IS THERE A RELATIONSHIP BETWEEN EATING FREQUENCY AND OVERWEIGHT STATUS IN CHILDREN?

IS THERE A RELATIONSHIP BETWEEN EATING FREQUENCY AND OVERWEIGHT STATUS IN CHILDREN? A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute By Elizabeth Anne Conklin April 11, 2008 Washington, DC

IS THERE A RELATIONSHIP BETWEEN EATING FREQUENCY AND OVERWEIGHT STATUS IN CHILDREN? Elizabeth Anne Conklin, B.A. Thesis Advisor: Harriet Komisar, Ph.D. ABSTRACT Over the last three decades, the prevalence of overweight among children has increased significantly, putting them at greater risk for hypertension, type-2 diabetes, and some cancers. Genetics play a role in determining children s overweight status but environmental and behavioral factors play a role as well, providing researchers with the opportunity to explore options to help curb the childhood overweight trends. One of the factors being explored is eating frequency, which may have a negative or positive association with being overweight for children, holding caloric intake constant. A positive relationship may exist if children who condense their daily caloric intake into fewer meals metabolize their food more efficiently, thereby reducing their probability of being overweight. On the other hand, a negative relationship may exist if spreading caloric intake out over the course of the day by eating more meals and snacks provides children energy at a more efficient rate, allowing them to maintain a healthier weight status. This study analyzes data on children ages 6-12 from the Continuing Survey of Food Intakes by Individuals, a nationwide survey conducted in 1994-96 and 1998. Approximately 37 percent of the study population are at risk for overweight or overweight. Findings indicate that the relationship between eating frequency and weight ii

status in children is negative and statistically significant. An increase of one in the average number of meals and snacks eaten per day decreases the odds that a child will be at risk for overweight or overweight by 11 percent, holding caloric intake and demographic, activity level, health indicator, and socioeconomic factors constant. Another key result is a positive relationship between hours spent watching television and a child s risk of being at risk for overweight or overweight. Increasing hours spent watching television by one hour raises a child s odds of being at risk for overweight or overweight by 8 percent, holding other factors constant. These findings suggest that it would be beneficial to educate children and their parents about the role these modifiable behaviors have on a child s risk of being an unhealthy weight. iii

TABLE OF CONTENTS Introduction... 1 Background: Defining Overweight Status For Children... 3 Literature Review... 5 Conceptual Framework and Hypothesis... 10 Data and Methods... 12 Data Source... 12 Analysis Plan... 13 Results... 20 Descriptive Results... 20 Regression Results... 27 Discussion... 34 References... 38 iv

LIST OF TABLES AND CHARTS Table 1: Descriptive Statistics of the Study Population of Children Ages 6-12... 21 Table 2: Average Number of Meals and Snacks per Day, Food Intake, and Activity Levels of Children Ages 6-12... 23 Chart 1: Percentage of Children at Risk for Overweight or Overweight by Average Number of Meals and Snacks per Day... 25 Chart 2: Percentage of Children at Risk for Overweight or Overweight by Age... 26 Chart 3: Percentage of Children at Risk for Overweight or Overweight by Race and Ethnicity... 28 Table 3: Logistic Regression Results: At Risk for Overweight or Overweight among Children Ages 6-12... 30 v

INTRODUCTION In the past three decades, the proportion of children who are overweight in America has significantly risen. Approximately 6.5 percent of children ages 6-11 were overweight from 1976-1980 but this increased to 18.8 percent from 2003-2004 (CDC Prevalence 2007). The health implications of this are serious and include increased risks of hypertension, type-2 diabetes, and some cancers (CDC Health Consequences 2007). While genetics are a known contributing factor to overweight status in children, environmental factors such as consumption of a high fat diet likewise have played a significant role in the recent increased prevalence (Hebebrand et al. 2000; Hill et al. 2000). It is on this basis that researchers have been conducting studies on prevention and intervention policy options. Recently, the average number of meals and snacks per day has been examined as one of these potential behavioral influences on weight status in children. This relationship has been established in adults more meals are associated with lower rates of obesity (Ma et al. 2003; Yunsheng et al. 2003; Ruidavets et al. 2002; Drummond et al. 1998; Metzner et al. 1977). However, being overweight as a child has not been clearly linked with eating frequency and, since children metabolize food differently than adults, the relationship may not be the same. Children who condense their daily caloric intake into fewer meals may metabolize their food more efficiently, thereby reducing their probability of being overweight. On the other hand, spreading caloric intake out over the course of the day by eating more meals and snacks may provide children energy at a 1

more efficient rate, allowing them to maintain a healthier weight status. While some studies conclude that the relationship for children is more like the latter (Franko et al. 2007; Toschke et al. 2005), others have found no significant results (Nicklas et al. 2004; Nicklas et al. 2003). This study uses data on children ages 6-12 from the Continuing Survey of Food Intakes by Individuals, collected from a nationwide sample in 1994-96 and in 1998, to examine this relationship. Additional evidence suggesting a relationship exists between eating frequency and overweight status in children will enable health programs to encourage eating habits that help children maintain a healthy weight. Promoting the consumption of a healthy number of meals and snacks per day at a young age is especially important because studies have shown eating habits are formed early in life (Crockett et al.1995) and are often carried into adulthood (Nicklas et al. 1992). Because parents help form these habits, they too will need to be targeted by education campaigns about the relationship between eating frequency and their child s odds of being at risk for overweight or overweight. 2

