PREVALENCE AND RISK FACTORS FOR OBESITY AND OVERWEIGHT AMONG ELEMENTARYSTUDENTS AT WEST VISAYAS...

PREVALENCE AND RISK FACTORS FOR OBESITY AND OVERWEIGHT AMONG ELEMENTARYSTUDENTS AT WEST VISAYAS STATE UNIVERSITY –

INTEGRATED LABORATORY SCHOOL IN 2013

A Clinical – based Research Paper Submitted to the

Office of Research West Visayas State University – College of Medicine

Atas, Bryan Altillero, Melchor Jr. Albay, Shiela Mae

Hechanova April Rose Pe, Clarisse Jane

Langurayan, Ma. Nerissa Abelita, Monica Joanne

Yap, Jeremy Brian Lim, MarijaMicah

Pedregosa, Lawrence Sison, Angeli

Diamante, Daniel Ken

March 2014

PREVALENCE AND RISK FACTORS FOR OBESITY AND OVERWEIGHT AMONG ELEMENTARY STUDENTS AT WEST VISAYAS STATE UNIVERSITY –

INTEGRATED LABORATORY SCHOOL IN 2013

A Clinical – based Research Paper Submitted to the

Office of Research West Visayas State University – College of Medicine

Atas, Bryan Altillero, Melchor Jr. Albay, Shiela Mae

Hechanova April Rose Pe, Clarisse Jane

Langurayan, Ma. Nerissa Abelita, Monica Joanne

Yap, Jeremy Brian Lim, MarijaMicah

Pedregosa, Lawrence Sison, Angeli

Diamante, Daniel Ken

_______________________ JerushaComuelo, MD

Adviser

March 2014

1

INTRODUCTION

Background of the Study

Childhood obesity is one of the most serious public health challenges of the 21st century. The problem is global and is steadily affecting many low- and middle-income countries, particularly in urban settings. The prevalence has increased at an alarming rate. The worldwide prevalence of childhood overweight and obesity increased from 4.2 percent in 1990 to 6.7 percent in 2010.1Globally, in 2010 the number of overweight children under the age of five is estimated to be over 42 million. Close to 35 million of these are living in developing countries.2

The World Health Organization (WHO) predicts that by 2015 approximately 2.3 billion

adults will be overweight and more than 700 million will be obese. In 2005, at least 20 million children under the age of 5 were overweight. Overweight is one of the leading causes of lifestyle-related diseases such as hypertension, diabetes mellitus, strokes, muscle and bone disorders, and certain cancers.3

The National Nutrition Survey (NNS) by the Food and Nutrition Research Institute of the Department of Science and Technology (FNRI-DOST) revealed that 4.3 percent of children are overweight for their age in 2011.4

The highest prevalence of childhood overweight is in the upper middle-income countries,

and, when taken as group, low-income countries have the lowest prevalence rate. However, overweight is rising in almost all countries, with prevalence rates growing fastest in the lower middle-income countries.5

The high prevalence of overweight and obesity has serious health consequences.

Children who are overweight or at-risk-for-overweight are at dramatically increased risk for many chronic medical conditions.6Elevated body mass index (BMI) is a major risk factor for diseases such as cardiovascular disease, type 2 diabetes, liver disease and many cancers 7 Menstrual abnormalities, impaired balance, orthopedic problems, asthma and obstructive sleep apnea are also associated with obesity. 8 9 It not only causes premature mortality, but also long-term morbidity.8

Children are a vulnerable age group in our society, and they are most particularly

affected by the ill-effects of this disorder, not only medically but also psychologically. Overweight and obesity in children predisposes them to unhealthy behaviours due to psychological stress like depression, limited mobility, decreased physical activity, low self – esteem as well as social discrimination.10 ,11,12,13 ,14

Childhood obesity has both immediate and long-term effects on health and well-being. The most immediate consequence of overweight, as perceived by children themselves, is social discrimination.15

Children and adolescents who are obese are at greater risk for social and psychological

problems, such as stigmatization and poor self-esteem.8For instance, overweight adolescents are more likely than the non-overweight to engage in unhealthy disordered eating behaviors such as binge eating and chronic dieting.16Majority of the researchers, academicians, and those

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involved in health services agree that prevention should start in order to counteract the rise in prevalence of obesity and diseases associated with obesity.17

Studies done in the prenatal period postulated a link between the severity of maternal

smoking during pregnancy and future development of childhood obesity.18 Children of mothers who were overweight during pregnancy are known to be overweight in their early childhood. Early studies on childhood obesity have reported that an overprotective dominant mother, a timid father and lack of warmth within the family are all risk factors towards the development of obesity in children.

Although no specific underlying factor or mechanism has been hypothesized to explain the role of family factors in obesity, studies have proposed the lack of family cohesion, presence of social isolation, conflicts and disorganization of family structure with role reversal as factors that may contribute to the development of obesity in children.

There are a number of factors in the child that determine his tendency to be obese. It

has been noted that children who are obese tend to be more inactive, have increased energy intake and decreased energy expenditure. It has been found that television viewing is a risk factor for childhood obesity.

Increased consumption of soft drinks and sugary foods in childhood has also been linked to overweight and obesity. It has been noted that children who sleep less are more prone to childhood obesity and likewise children with obesity have disturbed sleep due to breathing problems in sleep. The presence of fast food restaurants, school canteens that specialize in low cost high fat meals and the relentless television marketing of foods high in sugar and fat contribute to what has been described by some authors as a ‗toxic‖ food environment.19

The metabolic and physiologic changes in obesity are carried into adult life and eventually predispose the individual to metabolic diseases, disability and death. 20 It is also estimated that if current childhood obesity rates persist, children will live 3 to 4 years less than today‘s adults due to obesity.21

Significance of the Study

Obesity is now reaching pandemic proportions across much of the world, and its consequences are set to impose unprecedented health, financial and social burdens on global society, unless effective actions are taken to reverse the trend.10,22Special focus must be given to the children and adolescents. It is at the early ages that obesity must be controlled especially because about half of overweight school–age children become overweight as adults. 23,24Apparently, primary or secondary prevention could be the key for controlling the current epidemic of obesity and these strategies seem to be more effective in children than in adults.25

Though at a young age, understanding the devitalizing effects of being overweight and

obese would create awareness in the child, initiating cooperation to the necessary lifestyle changes imposed at school and at home.

