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PEDIATRICS Vol. 110 No. 2 August 2002, pp. 307-314

Assessing Risk Factors for Obesity Between Childhood and Adolescence: II. Energy Metabolism and Physical Activity

Arline D. Salbe, PhD*, Christian Weyer, MD*, Inge Harper, BS*, Robert S. Lindsay, MD{ddagger}, Eric Ravussin, PhD§ and P. Antonio Tataranni, MD*

* Clinical Diabetes and Nutrition Section
{ddagger} Diabetes and Arthritis Epidemiology Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
§ Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Objective. To assess the effect of energy expenditure, including resting metabolic rate (RMR), total energy expenditure (TEE), and activity energy expenditure (AEE), as well as substrate oxidation (respiratory quotient [RQ]), on the development of obesity in a large cohort of Native American children with a high propensity for obesity.

Methods. During the summer months of 1992 to 1995 and again 5 years later, 138 (65 boys and 73 girls) 5-year-old Pima Indian children were studied. At baseline and follow-up, height and weight were measured; body composition was assessed with the use of 18O dilution; RMR and RQ were assessed with the use of indirect calorimetry; TEE was measured with the use of the doubly-labeled water method; and AEE was calculated (TEE – [RMR + 0.1 x TEE]). In addition, an activity questionnaire was used to assess participation in sporting activities as well as television viewing during the previous year. Linear regression models were used to assess the effects of the baseline variables on the development of obesity.

Results. Pima Indian children were markedly overweight at both 5 and 10 years of age. Cross-sectionally, percentage of body fat and body weight at 5 and 10 years of age were negatively correlated with sports participation and positively correlated with television viewing. Most important, there was a marked change in the correlation between body size and activity between 5 and 10 years of age: at age 5 years, weight was positively correlated with AEE and PAL, but at age 10 years, the correlation with AEE was lost and that with PAL was negative. However, prospectively, none of the variables measured at baseline was a predictor of percentage of body fat at age 10 years after adjustment for percentage of body fat at age 5 years.

Conclusions. At age 5 years, obesity is associated with decreased participation in sports and increased television viewing but not with a decreased PAL. At age 10 years, obesity is associated with decreased participation in sports, increased television viewing, and a decreased PAL, suggesting that a decrease in PAL in free-living conditions seems to follow, not precede, the development of obesity.

Key Words: childhood obesity • growth and development • energy metabolism • physical activity • Pima Indians

Abbreviations: RQ, respiratory quotient • DEXA, dual-energy x-ray absorptiometry • TEE, total energy expenditure • RMR, resting metabolic rate • AEE, activity energy expenditure • PAL, physical activity level • BMI, body mass index


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
The prevalence of obesity has increased in all segments of the population in recent years, both in the United States and worldwide.1,2 For developing effective prevention strategies, it is important to understand the causes and mechanisms of weight gain. Obesity results from a chronic energy imbalance such that the rate of energy intake exceeds the rate of energy expenditure.3 Evidence seems to suggest that in the past century, food intake, especially high-fat food intake, has increased,4,5 although the results are conflicting.6 At the same time, occupational energy expenditure has declined and household chores have become increasingly automated, resulting in sedentary lifestyles.5,7,8 Compensating for this energy expenditure deficit with leisure time physical activity has been unsuccessful.9

The pathophysiological importance of increased energy intake and decreased energy expenditure to the problem of obesity is difficult to assess. In recent years, it has become feasible to assess energy expenditure in free-living conditions,10–12 but energy intake remains difficult to measure because estimations of food intake on both the individual13,14 and population15,16 levels generally are inaccurate. In addition, many studies of energy metabolism in obesity are cross-sectional in nature and, therefore, by design unable to distinguish causal relationships.

The Pima Indians of Arizona are a population uniquely prone to the development of obesity.17 Previous prospective studies in adult Pima Indians have shown that a relatively low resting metabolic rate,18 a high respiratory quotient (RQ) indicating a low ratio of fat-to-carbohydrate oxidation,19 low levels of spontaneous physical activity, 20 and low sympathetic nervous system activity21 all are predictive of weight gain in adult Pima Indians. Prospective studies in several22–26 but not all27–29 other populations have shown that relatively low levels of energy expenditure and a relatively high RQ predict weight gain.

