Context Some observational studies have found an association between television
viewing and child and adolescent adiposity.
Objective To assess the effects of reducing television, videotape, and video game
use on changes in adiposity, physical activity, and dietary intake.
Design Randomized controlled school-based trial conducted from September 1996
to April 1997.
Setting Two sociodemographically and scholastically matched public elementary
schools in San Jose, Calif.
Participants Of 198 third- and fourth-grade students, who were given parental consent
to participate, 192 students (mean age, 8.9 years) completed the study.
Intervention Children in 1 elementary school received an 18-lesson, 6-month classroom
curriculum to reduce television, videotape, and video game use.
Main Outcome Measures Changes in measures of height, weight, triceps skinfold thickness, waist
and hip circumferences, and cardiorespiratory fitness; self-reported media
use, physical activity, and dietary behaviors; and parental report of child
and family behaviors. The primary outcome measure was body mass index, calculated
as weight in kilograms divided by the square of height in meters.
Results Compared with controls, children in the intervention group had statistically
significant relative decreases in body mass index (intervention vs control
change: 18.38 to 18.67 kg/m2 vs 18.10 to 18.81 kg/m2,
respectively; adjusted difference −0.45 kg/m2 [95% confidence
interval {CI}, −0.73 to −0.17]; P=.002),
triceps skinfold thickness (intervention vs control change: 14.55 to 15.47
mm vs 13.97 to 16.46 mm, respectively; adjusted difference, −1.47 mm
[95% CI, −2.41 to −0.54]; P=.002), waist
circumference (intervention vs control change: 60.48 to 63.57 cm vs 59.51
to 64.73 cm, respectively; adjusted difference, −2.30 cm [95% CI, −3.27
to −1.33]; P<.001), and waist-to-hip ratio
(intervention vs control change: 0.83 to 0.83 vs 0.82 to 0.84, respectively;
adjusted difference, −0.02 [95% CI, −0.03 to −0.01]; P<.001). Relative to controls, intervention group changes
were accompanied by statistically significant decreases in children's reported
television viewing and meals eaten in front of the television. There were
no statistically significant differences between groups for changes in high-fat
food intake, moderate-to-vigorous physical activity, and cardiorespiratory
fitness.
Conclusions Reducing television, videotape, and video game use may be a promising,
population-based approach to prevent childhood obesity.
The United States has experienced alarming increases in obesity among
children and adolescents.1 However, most available
treatments for obese children have yielded only modest, unsustained effects.2 Consequently, prevention is considered to hold the
greatest promise.3 Unfortunately, most prevention
programs that specifically attempt to reduce fat and energy intake and increase
physical activity have been ineffective at changing body fatness.4,5 As a result, there is a need for innovative
approaches to prevent obesity.
There is widespread speculation that television viewing is one of the
most easily modifiable causes of obesity among children. American children
spend more time watching television and videotapes and playing video games
than doing anything else except sleeping.6
Two primary mechanisms by which television viewing contributes to obesity
have been suggested: reduced energy expenditure from displacement of physical
activity and increased dietary energy intake, either during viewing or as
a result of food advertising.
Cross-sectional epidemiological studies have consistently found relatively
weak positive associations between television viewing and child and adolescent
adiposity.7-21
Prospective studies are less common and have produced mixed results.7,14 The consistently weak associations
found in epidemiological studies may be due to the measurement error in self-reports
of television viewing. As a result, additional epidemiological studies would
not be expected to clarify the true nature of this relationship.22
A causal relationship can only be demonstrated in an experimental trial,
in which manipulation of the risk factor changes the outcome.23
Therefore, we conducted a randomized, controlled, school-based trial of reducing
third- and fourth-grade children's television, videotape, and video game use
to assess the effects on adiposity and the hypothesized mechanisms of physical
activity and dietary intake. We hypothesized that compared with controls,
children exposed to the television reduction intervention would significantly
decrease their levels of adiposity.
All third- and fourth-grade students in 2 public elementary schools
in a single school district in San Jose, Calif, were eligible to participate.