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This study would be of greatest importance to the family as they play a pivotal role in the long term management of childhood obesity and in the successful management of weight. Proper health habits especially that of diet, rest and activity should be started at home, and the parents as well as the guardians should serve as stewards for a healthy lifestyle.

The results of this study would help school administrators and teachers in the creation of

a healthy environment for children, considering that majority of the child‘s time are spent at school.

Even though the health consequences of obesity are most commonly seen during adulthood, the underlying factors of these diseases could originate during childhood. It is therefore vital to know exactly how early the health consequences and risk factors for these serious diseases occur, and how early they can be detected if they are to be addressed successfully. Prevention of obesity, therefore, is thought to be vital in decreasing morbidities thought to be associated to obesity.

Numerous studies have been conducted worldwide with regards to monitoring childhood obesity and the risk factors involved in developing it. It is however crucial that childhood obesity in the local scenario must be monitored since society and local health protocols play a big factor in its prevention.

General Objective

This study aimed to determine the prevalence of childhood overweight and obesity and its association with risk factors among elementary students of West Visayas State University -Integrated Laboratory School.

Specific Objectives

1. Determine the prevalence of overweight and obesity among elementary school students when grouped according to: a. General population of elementary students b. Age c. Sex

2. Determine the association between overweight and obesity and the following risk

factors: a. paternal BMI b. maternal BMI c. parental educational level d. household income e. child birth weight f. breast fed g. number of children in the family h. order of birth i. physical activity j. amount of sleep k. passive entertainment l. eating behavior m. food preferences

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Conceptual Framework

parents' BMI

Childhood Overweight and

Obesity

parents' educational level

household income

child birth weight

breast fed

number of children in the family order of birth

physical activity

amount of sleep

passive entertainment

eating behavior

food preferences

Independent Variables Dependent Variables

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METHODS

Study Design This cross-sectional study aimed to determine the prevalence and risk factors of

overweight and obesity among elementary students of West Visayas State University - Integrated Laboratory School (WVSU – ILS) in Iloilo City.

Study Setting

This study gathered data from West Visayas State University – Integrated Laboratory School (WVSU-ILS), Gen. Luna St., Lapaz, Ilo-ilo City. The measurement of BMI was done in the school clinic. Self –administered questionnaires were sent to the parents.

Study Period The gathering of data was done during the second semester of the academic school year 2013 - 2014 in the months of November, December and January of the following year.

Study Population This study measured the weight and height of elementary students from grade 1 to 6 in WVSU – ILS. Data were also gathered from the parents or guardians of the said students.

Inclusion Criteria

The study only gathered data from grade 1 to grade 6 elementary students of WVSU – ILS who are enrolled in the academic school year of 2013 – 2014 whose parents or guardians have consented to their participation.

Exclusion Criteria

The study did not gather data from children who refused to participate, or whom parents did not consent to their child‘s participation. Excluded also are those respondents who failed to answer the questionnaires adequately or who were unavailable during the said study period. Students with no birth weights in their respective student records at the Principal‘s office were also not included.

Children 12 years old or older who did not give their assent, despite the signed consent

forms from their parents, were not included in the study.

Sampling procedure

The study population comprised of those children who returned and adequately

answered the questionnaires and whose parents consented to the study.

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Operational Definition of Terms

1. Obesity – the state of being obese wherein the BMI is greater than 2 standard deviations

from the mean.26

2. Overweight – the state of being overweight wherein the BMI is greater than 1 standard

deviations but less than 2 standard deviations from the mean.59

3. Body Mass index (BMI) – an assessment tool calculated as weight in kilograms divided

by the square of height ( ).27

4. Parents' weight – the self-reported weight of the parents in kilograms.

5. Parent‘s height – the self-reported height of the parents in meters.

6. Parents' educational level – the level of education that the parents reached as to

elementary, high school, college, graduate course or etc.

7. Household income – the self – reported net income per month of the household which

supports the child. The monthly income was categorized into quintiles based on the

National Statistics Office (NSO) ‗Family Income and Expenditure Survey (FIES) of

2012.28

8. Child birth weight – the child‘s weight at birth as reflected in the birth certificate of the

child.

9. Breast fed - the reception of nutrition from breast milk, either through direct contact with

the mother‘s breast or from expressed breast milk in a bottle.29

10. Number of children in the family – the number of children which the parents or guardian consider as part of their family regardless of biological parent.

11. Order of birth – the order of the child‘s birth with respect to the birth of his or her biological siblings.

12. Physical activity - any endeavor which results in expenditure of energy, which is done outside and during school hours. These may include, but not limited to, walking to or from school, sports, dancing, and swimming, among others. They may also include physical education classes. 30

13. Amount of sleep - a self-reported total number of hours spent in sleeping, from the time one lies down in bed to the time of awakening. 31

14. Passive Entertainment – the amount of time spent watching television or playing video games in hours per week.

15. Eating behaviour - defined as the attitude towards food as what and how to eat, the selection of food, the way of getting food.32

16. Food preferences – the selection of one‘s food or beverage

Maneuvers

The proposal of the study has undergone technical and ethical reviews. A verbal and written explanation of the study and permission to conduct the study were obtained from the concerned school authorities. Specifically, the researchers received permission from Dr. Emellie

Annual income cutoffs (Php) Q1 Q2 110,986.5 Q3 156, 011 Q4 234, 423.5 Q5 500, 930.5

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G. Palomo, PhD, WVSU – ILS Director, and Atty. Paulino Salmon, President of the Parent-Teachers Association. The researchers also received permission from the WVSU – ILS Office of Director in obtaining the birth certificates of the children participants in their registry to obtain the birth weight of the children.

