1 Department of Public Health, Régie régionale de la santé et des services sociaux de Montréal-Centre, 1301, Montreal, Quebec, Canada.
2 Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
3 School of Dietetics and Human Nutrition, Macdonald Campus of McGill University, Ste-Anne de Bellevue, Quebec, Canada.
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ABSTRACT |
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adolescence; child; cohort studies; ethnic groups; income; obesity; regression analysis
Abbreviations: BMI, body mass index; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey; OR, odds ratio
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INTRODUCTION |
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Obesity is caused by a positive energy balance over long periods that is related to both genetic predisposition and environmental factors, including energy intake and energy expenditure (1315
). However, dietary fat intake has decreased substantially in both Canada and the United States in recent years, and total energy intake has remained relatively constant (16
), indicating that diet might not be a major factor in the current obesity epidemic. In contrast, technologic advances have resulted in people expending markedly less energy carrying on their daily lives (3
, 15
). Parental obesity is consistently a strong predictor of obesity in children (12
, 17
22
), but it is not clear whether this represents family eating habits, physical activity patterns, and/or genetic factors.
Previous attempts to investigate dietary factors and levels of physical activity as causes of overweight in children have been limited by methodological problems and have yielded inconsistent results. Cross-sectional studies (17, 22
29
) cannot establish temporal sequence between overweight and potential causal factors, while the few longitudinal studies of children to date (19
21
, 30
) were conducted in small, select study populations.
In this study, data from the evaluation of a school-based heart health promotion program were used to examine predictors of large increases in body mass index (BMI) in preadolescent children over 1 and 2 years. Earlier reports indicated a high prevalence of overweight in this study population from low-income, ethnically diverse neighborhoods in Montreal, Canada (22, 24
).
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MATERIALS AND METHODS |
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Procedure
Data were collected each year in two visits to each school. First, height and weight were measured according to a standardized protocol (34). To assess interrater reliability, we obtained repeat measures in a systematic one-in-10 subsample of students (n = 227) in the 1993 survey. Interrater reliabilities of 0.99 were observed for both height and weight.
During the second visit, students completed an in-class questionnaire administered in French or English, which collected data on age; gender; family composition; language(s) spoken; number of years lived in Canada; country of birth of each of the subject, mother, and father; and parents' employment status, as well as on smoking status, level of physical activity, and diet.
Measures
Level of physical activity was measured by using three indicators. First, frequency of physical activity was determined in an adaptation of a validated 7-day recall in which students checked which of 25 physical activities they had participated in on each of the preceding 7 days. The original instrument was moderately correlated with an objective activity measure (r = 0.34, p < 0.01) (35). The list of activities for our study included those most frequently engaged in by this age group during the spring months (33
). A frequency score, which ranged between 0 and 105, was computed by summing the total number of activities checked. Subjects were categorized into age- and gender-specific quintiles for analysis.
Second, participation in school sports teams was measured by: "Think about sports teams at school. Since school started last fall, you belonged to the school...cross-country ski team, basketball team, volleyball team, gymnastics team, handball team, floor hockey team, other." Students were instructed by the interviewers not to include activities that were part of their regular weekly gym class. Subjects responded yes or no to each item. "School sports teams" was categorized as "yes" if students responded yes to one or more items.
Third, participation in organized sports outside school was measured in two items by: 1) "Now think about sports teams outside of school. Since last summer, you belonged to a...basketball team, volleyball team, soccer team, gymnastics team, hockey team, football team, swimming team, baseball team, judo or karate or tai chi team, other"; and by 2) "Now think about sports or dance lessons. Since last summer, you took...swimming lessons, downhill ski lessons, hockey school, dance or ballet lessons, judo or karate or tai chi lessons, gymnastics lessons, skating lessons, other." Students responded yes or no to each item. Because sports or dance lessons are often taken in the context of team activities and because these two items were correlated (Spearman rank order correlation coefficient r = 0.43, p = 0.001), we created a single "sports outside school" variable, which was categorized as yes if students had belonged to any team and/or taken any lessons.
Two indicators of sedentary behavior were obtained by 1) "On school days, you usually watch...6 or more TV programs a day, 4 or 5 TV programs a day, 2 or 3 TV programs a day, 1 TV program a day, you don't watch TV on school days"; and 2) "Usually you play video games like Gameboy or Nintendo...everyday, a couple of times each week, hardly ever, never."
