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* Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
Departments of Nutrition
Epidemiology, Harvard School of Public Health, Boston, Massachusetts
|| Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Design. Cohort study using data from 2 mailed questionnaires, 1 year apart.
Participants. A total of 11 887 boys and girls, aged 10 to 15, who returned questionnaires in both 1997 and 1998 as part of the Growing Up Today Study.
Outcome Measure. Change in BMI from 1997–1998, accounting for increases in BMI associated with growth and development.
Results. An increase in physical activity from 1997–1998 was associated with decreasing relative BMI in girls (-0.06 kg/m2 per hour increase in daily activity; 95% confidence interval [CI]: -0.11, -0.01) and in overweight boys (-0.22 kg/m2; CI: -0.33, -0.10). An increase in inactivity was associated with increasing BMI in girls (+0.05 kg/m2 per hour increase in daily TV/videos/video games; CI: +0.02, +0.08). Effects were generally stronger among overweight children. Increasing time doing aerobics/dancing and walking were associated with BMI declines for some groups of children.
Conclusions. Many children may benefit by increasing their physical activity and by reducing time watching TV or videos and playing video games. In particular, 2 activities accessible to most children, aerobics/dancing and walking, also appeared beneficial.
Key Words: physical activity • strength training • aerobics • dancing • walking • seasonal activity • inactivity • television • videos • computer games • video games • BMI • adiposity • overweight • obesity • preadolescent • adolescent • children • growth • weight change • weight loss • longitudinal
Abbreviations: TV, television • BMI, body mass index • SD, standard deviation • FFQ, food frequency questionnaire • MET, metabolic equivalent • CI, confidence interval
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Childhood and adolescent obesity have been linked to higher all-cause mortality in adulthood, as well as childhood morbidity (diabetes, sleep disorders, asthma) and cardiovascular risk factors.3,7–19 Obesity also has psychosocial effects and economic costs, for the individual and society.20–23
Because obesity as well as behaviors that may be causally related to weight gain track into adulthood, it is important to assess the effects of modifiable behaviors on adolescent adiposity. Numerous cross-sectional studies have reported associations between measures of adiposity in children and levels of physical activity and inactivity, particularly television (TV) viewing.2,24–31 Furthermore, a recent longitudinal study by our group provided evidence that 1-year changes in body mass index (BMI) were linked to levels of activity and inactivity over the same time period,32 but comparable year-to-year data were not yet available for estimating changes in activity and changes in inactivity. There are few studies that address whether children who modify their personal behaviors will experience changes in adiposity. In a recent review article, Fulton and co-authors stated that "To achieve the best weight loss, we need to know whether increased activity or decreased inactivity achieves the best long-term results, and what physical activity type, intensity, duration and choice of activity we should promote."12 Published controlled trials demonstrated that school-based interventions may be effective in reducing TV time and increasing physical activity in children,19,33,34 thus reducing body fat.35–37 In a study of young adults, aged 18 to 30 at baseline, increasing physical activity over a 10-year period was associated with decreasing body weight.38 But there are no large nationwide longitudinal studies of youth that consider personal changes in physical activity (including season-specific changes), changes in inactivity, and also changes in particular types of activities and inactivities, and the corresponding change in BMI. Such epidemiologic studies, though not providing conclusive evidence of causation, would provide stronger evidence for causes of weight gain or loss than studies that do not analyze within-child changes in activity and inactivity.
Using data from the longitudinal Growing Up Today Study of over 10 000 US children, aged 10 through 15 in the fall of 1997, we examined the relationships between 1-year changes in total physical activity, season-specific activity, total inactivity, certain activities and inactivities separately, and concurrent changes in BMI. We looked at these relationships separately for children who were overweight versus more lean.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Measures
BMI
Children self-reported their height and weight annually on our questionnaire, which provided specific measuring instructions but suggested they ask someone to help; because their mothers are nurses, assistance is available. A previous study reported high validity for self-reported heights and weights in adolescent children.40 We assessed adiposity by computing BMI (BMI = weight/height2 [kg/m2]). The International Obesity Task Force supports the use of BMI to assess fatness in children and adolescents41; childhood BMI is related to other measures of adiposity that were not feasible to obtain on our cohort.42 A recent study43 supported the validity of BMI computed from self-reported height and weight, with a correlation of 0.92 between BMI computed from children’s (grades 7–12) self-reports and measured values. We estimated the 1-year change in adiposity by BMI1998-BMI1997, adjusting for the exact time interval between the child’s 2 surveys.