A complete list of elementary students was obtained from the WVSU – ILS Office of the

Director. On the card giving day of the elementary, informed consent forms were given and explained personally to the parent(s) or guardian. After they had given consent, 2 self – administered questionnaires were given for them to answer for 15 – 20 minutes in the WVSU – ILS classrooms. An assent form was given and explained to children 12 years old or older. The said children will only be included in the study after they have given their assent. Questionnaires were allowed to be taken out and answered at home for the respondents‘ convenience. The researchers coordinated with the classroom advisers and school Guidance Office for the return of the questionnaires.

Two self – administered questionnaires were used as a tool for data gathering in the study. The questionnaires were modified and adapted from a previous study. A permission to use the adapted questionnaire has been given by the authors of the previous study. After the approval, a pretest was done on selected elementary students of WVSU – ILS to validate the questionnaires. The data taken from the pretest were excluded from the official list of respondents of the research study.

The first questionnaire was modified to assess the physical activity and food preference

of the child. The questionnaire contains questions pertaining to the parents' weight and height, parents' educational level, household income, duration of breast feeding, number of children in the family, child‘s age and sex, child‘s birth weight, order of birth of the child, physical activity of the child, amount of sleep of the child, passive entertainment, eating behaviour of the child and food preferences of the child.

The second questionnaire assessed the child‘s eating behaviour. It was adopted and

translated from the Child Eating Behaviour Questionnaire (CEBQ) by Wardle et al. It has 35 questions that evaluates Food Responsiveness (FR; 5 items), Enjoyment of Food (EF; 4 items), Emotional Over – Eating (EOE; 4 items), Desire to Drink (DD; 3 items), Slowness in Eating (SE; 4 items), Satiety Responsiveness (SR; 5 items), Food Fussiness (FF; 6 items) and Emotional Under – Eating (EUE; 4 items). Each item was answered in a Likert–type scale with scores from 1 to 5: Never=1, Rarely=2, Sometimes=3, Often=4, Always=5.33

The CEBQ assesses eating behaviour traits in children and may predict risks of eating

disorders and body–weight related problems such as obesity. The data was used to assess association between specific eating behaviour scores and childhood obesity.

The study measured the weight and height of the pupils included in the study on a

scheduled date approved by the principal. It was done inside the WVSU school clinic. During the measurement, the participants were in their school uniform with their shoes removed and their pockets emptied. A weigh beam weighing scale (Detecto) was used to measure the weight of each participant. The height of the students will also be measured using the height rod in the weigh beam scale. The participants stood straight with their back against the height rod. Each participating child was then be classified whether they are thin, normal, overweight, or obese based on the BMI-for-age chart of the World Health Organization.

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The prevalence of obesity and overweight were then computed by dividing the number of obese and overweight children over the total number of children involved in the study. Statistical analysis was used to associate the risk factors in the questionnaires to childhood obesity and overweight.

The classification of children‘s BMI was based on the WHO charts of BMI-for-age of boys and girls aged 5 to 19 years old. Parental BMI was based on the WHO standards for adults.34 The monthly income was categorized into quintiles based on the National Statistics Office (NSO) Family Income and Expenditure Survey (FIES) of 2012.35

Data Processing and Analysis Statistical Package for Social Sciences (SPSS) version 20 (Chicago, Illinois) was used to analyze the data. Multiple Regression Analysis was used to identify the risk factors that influence obesity and overweight in the randomly selected elementary students. Mean, frequency and standard deviation was computed to determine the obese, overweight, normal or underweight children with respect to children‘s age, and sex.

Ethical Considerations Informed consent forms were distributed and explained to the parents or guardians of the elementary pupils to ensure that they understood the terms and conditions of the study. Signed consent forms indicated the voluntary participation of the parents or guardians and allowed their child as a participant. Failure of submission of signed consent forms implied that both the child and the parent or guardian refused participation. An Assent form was also given to children 12 years old or older whose parent(s) or guardians consented to their child‘s participation in the study. A signed assent form indicated that the child agreed with the parent(s)‘s or guardian‘s decision to include him or her in the study.

Parents of students identified as obese/overweight will be sent a letter stating the nutritional status (BMI) of the child. Parents will be advised for their child‘s consult to a pediatric specialist concerning weight. Separate forums for children as well as parents will be organized.

Results of the study will be presented to the school principal and PTA officers for necessary interventions to be initiated in cooperation with the research group. The results will be discussed as well as measures in preventing and managing childhood obesity.

Confidentiality will be highly observed such that a serial number will be assigned to each participant instead of their names. All participants in this study will be given utmost respect and courtesy.

Scope and Limitations This study did not look into the nutritional intake of the children. Some measurable risk factors could not be measured or verified by the researchers; instead they will be self – reported by the parents or guardians. The said risk factors include: mother‘s height and weight, father‘s height and weight, and household income.

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RESULTS There were a total of 308 pupils from grade 1 to grade 6 that were officially enrolled in West Visayas State University – Integrated Laboratory School. This study managed to gather data from 114 (37%) of the students and their parent(s) or guardian(s).

Out of the 114 respondents, 72(63.2%) are female, while 42(36.8%) are male. In terms of age, 23(20.2%) are 11 years old while only 5(4.4%) are 6 years old. A greater number, 29(25.4%) belong to the 4th grade, while 6(5.3%) are from the 3rd grade. Table 1. Student demographics (N=114)

No. of students Percent (%)

Sex Female 72 63.2%

Male 42 36.8%

Age (years)

6 5 4.4%

7 15 13.2%

8 14 12.3%

9 14 12.3%

10 18 15.8%

11 23 20.2%

12 18 15.8%

13 7 6.1%

Grade Level

1 19 16.7%

2 21 18.4%

3 6 5.3%

4 29 25.4%

5 18 15.8%

6 21 18.4%

Among the 228 parents (114 fathers and 114 mothers) of elementary students, 56.6%

(129) had a normal body mass index, 36.4% (83) were overweight, 4.4%(10) were obese and 2.6% (6) were thin.