BACKGROUND: DEFINING OVERWEIGHT STATUS FOR CHILDREN Determining the relationship between eating frequency and overweight status in children requires a reliable method of deciding whether or not a child is overweight. Over the years, the standard for measuring an individual s weight status has been to use a body mass index (BMI) score. A BMI score is a measure of weight status derived from an individual s height and weight. The score is calculated by dividing weight in pounds by height in inches and multiplying by 703. For adults, men and women with BMI scores below 18.5 are considered underweight and scores between 18.5 and 24.9 are considered normal weight. A score falling between 25.0 and 29.9 signifies the individual is overweight and BMI scores over 30.0 conclude the individual is obese (CDC Adults 2007). While a BMI score is derived the same way for all age groups, a child s BMI score is not interpreted directly like it is for an adult. A child s BMI score is compared against a gender- and age-specific growth chart to control for the fact that body fat is different for young boys and girls and changes with age (CDC Children and Teens 2007). The growth charts contain a series of curves that describe the distribution of body weight measurements in children in the U.S. The Centers for Disease Control and Prevention (CDC) recently updated the growth charts distribution of body weight measurements in 2000 using data from the National Health and Nutrition Examination Survey, which has collected American s height and weight information since the 1960 s (CDC Growth Charts 2007). 3

Comparing children s BMI scores to the appropriate boys or girls BMI-for-age growth chart provides their percentile ranking, indicating how their BMI score compares to other children of the same age and sex. Children are considered underweight if their BMI score is in less than the fifth percentile, healthy weight if it is in the fifth to less than the 85 th percentile, at risk for overweight if it is in the 85 th to less than the 95 th percentile, and overweight if it is in the 95 th or above percentile (CDC Children and Teens 2007). Although BMI does not measure an individual s body fat directly, studies have found it to be an approximate estimator (Mei et al. 2002). Multiple expert committees looking at the issue of overweight children have recommended that BMI-for-age percentiles be used to determine a child s weight status (CDC Growth Charts 2007). For the purposes of this study, BMI-for-age percentiles will be used to determine the participants weight status, the outcome variable of interest in determining the correlation with eating frequency. 4

LITERATURE REVIEW With one exception (Howarth et al. 2007), recent studies have found that adults who eat more meals during the day have a lower likelihood of being obese (Ma et al. 2003; Yunsheng et al. 2003; Ruidavets et al. 2002; Kirk 2000; Drummond et al. 1998; Metzner et al. 1977). Ma and colleagues (2003) found that eating more meals during the day decreased the risk for being an obese adult, holding constant age, gender, education level, total energy intake, and physical activity. To obtain these results, a logistic regression was run on data collected from 1994-1997 on adults ages 20-70 living in Worcester County, Massachusetts. Similarly, Metzner and colleagues (1977) found that men and women s adiposity indexes got smaller as number of meals increased from two to six, controlling for calories. The researchers used data from the Tecumseh Community Health Study, a longterm study on a community of 10,000 citizens, to perform an analysis of covariance on the number of meals eaten by sex. Contrary to these findings, Howarth and colleagues (2007) research found that eating more than three times a day was associated with being overweight or obese for younger and older adults, controlling for age, sex, race, activity level, education, health indicators, and location. Data from the 1994-1996 Continuing Survey of Food Intakes by Individuals were used to obtain these results. 5

Because children gain and lose weight differently than adults, researchers have been studying this as a separate issue. While a few studies on the topic have been conducted, they have come to conflicting conclusions. In their study, Franko and colleagues (2007) use data on African American and white girls ages 9 and 10 to analyze the relationship between eating frequency and BMI. The data used in the study is from the National Heart, Lung and Blood Institute Growth and Health Study, which collected dietary, physical activity, and psychosocial information on African American and white girls ages 9 and 10 from 1987-1996 in three different cities: Richmond, California; Cincinnati, Ohio; and the Washington, DC area (NHLBI NHLBI Growth and Health Study 2008). Their analysis uses linear regression modeling the girls BMI-for-age percentiles relative to other girls of the same age, and logistic regression modeling overweight, defined as being at or above the 95 th percentile. The regressions include the number of days the participants ate three or more meals (snacks were defined as a meal), study year, and an interaction between the two. Study site, demographic variables, and indicators of average daily calorie intake and expenditure (in the form of hours watching television per week and a physical activity score based on type and frequency of participation in physical activity) are held constant. Using regressions specific to each race, the researchers found that African American and white girls ages 9 and 10 who eat three or more meals on more days have lower BMI scores relative to other girls of the same age (Franko et al. 2007). African American girls who eat three or more meals a day are also less likely to be overweight. 6