Before computing BMI, we excluded any height that was >3 standard deviations (SDs) beyond the gender- and age-specific mean height (1997: 16 girls, 16 boys; 1998: 34 girls, 19 boys), and any 1-year (1997–1998) height change that declined by >1 inch or increased by 9 inches or more (mean change +3 SDs; 121 girls, 63 boys; their BMI changes were also excluded). We further excluded any BMI < 12.0 kg/m2 as a biological lower limit (clinical opinion), and any BMI 3 SDs beyond the gender and age specific mean (in log[BMI] scale; 1997: 53 girls, 45 boys; 1998: 61 girls, 40 boys). Because all BMI changes, computed from the remaining data, represented realistic changes over 1 year, there were no additional exclusions.
We grouped children, based on 1997 BMI and age (months), into those above and below the gender-age-specific 85th percentile on the Centers for Disease Control and Prevention BMI charts;44 children above the 85th percentile are at risk of overweight (85th–95th percentile) or are overweight (>95th percentile). For simplicity, we refer to all children whose 1997 BMI exceeded the 85th percentile as overweight, and those below as not overweight.
Physical Activity
We developed a physical activity questionnaire for youth that asked them to recall the typical number of hours per week, within each season over the past year, during which they participated in 17 activities and sports outside of gym class. Response categories ranged from zero to 10+ hours/week. We computed each child’s typical hours/day for each season and over the entire year. Assessments of an earlier nonseasonal version of this instrument (which we used in 1996) found that estimates of total physical activity were moderately reproducible and reasonably correlated with cardiorespiratory fitness, thus providing evidence of validity.45 Another validation study reported a correlation of r = 0.80 between survey self-reports and 24-hour recalls.37 We developed the seasonal version, which appeared on both 1997 and 1998 questionnaires, to further improve reliability and validity.46
Estimates of total physical activity that exceeded 40 hours/week (1997: 201 girls, 275 boys; 1998: 151 girls, 192 boys) were deemed unreliable and thus excluded.
We estimated each child’s change in total physical activity by subtracting 1997 activity from 1998 activity, and we similarly computed change in 3 particular activities (aerobics/dancing, strength training, walking) that were chosen a priori, and change in season-specific total activity.
We were unable to ascertain details of varying intensity (within sport), or duration per session of activity, which may be related to obesity.47 However, we assigned a metabolic equivalent (MET), based on a compendium by Ainsworth et al,48 to individual sports/activities and estimated total METs per day for each child, and then computed change in METs from 1997–1998.
Children also reported the number of gym (physical education) classes they participated in weekly at school, and we computed 1-year changes.
Inactivity
A series of questions were designed to measure weekly hours of recreational inactivity: ‘watching TV,’ ‘watching videos or VCR,’ and ‘Nintendo/Sega/computer games (not homework).’ For each of these, children selected their usual number of hours, separate for weekdays and for weekends, from options ranging from 0 to 31+ hours. Gortmaker and colleagues37 reported moderate validity for recalled total inactivity from a similar instrument. We excluded totals exceeding 80 hours/week (1997: 30 girls, 82 boys; 1998: 24 girls, 88 boys) before computing the 1-year change in inactivity.
Adjustment Factors
At baseline children reported their race/ethnic group by marking all that applied (6 options). We assigned each child to a race/ethnic group following US Census definitions, except Asians remained separate rather than pooled with ’Other.’1,49 Each year children reported their Tanner maturation stage (a validated self-rating50 of sexual maturity with 5 stages of pubic hair development), and girls reported whether/when their menstrual periods began. We derived a 3-group menstrual history variable: premenarche in both 1997 and 1998, periods began between 1997 and 1998, and postmenarche in 1997.
We designed a self-administered semiquantitative food frequency questionnaire (FFQ),51 specifically for older children and adolescents, which is inexpensive and easy to administer to large populations and has been shown to be valid52 and reproducible.51 (see Berkey et al32 for more details.) We excluded energy intakes <500 kcal/day or >5000 kcal/day (1998: 32 girls, 27 boys). All models adjusted for past-year energy intake.
Sample for Analysis
Aside from the previously described values that we excluded, missing values were present for the outcome, 1-year change in BMI, or for 1 or more adjustment variables for some participants. We included in our multivariate models the 6171 girls and 4725 boys who provided complete data on the outcome variable and these covariates.
Statistical Analysis
To assess the potential for bias we compared the baseline (1996) values of age, BMI, activity, inactivity, and energy intake of those children who returned surveys in both 1997 and 1998 with those who did not.
