degree, (Mantel Haenszel = 5.6 [df = 1]; P < .02). After statistical adjustment for race/ethnicity, however, these educational differences did not persist.
The percentage of children watching TV/video increased with increasing child age (Table 2). Eight-two percent of 1-year-old children watched TV/ video, whereas 95%, 96%, and 98% of 2-, 3-, and 4-year-old children, respectively, watched TV/video (Mantel Haenszel [chi square] = 124 [df = 1]; P < .0001). Among 1-year-old children, the percentage who did not watch TV/video was higher among whites (29%) than Hispanics (11%), blacks (15%) or others (20%), ([chi square] = 31 [df = 2]; P < .0001). The percentage of children who watched TV/video did not differ by parental educational attainment.
Although mean weekly TV/video viewing hours did not differ between survey years (15.0 vs 14.5 hours per week; t = 1.3 [df = 2645]; P = .2), more children were found to be watching at least some TV/video (90% in 1999% vs 94% in 2000, [chi square] = 17; P < .0001). Most of this increase was among the 1-year-old children, where the percentage increased from 78% in 1999% to 85% in 2000 ([chi square] = 5.7 [df = 1]; P < .02). When stratified by race/ethnicity, only white 1-year-old-children were found to have a significant increase in the proportion of children watching TV/ video (66% in 1999 to 78% in 2000; [chi square] = 4.5; P < .04; Fig 1).
[FIGURE 1 OMITTED]
Children’s weekly mean TV/video viewing hours differed by race/ethnicity (Table 2). Hispanic children watched (mean [+ or -] SEM) 15.0 [+ or -] 0.6 hours per week, black children watched 17.5 [+ or -] 0.8 hours per week, white children watched 12.7 [+ or -] 0.5 hour per week, and other race children watched 14.9 [+ or -] 1.2 hours per week (F = 22 [df = 3; 2731]; P < .0001 by analysis of variance). Posthoc analysis using Scheffe test showed that this significant overall F was attributable to differences between blacks and each of the other three racial/ethnic groups (all P < .05).
Older children spent more time watching TV/videos than younger children (Table 2). The 1-, 2-, 3-, and 4-year-old children watched, on average (mean [+ or -] SEM), 10.9 [+ or -] 0.6, 14.9 [+ or -] 0.6, 16.3 [+ or -] 0.7, and 18.4 [+ or -] 0.8 hours per week of TV/video, respectively (F = 93 [df = 3; 2752]; P < .0001 by analysis of variance). Posthoc analyses showed that all age groups, except the 2-year-old children versus the 3-year-old children, watched significantly different amounts of TV (all P < .05). Consistent with the differences in total viewing hours, the percentage of children watching TV/video 2 or more hours per day increased with increasing child age (Mantel Haenszel [chi square] = 137 [df = 1]; P < .0001), and differed by race/ethnicity ([chi square] = 67 [df = 3]; P < .0001; Table 2).
Children, whose parents had not completed high school spent more time (mean [+ or -] SEM) watching TV/videos (15.8 [+ or -] 0.7 hours per week) than children whose parents had completed high school (14.5 [+ or -] 0.5 hours per week), attended college (14.5 [+ or -] 0.5), or had a college degree (11.6 [+ or -] 0.6 hours per week; F = 4.9 [df = 3; 2478]; P < .002 by analysis of variance). Girls and boys watched similar amounts of TV/videos (14.8 [+ or -] 0.4 vs 14.7 [+ or -] 0.5 hours per week, respectively; t value = 1.6 [df = 2759]; P = .1).
In multiple logistic regression, after statistical adjustment for child age, child sex, parental educational attainment, and race/ethnicity, the prevalence of overweight children was significantly related to the amount of time (hours per day) that the children spent watching TV/videos (Table 3). The odds ratio (OR) for the prevalence of children with a BMI >85th percentile associated with each additional hour per day that the children watched TV/videos was 1.06 (95% confidence interval [CI]: 1.01-1.15). In addition, Hispanics were significantly more likely to be overweight than a white reference group (OR: 1.54; 95% CI: 1.26-1.87). Interaction terms were added to the model but found to be nonsignificant. When nonsignificant covariates were dropped from the model, the OR for the prevalence of child overweight associated with each additional hour per day of TV/video watched was virtually unchanged (OR: 1.08; 95% CI: 1.03-1.13; P < .004), as was the OR for child overweight among Hispanics versus the white reference group (OR: 1.52; 95% CI: 1.29-1.79; P < .0001).