Table 2. Parental BMI classification (N=114) BMI classification Father Mother Total Percent (%)

Obese 4 6 10 4.4

Overweight 48 35 83 36.4

Normal 58 71 129 56.6

Thin 4 2 6 2.6

Very thin - - - -

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Most of the parents, 191 (83.8%) were college graduates, 35(15.4%) post graduates,

and 2(0.9%) were high school graduates. Table 3. Parental educational level classification (N=114) Paternal education Maternal education Total Percent (%)

Elementary graduate - - - -

High school graduate - 2 2 0.9

College graduate 100 91 191 83.8

Post graduate 14 21 35 15.4

Obese children had the highest average household income (Php 51,916) followed by children with normal BMI (Php 49,732). Interestingly though, the overweight children had the lowest average household income at Php 35,662.

Half (57/114) of the study population were in the 5th quintile. About one – half (51%) of those in the 5th quintile had normal BMI while only a quarter (24.6%) were obese. Table 4. Monthly income profile per children’s BMI classification (N=114)

Children BMI classification

Q1 Q2 Q3 Q4 Q5 mean income (Php)

(Php)

n

(Php) n

(Php)

n

(Php) n

(Php)

n

very thin . 0 . 0 . 0 . 0 47152 1 47,152

thin . 0 . 0 18000 1 20000 2 56230 7 45,161

normal 6125 4 10000 1 15250 4 29727 22 77049 29 49,732

overweight . 0 12000 1 18549 1 30560 10 50960 6 35,662

obese . 0 10000 4 . 0 30714 7 74494 14 51,916

4 6 6 41 57 47,565

Of the 114 households, 17.5% (20) only had one child, 41.2% (47) had two children and

30.7% (35) had three children. Table 5. Number of children in the family of the study population (N=114)

No. of children Frequency Percent (%)

1 20 17.5

2 47 41.2

3 35 30.7

4 8 7.0

5 2 1.8

6 1 0.9

7 1 0.9

The average birth weight of the respondents were 3,003 grams. Table 6. Child’s birth weight descriptives (N=114)

Min Max Mean Standard deviation

Birth weight 1200 4173 3003.2 539.1

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Out of the 114 students, there were 76 who were breastfed and 38 who were not breastfed. Among those who were breastfed, 30 were males and 46 were females. On the other hand, among those who were not breastfed, 12 were males and 26 were females.

Table 7. Frequency of breast fed respondents (N=114)

breastfed not breastfed

Male 30 12

female 46 26

Total 76 38

Fifty percent of the students were 1st born, followed by 27% who were born second, 21%

who were born third, and 0.9% who were born 6th and 7th each.

Table 8. Frequency of birth orders of children (N=114)

Birth order Number Percent (%)

1st 54 50

2nd 29 27

3rd 23 21

4th - -

5th - -

6th 1 0.9

7th 1 0.9

Out of the 114 respondents, only 14 (12.3%) respondents spent at least 20 minutes

each day of the week for physical activities that resulted to sweating or breathing hard, and 8 (7%) of the respondents didn‘t spend any time for physical activities. Most of the respondents, 23(20%), only spent 1 day of at least 20 minutes physical activities in a week. Table 9. Number of days in a week that the child spent in physical activities lasting for at least 20 minutes each that resulted to sweating or breathing hard (N=114)

Days Frequency Percent (%)

0 8 7.0 1 23 20.2 2 10 8.8 3 15 13.2 4 5 4.4 5 16 14.0 6 2 1.8 7 14 12.3 don‘t know or unsure 21 18.4

Most of the respondents, 31 (27.2%), spent about 1 hour and 1.6 hours of the day

respectively for exercise. Only 1 (0.9%) of the respondents reported having spent 6-9 hours a day for exercise, while 14 (12.3%) don‘t exercise at all.

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Table 10. Number of hours of exercise the child spends per day (N=114)

Hours Frequency Percent (%)

0.0 14 12.3 0.3 2 1.8 0.4 1 0.9 0.5 5 4.4 1.0 31 27.2 1.5 1 0.9 1.6 31 27.2 2.0 15 13.2 3.0 6 5.3 4.0 3 2.6 5.0 2 1.8 6.0 1 0.9 7.0 1 0.9 9.0 1 0.9

The results showed that among 114 children, 107 (93.9%) had hours of sleep of 7-10

hours, 5 (4.3%) had less than 7 hours of sleep and 2 (1.8%) had more than 10 hours.

Table 11. Hours of sleep per day (N=114)

Number of hours Number Percent (%)

less than 7 5 4.3

7 to 10 107 93.9

more than 10 2 1.8

Of the total sample population, 22.8%(26) watched TV or played computer games for 4 hours during the weekdays, and 10.5%(12) for 1 hour and less. On the other hand, during the weekends, 33,3%(38) watched TV or played computer games for 2 hours, 14.0%(16) for 3 hours, 8.8%(10) for 4 hours and another 5.3%(6) for 6 hours or more. Table 12. Hours spent on tv or computer games during weekends and weekdays (N=114)

Weekdays Weekends

Hours spent Number Percent (%) Number Percent (%)

0 2 1.8 4 3.5

1 12 10.5 35 30.7

2 17 14.9 38 33.3

3 21 18.4 16 14.0

4 26 22.8 10 8.8

5 13 11.4 3 2.6

6 21 18.4 6 5.3

Don‘t know/not sure 2 1.8 2 1.8

Out of the 114 respondents, only 2 (1.8%) were drinking carbonated beverages at a

frequency of three or more times a week, while 42 (36.8%) only had one. 34(29.8%) of the respondents were not carbonated beverage drinkers.