This last relationship is not statistically significant for white girls or for the entire sample population of African American and white girls. A German study conducted by Toschke and colleagues (2005) uses data collected from 2001-2002 at six Bavarian public health offices for school health examinations to look at the relationship between eating frequency and being an overweight child. Data on 2,070 girls and boys ages five and six are analyzed. A logistic regression was run on overweight, a binary variable that was defined by sex- and age-specific BMI cutoff points supplied by the International Obesity Task Force. Parental education, gender, parental obesity, and breastfeeding are all included in the model. The results of the study indicate that a negative relationship exists between eating frequency and overweight status (Toschke et al. 2005). That is, eating more meals decreases the likelihood that a child is overweight. The researchers also found that eating more meals is associated with: high parental education; low likelihood that a child would eat a meal alone; watching no more than one hour of television per day; not snacking in front of the television; having siblings; mother not smoking during pregnancy; and breastfeeding the child for more than one month from birth. Other studies, however, have different results. Nicklas and colleagues (2003, 2004) conducted two studies to look at the relationship between eating patterns and overweight status in children. In both studies they used Bogalusa Heart Study data, which examined seven cross-sections of fifth-grade students in the Bogalusa, Louisiana school system from 1973 to 1994. 7

The first study, conducted in 2003, looks at the 10-year-olds eating patterns across the entire 20 years of data (Nicklas et al. 2003). The dependent variable in their model is overweight, defined as having an 85 th BMI-for-age percentile or higher. That is, children who are overweight or at risk for overweight are defined as being overweight. Multiple logistic models are run using different indicators for eating pattern including: consumption patterns; total grams of food and beverages consumed by meal; total eating episodes; total number of meals and snacks; and total gram amount of high- and lowquality foods. Gender, ethnicity, a gender-ethnicity interaction variable, total calorie intake, and study year are held constant in all models. Their study found the eating patterns positively associated with overweight status include total gram amount of food and beverages, particularly from snacks, and consumption of sweets and meats, but the percentage of variance explained in these models is small (Nicklas et al. 2003). They also note that eating patterns associated with obesity may vary by gender and ethnicity. However, their data are limited to a specific community so they encourage other research be conducted using national surveys that represent a more diverse geographic sample. In the second Nicklas and colleagues (2004) study, a different approach is used to analyze the relationship between eating patterns and overweight status. In this study, meal pattern is defined as eating frequency, source of meal, eating episode, and the amount of time between the first and last meals eaten during a day. Consumption of food is counted as an eating episode if the food or drink eating period is separated by at least 15 minutes from other eating episodes. Mealtime span measures the time between the 8

first food or drink consumed in the morning and the last eating episode at night. Among other things, logistic regressions are run to determine the association between the meal patterns and BMI, controlling for study year, total energy intake, ethnicity, gender, and the extra affect between gender and ethnicity through use of an interaction variable. No significant associations with meal patterns and overweight status are found in this study (Nicklas et al. 2004). However, because their data contain eating information on 10-year-olds across approximately 20 years, they are able to determine that from 1973 to 1994, the average number of eating episodes per day decreased from an average of 6.6 to 5.2 and that mealtime span shrunk from 12.4 hours in 1973-74 to 11.5 hours in 1993-94. Although these studies all offer insights into the relationship between eating frequency and overweight status in children, the findings are not generalizable to all American children. The Franko and colleagues (2007) study is restricted to girls ages 9 and 10, the Toschke and colleagues (2005) focuses on German children, and the Nicklas and colleagues (2003, 2004) studies use data that focus on children in a specific community. 9

CONCEPTUAL FRAMEWORK AND HYPOTHESIS In addition to genetics, environmental and behavioral factors play a role in determining a child s overweight status (Hebebrand et al. 2000). Eating habits, in this case eating frequency, are some of the behavioral factors that are being examined as influences on children being overweight. Logically, eating frequency could affect a child s risk of being overweight in two ways. The first possibility is that a decrease in meals and snacks could reduce the likelihood of a child being overweight, that is there is a positive relationship, if condensing a daily intake of calories into fewer meals is an efficient way to consume food that allows children to maintain a healthy weight. However, it is also possible that children who eat more meals and snacks spread their caloric intake out over the day, allowing their bodies to metabolize food at a more efficient and healthy rate, thereby reducing their probability of being overweight or at risk for overweight. I hypothesize that the latter, negative relationship is true. Currently, the research on this topic has shown contradicting results for children. Some studies have found significant negative associations between eating frequency and being an overweight child and others have found no significant relationships. In this study, I will continue to test the hypothesis that eating frequency is associated with a child s weight status. The main factor of interest is eating frequency and the outcome is overweight status. In addition to eating frequency, other consumption factors are taken into consideration as well. Among the factors are specific food intake information, including 10

what kinds of foods the participants eat and the total number of calories they consume, all of which influence a child s weight status. Demographics also play a role in predicting a child s weight status. Whether or not a child is overweight may vary depending on how old they are, whether they are a boy or a girl, and their racial or ethnic background. However, these demographics may also be associated with the number of meals and snacks a day a child consumes, which is the key behavior being examined as a factor of a child s weight status. Activity levels and overall health status can influence the probability of a child being overweight as well. Activity level can be defined in two ways: sedentary time, which may be spent watching television or using a computer; and time spent exercising, including participation in school programs, organized sports, or activities with family and friends. If a child exercises on a regular basis, it is less likely that he or she will be overweight. Similarly, if a child feels healthy versus unhealthy, it may be more likely that he or she is a normal, healthy weight. Finally, the study must include factors that influence a child s upbringing, including family characteristics. Household income, for example, may play a role in determining whether or not a child is overweight. If household income is high, parents may be able to enroll their children in more activities, enabling the children to have more vigorous exercise time during the day. In this case, household income is a proxy for activity-level factors that cannot be directly controlled for in the model. 11