For those surveyed in 2000, each parent/guardian also reported his/her weight, height, and whether his/her child had a TV in his/her bedroom. More black children (51%) and more Hispanic children (50%) had a TV set in their bedroom than white children (20%) or other race children (31%; [chi square] = 120 [df = 3]; P < .0001; Fig 2). Children with a TV set in their bedroom, compared with those without, spent an additional 4.6 per week watching TV/video (17.4 [+ or -] 0.5 vs 12.8 [+ or -] 0.3 hours per week; t value = 7.8 [df = 1374]; P 85th percentile was also higher among children with a TV in their bedroom compared with those without (44.4% vs 35.3%; [chi square] = 11 [df = 1]; P 85th percentile (OR: 1.31; 95% CI: 1.01-1.69; P 85th percentile also increased with higher maternal BMI (OR: 1.85; 95% CI: 1.32-2.60, for 30 [less than or equal to] BMI < 35 kg/[m.sup.2]; and OR: 1.59; 95% CI: 1.07-2.35, for BMI [greater than or equal to] 35 kg/[m.sup.2]; P < .02).
[FIGURE 2 OMITTED]
CONCLUSION
In this study of low-income preschool children, we found that the amount of time spent viewing TV/ video was significantly related to the prevalence of child overweight (BMI >85th percentile). Although direct comparisons to previous studies are difficult, the OR we observed of 1.06 (95% CI: 1.01-1.15) for the prevalence of child overweight associated with each additional hour per day of TV/video viewed is in good agreement with the OR of 1.12 (95% CI: 1.02-1.22) reported for Mexican children, aged 9 through 16 years. (14) It also appears consistent with the reported 2% increase in prevalence of obesity for each additional hour per day of TV viewed by 12- through 17-year-old adolescents. (6,8)
The mean weekly TV/video viewing times we observed (14.9, 16.3, and 18.4 hours per week for the 2-, 3-, and 4-year-old children, respectively) are comparable with the mean of 18.2 hours per week reported for 2- through 5-year-old children in the 1998 Annenberg National Survey. (26) Consistent with age trends, the younger preschool-aged children in this study watched fewer hours per week of TV/video than did older children in other studies, (7,8,10,13) but more than the 14 hours per week reported in an earlier Canadian study of children aged 3 through 10 years. (22) This may reflect time trends and/or differences between study populations.
We also found that the amount of time children spent watching TV/video was strongly influenced by demographic factors. As seen in studies of school-aged children and adolescents, black children spent the most time watching TV/videos, whereas the white children spent the least time. (7,11,25) Although previous studies have reported that children’s TV viewing varied inversely with parental educational attainment, we found in this low-income population that after statistical adjustment for racial/ethnic differences, parental educational attainment was no longer related to children’s TV/video viewing.