Because many Pima Indians become obese well before reaching adulthood, 30 we conducted a longitudinal study in Pima Indian children. Energy expenditure, physical activity, and substrate oxidation were measured in a large cohort of 5-year-old Pima Indian children who were then followed over 5 years. The aims of the study were 1) to assess total energy expenditure, resting metabolic rate, physical activity level, and the RQ at 5 and 10 years of age; 2) to examine metabolic predictors of obesity between childhood and the onset of adolescence; and 3) to determine whether these predictive characteristics track in a longitudinal manner.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 
Subjects
During the summer months of 1992 to 1995 (baseline) and again 5 years later (follow-up), 176 Pima Indian 5-year-old children were studied. Previous partial reports on low levels of physical activity in 5-year-old children31 and the association between leptin concentrations and energy expenditure32 contained some of the data presented here. Briefly, children were studied at the National Institutes of Health Field Clinic located in the Gila River Indian Community in Sacaton, Arizona, approximately 40 miles southeast of Phoenix. Pima Indian children were of full Indian and at least 75% Pima-Papago heritage. Children arrived at the clinic at 8:00 AM in the fasting state, accompanied by 1 of their parents, and their health status was determined by medical history and physical examination. Studies were conducted in healthy children after a 10-hour overnight fast on 2 occasions, 1 week apart, for approximately 6 hours on the first occasion and subsequently for approximately 3 hours between 8:00 AM and 2:00 PM. Because diabetes during pregnancy is known to affect the risk of obesity in the offspring,33 children whose mothers were known to have had diabetes before or during the pregnancy of interest were excluded from the analysis, reducing the number of children in this report to 138. Before participation, volunteers and their parents were fully informed of the nature and purpose of the study and written informed consent/assent was obtained. The experimental protocol was approved by the Institutional Review Board of the National Institute of Diabetes and Digestive and Kidney Diseases and by the Tribal Council of the Gila River Indian Community.

Anthropometry
Anthropometric measurements on all 5-year-old children and 53 10-year-old children were performed during 2 clinic admissions, 1 week apart, and results represent the means of the 2 measurements. Because of the worldwide shortage of doubly-labeled water,34 only 53 of the 138 10-year-old children were able to participate in the follow-up doubly-labeled water measurements. As a result, anthropometric assessments on the remaining 85 10-year-old children were made on just 1 occasion. Height was measured without shoes. Body weight was measured while the children were wearing light summer clothing.

Body Composition
Body water, calculated from 18O dilution spaces, was used to assess body composition in all 5-year-old children and 53 10-year-old children with the assumption that water is 75% of the fat-free mass in girls and 74% in boys.35 At follow-up, body composition was measured by 18O dilution in 53 of the 10-year-old children; in the 85 remaining children, body composition was determined using dual-energy x-ray absorptiometry (DEXA) as previously described.36 The following regression equation, developed using the percentage of body fat values of 64 10-year-old children who had both DEXA and 18O measurements, was used to convert percentage of body fat measured by DEXA to percentage of fat measured by 18O when the latter measurements were lacking: %Body Fat 18O = 0.835 x %Body Fat DEXA + 9.13 (R2 = 0.95, standard error of the estimate = 2.1%, P < .0001).

Total Energy Expenditure
Total energy expenditure (TEE) was measured using the doubly-labeled water method. Subjects were asked to provide 1 baseline urine sample collected at home the evening before the test. On arrival at the clinic on the first study day, a second baseline urine sample was collected before children were dosed with doubly-labeled water as previously described.37 Briefly, the dose contained 0.132 g of 100% 2H2O/kg total body weight and 2.508 g of 10% H218O/kg total body weight (Isotec, Inc, Miamisburg, OH). The dosing container was rinsed with 100 mL of tap water, which was given to the children to ensure complete consumption of labeled water. Complete urine collections were made at approximately 1.5, 2.5, 3.5, and 4.5 hours after dosing on day 1 and twice during a 3-hour period 7 days later. The sample collected at 1.5 hours was discarded. Isotopic enrichment of the 2 baseline urine samples was averaged to provide 1 baseline value. The disappearance rates of the 2 stable isotopes were determined as previously described,38 using a food quotient of 0.866 to determine CO2 production. This assessment was made in all 5-year-old children and in 53 children at follow-up.