DATA AND METHODS Data Source The Continuing Survey of Food Intakes by Individuals (CSFII), sponsored by the United States Department of Agriculture s Agriculture Research Service, will be used to look at the relationship between eating frequency and overweight children in this study. From 1994-96, two days of food intake data was collected from a nationallyrepresentative sample of individuals residing in households. In 1998, another series of food intake data was collected on children ages zero to nine to complete the original dataset, which did not contain an accurate representation of participants from that age range. Therefore, this study will use both the 1994-96 and 1998 data, as is recommended by the CSFII documentation for studies on children. Identical collection methods were used for both the 1994-96 and 1998 datasets. Information was collected through a series of interviews, the first of which was a screening interview where basic demographics were collected on the household and on the people living in the household. Two days of food intake data were collected using inperson questionnaires. Respondents were not notified in advance when this information would be collected. All interviews were conducted in person. Proxy interviews were conducted for child sample persons under the age of 6 and for adults who could not report the information themselves due to physical or mental limitations. Child sample persons ages 6 to 11 (6 to 9 in the 1998 dataset) provided their food and drink intake with the 12

assistance of an adult household member, generally the person who was responsible for preparing their meals. Interviews began with the sample person first being asked to report anything eaten or drunk the previous day between midnight and midnight. The sample person then reported the name of the eating occasion for each food or drink consumed and the time it began. A Food Instruction Booklet was used to aid the interviewer in collecting more specific information about the quantities of the food and drinks the sample person had consumed. In addition to food and drink consumption, the day one interview obtained information about the sample person s self-reported height and weight, health status, and number of hours spent watching television or videos. The 1994-96 day one response rate was 80.0 percent and the overall two-day response rate was 76.1 percent. In 1998, the day one response rate was 85.6 percent and the overall two-day response rate was 81.7 percent. Analysis Plan Because this study focuses specifically on children s eating patterns and overweight status, only CSFII participants ages 6-12 are included in the analysis. The rationale behind using this particular sub-sample is that it is similar to the age range used in other studies looking at children s weight. Also, in this particular dataset, children began providing their own food and beverage consumption information at age 6. Age 12 was chosen as an upper limit because at age 13, children are considered to have moved from 13

the child part of their life to the teen part of their life and this study focuses on children. The survey data include a sample of 2,188 children ages 6-12, but because of data limitations, the sample in the study consists of 1,898 children. Specifically, because complete height or weight information was not collected for 290 children, the BMI-forage percentile, which is needed for the binary independent variable signifying weight status, could not be calculated for these participants. No other information collected in the CSFII dataset would allow for an approximation of the participants weight status so these individuals were excluded from the study population. A logistic regression model is used to look at the relationship between eating frequency and at risk for overweight and overweight status in children. A BMI-for-age percentile was calculated for each child to determine weight status and is used to construct the binary dependent variable equaling 1 if the child has a BMI-for-age percentile at or above the 85 th percentile. Using a logistic model estimates the odds of a child being at risk for overweight or overweight given his or her eating frequency, holding other variables in the model constant. Here is a breakdown of the model, including an explanation of the variables that will be included: Pr( Overweight = 1) ln = β 0 + β 1 Number of Eating Episodes+ β 2 Grams of Fat 1 (Pr( Overweight = 1)) + β 3 Grams of Meat + β 4 Grams of Eggs + β 5 Grams Legumes + β 6 Grams of Nutseeds + β 7 Grams of Vegetables + β 8 Grams of Fruits + β 9 Grams of Grains + β 10 Grams of Dairy Products + β 11 Grams of Beverages + β 12 Grams of Sugars + β 13 Total Caloric 14

Intake + β 14 Hours Spent Watching TV + β 15 Female + β 16 Age + β 17 African American + β 18 Other Race + β 19 Hispanic + β 20 Good Health + β 21 Very Good Health + β 22 Excellent Health + β 23 Household Size + β 24 Household Income + β 25 High School Diploma Highest Level of Education in Household + β 26 Some College Highest Level of Education in Household + β 27 College Highest Level of Education in Household + β 28 Midwest + β 29 Northeast + β 30 West + β 31 Data Collected in 1995 + β 32 Data Collected in 1996 + β 33 Data Collected in 1998+ u Dependent Variable Overweight: According to the CDC, at risk for overweight and overweight are labels that refer to weight statuses considered to be above a normal, healthy weight (CDC Defining 2007). Therefore, overweight is a binary variable that equals 1 if the child s BMI-for-age percentile is at or above the 85 th percentile and 0 if not. Key Independent Variable of Interest Average Number of Meals and Snacks per Day: Average number of meals and snacks per day is the key independent variable of interest. It is a continuous variable that specifies the number of eating episodes the child had during the day, including breakfast, brunch, lunch, dinner, supper, and snacks. The variable was constructed by counting the reported number of meals and snacks the child had on the two days the information was collected, and then averaging them. If a participant was not interviewed for a second day, the information from day one is used. The expected relationship between being at risk for overweight or overweight and eating frequency is negative. 15