Although the racial/ethnic distribution of the study population differed from the total WIC population at the surveyed agencies, most of the analyses were stratified by race/ethnicity or included race/ ethnicity as a covariate. Thus, our findings are unlikely to be biased because of these differences. Our study population consisted entirely of children from low-income families, with relatively low parental educational attainment. This is both a limitation, as it limits generalizability of the findings, and a strength, because it decreases confounding by income level. It should be noted that the earliest studies linking TV viewing to obesity were among children with low-income, less educated parents, (6,8) as was a recent Mexican study. (14) In a study of sixth grade children, TV viewing and adiposity (triceps skinfold thickness in the boys and BMI in the girls) were found to be statistically associated among children from less affluent school districts, but not among those from more affluent school districts. (32) A stronger association between TV viewing and obesity has also been observed among low-income, less educated women than among high-income, more educated women. (33) Among the few studies of school children that failed to find a relationship between TV viewing and obesity, one study was conducted with a highly educated population, (34) while another gave no information regarding parental educational attainment or income level. (25)
The relationship between TV viewing and adiposity may be stronger in low-income populations, or alternately, there may be other factors that covary with TV viewing and contribute to the risk of obesity that are present to a greater degree in low-income families. An Italian study of children aged 2 through 8 years reported that less-educated mothers had more positive attitudes about TV, believing that TV shows were instructive and stimulated interests, and had fewer concerns about negative values, such as exposure to violence. (35) Higher educated mothers were more likely to prohibit certain shows and monitor their child’s TV use. Consequently, children of less-educated mothers not only watched more hours per week of TV but also had greater uncritical exposure to messages on TV. Children of higher educated mothers had less total TV exposure but also had qualitatively “better” exposure to TV programs because of greater parental selectivity of the programs watched. (35) Additional potential confounding factors might also include lower cognitive stimulation, which Strauss (36) suggests is an independent risk factor for the development of child obesity. It is possible that the increased rates of obesity associated with increased TV viewing might, in part, be mediated through the confounding effects of qualitative differences in the TV/video shows viewed and lower levels of cognitive stimulation, which are found more often in low-income populations. The relationship between TV viewing and obesity, however, is not found only among low-income populations. Two recent clinical trials in relatively highly educated populations demonstrated that reductions in TV/video viewing were associated with reductions in adiposity. (17,18)
The findings that 38% of 1- through 4-year-old children, 43% of third- and fourth- grade California children (17) and 54% of sixth- and seventh-grade Boston children had TV sets in their bedroom (27) suggest a trend with age. These percentages, however, are higher than previously reported (31% of children, aged 2 through 5 years, and 34.5% of children, aged 6 through 11 years), (26) perhaps reflecting time trends. The differences we observed between the percentages of black, Hispanic, and white children with TV sets in their bedroom are only partially explained by differences in parental educational attainment. The children’s living situations might contribute, in part, to the differences as the Hispanics and blacks in this study tended to live in metropolitan New York City where apartments are smaller than in upstate NY. Therefore, Hispanic children and Black children might be more likely than white children to share a bedroom with older siblings and/or parents.
Our finding that children with a TV set in their bedroom watched 4.8 hours per week more TV/ video, than those without, is similar to the 4.5 hours per week (0.64 hours per day) difference noted for sixth- and seventh-grade children. (27) It is of note that a TV set in the child’s bedroom was more strongly associated with increased risk of child overweight than the child’s weekly TV/video viewing hours, after adjustment for potential confounders, including race/ethnicity, maternal education, and maternal obesity. The stronger association could be attributable to less measurement error in parental assessment of whether or not the child has a TV in his/her bedroom than in estimation of the child’s usual TV/ video viewing time. Alternately, children with a TV set in their bedroom might watch even more TV than parents are aware, or a TV in the bedroom might be a marker for other behaviors that contribute to and/or covary with child obesity.
In this study, we chose child BMI >85th percentile as the “overweight” criterion. The prevalence of child overweight, at risk of overweight, and/or obesity will vary depending on the definition or criteria used. Experts in the United States and worldwide have called for a standard definition for child overweight, recommending that sex-specific BMI for age percentiles be used to screen for children who are “overweight” or “at risk of being overweight.” (30,37) Strong correlation coefficients have been observed between BMI and both total body fat and percent of body fat (assessed using dual energy radiograph absorptiometry or hydrodensitometry), indicating that BMI can provide a general description of the adiposity characterization of a population. BMI, however, has been shown to be a poor predictor for the individual child. (38,39) In children aged 1 to 7 years, decreases in BMI with age were primarily attributable to decreases in fat mass/stature, (2,40) whereas in children aged 8 through 18 years, increases in BMI with age were attributable to increases in both fat-free mass/stature (2) as well as total body fat/stature. (2,41) These associations also varied with gender and race/ ethnicity. (38,40) Because the relationship between BMI and percent fat among children has been evaluated only in blacks and whites, the validity of using BMI cutpoints to assess the prevalence of overweight status among Hispanic children and other racial/ethnic groups requires additional study.