Schools were sociodemographically and scholastically matched by district personnel.
School principals and teachers agreed to participate prior to randomization.
Parents or guardians provided signed written informed consent for their children
to participate in assessments and for their own participation in telephone
interviews. One school was randomly assigned to implement a program to reduce
television, videotape, and video game use. The other school was assigned to
be an assessments-only control. Participants and school personnel, including
classroom teachers, were informed of the nature of the intervention but were
unaware of the primary hypothesis. The study was approved by the Stanford
University Panel on Human Subjects in Research, Palo Alto, Calif.
To test the specific role of television, videotape, and video game use
in the development of body fatness, as well as effects on dietary intake and
physical activity, it was necessary to design an intervention that decreased
media use alone without specifically promoting more active behaviors as replacements.
This was accomplished by limiting access to television sets and budgeting
use while simultaneously becoming more selective viewers or players.
The intervention, which was based in Bandura's social cognitive theory,24 consisted of incorporating 18 lessons of 30 to 50
minutes into the standard curriculum that was taught by the regular third-
and fourth-grade classroom teachers. The teachers were trained by the research
staff, and the majority of lessons were taught during the first 2 months of
the school year. Early lessons included self-monitoring and self-reporting
of television, videotape, and video game use to motivate children to want
to reduce the time they spent in these activities. These lessons were followed
by a television turnoff,25 during which children
were challenged to watch no television or videotapes and play no video games
for 10 days. After the turnoff, children were encouraged to follow a 7-hour
per week budget. Additional lessons taught children to become "intelligent
viewers" by using their viewing and video game time more selectively. Several
final lessons enlisted children as advocates for reducing media use. The entire
curriculum consisted of approximately 18 hours of classroom time. Newsletters
that were designed to motivate parents to help their children stay within
their time budgets and that suggested strategies for limiting television,
videotape, and video game use for the entire family were distributed to parents.
To help with budgeting, each household also received an electronic television
time manager (TV Allowance, Mindmaster, Inc, Miami, Fla). This device locks
onto the power plug of the television set and monitors and budgets viewing
time for each member of the household through use of personal identification
codes. Because it controls power to the television, it also controls video
cassette recorder (VCR) and video game use. Families could request additional
units for every television in their homes, at no cost.
Assessments were performed by trained staff, blinded to the experimental
design, at baseline (September 1996) and after the completion of the intervention
(April 1997). At each time point, on the same days in both schools, children
completed self-report questionnaires on 2 non-Monday weekdays. A research
staff member read each question out loud. Classroom teachers did not participate
in the assessments. Physical measures were performed during 2 physical education
periods at each time point, by the same staff in both schools. Parents were
interviewed by telephone at baseline and after the intervention by trained
interviewers following a standardized protocol. Parents, children, and teachers
were not aware that the primary outcome was adiposity.
Body mass index (BMI), defined as the weight in kilograms divided by
the square of the height in meters, was the primary measure of adiposity.26,27 Standing height was measured using
a portable direct-reading stadiometer and body weight was measured using a
digital scale, according to established guidelines.28,29
Test-retest reliabilities were high (intraclass Spearman r>0.99 for height, r>0.99 for weight). Triceps
skinfold thickness was included as a measure of subcutaneous fat and was measured
on the right arm, according to established guidelines.28,29
Test-retest reliability was r>0.99 and skinfold thickness
was highly correlated with BMI (r=0.82).
Waist and hip circumferences were measured with a nonelastic tape at
the level of the umbilicus and the maximal extension of the buttocks, respectively,
according to established guidelines.28,29
Test-retest reliabilities were r>0.99. Waist and
hip circumferences were correlated with BMI (r=0.87, r=0.90, respectively) and triceps skinfold thickness (r=0.72, r=0.78, respectively).
The waist-to-hip ratio was calculated as a measure of body fat distribution.