Resting Metabolic Rate and RQ
After ingesting the doubly-labeled water, the children rested comfortably on a bed for 10 minutes, after which the resting metabolic rate (RMR) was measured for 20 minutes using a DeltaTrac Metabolic Monitor (SensorMedics Corp, Yorba Linda, CA) as previously described.39 Children were carefully instructed about the testing procedure before the measurement began. During the time, a parent was nearby completing a detailed activity questionnaire, the child was allowed to watch a nonviolent cartoon video during the measurement. The RMR measurement was repeated at the return visit 1 week later, and the results of the 2 measurements were averaged to obtain the mean of the 2 values for all 5-year-old children and 53 children at follow-up. In the remaining 85 children at follow-up, the RMR measurement was made only once but was conducted for a 25-minute period.

Carbon dioxide production and oxygen consumption were calculated for each minute of the 20- 25-minute period during which the RMR was measured, and all values were averaged to obtain the means. The fasting RQ was calculated as the ratio of mean carbon dioxide production and mean oxygen consumption.

Physical Activity
Energy expended in physical activity was estimated by difference as follows: activity energy expenditure (AEE) = TEE – (RMR + 0.1 x TEE) where 0.1 x TEE represents an estimate of the thermic effect of food.40 The following indices of physical activity were also calculated: 1) the physical activity level (PAL) as the ratio of TEE to RMR and 2) the adjusted PAL as TEE adjusted for RMR and gender using linear regression analysis.

Physical Activity Questionnaire
A physical activity questionnaire, completed by the parent, was used to assess average hours per week during the past year in which the child was typically engaged in sports and recreational activities that required a greater expenditure of energy than that normally needed for daily grooming, bathing, and eating.41,42 Additional questions assessed the number of hours that children engaged in sedentary activities such as napping, sleeping, watching television, and playing computer and video games.

Statistical Methods
All statistical analyses were performed using software of the SAS Institute (Cary, NC). Throughout the text, the data are expressed as means ± standard deviation. However, the median and 50% confidence intervals are given for variables derived from the physical activity questionnaire because of the skewed nature of the results.

Spearman rank correlation coefficients were used to quantify the relationships among the variables of interest and to assess the tracking of the energy expenditure variables. Linear regression models were used to assess the effect of baseline variables on the development of obesity; percentage of body fat at age 10 years was used as the dependent variable and percentage of body fat at age 5 years was used as the independent variable in all models. Because the children were growing at the same time as obesity was developing and because weight has been used as an indicator of growth and/or obesity, models were also developed in which body weight at age 10 years was used as the dependent variable and body weight at age 5 years was used as the independent variable.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 CONCLUSION
 REFERENCES
 
Cross-Sectional Analysis: Characteristics at Baseline and Follow-up
The physical characteristics of the children at 5 and 10 years of age are shown in Table 1. By the age of 10 years, Pima Indian children had more than doubled their weight and tripled their fat mass. There were no significant gender differences in body weight or body mass index (BMI) at 5 or 10 years of age, and there was no gender difference in the change in weight (data not shown).


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  		TABLE 1. Physical Characteristics of Pima Indian Children at 5 and 10 Years of Age (N = 138; 65 Boys and 73 Girls)

 
Childhood energy expenditure (Table 2), as TEE, RMR, or AEE, was similar in value to previously published reports.37,43 The mean physical activity level (PAL, TEE:RMR) of 1.36 at age 5 years was also similar to previously published values.37 At age 10 years, TEE had increased by 60% and RMR had increased by 40%. In contrast, AEE increased 150%, and the PAL increased to a mean of 1.57. There were no gender differences in any of the energy expenditure parameters at either age point.


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  		TABLE 2. Significance of the Factors Used to Adjust Energy Expenditure Variables

 
Table 2 indicates that at both 5 and 10 years of age, the major anthropometric factors associated with RMR were fat-free mass, fat mass, and gender (all P = .0001). At 5 and 10 years of age, fat-free mass (P = .0001, P = .0003, respectively) and gender (both P = .01) were most significantly associated with TEE; at 10 years of age, fat mass was also associated (P = .01). At 5 years of age, fat-free mass (P = .0001) and fat mass (P = .005) were significantly associated with AEE, with fat mass having a negative effect. At 10 years of age, neither fat mass nor fat-free mass was significantly associated with AEE.