Because these study data are cross-sectional, causality cannot be inferred nor implied. Prospective studies, with more sociodemographically diverse populations and inclusion of other variables that contribute to the development of child obesity, such as paternal adiposity, child diet, parent-child feeding behaviors, and measures of child physical activity, are needed. Assessment of the types of TV shows or videotapes viewed, exposure to food advertisement, parental coviewing, and measures of the cognitive stimulation of the child are also needed to further explore the association between TV/video viewing and overweight status among young preschool-aged children.
In our study population, most parents reported that their child “usually” watched TV/videos. Only 20% of the 1-year-old children did not watch TV/ video, whereas 25% of 1-year-old children averaged >2 hours per day, despite recommendations by the American Academy of Pediatrics that TV viewing should be discouraged for this age group. (42,43) Fifty-seven percent of the 4-year-old children in this study exceeded the recommended maximum media viewing time of 2 hours per day. (42,43) It is possible that parents are not aware of the recent recommendations by the American Academy of Pediatrics, Committee on Public Education (43) or do not agree with them. Alternately, market forces encouraging parents to provide TV/video viewing opportunities for their young children may. be more persuasive. Given that most 1-year-old children in this study are watching TV/videos, however, we need to understand why these recommendations are not being followed and what are parent’s expectations and beliefs about TV viewing by young children. Only then can effective educational guidelines about TV viewing be developed and delivered by pediatric health care providers, WIC nutrition staff, public health educators, and other sources.
CONCLUSION
Our findings suggest that the previously observed association between TV viewing and increased risk of overweight extends to an even younger age group, 1- through 4-year-old children. Future studies are needed to determine whether reductions in TV/ video viewing and/or removing TV sets from children’s bedrooms will contribute to population-based efforts to prevent child obesity.
TABLE 1. Distribution of Child Age, Sex, BMI, Parental/Guardian
Educational Attainment, and Relationship to Child by Race/
Ethnicity (N, %)
Hispanic Black White
(N = 911) (N = 621) (N = 949)
Child
Age (y) * N = 910 N = 620 N = 947
1 274 (30.1) 179 (28.9) 261 (27.6)
2 247 (27.1) 164 (26.5) 250 (26.4)
3 217 (23.9) 156 (25.2) 256 (27.0)
4 172 (18.9) 121 (19.5) 180 (19.0)
Sex (male) 482 (52.9) 294 (47.3) 484 (51.0)
BMI ([dagger]) N = 911 N = 621 N = 949
>85th percentile 390 (42.8) 215 (34.6) 310 (32.7)
>95th percentile 223 (24.5) 124 (20.0) 172 (18.1)
Parent/guardian
Relationship to child N = 869 N = 616 N = 946
Mother 809 (93.1) 549 (89.1) 866 (91.5)
Father 25 (2.9) 24 (3.9) 31 (3.3)
Other 35 (4.0) 43 (7.0) 49 (5.2)
Educational attainment N = 882 N = 614 N = 941
([double dagger])
[less than or equal to] 11th 388 (44.0) 150 (24.4) 200 (21.3)
grade
High school degree 311 (35.3) 264 (43.0) 449 (47.7)
Some college 151 (17.1) 165 (26.9) 254 (27.0)
4-year college degree 32 (3.6) 35 (5.7) 38 (4.0)
Other Total
(N = 251) (N = 2761)
Child
Age (y) * N = 250 N = 2756
1 76 (30.4) 798 (29.0)
2 68 (27.2) 737 (26.7)
3 59 (23.6) 694 (25.2)
4 47 (18.8) 527 (19.1)
Sex (male) 131 (52.2) 1405 (50.9)
BMI ([dagger]) N = 251 N = 2761
>85th percentile 75 (29.9) 955 (36.0)
>95th percentile 36 (14.3) 555 (20.3)
Parent/guardian
Relationship to child N = 242 N = 2698
Mother 212 (87.6) 2454 (91.0)
Father 24 (9.9) 108 (4.0)
Other 6 (2.5) 136 (5.0)
Educational attainment N = 243 N = 2702
([double dagger])
[less than or equal to] 11th 50 (20.6) 790 (29.2)
grade
High school degree 96 (39.5) 1130 (41.8)
Some college 72 (29.6) 649 (24.0)
4-year college degree 25 (10.3) 133 (4.9)
* Child distribution varies by age (ie, includes more younger children
than older children), for each race/ethnic group and for the total
group; [chi square] = 58.7; P < .0001.