Daily Food Intake Variables Food Content: Detailed food content information is available in the CSFII dataset. Total grams for each of the following categories are included in the model: fats, meats, eggs, legumes, nutseeds, vegetables, fruits, grains, dairy products, beverages, and sugars. The expected relationship between grams of fat consumed and being at risk for overweight or overweight is positive, between grams of meat and overweight is positive, between grams of egg and overweight is positive, between grams of legumes and overweight is negative, between grams of nutseeds and overweight is positive, between grams of fruit and overweight is negative, between grams of grain and overweight is positive, between grams of dairy products and overweight is positive, between grams of beverages and overweight is positive, and between grams of sugars and overweight is positive. Total Caloric Intake: Total caloric intake is a continuous independent variable that will describe the children s average caloric intake over the two days of interviewing. If a child was not interviewed for a second day, the information from the first day is used. The expected relationship between total caloric intake and being at risk for overweight or overweight is positive. Activity Level Variable Hours Spent Watching Television: Hours spent watching television is a continuous independent variable that tells the average number of hours of television children watched over the two days they were interviewed. If a child was only interviewed on one day, the information from that day is used. This variable will serve as a 16

proxy for activity level because information on the children s exercise habits was not collected during the 1998 study. The expected relationship between hours spent watching television and being at risk for overweight or overweight is positive because the more time children spend watching television, the less time they have to exercise. Their chances of being overweight should increase as a result. Demographic Variables Age: Age is a continuous independent variable that will tell the child s age, which ranges from 6 to 12. The expected relationship between age and being at risk for overweight or overweight is positive. Gender: Female is a binary independent variable. The expected relationship between being female and being at risk for overweight or overweight is positive. Race: African American is a binary independent variable. Other race is a binary independent variable that equals 1 if the child is of some race other than white or African American, including Asian/Pacific Islander, or American Indian/Native American, and 0 if not. White is the reference category. The expected relationship between being African American and at risk for overweight or overweight is positive, and being a race other than African American or white and at risk for overweight or overweight is negative. Hispanic: Hispanic is a binary independent variable that equals 1 if the child is of Spanish or Hispanic origin and 0 if not. The reference group is non-hispanic. The 17

expected relationship between being Hispanic and at risk for overweight or overweight is positive. Health Status Variables Health Status: Health status is included in the model through the use of three binary independent variables: good, very good, and excellent. Fair or poor health status is the reference category. The expected relationship between being at risk for overweight or overweight and being in good, very good, or excellent health compared with fair or poor health is negative because, logically, children who feel healthy are more likely to be a healthy weight. Household Information Variables Household Size: Household size is a continuous independent variable that describes the number of individuals living in the household with the child. The expected relationship between household size and being at risk for overweight or overweight is positive because households with more people may have less time to make healthy, home-cooked meals, exercise, or encourage other healthy lifestyle habits. Household Income: Income is a continuous independent variable measured in thousands of dollars that reports the child s household income. The expected relationship between income and being at risk for overweight or overweight is negative because families with more financial resources are in a position to maintain a healthier lifestyle. 18

Parental Education: A total of three binary independent variables are included in the model to describe the highest level of education achieved by the child s mother or father: obtainment of a high school diploma or General Education Diploma (GED); attended some college; or completed college. The reference group is the highest level of education achieved by the child s mother or father is less than a high school diploma or GED. The expected relationship between educational achievement and the child being at risk for overweight or overweight is negative. The more education a parent has received, the more knowledge and resources they should have to help their child lead a healthy lifestyle and maintain a healthy weight. Region: Three binary independent variables are included to indicate the region of the country in which the child lives: Midwest, Northeast, or West. The reference group for region is the South. Year Data Collected Variables Year Data Collected: Three binary independent variables are included to describe the years in which the data were collected on each child. The three years included in the model are 1995, 1996, and 1998; the reference year is 1994. These variables allow for a potential trend over time due to factors that are not otherwise controlled for in the model. 19

RESULTS Descriptive Results According to the weighted sample data, roughly 49 percent of children ages 6-12 are female (Table 1). Approximately 75 percent are white, 16 percent are African American, and 9 percent are a race other than white or African American. About 89 percent are non-hispanic. Among children ages 6-12, the mean household size is 4.5 people and the average family income is $45,000 a year. Roughly 37 percent of the children in the study population are at risk for overweight or overweight. The majority (60 percent) consider themselves to be in excellent health. Children in this age range have daily averages of watching 2.7 hours of television, consuming 1,958 total kilocalories, and eating 4.6 meals and snacks. Differences in the food consumption and activity levels of the children emerge when the data are separated by weight status. Non-overweight children eat an average of 4.7 meals and snacks per day, compared to an average of 4.5 among children at risk for overweight or overweight, a difference that is statistically significant (Table 2). Additionally, children at risk for overweight or overweight eat, on average, 28.9 grams (17.4 percent) more meat and drink 35.8 grams (8.1 percent) more beverages than do non-overweight children. Conversely, they consume 35.4 fewer grams (8.3 percent) of dairy products than non-overweight children. The differences in average quantities of meat, beverages, and dairy products consumed by non-overweight and at risk for overweight or overweight children are all statistically significant. 20