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Table 13. Frequency of drinking carbonated beverage per week (N=114)

Frequency Number Percent (%)

None Once Twice Three or more times Don‘t know/not sure

34 42 24

2 12

29.8 36.8 21.1 1.8

10.5

Out of the 114 respondents, almost half 53(46%) of them drink sweetened beverages once a day and about a third 35(31%) drinks twice a day. Ten percent(11) were not sure, 8(7%) don‘t drink and 7(6%) drink three or more times a day of sweetened beverages. Table 14. No of frequency of drinking sweetened beverage per day (N=114)

Frequency Number Percent (%)

None Once Twice Three or more times Don‘t know/not sure

8 53 35 7 11

7 46 31 6 10

The result showed that among 114 children, 53 (46.5%) had fast food once a week, 9

(7.9%) had less than a week, 28 (24.6%) had twice a week, 16 (14.0%) had 3-5 times a week, and only 1 (0.9%) had more than 5 times a week. Table 15.Frequency of eating fast foods (N=114)

Times per week Number Percent (%)

Less than once per week 9 7.9

Once 53 46.5

Twice 28 24.6

Three to five times 16 14.0

More than five times 1 0.9

Don‘t know not sure 7 6.1

The results showed that 27(23.7%) of the respondents drink 1 glass of milk per day. Almost a fifth, 24(21.1%) of the parents reported that they are unsure how much glasses the child drinks per day. Only 2(1.8%) of the children drink 4 or more glasses of milk per day. Table 16. Amount of milk the child typically drinks in a day (N=114)

Glasses of milk drank per day Number Percent (%)

None 6 5.3

Less than 1 14 12.3

1 27 23.7

2 21 18.4

3 16 14.0

4 or more 2 1.8

Don‘t know/not sure 24 21.1

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The most frequent milk type that is consumed by children is flavoured low-fat or skim milk (35.1%) followed by low-fat (24.6%) and skim or non-fat (12.3%). Seven percent reported that they their children do not drink milk. Table 17. Type of milk the child usually drinks (N=114)

Type of milk Number Percent(%)

None 8 7.0

Skim or non-fat 14 12.3

Flavored low-fat or skim 40 35.1

Low fat (1/2 - 1%) 28 24.6

Flavored 2% or whole 13 11.4

Reduced fat (2%) 1 0.9

Whole 7 6.1


Among the 114 children, 51 (44.7%) ate chips once a day, 25 (21.9%) ate chips twice a day, 10 (8.8%) ate chips three or more times a day, 19 (16.7%) do not eat chips at all. Table 18. Amount of chips consumption (N=114)

Times per day Number Percent (%)

None 19 16.7

Once 51 44.7

Twice 25 21.9

Three or more times 10 8.8


Of the study population, 9.7% of the students don‘t eat vegetables. 52.6% of them had 1 serving a day; 28.1% had 2 servings a day; 7% had 3 servings or more. 2.6% either did not know or were not sure. Of the study population, 7.1% of the pupils don‘t eat fruits. 48.2% had 1 serving a day; 26.3% of them had 2 servings a day; 12.3% had 3 or more servings a day. 6.1% of the students were either not sure or did not know. Table 19. Amount of vegetable and fruit consumption per day (N=114)

Vegetables Fruits

No. of servings Number Percent (%) Number Percent (%)

None 11 9.7 8 7.1

1 60 52.6 55 48.2

2 32 28.1 30 26.3

3 or more 8 7.0 14 12.3

Don‘t know/not sure 3 2.6 7 6.1

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Prevalence of obesity The overall prevalence of childhood obesity in the study was 21.9%. Almost half (52.6%) had normal BMI while 15.8% were overweight. The average BMI of the pupils were 19.99. Table 20. Prevalence of childhood obesity in WVSU-ILS (N=114)

BMI classification Frequency Percent (%)

Obese 25 21.9 Overweight 18 15.8 Normal 60 52.6 Thin 10 8.8 Very thin 1 0.9

Total 114 100.0

Table 21. Body-Mass Index profile of study participants (N=114)

Min Max Mean Standard deviation

BMI 11.8 33.6 20.00 4.0

When Body Mass Index (BMI) was categorized as to age in years, results showed that 64.3% (9 students) of 9 years old students, 28.6%(4 students) of 8 year old students and 26.1%(6 students) of 11 years old students were obese. While 27.8% (5 students) of 10 year old students, 26.1% (6 students) of 11 years old students, 16.7%(3 students) of 12 years old students were overweight. Table 22. BMI classification of children per age (N=114)

Age Obese Overweight Normal Thin Very thin

Total n % n % n % n % n %

6 - - - - 4 80.0 1 20.0 - 0.0 5

7 - - - - 6 40.0 8 53.3 1 6.7 15

8 4 28.6 2 14.3 8 57.1 - - - - 14

9 9 64.3 1 7.1 3 21.4 1 7.1 - - 14

10 4 22.2 5 27.8 9 50.0 - - - - 18

11 6 26.1 6 26.1 11 47.8 - - - - 23

12 1 5.6 3 16.7 14 77.8 - - - - 18

13 1 14.3 1 14.3 5 71.4 - - - - 7

Total 25 21.9% 18 15.8% 60 52.6% 10 8.8% 1 0.9% 114

When BMI was categorized as to the respondents‘ sex, of the 72 females, 13.9% were

obese (10 students), and 15.3% (11 students) were overweight. Of the 42 males, 35.7% (15 students) belong to the obese category while 21.9% (25 students) were in the overweight category.

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Table 23. BMI classification per sex (N=114)

BMI classification

Total Obese Overweight Normal Thin Very thin

n % n % n % n % n %

sex Female 10 13.9% 11 15.3% 44 61.1% 6 8.3% 1 1.4% 72

Male 15 35.7% 7 16.7% 16 38.1% 4 9.5% - - 42

25 21.9% 18 15.8% 80 52.6% 10 8.8% 1 0.9% 114

Only 25% of the obese fathers also had an obese child while 50% of those fathers had

an overweight child. Among the fathers who were overweight, 20.8% had an obese child and 12.5% had an overweight child.