([dagger]) The prevalence of children with a BMI for age >85th
sex- specific percentile differs by race/ethnicity, [chi square] = 27.4
(df = 3); P < .0001.
([double dagger]) Parental educational attainment differs by
race/ethnicity; Mantel-Haenszel [chi square] = (df = 71.0); P < .0001.
TABLE 2. Children’s Usual TV/Video Viewing Habits By Race/Ethnicity
and Age
Children Viewing Hispanic Black White
Any TV/video
(N [%]) *
1-y-old 245/274 (89.4) 152/179 (84.9) 186/261 (71.3)
2-y-old 241/247 (97.6) 157/164 (95.7) 227/250 (90.8)
3-y-old 217/217 (100) 153/156 (98.0) 236/256 (92.2)
4-y-old 171/172 (99.4) 119/121 (98.4) 173/180 (96.1)
Mean h/wk (SEM)
([dagger])
1-y-old 11.6 (0.6) 13.8 (1.0) 8.6 (0.5)
2-y-old 14.7 (0.6) 17.6 (0.8) 13.2 (0.6)
3-y-old 18.1 (0.8) 17.5 (0.9) 14.0 (0.6)
4-y-old 17.0 (0.7) 23.0 (1.3) 16.4 (0.8)
>2 h/d ([double
dagger]) (N [%])
1-y-old 78/274 (28.5) 68/179 (38.0) 45/261 (17.2)
2-y-old 101/247 (40.9) 92/164 (56.1) 87/250 (34.8)
3-y-old 120/217 (55.3) 83/156 (53.2) 91/256 (35.6)
4-y-old 97/172 (56.4) 87/121 (71.9) 87/180 (48.3)
Children Viewing Other Total
Any TV/video
(N [%]) *
1-y-old 61/76 (80.3) 644/790 (81.5)
2-y-old 66/68 (97.1) 691/729 (94.8)
3-y-old 57/59 (96.6) 663/688 (96.4)
4-y-old 45/47 (95.7) 508/520 (97.7)
Mean h/wk (SEM)
([dagger])
1-y-old 9.1 (1.2) 10.9 (0.4)
2-y-old 16.4 (1.4) 14.9 (0.4)
3-y-old 17.0 (1.3) 16.3 (0.4)
4-y-old 19.6 (2.3) 18.4 (0.5)
>2 h/d ([double
dagger]) (N [%])
1-y-old 11/76 (14.5) 202/790 (25.6)
2-y-old 36/68 (52.9) 316/729 (43.4)
3-y-old 34/59 (57.6) 328/688 (47.7)
4-y-old 26/47 (55.3) 297/520 (57.1)
* The percentage of children watching TV/videos increased with
increasing child age (Mantel Haenszel [chi square] = 124.2 [df = 1];
P < .0001). Among 1-year-old children, the prevalence of children not
watching TV/videos was higher among whites (29%) than Hispanics (11%),
blacks (15%), or others (20%), ([chi square] = 31.0, [df = 2];
P < .0001).
([dagger]) Children’s mean time viewing TV/video (hours per week)
differed by race/ethnicity (F = 22.0 [df = 3; 2731]); P < .0001 by
analysis of variance). Post hoc analysis using Scheffe’s test showed
that this significant overall F was attributable to differences between
blacks and each of the other 3 racial/ethnic groups (all P < .05).