At 5 years of age, there was a positive relationship between PAL and body weight (r = 0.19, P = .03) but not with percentage of body fat (r = –0.05, P = .55). At age 10 years, however, there was an inverse relationship between PAL and body weight (r = –0.37, P = .006) as well as percentage of body fat (r = –0.28, P = .05).

Table 3 shows the correlations between the questionnaire variables and anthropometric characteristics of the children at baseline and follow-up. At both 5 and 10 years of age, the number of sporting activities was negatively correlated with and the number of hours of television viewing was positively correlated with percentage of body fat and body weight at the respective age. There were no gender differences in any of the activity questionnaire variables at baseline (data not shown); however, at follow-up, girls were reported to have spent less time participating in sports than boys (10.3 h/wk vs 14.5 h/wk).


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  		TABLE 3. Correlations Between Activity Questionnaire and Anthropometric Variables at Baseline and Follow-up

 
Surprising is that there were no significant associations between the activity questionnaire and energy expenditure variables at 5 or 10 years of age. This might have resulted from a reporting error in gathering the questionnaire data, although previous reports suggest that the 2 variables may be assessing different aspects of activity.44 At 10 years of age, there was a trend toward a significant correlation (r = 0.23, P = .10) between PAL and hours reportedly spent participating in sports.

Longitudinal Results: Tracking of Energy Expenditure and Physical Activity
The recently published National Center for Health Statistics growth charts45 established the following age- and gender-adjusted criteria for overweight in children and adolescents: BMI <85th percentile is considered to be within the normal range, 95th > BMI ≥ 85th percentile is considered "at risk of becoming overweight," and BMI ≥95th percentile is considered overweight. With the use of these criteria, the children in the present cohort were classified into 3 groups: 1) low-risk children (n = 48), including those whose BMI stayed within the normal range (n = 43) at both 5 and 10 years of age, those who stayed in the "at risk" category (n = 2) at both 5 and 10 years of age, and those whose BMI classification improved between baseline and follow-up (n = 3) from the overweight to the "at risk" category; 2) gainers (n = 54), including those whose BMI was within the normal range at 5 years of age but who became at risk of overweight (n = 17) or overweight (n = 19) at 10 years of age, and those who were considered "at risk" at 5 years of age and became overweight at 10 years of age (n = 18); and 3) high-risk children (n = 36), those who were already considered overweight at baseline and remained so at follow-up. Figure 1 shows the longitudinal relationships of the energy expenditure variables among these 3 groups of children.


Figure 1
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  		Fig 1. Longitudinal relationships of the energy expenditure variables at 5 and 10 years of age in low-risk (green) (n = 48), gainers (blue) (n = 54), and high-risk (red) (n = 36) Pima Indian children. The regression line represents the unadjusted energy expenditure values for all 5- and 10-year-old children.

 
The responses to the activity questionnaires were correlated at baseline and follow-up, including the number of sports in which the children participated (r = 0.32, P = .0004), the number of hours they were engaged in sports activities (r = 0.33, P = .0003), and the number of hours they spent watching television (r = 0.22, P = .01).

Prospective Analysis
There was a minimal but significant positive relationship between energy expenditure and physical activity at baseline and percentage of body fat and body weight at follow-up (Table 4). This effect was small, but in all cases, the addition of the unadjusted baseline energy expenditure characteristic to the models predicting percentage of body fat increased the explained variance of the model (R2) by approximately 1% to 5%. The results for the model explaining follow-up body weight were somewhat less robust (Table 4).


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  		TABLE 4. Predictive Effect of Baseline Energy Expenditure, Physical Activity, and Substrate Oxidation on Adiposity and Body Weight

 
In contrast to the energy expenditure and physical activity results, the number of recreational activities in which the children were said to have participated at baseline was negatively correlated with percentage of body fat and body weight at follow-up (r = –0.23, P = .01; r = –0.26, P = .004, respectively), whereas the number of hours spent watching television was positively correlated with follow-up percentage of body fat and body weight (r = 0.09, P = .31; r = 0.17, P = .04, respectively). However, in the regression models that included baseline percentage of body fat or body weight, the activity questionnaire variables were not significant predictors of adiposity or weight.

There was a trend toward a positive association between the fasting respiratory quotient at baseline and percentage of body fat at follow-up; however, this relationship did not reach statistical significance (Table 4).