Data on diet during the previous week were collected in a 34-item food frequency questionnaire. Principal components analysis yielded a high fat/junk food factor (Cronbach's = 0.77) that comprised 10 food items/groupings and a healthy fruit and vegetable food factor (Cronbach's
= 0.61) that comprised five food items. To create the "high fat/junk food" and "healthy food" consumption scores, each item was scored as 1 (never), 2 (one or two times, once or a couple of times), or 3 (three or more times, everyday, often), according to the student's response, and items for each factor were summed. For analysis, subjects were categorized into age- and gender-specific quintiles. A separate study to validate these factors against four 24-hour diet recalls in 142 schoolchildren aged 8 and 13 years from four nonparticipating schools indicated moderate correlation between the high fat/junk food factor and grams of fat (Pearson product moment correlation coefficient r = 0.30, p = 0.0003). The healthy food factor showed moderate correlations with milligrams of folate (r = 0.39, p = 0.0001) and with milligrams of vitamin C (r = 0.32, p = 0.0001) (Gray-Donald et al., unpublished manuscript).
BMI was computed as weight (kg)/height (m)2. In the 1993 survey, which collected data on triceps skinfold thickness in addition to height and weight, the Pearson product moment correlation coefficient between triceps skinfold thickness and BMI was r = 0.83 (p 0.0001) (22
), indicating that BMI correlated well with body fatness in this study population. Students' baseline BMI was categorized on the basis of age- and gender-specific percentiles for BMI from the National Health and Nutrition Examinations Survey I and II (36
) into 85th percentile or less, 8589th percentile, or 90th percentile or more.
A 1-year change in BMI was computed as BMI at 1-year follow-up minus baseline BMI. Similarly, 2-year change in BMI was computed as BMI at the 2-year follow-up minus baseline BMI. Subjects were categorized by decile of change in BMI. "Excess weight gain" was defined as a change in BMI equal to or greater than the 90th percentile change in BMI for same-age, same-gender students in the study population.
Analysis
Data for this analysis were drawn from comparison schools only. For the 1-year follow-up, we identified all fourth- and fifth-grade students aged 912 years with 1-year follow-up data. Children with 1-year follow-up data from fourth to fifth grade and again from fifth to sixth grade were included twice in the database. For the 2-year follow-up, we identified all fourth-grade students aged 911 years with 2-year follow-up data from the 5 years of data collection.
One- and 2-year predictors of excess weight gain were identified in multiple logistic regression analysis in which the dependent variable was whether or not the subject was in the highest age- and gender-specific decile of change in BMI. Potential predictors investigated included sociodemographic characteristics, level of physical activity, frequency of television viewing, frequency of video game playing, and the diet indicators. All potential predictors associated with the dependent variable in univariate analyses were entered into the multivariate models concurrently. Both the univariate and multivariate analyses suggested that predictors differed by gender, and therefore, the analyses are presented separately for boys and girls. All analyses controlled for age at baseline (912 years), grade (4), and year of cohort (1993, 1994, 1995, 1996). "School" (116) was included in the final models to control for possible clustering related to homogeneity of students within schools. In addition, a variable to control for dependence between observations of the same subject in the 1-year follow-up database was included in the final models.
Use of the odds ratio as the parameter of interest in a prospective longitudinal study is somewhat controversial (37, 38
), in part because it could overestimate the relative risk if the outcome studied is not rare. However, because the outcome of interest in this study was binary and relatively rare (10 percent), because risk estimates and odds estimates are mathematically codependent, and because of its common usage and ease of interpretation, we report the odds ratios.
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RESULTS |
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Changes in BMI over 1 and 2 years
Mean values of height, weight, and BMI showed the expected monotonic increase (31) with age in both boys and girls (table 2). Students in the top decile of change in BMI increased 22
BMI units over 1 year compared with 1 BMI unit or less among those at the 50th percentile. Over 2 years, students in the top decile increased three to four BMI units, compared with one to two BMI units among those at the 50th percentile.
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Baseline BMI was the only consistent independent predictor of excess weight gain in all four multivariate models (table 3). Boys and girls who were obese at baseline were two to three times more likely to gain excess weight for height. Not participating in organized sports outside school was a significant predictor in two of the four models. Playing video games a couple of times or more each week was retained in the 1-year model for girls. None of the terms to test for interactions between either baseline BMI or grade and each of the other independent predictors were significant, and an age x grade interaction term was not significant in the 1-year model (analyses not shown).
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DISCUSSION |
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Only four studies, two among preadolescents and two among preschool children, have investigated physical activity and/or dietary factors longitudinally as determinants of obesity in children. Goran et al. (20) found no relation between energy expenditure and obesity in a 4-year follow-up of 75 white preadolescent children. The main predictors of change in fat mass relative to fat-free mass in this study were gender, initial fatness, and parental fatness. Similarly Maffeis et al. (19
) reported that neither physical activity nor energy and nutrient intakes significantly affected the change in BMI over 4 years in 112 prepubertal children with a mean age of 8.6 years at baseline after parental obesity was taken into account. BMI at baseline was the only variable retained in multivariate analyses.
In contrast, two longitudinal studies of younger preschool children have provided evidence for an association. Among 97 White children aged 35 years at baseline in the Framingham Children's Study, those with low levels of physical activity gained substantially more fat than did more active children as they were followed into first grade (30). Similarly Klesges et al. (21
) reported that dietary intake and physical activity accounted for more of the variance in changes in BMI than did nonmodifiable variables such as parental obesity in 146 White, middle-class, preschool children over a 3-year follow-up.