Older children spent more time watching TV/videos than younger children
(F = 93.0 [df = 3; 2752]; P < .0001 by analysis of variance). Post hoc
analyses showed that all age groups, except the 2-year-old children
versus the 3-year-old children, watched significantly different amounts
of TV (all P < .05).
([double dagger]) The percentage of children watching more than an
average of 2 hours per day of TV/videos increased with increasing
child age (Mantel Haenszel [chi square] = 136.5 [df = 1]; P < .0001)
and also differed by race/ethnicity ([chi square] = 66.5 [df = 3];
P < .0001).
TABLE 3. Multiple Logistic Regression: Child BMI for Age >85th
Sex-Specific Percentile, Year 1999 and 2000 Survey Data (N = 2761)
Parameter Estimate P Value < OR 95% CI
Intercept -0.92 <.0001
TV/video viewing (h/d) 0.06 .03 1.06 (1.01-1.15)
Child age (y) 0.04 .27 1.04 (0.97-1.12)
Child sex 0.05 .54 1.06 (0.90-1.24)
Parental educational
attainment
Less than high school -0.09 .4 0.92 (0.76-1.12)
Some college -0.13 .2 0.87 (0.72-1.06)
Black race 0.07 .54 1.07 (0.86-1.33)
Hispanic ethnicity 0.43 <.0001 1.54 (1.26-1.87)
Other race -0.18 .25 0.83 (0.61-1.14)
White race, female child, and high school parental educational
attainment are the reference groups.
TABLE 4. Multiple Logistic Regression: Child BMI for Age >85th
Sex-Specific Percentile, Year 2000 Survey Data, Limited to
Mother-Child Pairs (N = 1182)
Parameter P Value < OR for (95% CI)
Estimate Child
Over-
weight
Intercept -0.86 .0001
TV set in bedroom 0.27 .045 1.31 (1.01-1.69)
TV/video viewing (hours -0.01 .8 0.99 (0.91-1.08)
per day)
Child age (y) 0.04 .5 1.04 (0.93-1.16)
Child sex 0.01 .9 1.01 (0.80-1.28)
Maternal educational
attainment
Less than high school -0.22 .13 0.80 (0.60-1.06)
Some college -0.28 .07 0.75 (0.56-1.02)
Black race 0.12 .5 1.13 (0.81-1.59)
Hispanic ethnicity 0.44 .004 1.56 (1.16-2.10)
Other race/ethnicity -0.47 .07 0.63 (0.38-1.04)
Maternal BMI (kg/
[m.sup.2])
25 [less than or equal 0.25 .10 1.28 (0.96-1.71)
to] BMI < 30
30 [less than or equal 0.62 .0004 1.85 (1.32-2.60)
to] BMI < 35
35 [less than or equal 0.46 .02 1.59 (1.07-2.35)
to] BMI
Reference groups are female child, maternal BMI <25 kg/[m.sup.2], high
school maternal educational attainment, white race, and no TV set in
child’s bedroom.
ACKNOWLEDGMENTS
This project was supported, in part, by an American Heart Association Established Investigator Award 96-40256N (Dr Dennison).
We thank Deborah J. Gregg, RD, MPH, at the New York State Department of Health, for assistance in collecting the data.