Table 24. Distribution of children’s BMI with respect to paternal BMI (N=114)

A third (33.3%) of the obese mothers also had an obese child. Among the mothers who were overweight, 34.3.8% had an obese child and 11.4% had an overweight child. Table 25. Distribution of children’s BMI with respect to maternal BMI (N=114)

Children‘s BMI

Obese Overweight Normal Thin Very thin

n % n % n % n % n %

Paternal BMI

Obese 1 25.0% 2 50.0% 1 25.0% - - - -

Overweight 10 20.8% 6 12.5% 28 58.3% 4 8.3% - -

Normal 14 24.1% 8 13.8% 29 50.0% 6 10.3% 1 1.7%

Thin - - 2 50.0% 2 50.0% - - - -

Very thin - - - - - - - - - -

Total 25 21.9% 18 15.8% 60 52.6% 10 8.8% 1 0.9%

Children‘s BMI


n % n % n % n % n %

Maternal BMI

Obese 2 33.3% - - 4 66.7% - - - -

Overweight 12 34.3% 4 11.4% 16 45.7% 3 8.6% - -

Normal 11 15.5% 14 19.7% 38 53.5% 7 9.9% 1 1.4%

Thin - - - - 2 100.0% - - - -

Very thin - - - - - - - - - -

Total 25 21.9% 18 15.8% 60 52.6% 10 8.8% 1 0.9%

17

Of the children with college graduate fathers, 22% were obese, while 17% were overweight. For those with fathers having completed post-graduate education, 21.4% were obese, while 7.1% were overweight. Table 26. Distribution of BMI based on father’s educational level (N=114)

Of the children with college graduate mothers, 22% were obese, while 16.5% were

overweight. For those having mothers having post graduate degrees, 23.8% had children who were obese, while 14.3% were overweight. Table 27. Distribution of BMI based on mother’s educational level (N=114)

Twenty percent of families with only 1 child, had obese children and overweight children,

respectively. For families with 3 children, 28.6% had obese children while 17.1% had overweight children. For families having 4 children, 25% of them had obese children, while 12.5% had overweight children.

Children‘s BMI

Obese Overweight Normal Thin Very thin Total

n % n % n % n % n % N %

Father‘s

education

college graduate 22 22.0 17 17.0 51 51.0 9 9.0 1 1.0 100 100

Completed postgraduate

course 3 21.4 1 7.1 9 64.3 1 7.1 - -

14

100

Children‘s BMI


n % n % n % n % n % N %

Mother‘s

education

Highschool

graduate - - - - 2 100.0% - - - - 2 100.0%

College graduate 20 22.0% 15 16.5% 47 51.6% 8 8.8% 1 1.1% 91 100.0%

Postgraduate

course 5 23.8% 3 14.3% 11 52.4% 2 9.5% - - 21 100.0%

18

Table 28. Distribution of BMI with respect to no. of children in the family (N=114)

Breastfed and non-breast fed children showed close values in terms of obese children,

22.4% and 21.1% respectively. Meanwhile 13.2% of breastfed children and 21.1% of non- breastfed children were overweight. Table 29. Distribution of BMI in terms of breastfeeding history (N=114)

Children‘s BMI


n % n % n % n % n % n %

Breastfeeding

Not

breastfed 8 21.1% 8 21.1% 16 42.1% 6 15.8% - - 38 100.0%

breastfed 17 22.4% 10 13.2% 44 57.9% 4 5.3% 1 1.3% 76 100.0%

Among the first borns, 20.4% were obese while 16.7% were overweight. Meanwhile,

22.9% of second borns were obese while 11.4% were overweight. Among the 3 rd borns, 26.1% were obese while 17.4% were overweight.

Children‘s BMI


n % n % n % n % n % N %

Number of

children in

the

family

1 4 20.0% 4 20.0% 11 55.0% 1 5.0% 0 0.0% 20 100.0%

2 8 17.0% 6 12.8% 27 57.4% 5 10.6% 1 2.1% 47 100.0%

3 10 28.6% 6 17.1% 17 48.6% 2 5.7% - - 35 100.0%

4 2 25.0% 1 12.5% 4 50.0% 1 12.5% - - 8 100.0%

5 1 50.0% - - - - 1 50.0% - - 2 100.0%

6 - - 1 100.0% - - - - - - 1 100.0%

7 - - - - 1 100.0% - - - - 1 100.0%

19

Table 30. Distribution of BMI with respect to birth order (N=114)

Children‘s BMI


n % n % n % n % n % n %

Birth order

1st 11 20.4% 9 16.7% 26 48.1% 7 13.0% 1 1.9% 54 100.0%

2nd

8 22.9% 4 11.4% 22 62.9% 1 2.9% - - 35 100.0%

3rd

6 26.1% 4 17.4% 11 47.8% 2 8.7% - - 23 100.0%

6th - - 1 100.0% - - - - - - 1 100.0%

7th - - - - 1 100.0% - - - - 1 100.0%

Among the obese children, only 12% don‘t exercise, while 16% were reported to exercise for at least 20 minutes everyday. Among the overweight children, same values of 11.1% were reported for those who don‘t exercise and for those exercising everyday. Table 31. Distribution of BMI with respect to no. of days of in a week of exercise (N=114)

All obese children and almost all (94.4%) of overweight children sleep 7-10 hours a day.

BMI category


No. % No. % No. % No. % No. %

Exercise

days

0 3 12.0% 2 11.1% 3 5.0% - - - -

1 4 16.0% 3 16.7% 9 15.0% 7 70.0% - -

2 - - 3 16.7% 7 11.7% - - - -

3 4 16.0% 2 11.1% 9 15.0% - - - -

4 1 4.0% 1 5.6% 2 3.3% 1 10.0% - -

5 3 12.0% 4 22.2% 8 13.3% 1 10.0% - -

6 - - - - 2 3.3% - - - -

7 4 16.0% 2 11.1% 8 13.3% - - - -

Don‘t know 6 24.0% 1 5.6% 12 20.0% 1 10.0% 1 100.0%

20

Table 32. Distribution of BMI with respect to sleeping hours (N=114)

Among the obese children, 44% played computer games/watched television for 2 hours during weekends while 8% for 6 hours or more. Among the overweight, 33.3% played computer games/watched television for 2 hours while 16.7% for 6 hours or more.