To estimate the effects of 1-year changes in activity and inactivity on change in BMI from 1997–1998, we used regression models with change in BMI as the continuous outcome. All models, fit separately for boys and girls, included race/ethnic group and energy intake, and to account for changes in BMI that normally occur during growth and maturation, height growth from 1997–1998, menstrual history (girls), age, Tanner stage, and BMI in 1997.5,53–56 We fit a series of models denoted ‘S-Models,’ because each examined a single activity or inactivity variable. Each S-Model included all the adjustment factors and a single activity or inactivity change variable, along with its 1997 value. For example, one S-Model considered change in total recreational inactivity (TV/videos/video games) from 1997–1998, and adjusted for 1997 inactivity, but no activity variables were included in this particular model. A second S-Model examined change in total physical activity from 1997–1998, adjusting for 1997 activity. Three other models considered activities, selected a priori, that could be pursued by most children: aerobics/dancing, strength training (lifting weights, push-ups), and walking. Season-specific changes in activity were evaluated by four other S-Models. In each model, we considered nonlinear associations with BMI change.
We then fit the ‘J-Model,’ which included change in total activity and change in total inactivity, jointly, along with 1997 activity, 1997 inactivity, and all the adjustment factors.
Then we reestimated all S- and J-Models so that overweight and more lean children had separate estimates of the effects of each change variable.
In all regression models, we used techniques that took into account the correlations among the small numbers of siblings of the same gender.57,58
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RESULTS |
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Children who did not return both the 1997 and 1998 surveys were slightly older (girls by 0.3 years; boys by 0.4 years), and at baseline were more physically active (by 0.1 hour/day) and more inactive (0.3 hour/day; both age-adjusted; all P < .05), but they did not differ by age-adjusted BMI or total energy intake. The loss of more inactive children might remove some who later experienced declines in inactivity, thus weakening estimated associations with BMI change.
Table 1 presents gender-age-specific means for BMI, energy intake, inactivity and activity, and menarche status (girls). For boys, the daily inactivity (TV/videos/games) mean was near 3.5 hours/day for all age groups, while girls reported around 2.5 hours/day; boys spent considerably more time than girls playing video games. Mean physical activity was higher for boys than for girls. Activity means were higher for older than younger children, consistent with a lower prevalence of overweight among the older boys (27.3% of 10-year-olds were overweight vs 17.3% of 15-year-olds) and girls (17.5% vs 13.6%). Activity levels were lowest in winter.
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For girls, an increase in physical activity from 1997–1998 was associated with a relative decline in BMI (-0.055kg/m2; CI: -0.10, -0.01), and an increase in aerobics/dancing was associated with an even larger BMI decline (-0.197 kg/m2; CI: -0.364, -0.030) (Table 3). Increasing inactivity was associated with an increase in BMI (+.045 kg/m2; CI: +0.018, +0.073). For TV alone (not shown), the effects were stronger (+0.076; CI: +0.036, +0.117). Weaker evidence suggested that overweight girls who increased strength training (P = .09) and walking (P = .11) experienced declines in BMI. The strongest season-specific effects were for increasing winter physical activity among overweight girls (-0.168; CI: -0.298, -0.039).
The J-Models for girls support the independence of the activity and inactivity effects, with conclusions similar to S-Models (Table 3, bottom).
Figure 1 shows the predicted 1-year change in BMI for a child who replaces 1 hour/day of TV/videos/video games with physical activity.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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We had selected a priori 3 activities, from the many on our survey, which should be available to most children regardless of access to facilities or teams, neighborhood safety, or weather conditions. In separate analyses of these, increasing aerobics/dance was associated with BMI declines in boys and girls, but walking was accompanied by BMI declines only among overweight boys (and marginally among overweight girls), and strength training was associated (marginally) with weight loss only among overweight girls. Many overweight children may not enjoy competitive sports, so these may be appealing alternatives, though few boys participate in aerobics/dance. Strength training had no effect on overweight boys and actually increased BMI in leaner boys, illustrating a limitation of BMI as a measure of adiposity.
Season-specific analyses revealed that increasing winter activity was strongly associated with BMI declines among overweight girls. Because activity levels were much lower during the winter for both genders, we should promote ways for children to maintain activity levels throughout the year.
Although the magnitudes of our estimated effects on BMI change were small, if repeated year after year they would cumulate to effects which are clinically meaningful. For example, the effects for overweight boys and girls in Fig 1 would cumulate to a relative loss of 1.0 kg/m2 after 4 years.