REFERENCES
(1.) Kuczmarski RJ, Carroll MD, Flegal KM, Troiano RP. Varying body mass index cutoff points to describe overweight prevalence among US adults: NHANES III (1988 to 1994). Obes Res. 1997;5:542-548
(2.) Troiano RP, Flegal MD. Overweight children and adolescents: description, epidemiology, and demographics. Pediatrics. 1998;101:497-504
(3.) Mei Z, Scanlon KS, Grummer-Strawn LM, Freedman DS, Yip R, Trowbridge FL. Increasing prevalence of overweight among US low-income preschool children: the Centers for Disease Control and Prevention, Pediatric Nutrition Surveillance, 1983 to 1995. Pediatrics. 1998;101(1). Available at: http://www.pediatrics.org/cgi/content/full/101/1/e12
(4.) Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med. 1993;22:167-177
(5.) Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337:869-873
(6.) Dietz WH, Gortmaker SL. Do we fatten our children at the television set? Obesity and television viewing in children and adolescents. Pediatrics. 1985;75:807-812
(7.) Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey. JAMA. 1998;279:938-942
(8.) Gortmaker SL, Dietz WH, Cheung LW. Inactivity, diet and the fattening of America. J Am Diet Assoc. 1990;90:1247-1255
(9.) Guillaume M, Lapidus L, Bjorntorp P, Lambert A. Physical activity, obesity, and cardiovascular risk factors in children. The Belgian Luxembourg Child Study II. Obes Res. 1997;5:549-556
(10.) Gortmaker SL, Must A, Sobol AM, Peterson K, Colditz GA, Dietz WH. Television viewing as a cause of increasing obesity among children in the United States, 1986-1990. Arch Pediatr Adolesc Med. 1996;150: 356-362
(11.) Obarzanek E, Schreiber GB, Crawford PB, et al. Energy intake and physical activity in relation to indexes of body fat: the National Heart, Lung, and Blood Institute Growth and Health Study. Am J Clin Nutr. 1994;60:15-22
(12.) Locard E, Mamelle N, Billette A, Miginiac M, Munoz F, Rey S. Risk factors of obesity in a five-year-old population. Parental versus environmental factors. Int J Obes. 1992;16:721-729
(13.) Hanley AJG, Harris SB, Gittelsohn J, Wolever TMS, Saksvig B, Zinman B. Overweight among children and adolescents in a Native Canadian community: prevalence and associated factors. Am J Clin Nutr. 2000;71: 693-700
(14.) Hernandez B, Gortmaker SL, Colditz GA, Peterson KE, Laird NM, Para-Cabrera S. Association of obesity with physical activity, television programs and other forms of video viewing among children in Mexico City. Int J Obes Relat Metab Disord. 1999;23:845-854
(15.) Salmon J, Bauman A, Crawford D, Timperio A, Owen N. The association between television viewing and overweight among Australian adults participating in varying levels of leisure-time physical activity. Int J Obes Relat Metab Disord. 2000;24:600-606
(16.) Gortmaker SL, Peterson K, Wiecha J, et al. Reducing obesity via a school-based interdisciplinary intervention among youth: planet health. Arch Pediatr Adolesc Med. 1999;153:409-418
(17.) Robinson TN. Reducing children’s television viewing to prevent obesity: a randomized controlled trial. JAMA. 1999;282:1561-1567
(18.) Epstein LH, Paluch RA, Gordy CC, Dorn J. Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med. 2000;154: 220-226
(19.) DuRant RH, Baranowski T, Johnson M, Thompson WO. The relationship among television watching, physical activity, and body composition of young children. Pediatrics. 1994;94:449-455
(20.) Anderson DR, Field DE, Collins PA, Lorch EP, Nathan JG. Estimates of young children’s time with television: a methodological comparison of parent reports with time-lapse video home observations. Child Dev. 1985;56:1345-1357
(21.) Dennison BA, Gedmintas L, Russo TJ, Burdick P. Age-appropriateness and educational value of TV/video programs viewed by preschool children. Pediatr Res. 2002. In press
(22.) Bernard-Bonnin AC, Gilbert S, Rousseau E, Masson P, Maheux B. Television and the 3- to 10-year-old child. Pediatrics. 1991;88:48-54
(23.) Medrich EA. Constant television: a background to daily life. J Commun. 1979;29:171-176
(24.) Kimm SYS, Obarzanek E, Barton BA, et al. Race, socioeconomic status, and obesity in 9- to 10-year-old girls: the NHLBI growth and health study. Ann Epidemiol. 1996;6:266-275