Table 1: Descriptive Statistics of the Study Population of Children Ages 6-12 Unweighted Weighted Variable Number of Observations % of Study Population or Mean Number of Observations (in thousands) % of Study Population or Mean Total Survey Population (Children ages 6-12) 1,898 100.0% 24,246 100.0% Weight Status At Risk for Overweight or Overweight Children (BMI-for-age percentiles of 85 and above) 717 37.8% 8,866 36.6% Children not at risk for Overweight or Overweight (BMI-for-age percentiles below 85) 1,181 62.2% 15,380 63.4% Average Number of Meals and Snacks per Day 1,898 4.6 24,246 4.6 Daily Food Intake (in grams) Fats 1,898 7.3 24,246 7.7 Meats 1,898 142.2 24,246 147.8 Eggs 1,898 11.7 24,246 11.5 Legumes 1,898 11.3 24,246 10.9 Nutseeds 1,898 4.8 24,246 4.8 Vegetables 1,898 116.2 24,246 120.5 Fruits 1,898 170.9 24,246 171.3 Grains 1,898 297.0 24,246 304.0 Dairy Products 1,898 413.9 24,246 414.8 Beverages 1,898 391.0 24,246 415.1 Sugars 1,898 38.5 24,246 39.6 Total Calories (in kilocalories) 1,898 1913.0 24,246 1958.4 Activity Level Hours watching TV 1,898 2.7 24,246 2.7 Gender Male 971 51.2% 12,385 51.1% Female 927 48.8% 11,860 48.9% Age (in years) 6 511 25.5% 3,196 13.2% 7 265 13.5% 3,299 13.6% 8 258 13.3% 3,223 13.3% 9 256 13.9% 3,593 14.8% 10 229 12.3% 3,735 15.4% 11 213 12.2% 3,718 15.3% 12 166 9.3% 3,481 14.4% Race White 1,414 74.5% 18,225 75.2% African American 297 15.7% 3,954 16.3% Other Race 187 9.9% 2,068 8.5% Hispanic Non-Hispanic 1,646 86.7% 21,529 88.8% Hispanic 252 13.3% 2,716 11.2% 21

Table 1 (Continued) Unweighted Weighted Variable Number of Observations % of Study Population or Mean Number of Observations (in thousands) % of Study Population or Mean Health Status Poor or Fair Health 1,756 87.7% 511 2.1% Good Health 233 12.3% 3,147 13.0% Very Good Health 477 25.1% 6,426 26.5% Excellent Health 1,147 60.4% 14,162 58.4% Household Information Household Size (number of people) 1,898 4.5 24,246 4.5 Household Income (in thousands of dollars) 1,898 44 24,246 45 Education (Highest Level Achieved in Household) No High School Diploma 178 9.4% 2,147 8.9% High School Diploma 537 28.3% 6,726 27.7% Some College 494 26.0% 6,214 25.6% College Graduate 678 35.7% 9,015 37.2% No Information 11 0.6% 145 0.6% Region South 685 36.1% 8,749 36.1% Midwest 477 25.1% 6,043 24.9% Northeast 329 17.3% 4,753 19.6% West 407 21.4% 4,701 19.4% Source: Author's analysis of data from Continuing Survey of Food Intakes by Individuals 1994-96 and 1998. 22

Table 2: Average Number of Meals and Snacks per Day, Food Intake, and Activity Levels of Children Ages 6-12 At Risk for Overweight or Overweight (n=8,866,000) Non- Overweight (n=15,380,000) Variable Mean Mean Average Number of Meals and Snacks per Day 4.4 4.7 *** Food Intake (in grams) Fats 8.2 7.4 Meats 166.2 137.3 *** Eggs 10.7 12.0 Legumes 9.4 11.8 Nutseeds 4.4 5.0 Vegetables 123.5 118.8 Fruits 164.3 175.3 Grains 296.4 308.4 Dairy Products 392.3 427.7 *** Beverages 437.8 402.0 ** Sugars 39.9 39.4 Total Calories (in kilocalories) 1947.8 1964.5 Activity Level Hours watching TV 3.1 2.6 *** Source: Author's analysis of data from Continuing Survey of Food Instakes by Individuals 1994-96 and 1998. *** Difference is significant at the 0.01 level ** Difference is significant at the 0.05 level * Difference is significant at the 0.10 level 23

Examining the average number of meals and snacks eaten per day by at risk for overweight or overweight children reveals interesting patterns. The proportion of at risk for overweight or overweight children steadily declines as the average number of meals and snacks per day increases (Chart 1). Among children who eat an average 2.5 or fewer meals or snacks per day, 77 percent are at risk for overweight or overweight (although only 2 percent of the study population eat 2.5 or fewer meals a day). Approximately 42 percent of the children who eat an average 3 to 3.5 meals and snacks per day are at risk for overweight or overweight and this proportion slowly declines with increases in eating frequency up until 7 or more meals where only 28 percent are at risk for overweight or overweight. Breaking down the proportion of at risk for overweight or overweight versus nonoverweight children by various demographic characteristics also reveals interesting relationships. For example, the distribution over age of at risk for overweight or overweight children is fairly even (Chart 2). However, comparing males and females at different ages reveals an interesting trend where a higher percentage of females are at risk for overweight or overweight at younger ages and slightly more males are at risk for overweight or overweight at ages 11 and 12. At ages 6 and 7 these differences are marginally statistically significant. The proportion of at risk for overweight or overweight 6-year olds is 6 percentage points higher for females than males and is 9 percentage points higher for 7-year old females than males. The proportion of children at risk for overweight or overweight also varies by race. The percentage of at risk for overweight or overweight African American children 24