Children reported the time they spent "watching television," "watching
movies or videos on a VCR," and "playing video games," separately for before
school and after school, "yesterday" and "last Saturday" on the first assessment
day, and "yesterday" on the second assessment day. Prior to reading these
items, the research staff led children through several participatory time-estimating
exercises. This instrument was adapted from a similar instrument previously
used in young adolescents with high test-retest reliability (r=0.94).15
Parents estimated the amount of time their child spent watching television,
watching videotapes on the VCR, and playing video games on a typical school
day and on a typical weekend day. Similar items have produced accurate estimates
compared with videotaped observation.30 There
was moderate agreement between parent and child reports of children's media
use (Spearman r=0.31, P<.001
for television viewing; r=0.17, P=.03 for videotape viewing; r=0.49, P<.001 for video game playing). A previously validated
4-item instrument was used to assess overall household television viewing.31
Children and parents also estimated the amount of time the child spent
in other sedentary behaviors, including, using a computer, doing homework,
reading, listening to music, playing a musical instrument, doing artwork or
crafts, talking with parents, playing quiet games indoors, and at classes
or clubs (parent-child agreement Spearman r=0.16, P<.05).
On both days children reported their previous day's out-of-school physical
activities, using a previously validated activity checklist.32
Responses from the 2 days were averaged and weighted for levels of intensity
using standard energy expenditure estimates.33
Parents estimated the amount of time their child spent in organized physical
activities (such as teams or sports classes) and nonorganized physical activities
(such as playing sports, bicycling, rollerblading, etc) (parent-child agreement
Spearman r=0.16, P=.05).
On both days, children completed 1-day food frequency recalls for 60
foods in 26 food categories, based on instruments previously validated in
third- through sixth-grade children.34,35
High-fat foods were those previously identified as the major contributors
of fat in the diets of children35 and adults,36 and were identified through focus groups with children,
parents, and school lunch personnel. Highly advertised foods included 3 categories
representing sugary cereals, carbonated soft drinks, and foods from fast-food
restaurants.
Children also reported how often they ate breakfast and dinner in a
room with the television turned on during the past week, on 4-point scales
ranging from never to every day, and they reported the proportion of time
they were eating or drinking a snack (not including meals) while watching
television or videotapes or playing video games, on a 3-point scale. Parents
responded to the same questions about their children, reporting the number
of days in the last week for meals (parent-child agreement Spearman r=0.24, P=.003) and the percentage
of time for snacking (parent-child agreement Spearman r=0.02, P>.05).
The maximal, multistage, 20-m, shuttle run test (20-MST) was used to
assess cardiorespiratory fitness.37 The 20-MST
has been found to be reliable (test-retest r=0.73-0.93),37-39 a valid measure of
maximum oxygen consumption as measured by treadmill testing (r=0.69-0.87),38-42
and sensitive to change42 in children.
Baseline comparability of intervention and control groups was assessed
using nonparametric Wilcoxon rank sum tests for scaled variables and χ2 tests for categorical variables. As a primary prevention program,
the intervention was designed to target the entire sample. Effects were expected
and intended to occur throughout the entire distribution of adiposity in the
sample—not just around a defined threshold. Thus, for purposes of establishing
the efficacy of this intervention, it is most appropriate to compare the full
distributions of BMI between intervention and control groups. Therefore, to
test the primary hypothesis, accounting for the design with school as the
unit of randomization (adjusting for intraclass correlation), a mixed-model
analysis of covariance approach was used, with postintervention BMI as the
dependent variable; the intervention group (intervention vs control) as the
independent variable; and baseline BMI, age, and sex as covariates (SAS MIXED
procedure, SAS version 6.12, SAS Institute Inc, Cary, NC).43
The same analysis approach was used for all secondary outcome variables, triceps
skinfold thickness, waist and hip circumferences, waist-to-hip ratio, and
measures of dietary intake and physical activity. Each outcome also was tested
for intervention by sex and intervention by age interactions. All analyses
were completed on an intention-to-treat basis, and all tests of statistical
significance were 2-tailed with α=.05.
With an anticipated sample size of approximately 100 participants per
group and using the above analysis, the study was designed to have 80% power
to detect an effect size of 0.20 or greater. This corresponded to estimated
differences between groups of about 0.75 BMI units, 1.2 mm of triceps skinfold,
1.8 cm of waist circumference, and 2 hours per week of television, videotape,
and video game use.