(25.) Wolf AM, Gortmaker SL, Cheung L, Gray HM, Herzog DB, Colditz GA. Activity, inactivity, and obesity: racial, ethnic, and age differences among schoolgirls. Am J Public Health. 1993;83:1625-1627
(26.) Stanger JD. Television in the Home 1998: The Third Annual National Survey of Parents and Children. Philadelphia, PA: Annenberg Public Policy Center; 1998
(27.) Wiecha JL, Sobol AM, Peterson KE, Gortmaker SL. Household television access: associations with screen time, reading and homework among youth. Ambulatory Pediatr. 2001;1:244-251
(28.) New York State Department of Health. WIC Program Manual. Section #1183-C1-Anthropometry for Infants and Children. 1996:1-41
(29.) Frisancho AR. Anthropometric Standards for the Assessment of Growth and Nutritional Status. Ann Arbor, MI: University of Michigan Press; 1990
(30.) Barlow SE, Dietz WH. Obesity evaluation and treatment: expert committee recommendations. Pediatrics. 1998;102(3). Available at: http:// www.pediatrics.org/cgi/content/full/102/3/e29
(31.) Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. CDC Growth Charts: United States. Advance Data From Vital and Health Statistics. No. 314. Hyattsville, MD: National Center for Health Statistics; 2000. Available at: http://www.cdc.gov/growthcharts
(32.) Shannon B, Peacock J, Brown MJ. Body fatness, television viewing and calorie-intake of a sample of Pennsylvania sixth grade children. J Nutr Educ. 1991;23:262-268
(33.) Jeffrey RW, French SA. Epidemic obesity in the United States: are fast foods and television contributing? Am J Public Health. 1998;88:277-280
(34.) Robinson TN, Hammer LD, Killen JD, et al. Does television viewing increase obesity and reduce physical activity? Cross-sectional and longitudinal analyses among adolescent girls. Pediatrics. 1993;91:272-280
(35.) Valerio M, Amodio P, Zio MD, Vianello A, Zacchello GP. The use of television in 2- to 8-year-old children and the attitude of parents about such. Arch Pediatr Adolesc Med. 1997;151:22-26
(36.) Strauss RS, Knight J. Influence of the home environment on the development of obesity in children. Pediatrics. 1999;103(6). Available at: http://www.pediatrics.org/cgi/content/full/103/6/e85
(37.) Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J. 2000;320:1240-1243
(38.) Ellis KJ, Abrams S, Wong WW. Monitoring childhood obesity: assessment of weight/height index. Am J Epidemiol. 1999;150:939-946
(39.) Pietrobelli A, Faith MS, Allison DB, Gallagher D, Chiumello G, Heymsfield SB. Body mass index as a measure of adiposity among children and adolescents: a validation study. J Pediatr. 1998;132:204-210
(40.) Wells JCK. A critique of the expression of pediatric body composition data. Arch Dis Child. 2001;85:67-72
(41.) Maynard LM, Wisemandle W, Roche AF, Chumlea WC, Guo SS, Siervogel RM. Childhood body composition in relation to body mass index. Pediatrics. 2001;107:344-350
(42.) American Academy of Pediatrics, Committee on Public Education. Media education. Pediatrics. 1999;104:341-343
(43.) American Academy of Pediatrics, Committee on Public Education. Children, adolescents, and television. Pediatrics. 2001;107:423-426
Barbara A. Dennison, MD * ([double dagger]); Tara A. Erb, MS *; and Paul L. Jenkins, PhD *
From the * Research Institute, Bassett Healthcare, Cooperstown, New York; and ([double dagger]) Department of Pediatrics, Columbia University, New York, New York. This paper was presented, in part, at the Annual Scientific Meeting of the North American Association for the Study of Obesity, October 31, 2000, Long Beach, CA [abstract appears in Obes Res. 2000;8(suppl 1):385] and at the Pediatric Academy Societies’ Meeting, May 1, 2001, Baltimore, MD [abstract appears in Pediatr Res. 2001;49:160A].
Received for publication Apr 16, 2001; accepted Dec 3, 2001.
The results reported are the sole responsibility of the authors and may not reflect the views of the New York State Department of Health or the American Heart Association.
Reprint requests to (B.A.D.) Research Institute, Bassett Healthcare, 1 Atwell Rd, Cooperstown, NY 13326. E-mail: barbara.dennison@bassett.org
COPYRIGHT 2002 American Academy of Pediatrics
COPYRIGHT 2002 Gale Group