Chart 1: Percentage of Children at Risk for Overweight or Overweight by Average Number of Meals and Snacks per Day 100% 80% 77% 60% 40% 20% 44% 33% 42% 25% 35% 35% 18% 18% 17% 17% 17% 31% 28% 17% 12% 0% 2.5 or 3 to 3.5 4 to 4.5 5 to 5.5 6 to 6.5 7 or more fewer (2%) (23%) (34%) (24%) (10%) (7%) Average Number of Meals and Snacks per Day (percent of population) Source: Author's analysis of weighted data from Continuing Survey of Food Intakes by Individuals 1994-96 and 1998. 14% 16% Overweight At Risk for Overweight 25

Chart 2: Percentage of Children at Risk for Overweight or Overweight by Age 100% 80% 60% 40% 20% 43% 39% 41% 41% 38% 37% 38% 33% 34% 36% 35% 34% 32% 28% Males Females 0% 6* 7* 8 9 10 11 12 Age Source: Author's analysis of weighted data from Continuing Survey of Food Intakes by Individuals 1994-96 and 1998. * Difference betwen males and females is significant at 0.15 level No significant differences exist between children at the median age 9 and any other age in the sample 26

is higher than for whites or other races (Chart 3). The proportion of African American children who are at risk for overweight is 50.5 percent and is statistically significantly higher than white children by 12.1 percent. Likewise, the proportion of African American children at risk for overweight or overweight is statistically significantly higher than children of a race other than African American or white by 10.4 percent. Looking at the differences between being a male or female of a particular race reveals that African American females have the highest percentage of being at risk for overweight or overweight than any other gender and race combination (Chart 3). Interestingly, a higher proportion of African American females than males are at risk for overweight or overweight, although the difference is not statistically significant. However, a higher proportion of males than females are at risk for overweight or overweight among both white children and children of a race other than white or African American and these differences are statistically significant. The percentage of white males and females who are at risk for overweight or overweight differs by a statistically significant 5 percent. Similarly, the proportion of males and females of a race other than white or African American who are at risk for overweight or overweight differs by a statistically significant 9 percent. Regression Results A logistic regression was run on the children s weight status to determine if a correlation exists between being at risk for overweight or overweight and the average number of meals and snacks per day when holding other factors constant. The results 27

Chart 3: Percentage of Children at Risk for Overweight or Overweight by Race and Ethnicity 100% 80% 60% 40% 20% 52% 46% 40% 49% 41% 36% 37% 31% 31% 34% Males Females 0% White* African American Other Race** Hispanic Non- Hispanic Race Ethnicity Source: Author's analysis of weighted data from Continuing Survey of Food Intakes by Individuals 1994-96 and 1998. ** Difference between males and females is significant at 0.05 level * Difference beween males and females is significant at 0.10 level Difference between white and African American is significant at 0.01 level Different between African American and other race is significant at 0.05 level 28

indicate that average number of meals and snacks eaten per day and being at risk for overweight or overweight have a statistically significant negative relationship, meaning a decrease in the number of meals and snacks increases the probability of being at risk for overweight or overweight (Table 3). An increase of one in average number of meals and snacks eaten per day decreases the odds a child will be at risk for overweight or overweight by 11 percent. Other statistically significant behaviors that influence the probability of being at risk for overweight or overweight include consumption of fat, meat, and beverages, as well as hours spent watching television, all of which have positive relationships with the dependent variable. Of the three statistically significant food types, consumption of fat has the biggest effect on the probability of being at risk for overweight or overweight. Increasing fat consumption by 10 grams raises the odds a child will be at risk for overweight or overweight by 7 percent, controlling for total kilocalories and other characteristics. Similarly, an additional hour per day spent watching television increases the odds a child will be at risk for overweight or overweight by 8 percent. Various demographic indicators in the model have statistically significant impacts on the probability of being at risk for overweight or overweight as well. Being African American has a positive statistically significant relationship with the dependent variable. Compared to white children, African American children have 32 percent greater odds of being at risk for overweight or overweight. Interactions between being female and being African American, a race other than white or African American, and Hispanic 29