In children of this age, BMI, triceps skinfold thickness, waist circumference,
and hip circumference were all expected to increase over the course of the
experiment, as part of normal growth, in both the intervention and control
groups. Therefore, effect sizes are reported as changes in the intervention
group relative to changes in the controls (relative differences). A negative
difference is termed a relative decrease in comparison
with the controls, even if the actual value increased as a result of normal
growth and development.
The study design and participation are shown in Figure 1. Ninety-two (86.8%) of 106 eligible children in the intervention
school and 100 (82.6%) of 121 eligible children in the control school participated
in baseline and postintervention assessments. Intervention and control participants,
respectively, were comparable in age (mean [SD], 8.95 [0.64] vs 8.92 [0.70]
years, P=.69), sex (44.6% vs 48.5% girls, P=.59), mean (SD) number of televisions in the home (2.7 [1.3] vs 2.7
[1.1], P=.56), mean (SD) number of video game players
(systems) (1.5 [2.3] vs 1.2 [1.7], P=.49) and percentage
of children with a television in their bedroom (43.5% vs 42.7%, P=.92). Physical measures but not self-reports were included in the
analysis for 11 children who were classified by their teachers as having limited
English proficiency or having a learning disability.
Baseline and postintervention telephone interviews were completed by
68 (71.6%) and 75 (72.8%) of the parents of participating children in the
intervention and control schools, respectively. Intervention school parents
reported greater maximum household education levels than participating control
school parents (45% vs 21% college graduates, P=.01)
but did not differ significantly in ethnicity (80% vs 70% white, P=.19), sex of respondent (82% vs 88% female, P=.33)
or marital status (77% vs 67% married, P=.22).
Participation in the Intervention
Teachers reported teaching all lessons, although we did not collect
detailed data determining whether the lessons were delivered as they were
intended. Ninety-five (90%) of 106 students in the intervention school participated
in at least some of the television turnoff and 71 (67%) completed the entire
10 days without watching television or videotapes or playing video games.
During the budgeting phase of the intervention, 58 (55%) of the students turned
in at least 1 signed parent confirmation that they had stayed below their
television and videotape viewing and video game playing budget for the previous
week. Forty-four parents (42%) returned response cards reporting they had
installed the TV Allowance and 29 families (27%) requested 1 or more additional
TV Allowances.
Results of anthropometric measures are presented in Table 1. At baseline, both groups were comparable (P>.10) on all baseline measures of body composition. As expected for
children of this age, BMI, triceps skinfold thickness, waist circumference,
and hip circumference all increased in both intervention and control children
during the course of the school year. However, compared with controls, children
in the intervention group had statistically significant relative decreases
in BMI, triceps skinfold thickness, waist circumference, and waist-to-hip
ratio (Table 1). There were no
significant interventions by sex or intervention by age interactions for any
of the body composition outcomes. The results did not change when ethnicity
and parent education were included as additional covariates for children with
completed parent interviews.
Although the sample size was insufficient to formally test for effects
within subgroups, it was desirable to further characterize the effects of
the intervention on participants with varying levels of adiposity, with a
descriptive analysis. Intervention and control group changes were compared
within strata defined by baseline levels of BMI, triceps skinfold, waist circumference,
and waist-to-hip ratio. For all body composition measures, effects of the
intervention occurred across the entire distribution of baseline adiposity,
with greater intervention vs control differences evident among the middle
and higher strata of body fatness.
Effects on Media Use, Diet, and Physical Activity
Child measures are presented in Table
2 and parent measures are presented in Table 3. Both groups were well matched at baseline, although intervention
group children reported eating significantly more meals while watching television,
and participating intervention group parents reported significantly less overall
household television use and that their children spent significantly more
time in other sedentary behaviors at baseline.
The intervention significantly decreased children's television viewing,
compared with controls, according to both child and parent reports (relative
reductions of about one third from baseline). Intervention group children
also reported significantly greater reductions in video game use than controls.