Table 33. Distribution of BMI with respect to passive entertainment during weekends (N=114)

Among the obese children, 20% played computer games/watched television for 3, 4 and 6 hours and more respectively during weekdays. Among the overweight, 27.8% played computer games/watched television for 5 hours while 5.6% for both 1 hour, and those not engaging in passive entertainment, respectively.

BMI category


No. % No. % No. % No. % No. %

Sleeping

hours

less than 7

hours - - 1 5.6% 4 6.7% - - - -

7 to 10 hours

per day 25 100.0% 17 94.4% 54 90.0% 10 100.0% 1 100.0%

more than 10

hours per day - - - - 2 3.3% - - - -

BMI


No. % No. % No. % No. % No. N %

Passive

entertainment

(hours)

0 1 4.0% 1 5.6% 2 3.3% - - - -

1 7 28.0% 4 22.2% 21 35.0% 3 30.0% - -

2 11 44.0% 6 33.3% 17 28.3% 4 40.0% - -

3 2 8.0% 2 11.1% 10 16.7% 2 20.0% - -

4 - - 1 5.6% 8 13.3% - - 1 100.0%

5 1 4.0% - - 2 3.3% - - - -

6 or more 2 8.0% 3 16.7% - - 1 10.0% - -

Don't know 1 4.0% 1 5.6% - - - - - -

21

Table 34. Distribution of BMI with respect to passive entertainment during weekdays (N=114)

Forty percent of the obese respondents drink carbonated drinks once a day, while only 4%(1) drinks three or more times a day. Among the overweight, 38.9%(7) drink once a day while 22.2%(4) drink carbonated beverages twice a day.

Table 35. Distribution of children’s BMI with respect to carbonated drinks consumption (N=114)

BMI category


n % n % n % n % n %

Carbonated drinks

consumption frequency

(a day)

None 4 16.0% 6 33.3% 21 35.0% 3 30.0% - -

1 time 10 40.0% 7 38.9% 19 31.7% 5 50.0% 1 100.0%

2 times 6 24.0% 4 22.2% 13 21.7% 1 10.0% - -

times 1 4.0% - - - - 1 10.0% - -

Don't know 4 16.0% 1 5.6% 7 11.7% - - - -

44% (11) among the obese respondents drink sweetened beverages twice a day, while

only 4% (1) drinks three or more times a day. Among the overweight, 50% (9) drink once a day while 16.7% (3) drink sweetened beverages three times or more a day.

BMI category


No. % No. % No. % No. % No. %

Passive

Entertainment

(hours)

0 - - 1 5.6% 1 1.7% - - - -

1 3 12.0% 1 5.6% 8 13.3% - - - -

2 4 16.0% 4 22.2% 6 10.0% 3 30.0% - -

3 5 20.0% 4 22.2% 11 18.3% 1 10.0% - -

4 5 20.0% 3 16.7% 16 26.7% 2 20.0% - -

5 2 8.0% - - 7 11.7% 3 30.0% 1 100.0%

6 or more 5 20.0% 5 27.8% 10 16.7% 1 10.0% - -

Don‘t know 1 4.0% - - 1 1.7% - - - -

22

Table 36. Distribution of children’s BMI with respect to Sweetened drinks consumption (N=114)

60% (15) among the obese respondents eats fast food once a week, while only 4%(1) eat more than five times a week. Among the overweight, 44.4% (8) respondents eat fast food once a week, while only 11.1% (2) eat less than once a week, or twice a week respectively.

Table 37. Distribution of children’s BMI with respect to fast food consumption (N=114

Among the obese respondents, 28%(7) consume 2 glasses of milk a day while only

4%(1) consumes 4 or more glasses. Among the overweight, 33.3%(6) consume 1 glass of milk a day while 16.7%(3) consume 3 glasses.

BMI category


No. % No. % No. % No. % No. %

Sweetened drinks

consumption

frequency

None - - 1 5.6% 5 8.3% 2 20.0% - -

1 time 8 32.0% 9 50.0% 32 53.3% 3 30.0% 1 100.0%

2 times 11 44.0% 4 22.2% 15 25.0% 5 50.0% - -

3 or more

times 1 4.0% 3 16.7% 3 5.0% - - - -

Don't know 5 20.0% 1 5.6% 5 8.3% - - - -

BMI category


No. % No. % No. % No. % No. %

Fastfood

consumption

Less than once

a week 1 4.0% 2 11.1% 5 8.3% 1 10.0% - -

Once a week 15 60.0% 8 44.4% 25 41.7% 4 40.0% - -

Twice a week 5 20.0% 2 11.1% 19 31.7% 3 30.0% - -

3 to 5 times a

week 3 12.0% 5 27.8% 7 11.7% 1 10.0% - -

More than 5

times a week 1 4.0% - - - - - - - -

Don't know - - 1 5.6% 4 6.7% 1 10.0% 1 100.0%

23

Table 38. Distribution of children’s BMI with respect to milk consumption (N=114)

Most of the obese respondents(32%) consume low fat milk while most of the overweight respondents(38.9%) consume flavoured low fat or skim milk.

Table 39. Distribution of children’s BMI with respect to type of milk consumed (N=114)

Among the obese respondents, 48%(12) consume one serving of chips a day while only 8%(2) consumes none at all, or 3 times or more serving, respectively. Among the overweight, 44.4%(8) consume one serving of chips a day while only 5.6%(1) consume none at all, or two servings, respectively

BMI category


No. % No. % No. % No. % No. %

Milk consumption

(a day)

None 1 4.0% 5 8.3% - - -

Less than

1 glass 4 16.0% 4 22.2% 5 8.3% 1 10.0% - -

1 glass 5 20.0% 6 33.3% 13 21.7% 3 30.0% - -

2 glasses 7 28.0% 4 22.2% 9 15.0% 1 10.0% - -

3 glasses 2 8.0% 3 16.7% 8 13.3% 3 30.0% - -

4 or more

glasses 1 4.0% - - 1 1.7% - - - -

Don‘t know 3 12.0% 1 5.6% 17 28.3% 2 20.0% 1 100.0%

BMI category

obese overweight normal thin very thin

No. % No. % No. % No

.