Multivariate Prediction Models
Results of the stepwise multiple linear regression models are shown in Table 5. The major determinants of percentage of body fat at age 10 years were percentage of body fat and TEE at age 5 years, explaining 55% of the variance. Baseline body weight and gender explained 74% of the variance in body weight at 10 years of age. None of the questionnaire variables was significant in these models. These results indicate that prospective analyses perform better with weight than with percentage of body fat. This is most likely because weight incorporates factors such as height, for which we found a correlation coefficient of 0.85 between measurements at 5 and 10 years of age.


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  		TABLE 5. Stepwise Linear Regression Models Predicting Obesity in Children at Age 10 Years

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
CONCLUSION
 REFERENCES
 
We previously reported that decreased energy expenditure and an increased RQ predict weight gain in Pima Indian adults.18,19 The results of this longitudinal study of obesity in young Pima Indian children indicate that neither reduced energy metabolism, assessed as RMR, TEE, or AEE, nor a low rate of fat oxidation measured at 5 years of age predicts the development of obesity at 10 years of age. On the contrary, although weight and adiposity at 5 years of age were the most important predictors of weight and adiposity at age 10 years, there was actually a positive relationship between baseline TEE and adiposity. Although these results are in contrast to our previously published findings in Pima Indian adults,18,19 they agree with previous longitudinal observations in children.43,46 Despite the lack of a predictive effect of decreased energy expenditure on the development of adiposity, however, the longitudinal relationships between the indices of physical activity at baseline and follow-up suggest that in this high-risk population, children who do not increase their physical activity as their weight increases have a greater risk of developing obesity.

Cross-Sectional Findings
In the United States, the estimated prevalence of obesity (BMI ≥95th percentile) in 5-year-old children was approximately 5% in boys, approximately 11% in girls,47 and approximately 13% in all children aged 6 to 11 years48 during the period corresponding to that of the current study. With the same criteria, the prevalence of obesity in these 5- and 10-year-old Pima Indian children was 28% and 53%, respectively (see also reference 49). Our present results indicate that Pima Indian children do not have gross abnormalities in energy expenditure or substrate oxidation that could account for this marked propensity for obesity. Measured RMR in these children was similar to results obtained using the gender-specific, weight-based Food and Agriculture Organization/World Health Organization equations.50 TEE was similar to previous reports from this37 and other laboratories.43 Although PAL was somewhat lower than the Food and Agriculture Organization/World Health Organization standard in 5-year-old children,50 at 10 years of age, PAL was appropriate according to that standard. Finally, the associations between the anthropometric characteristics and TEE and RMR in this study are similar to those previously reported in other pediatric populations51 and also to those reported in Pima Indian adults.52

At age 5 years, fat mass was a negative determinant of AEE, suggesting that children who were more obese expended less energy in physical activity than those who were thin. To our knowledge, this observation has not been previously reported; however, it agrees with the observation that weight and percentage of body fat at age 5 years were both negatively associated with the number of sporting activities and positively associated with the number of hours of television viewing in which the children were said to have engaged during the past year. Many cross-sectional studies have reported decreased physical activity and increased television viewing in obese children.53–57 Although we were unable to find a correlation between AEE and the activity questionnaire variables, both seem similarly linked to adiposity.

Gender was a significant determinant of TEE and RMR, with boys having higher rates than girls; however, there was no gender effect on AEE. A decline in physical activity in girls just before the onset of puberty has been reported.57 Although pubertal development was not assessed in this cohort, it is likely that a number of these children, especially the girls, were pubertal at the time of study. In agreement with this observation, 10-year-old girls in the present study were reported to have spent less time in sports activities than 10-year-old boys.

Prospective Findings
The data relating energy expenditure and obesity in children are somewhat inconclusive. Cross-sectional studies have found a relatively low RMR both in preadolescent girls at high risk of obesity58 and in black compared with white children.59,60 In a previous report from this laboratory, however, there were no differences in relative energy expenditure assessed as TEE, RMR, AEE, or PAL between high-risk Pima Indian and white children at 5 years of age.31 TEE in infants aged 3 months was found to affect weight at 1 year of age25; however, these results were not confirmed by others.27,28 Goran et al43 found that although neither low TEE nor low AEE predicted fat gain in children, 43 measures of physical inactivity44 as well as low aerobic capacity did.46 The results of the current study agree with those findings. Although differences in energy expenditure in our 5-year-old Pima Indian children may have been influenced by the large number of children already obese at that age, the children in the Goran cohort were not as obese. Identifying metabolic predictors of obesity may be a more difficult task in rapidly growing children than in adults, in whom weight gain generally is associated with increasing adiposity but not growth.