In our study, children who were most overweight at baseline gained the most weight for height at 1 and 2 years of follow-up. Because about half of obese school-aged children will be obese as adults (9), clinicians and public health practitioners must be made aware that many overweight children will not lose their overweight status through linear growth, and therefore, it is both justified and necessary to intervene at early ages.
Physical activity indicators also contributed independently to short-term excess weight gain in this study, even after baseline BMI was taken into account. Baseline BMI reflects accumulated effects of genetic and environmental factors on body weight, including past participation in physical activity. The finding that current participation in physical activity continues to be a predictor of excess weight gain, over and above accumulated effects of past participation, is important from a public health perspective and suggests that public health practitioners must actively endorse and promote programs and policies that support the practice of regular physical activity. These might include both those targeted to the environment (i.e., increasing access to recreational facilities, increasing availability of community-based sports programs, designing safe bicycle paths) and those targeted to the individual and his or her family (i.e., daily physical education classes at school, programs that promote active family lifestyles). While the diet indicators in our study were not important predictors of excess weight gain, it is possible that increasing physical activity will be more effective if combined with interventions to improve diet.
In girls, the effects of physical activity were apparent after 1, but not 2, years of follow-up, whereas in boys they became apparent only in the 2-year follow-up. Our earlier work suggests that there were marked declines in level of physical activity with age in both boys and girls in this population (33). It is possible that changing levels of physical activity over follow-up, which could not be measured by our baseline indicators, contributed to these apparently inconsistent findings. Future research should measure physical activity repeatedly and frequently during follow-up, so that changes in levels of physical activity over time can be detected and related to weight for height gain (39
).
Participation in organized sports outside school was a strong predictor of excess weight gain in both boys and girls, whereas participation in school sports was not significant. Future research should qualify differences between sports at and outside school in type of activity, in frequency and regularity of participation, and in activity time or exercise intensity, which might explain these findings. Our results suggest benefits of participating in organized sports outside school, although the appeal of these activities to, and their enjoyment by, overweight children must be investigated to assure that both overweight and normal-weight children can and do benefit.
Several studies report that increased television viewing by children is associated with increased obesity (40, 41
), possibly because television viewing is inversely associated with time spent in physical activity (42
). In our study, frequency of video game playing, but not television viewing, was associated with excess weight gain. Television viewing was not associated with level of physical activity in our data (33
), suggesting that, at least in this population, it might not be a good marker of physical inactivity. In contrast, the finding that frequent video game playing is important suggests that educational programs should inform parents and children that sedentary behaviors should be balanced by participation in physical activities that result in increased energy expenditure to reduce the risk of excess weight gain.
Limitations
Our measures of physical activity were based on self-reports by children, which are subject to measurement error related to problems with reliability, validity, and practicality of testing (23, 30
, 43
). Because of these difficulties, it has been recommended that researchers include multiple measures of physical activity in studies of children. Accordingly, we measured several indicators of both physical activity and sedentary behavior.
Similarly, our measures of high fat/junk and healthy food consumption were based on self-reports. Although moderately correlated with selected nutrients, these measures were possibly not sufficiently precise, or they might not have measured those aspects of diet that are related to excess weight gain. In addition, underreporting of dietary intake by heavier children may interfere with dietary evaluation in children (24).
Several authors suggest that BMI is a poor measure of adiposity during growth because it may not detect differences in fat distribution or it may fail to differentiate muscle from fat (18). However, in this study population, BMI correlated well with triceps skinfold thickness that is generally considered to be a good measure of body fatness. In addition, had there been a tendency for children to gain muscle rather than fat, one might expect physical activity indicators to be positively, rather than negatively, associated with excess weight gain.
Loss to follow-up was high in both the 1- and 2-year follow-up, reflecting that we surveyed classrooms in the same 16 schools each year rather than trace subjects individually over time. School records indicate that families living in these communities tend to move often, possibly in search of better housing or job opportunities, so that children change schools frequently. If moving relates to family economic circumstances, it is unlikely that a selection bias related to loss-to-follow-up affected internal validity. Finally, because of the unique characteristics of our sample, external generalizability is unknown.
Conclusion
This study provides compelling evidence that heavier children as young as 912 years are at risk of remaining overweight because of short-term excess weight for height gain. In addition, low levels of physical activity are associated with excess weight for height gain in preadolescent children. These observations from low income, ethnically diverse neighborhoods suggest an important need for interventions to promote increased physical activity in children and to decrease time spent in sedentary activities.
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ACKNOWLEDGMENTS |
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Dr. Jennifer O'Loughlin is a National Health Research Scholar.
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NOTES |
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REFERENCES |
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