The intervention also resulted in greater, but not statistically significant,
decreases in parent reports of children's video game use, parent and child
reports of videotape viewing, and parent reports of overall household television
viewing. There were no significant intervention by sex or intervention by
age interactions for any of the media use outcomes.
The intervention significantly reduced the frequency of children eating
meals in a room with the television turned on. Intervention group children
also reported relative reductions in servings of high-fat foods compared with
controls, although these differences were not statistically significant. There
were no significant intervention effects on reports of children's physical
activity levels or performance on the 20-MST of physical fitness. There were
no significant intervention by sex or intervention by age interactions for
any of the diet or activity outcomes.
This is the first experimental study to demonstrate a direct association
between television, videotape, and video game use and increased adiposity.
Because the intervention targeted reduction of media use alone, without substituting
alternative behaviors, a causal inference might be made.23
In one previous obesity treatment study, obese children who were reinforced
(ie, rewarded) for decreasing sedentary activity (including television viewing
and computer games, as well as imaginative play, talking on the telephone,
playing board games, etc) along with following an energy-restricted diet lost
significantly more weight than obese children reinforced for increasing physical
activity or those reinforced for both.44 Although
that study did not directly test the role of television, videotape, and video
game use, the similar findings support our results.
This experiment was designed to overcome the dependence of epidemiological
studies on error-prone measures of television viewing behaviors by using BMI
as the primary outcome. However, the intervention did produce statistically
significant decreases in reported television viewing and video game use, compared
with controls. Previous studies of reducing children's television viewing
have been uncontrolled and limited to a small number of families.45-47 This study, therefore,
also represents a promising model for studying other hypothesized effects
of television and videotape viewing and video game use.
Because this study involved children in only 2 elementary schools, the
possibility that the results were due to differences in the groups that were
unrelated to the intervention cannot be ruled out completely. This possibility
is made less likely, however, because the schools were in a single school
district and participants were comparable at baseline on almost all measured
variables. In addition, the patterns of the results strengthen the case for
causal inference. The crossover patterns of the changes in BMI, triceps skinfold
thickness, waist circumference, and waist-to-hip ratio lessen the likelihood
of scaling (a "ceiling effect"), regression, and selection-maturation biases
as alternative interpretations of the results.48,49
Effects of the intervention on diet and activity were less clear. Compared
with controls, children in the intervention group significantly reduced the
number of meals they reportedly ate in front of the television set. There
were no significant effects on reports of snacking while watching television
or intake of high-fat and highly advertised foods. However, because snacking
while watching television was assessed as a proportion, even no change in
this variable might result in decreased energy intake as total viewing was
decreased. Epidemiological studies have found associations among hours of
television viewing and children's fat and energy intakes,15,50
and experimental studies have shown that food advertising affects children's
snack choices and consumption.51,52
Some epidemiological studies have found weak inverse associations between
hours of television viewing and physical activity14,18
and fitness.8,16 Our intervention
did not result in a significant change in physical activity or cardiorespiratory
fitness. However, because only moderate- and vigorous-intensity activities
were assessed, it is also possible that reductions in television viewing resulted
in increased energy expenditure via more low-intensity activity. This is consistent
with the finding that reductions in television, videotape, and video game
use did not result in compensatory increases in other sedentary pursuits.
Larger experimental studies and improved measures of diet and activity are
needed to more definitively assess the specific mechanisms that account for
changes in adiposity in response to reduced television, videotape, and video
game use.
With a few exceptions, previous prevention interventions that have attempted
to increase physical activity and decrease dietary fat and energy intake have
been relatively ineffective at reducing body fatness.4,5
In contrast, this intervention targeting only television, videotape, and video
game use produced statistically significant and clinically significant relative
changes in BMI, triceps skinfold thickness, waist circumference, and waist-to-hip
ratio over a period of 7 months. These changes occurred over the entire sample,
shifting the entire distribution of adiposity downward. Even a small shift
downward in the population distribution of adiposity would be expected to
have large effects on obesity-related morbidity and mortality.53
Additional experimental studies with larger and more sociodemographically
diverse samples are needed to evaluate the generalizability of these findings.