Table 3: Logistic Regression Results: At Risk for Overweight or Overweight among Children Ages 6-12 Variable Intercept Average Number of Meals and Snacks per Day 30 Coeff. Odds Ratio Odds Ratio 95% Confidence Intervals P- Value 0.2307 0.66-0.1220 0.8852 [0.8071, 0.9707] 0.01 *** Daily Food Intake (in grams per day) Fats 0.0072 1.0072 [0.9991, 1.0152] 0.08 * Meats 0.0014 1.0014 [1.0002, 1.0025] 0.02 ** Eggs -0.0013 0.9987 [0.9946, 1.0026] 0.51 Legumes -0.0021 0.9979 [0.9947, 1.0010] 0.19 Nutseeds -0.0013 0.9987 [0.9930, 1.0043] 0.64 Vegetables 0.0005 1.0005 [0.9996, 1.0013] 0.26 Fruits 0.0002 1.0002 [0.9995, 1.0008] 0.60 Grains 0.0000 1.0000 [0.9993, 1.0006] 0.92 Dairy Products 0.0000 1.0000 [0.9994, 1.0006] 0.90 Beverages 0.0003 1.0003 [0.9999, 1.0007] 0.08 * Sugars 0.0011 1.0011 [0.9992, 1.0029] 0.23 Total Calories (in kilocalories) -0.0002 0.9998 [0.9994, 1.0001] 0.26 Activity Level Hours watching television 0.0800 1.0833 [1.0201, 1.1503] 0.01 *** Gender Male - - - - Female -0.1003 0.9046 [0.7388, 1.1075] 0.32 Age (in years) 0.0099 1.0100 [0.9655, 1.0564] 0.66 Race White - - - - African American 0.2767 1.3188 [0.9775, 1.7792] 0.07 * Other Race -0.0435 0.9574 [0.6065, 1.5112] 0.85 Hispanic Non-Hispanic - - - - Hispanic 0.1565 1.1694 [0.8423, 1.6233] 0.34 Health Status Poor or Fair Health - - - - Good Health -0.3674 0.6925 [0.2761, 1.2495] 0.36 Very Good Health -0.4964 0.6087 [0.2914, 1.2713] 0.18 Excellent Health -0.5320 0.5874 [0.3095, 1.5491] 0.16 Household Information Household Size -0.0417 0.9592 [0.8788, 1.0468] 0.34 Household Income (in thousands of dollars) -0.0057 0.9943 [0.9899, 0.9986] 0.01 *** Education (Highest Level of Attained in Household) No High School Diploma - - - - High School Diploma 0.1991 1.2203 [0.8616, 1.7283] 0.25 Some College 0.1155 1.1225 [0.8033, 1.5682] 0.49 College Graduate -0.0233 0.9770 [0.6667, 1.4315] 0.90 No Education Information 0.6498 1.9152 [0.3944, 9.2985] 0.41 Region South - - - - Midwest 0.0427 1.0436 [0.7474, 1.3120] 0.79 Northeast -0.0097 0.9903 [0.7611, 1.4310] 0.94 West -0.0535 0.9479 [0.6897, 1.3028] 0.74

Table 3 (Continued) Variable Coeff. Odds Ratio Odds Ratio 95% Confidence Intervals P- Value Year Data Collected 1994 - - - - 1995 0.3408 1.4061 [1.0290, 1.9212] 0.03 ** 1996 0.0895 1.0937 [0.7941, 1.5060] 0.57 1998 0.4594 1.5832 [1.1102, 2.2575] 0.01 *** N=1,898 Likelihood Ratio = 132.30 (df=34, p<.0001) Source: Author's analysis of data from Continuing Survey of Food Intakes by Individuals 1994-96 and 1998. *** Significant at the 0.01 level ** Significant at the 0.05 level * Significant at the 0.10 level 31

were additionally tested for significance. However, none of these relationships were found to be significant so results from the simpler model, which do not include these interactions, are reported. Household income has a significant negative relationship with a 6-12 year old child s odds of being at risk for overweight or overweight and the magnitude of the relationship is sizeable. Increasing household income by $10,000 decreases the odds a child is at risk for overweight or overweight by 6 percent. Interestingly, the year indicators for 1995 and 1998 (but not 1996) have positive statistically significant relationships with being at risk for overweight or overweight. Children whose data were collected in 1995 and 1998 are more likely than children whose data was collected in 1994 to be at risk for overweight or overweight, holding other factors constant. There is an exception to this pattern however, because data collected in 1996 was not significantly different than data collected in 1994. To illustrate further the magnitude of the relationship between a child being at risk for overweight or overweight and the number of meals and snacks eaten per day, the regression results were used to estimate the effect of increasing the number of meals and snacks on the probability that the study sample s average child will be at risk for overweight or overweight. For this illustration, the average child is defined as a 9- year-old non-hispanic white male in excellent health who watches an average of 2.7 hours of television per day and who lives in the South in a 4.5-person household where the highest education achieved is college and the average income is $45,000. He also eats the average number of grams for each of the food groups represented in this study and 32

consumes 1,958 kilocalories. Increasing the number of meals and snacks he eats from 4.6, the study population average, to 5.6 decreases the probability he will be at risk for overweight or overweight from 29.3 percent to 26.9 percent, a decrease of about 8 percent. This second example illustrates the probability of being at risk for overweight or overweight for the same average child, but compares 4 meals and snacks, the average number of meals and snacks at the first quartile, with 5.5 meals and snacks, the average number of meals and snacks at the third quartile. Increasing the number of meals and snacks from 4 to 5.5 decreases the probability that he will be at risk for overweight or overweight from 30.9 percent to 27.1 percent, a decrease of approximately 14 percent. The affect of hours spent watching television on a child s probability of being at risk for overweight or overweight is large so the third example illustrates the magnitude of that relationship for the same average child considered in the first example. Increasing the average daily time spent watching television by one hour from an average of 2.7 hours to 3.7 hours increases the child s probability of being at risk for overweight or overweight from 29.3 percent to 36.7 percent, an increase of 25 percent. The fourth example illustrates the difference in the probability of being at risk for overweight or overweight for otherwise similar children who are African American or white. The children in this example will be similar in other characteristics to the average child previously described in the first example. Being African American versus white increases the probability a child will be at risk for overweight or overweight from 29.3 percent to 35.4 percent, an increase of about 21 percent. 33