Tracking of Variables
Perhaps the most important observation of the present study is the dramatic change in the correlation of body size and activity between 5 and 10 years of age. Although the adjusted energy expenditure variables tracked between baseline and follow-up (RMR: r = 0.34, P = .007; TEE: r = 0.46, P = .003; PAL: r = 0.34, P = .008), when children were classified as being at low risk, gainers, or at high risk for the development of obesity, these relationships took on greater significance (Fig 1). Regardless of risk factor category, the longitudinal changes in RMR for all children fell along the cross-sectional prediction line (Fig 1A). Thus, unlike Pima Indian adults,61 there was no evidence of resting metabolic adaptation to weight gain in these Pima Indian children. At 10 years of age, however, low-risk children had TEE values above the regression line compared with high-risk children (Fig 1B). Most important, both indices of physical activity, AEE (Fig 1C) and PAL (Fig 1D), increased in low-risk children with increasing weight; gainers showed no change in either variable; and high-risk children who were obese at both 5 and 10 years of age fell below the regression line for each variable at both time points. One could argue, therefore, that in fact there is metabolic adaptation but that this includes TEE and PAL rather than RMR. Moreover, metabolic adaptation seems to occur in those who avoid becoming obese (ie, low-risk children). Even in the absence of a predictive effect of low energy expenditure on the risk of weight gain, these results are consistent with the notion that increased physical activity may prevent obesity.

A comparison of the anthropometric characteristics of children whose weight was within normal limits at baseline indicates that there were subtle but significant differences in BMI, body weight, relative weight, and percentage of body fat already present at 5 years of age in the gainers whose risk factor status changed at 10 years of age. It seems that in these children, being at risk of overweight at age 5 years actually means that they are likely to stay at risk or become frankly overweight during this 5-year period.


   CONCLUSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
REFERENCES
 
It is not possible to conclude this article without mentioning the possibility of measurement error, which might have affected some aspects of the analysis. First, the activity questionnaires used to assess behavior during the previous year are difficult to validate and the results do not seem to be correlated to physical activity energy expenditure assessed by doubly-labeled water. Second, the associations between some of the activity variables and adiposity and body weight are inconsistent, although this might also be as a result of variability in the assessment of body fat.62 However, it is important to note that previous studies43,44 have found a similar lack of association between physical AEE and time reportedly spent in vigorous activity, leading those authors to conclude that these methods assess different aspects of energy balance44: physical AEE reflects not only time spent on activity but also intensity and efficiency. Maintaining energy balance may be more easily achieved by engaging in longer, lower intensity bouts of physical activity that avoid sedentary behavior.44

This study confirms previous prospective investigations that have shown that low rates of energy expenditure and fat oxidation in early childhood do not predict the development of obesity in children 5 years later. However, the results do indicate that differential changes in physical activity take place in children who are at different risks of developing obesity and that failure to increase physical activity in response to weight gain may promote obesity in preadolescence. Therefore, efforts to decrease physical inactivity and increase physical activity in children, such as by limiting television viewing and encouraging participation in sports, respectively, should be promoted.


   ACKNOWLEDGMENTS
 
This work could not have been completed without the help of Michael R. Milner, PAC, Frank Gucciardo, PA, Deanna Francis, DO, the staff of the NIH Field Clinic, and many NIH-supported summer interns.

We thank the members of the Gila River Indian Community for support of this study. Most of all, we thank the children and families who participated.


   FOOTNOTES
 
Received for publication Jul 31, 2001; Accepted Jan 3, 2002.

Reprint requests to (A.D.S.) NIH/NIDDK/CDNS, 4212 North 16th St, Rm 541, Phoenix, AZ 85016. E-mail: arline_salbe{at}nih.gov

Dr Weyer’s current affiliation is Amylin Pharmaceuticals, Inc, San Diego, California.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSION
 REFERENCES
 

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