However, this study indicates that reducing television, videotape, and video
game use may be a promising, population-based approach to help prevent childhood
obesity.
1.Troiano RP, Flegal KM. Overweight children and adolescents: description, epidemiology, and
demographics.
Pediatrics.1998;101:497-504.Google Scholar 2.Epstein LH, Myers MD, Raynor HA, Saelens BE. Treatment of pediatric obesity.
Pediatrics.1998;101:554-570.Google Scholar 3.Hill JO, Peters JC. Environmental contributions to the obesity epidemic.
Science.1998;280:1371-1374.Google Scholar 4.Resnicow K. School-based obesity prevention: population versus high-risk interventions.
Ann N Y Acad Sci.1993;699:154-166.Google Scholar 5.Resnicow K, Robinson TN. School-based cardiovascular disease prevention studies: review and
synthesis.
Ann Epidemiol.1997;7(suppl 7):S14-S31.Google Scholar 6.The Annenberg Public Policy Center of the University of Pennsylvania. Television in the Home: The 1997 Survey of Parents
and Children. Philadelphia: University of Pennsylvania; 1997.
7.Dietz WH, Gortmaker SL. Do we fatten our children at the TV set? television viewing and obesity
in children and adolescents.
Pediatrics.1985;75:807-812.Google Scholar 8.Pate RR, Ross JG. The national children and youth fitness study II: factors associated
with health-related fitness.
J Phys Educ Recreation Dance.1987;58:93-95.Google Scholar 9.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.Google Scholar 10.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.Google Scholar 11.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.Google Scholar 12.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.Google Scholar 13.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.Google Scholar 14.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;91:273-280.Google Scholar 15.Robinson TN, Killen JD. Ethnic and gender differences in the relationships between television
viewing and obesity, physical activity and dietary fat intake.
J Health Educ.1995;26:S91-S98.Google Scholar 16.Tucker LA. The relationship of television viewing to physical fitness and obesity.
Adolescence.1986;21:797-806.Google Scholar 17.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.Google Scholar 18.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.Google Scholar 19.DuRant RH, Thompson WO, Johnson M, Baranowski T. The relationship among television watching, physical activity, and
body composition of 5- or 6-year-old children.
Pediatr Exerc Sci.1996;8:15-26.Google Scholar 20.Dwyer JT, Stone EJ, Yang M.
et al. Predictors of overweight and overfatness in a multiethnic pediatric
population.
Am J Clin Nutr.1998;67:602-610.Google Scholar 21.Armstrong CA, Sallis JF, Alcaraz JE, Kolody B, McKenzie TL, Hovell MF. Children's television viewing, body fat, and physical fitness.
Am J Health Promotion.1998;12:363-368.Google Scholar 22.Robinson TN. Does television cause childhood obesity?
JAMA.1998;279:959-960.Google Scholar 23.Kraemer HC, Kazdin AE, Offord DR, Kessler RC, Jensen PS, Kupfer DJ. Coming to terms with the terms of risk.
Arch Gen Psychiatry.1997;54:337-343.Google Scholar 24.Bandura A. Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice-Hall; 1986.
25.Winn M. Unplugging the Plug-in Drug. New York, NY: Penguin Books; 1987.
26.Kraemer HC, Berkowitz RI, Hammer LD. Methodological difficulties in studies of obesity, I: measurement issues.
Ann Behav Med.1990;12:112-118.Google Scholar 27.Dietz WH, Robinson TN. Use of the body mass index (BMI) as a measure of overweight in children
and adolescents.
J Pediatr.1998;132:191-193.Google Scholar 28.Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, Ill: Human Kinetics Publishers; 1988.
29.National Center for Health Statistics. NHANES III Anthropometric Procedures [videotape]. Washington, DC: US Government Printing Office; 1996. Stock No. 017-022-01335-5.
30.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 observation.
Child Dev.1985;56:1345-1357.Google Scholar 31.Medrich EA. Constant television: a background to daily life.