A major strength of our analysis was the longitudinal design, which allowed us to study changes over time in physical activity, in inactivity, and in BMI, while accounting for growth and maturation. Although our observational study cannot prove causation, evidence which links change in physical activity or change in inactivity to change in BMI is stronger than evidence from cross-sectional studies. However, residual and unmeasured confounding are still possible despite our extensive control for many important covariates. Another limitation of our study was the necessity to collect data on youth by self-report on mailed surveys, but with our large geographically-dispersed cohort alternatives were not feasible. No doubt there was considerable measurement error in these self-reported data, but the impact of random errors should be to bias estimates of effects toward the null, explaining why many of our estimates, though statistically significant, were quite small. Although our assessment of adiposity, using only height and weight, was crude, more reliable body composition techniques47 are not feasible for large epidemiologic studies. Detailed studies of indices that use weight and height suggested that BMI is the better measure of adiposity at all childhood ages.42,59–63 Although self-reported BMI has been validated among adolescents,43 we are not aware of any validation studies of change over time in self-reported BMI. Another limitation is that no one, to our knowledge, has studied reliability/validity of change over time in self-reported physical activity or inactivity.
Our longitudinal findings are consistent with a large body of cross-sectional evidence that among children and adolescents, physical activity and inactivity (particularly TV viewing) are associated with adiposity,2,24,25,27–31,64–69 although the TV association has not been confirmed by all studies.70 Data collection in some of these studies occurred before the arrival of VCRs, DVDs, and home computer/video games, which now contribute substantially to children’s lifestyles and which we did measure in our cohort. A recent study showed that computer use may be displacing leisure time physical activity among college students;71 our measure of inactivity included computer games but not other types of computer use.
School-based interventions have demonstrated some success in reducing TV time, increasing physical activity19,33–34 and reducing body fat.35–37 An intervention study of diet and sports in obese children,72 a longitudinal study of physical activity in preschool children,26 a study of aerobic fitness among 4- to 11-year-olds,73 and a randomized, controlled trial of obese children74 all provided evidence linking activity and inactivity with changes in body fatness. Among young adults, aged 18 to 30, followed 10 years by the CARDIA study, change in physical activity was inversely associated with change in body weight and, also similar to our findings, the effects were stronger among participants who were overweight at baseline.38
Our previous longitudinal study estimated the effects of activity and inactivity levels on BMI changes over 1 year.32 The present study goes beyond this, assessing the effects of changing activity and changing inactivity in this large nationwide free-living cohort of 10- to 15-year-old children. Thus, the benefits demonstrated in trials can be obtained in older children outside the context of a school-based program or intervention trial.
Although we cannot claim that our cohort of children of nurses is representative of US children, the associations among factors within our cohort should still be internally valid. We compared our data with national data to see how similar our cohort was on several measures. Our boys and girls had age-specific mean BMIs that were, as expected, 
0.5kg/m2 higher than mean BMIs of US children in earlier decades.75 In contrast, our cohort reported lower levels of TV viewing than national averages;24 43% of our girls and 55% of our boys reported 2 or more hours/day watching television or videos, compared with 67% in a report using 1988–1994 data from the Third National Health and Nutrition Examination Survey.24 We suspect, although we have no data available to test it, that many adolescents have replaced some TV viewing time with e-mail or instant-messaging on the Internet, which has become very popular among this age group just since 1995. Finally, gym glass participation in our cohort in 1997 was similar to Youth Risk Behavior Survey national data collected the same year.76
The Council on Scientific Affairs of the American Medical Association supports prevention of obesity as critical for adults, and for weight loss they support a nutritionally balanced, low-energy diet while increasing energy expenditure through regular physical exercise.77 Similar recommendations have been made for children.78 A recent review of studies of weight loss and prevention of abnormal weight gain in youth did note some strategies that were successful.12 Modifying dietary, activity, and inactivity patterns is difficult after lifelong habits are established, and thus changing these habits during adolescence before they become ingrained is critical. Thus, we need to know more about determinants of activity and inactivity in older children and what behavior change strategies will succeed in this age group. The encouragement of moderate and vigorous physical activity by families and schools, to achieve and maintain aerobic fitness in the preadolescent years, could help prevent the development of obesity.73 Our study provides evidence that, in older children and adolescents, increasing recreational physical activity and reducing time watching television/videos may be accompanied by reductions in adiposity. Because most physical activity in youth takes place in organized programs outside of school,28,76 increasing physical activity and reducing inactivity in children will likely require multiple approaches, including policy changes, environmental planning and educational efforts in schools and communities.79–81
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ACKNOWLEDGMENTS |
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Members of the Growing Up Today Study research group who provided ongoing assistance include Karen Corsano, Gideon Aweh, and Gary Chase. We are also grateful to Diane Sredl for technical assistance, and to the children (and their mothers) for careful completion of the questionnaires.
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FOOTNOTES |
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Reprint requests to (C.S.B.) Channing Laboratory, 181 Longwood Ave, Boston, MA 02115. E-mail: catherine.berkey{at}channing.harvard.edu
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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