J Communication.1979;29:171-176.Google Scholar 32.Sallis JF, Strikmiller PK, Harsha DW.
et al. Validation of interviewer- and self-administered physical activity
checklists for fifth grade students.
Med Sci Sports Exerc.1996;28:840-851.Google Scholar 33.Ainsworth BE, Haskell WL, Leon AS.
et al. Compendium of physical activities: classification of energy costs of
human physical activities.
Med Sci Sports Exerc.1993;25:71-80.Google Scholar 34.Baranowski T, Dworkin R, Henske JC.
et al. The accuracy of children's self reports of diet: family health project.
J Am Diet Assoc.1986;86:1381-1385.Google Scholar 35.Simons-Morton BG, Baranowski T, Parcel GS, O'Hara NM, Matteson RC. Children's frequency of consumption of foods high in fat and sodium.
Am J Prev Med.1990;6:218-227.Google Scholar 36.Block G, Clifford C, Naughton MD, Henderson M, McAdams M. A brief dietary screen for high fat intake.
J Nutr Educ.1989;21:199-207.Google Scholar 37.Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness.
J Sports Sci.1988;6:93-101.Google Scholar 38.Liu NY-S, Plowman SA, Looney MA. The reliability and validity of the 20-meter shuttle test in American
students 12 to 15 years old.
Res Q Exerc Sport.1992;63:360-365.Google Scholar 39.Mahoney C. 20-MST and PWC170 validity in non-Caucasian children in the UK.
Br J Sports Med.1992;26:45-47.Google Scholar 40.Boreham CAG, Paliczka VJ, Nichols AK. A comparison of the PWC170 and 20-MST tests of aerobic fitness in adolescent
schoolchildren.
J Sports Med Phys Fitness.1990;30:19-23.Google Scholar 41.van Mechelen W, Hlobil H, Kemper HCG. Validation of two running tests as estimates of maximal aerobic power
in children.
Eur J Appl Physiol.1986;55:503-506.Google Scholar 42.Ahmaidi SB, Varray AL, Savy-Pacaux AM, Prefaut CG. Cardiorespiratory fitness evaluation by shuttle test in asthmatic subjects
during aerobic training.
Chest.1993;103:1135-1141.Google Scholar 43.Murray DM. Design and Analysis of Group-Randomized Trials. New York, NY: Oxford University Press; 1998.
44.Epstein LH, Valoski AM, Vara LS.
et al. Effects of decreasing sedentary behavior and increasing activity on
weight change in obese children.
Health Psychol.1995;14:109-115.Google Scholar 45.Wolfe DA, Mendes MG, Factor D. A parent-administered program to reduce children's television viewing.
J Appl Behav Anal.1984;17:267-272.Google Scholar 46.Jason LA. Using a token-actuated timer to reduce television viewing.
J Appl Behav Anal.1985;18:269-272.Google Scholar 47.Jason LA, Johnson SZ, Jurs A. Reducing children's television viewing with an inexpensive lock.
Child Fam Behav Ther.1993;15:45-54.Google Scholar 48.Bracht GH, Glass GV. The external validity of experiments.
Am Educ Res J.1968;5:437-474.Google Scholar 49.Cook TD, Campbell DT. Quasi-Experimentation: Design & Analysis Issues
for Field Settings. Boston, Mass: Houghton Mifflin Co; 1979.
50.Taras HL, Sallis JF, Patterson TL, Nader PR, Nelson JA. Television's influence on children's diet and physical activity.
J Dev Behav Pediatr.1989;10:176-180.Google Scholar 51.Gorn GJ, Goldberg ME. Behavioral evidence for the effects of televised food messages on children.
J Consumer Res.1982;9:200-205.Google Scholar 52.Jeffrey DB, McLellarn RW, Fox DT. The development of children's eating habits: the role of
television commercials.
Health Educ Q.1982;9:78-93.Google Scholar 53.Rose G. Strategies of prevention: the individual and the population. In: Marmot M, Elliott P, eds. Coronary Heart Disease
Epidemiology: From Aetiology to Public Health. Oxford, England: Oxford
University Press; 1992.