% No. %

Milk type

consumed

None 3 12.0% - - 5 8.3% - - - -

Skim or non-fat 3 12.0% 3 16.7% 7 11.7% 1 10.0% - -

Flavored low-fat

or skim 5 20.0% 7 38.9% 25 41.7% 3 30.0% - -

Low fat (1/2 –

1%) 8 32.0% 2 11.1% 13 21.7% 4 40.0% 1 100.0%

Flavored 2% or

whole 4 16.0% 4 22.2% 5 8.3% - - - -

Reduced fat (2%) - - - - - - 1 10.0% - -

Whole 2 8.0% - - 4 6.7% 1 10.0% - -

Don't know - - 2 11.1% 1 1.7% - - - -

24

Table 40. Distribution of children’s BMI with respect to chips consumption (N=114)

Among the obese respondents, 56%(14) consume one serving of vegetable a day while only 8%(2) consume none at all, or 3 times or more serving, respectively. Among the overweight, 44.4%(8) consume one serving of vegetable a day while only 5.6%(1) consume none at all, or not sure respectively.

Table 41. Distribution of children’s BMI with respect to vegetable consumption (N=114)

Among the obese respondents, 44%(11) consume one serving of fruit a day while only

8%(2) consumes none at all, or are not sure, respectively. Among the overweight, 38.9% (7) consume one serving of fruit a day while only 5.6% (1) consume none at all, or not sure, respectively

BMI Category


No. % No. % No. % No. % No. %

Chips

consumption

None 2 8.0% 1 5.6% 11 18.3% 5 50.0% - -

Once 12 48.0% 8 44.4% 27 45.0% 3 30.0% 1 100.0%

2 times 9 36.0% 5 27.8% 10 16.7% 1 10.0% - -

3 or more

times 2 8.0% 1 5.6% 7 11.7% - - - -

Don't know - - 3 16.7% 5 8.3% 1 10.0% - -

BMI category

obese overweight normal thin very thin

No. % No. % No. % No. % No. %

Vegetables

None 2 8.0% 1 5.6% 6 10.0% 1 10.0% 1 100.0%

1 serving per

day 14 56.0% 8 44.4% 32 53.3% 6 60.0% - -

2 servings per

day 6 24.0% 6 33.3% 19 31.7% 1 10.0% - -

3 or more

servings per

day

2 8.0% 2 11.1% 2 3.3% 2 20.0% - -

Don't know 1 4.0% 1 5.6% 1 1.7% - - - -

25

Table 42. Distribution of children’s BMI with respect to Fruit consumption (N=114)

Child Eating and Behaviour Questionnaire

The mean (3.72) of enjoyment of food (EF) had the highest average CEBQ score in the respondents. It was followed by emotional under eating (EUE) and food responsiveness (FR) with means of 3.04 and 2.96 respectively. Emotional over eating (EOE) and by Slowness in eating (SE) were the lowest mean CEBQ scores at 2.42 and 2.65 respectively. Table 43. Average CEBQ scores per sub category of the respondents (N=114).

N Range Minimum Maximum Mean

Food responsiveness (FR) 114 4.0 1.0 5.0 3.0

Enjoyment of food (EF 114 3.0 2.0 5.0 3.7

Emotional Over Eating (EOE) 114 2.8 1.0 3.8 2.4

Desire to Drink (DD) 114 4.0 1.0 5.0 2.9

Slowness in Eating (SE) 114 4.0 1.0 5.0 2.6

Satiety Responsiveness (SR) 114 3.0 1.4 4.4 2.9

Food Fussiness (FF) 114 4.0 1.0 5.0 2.9

Emotional Under Eating (EUE) 114 3.8 1.3 5.0 3.0

Most of the obese respondents had higher mean scores in the eating behaviors such as

Enjoyment of food, Food Responsiveness and Desire to drink while among the overweight, higher mean scores were found to be in Enjoyment of Food, Emotional under eating and Satiety responsiveness.

BMI category


No. % No. % No. % No. % No. %

Fruits

None 2 8.0% 1 5.6% 3 5.0% 1 10.0% 1 100.0%

1 serving per

day 11 44.0% 7 38.9% 33 55.0% 4 40.0% - -

2 servings per

day 7 28.0% 6 33.3% 16 26.7% 1 10.0% - -

3 or more

servings per

day

3 12.0% 3 16.7% 5 8.3% 3 30.0% - -

Don't know 2 8.0% 1 5.6% 3 5.0% 1 10.0% - -

26

Table 44. Distribution of children’s BMI with respect to child eating behaviors (N=114)

Multiple Regression analysis

Multiple regression analysis showed that only frequency of sweetened beverage (OR=2.140), eating behaviors such as food responsiveness (OR=4.677), enjoyment of food(OR=7.313) and emotional over eating(OR=2.47) had strong positive associations with obesity while satiety responsiveness(OR=0.298) had a negative association with obesity. Frequency of sweetened beverages, Food responsiveness, Enjoyment of food and emotional overeating are therefore predictors for childhood obesity, while satiety responsiveness as a protective factor. None of the factors had any association with being overweight.

BMI category


Mean Mean Mean Mean Mean

FR 3.7 2.7 2.8 2.9 2.8

EF 4.2 3.8 3.6 3.4 2.8

EOE 2.7 2.1 2.4 2.3 2.0

DD 3.3 2.8 2.8 3.1 1.7

SE 2.4 2.4 2.7 3.3 5.0

SR 2.6 2.9 2.9 3.2 3.8

FF 2.9 2.9 2.9 3.3 3.5

EUE 3.1 3.2 3.0 2.9 3.0

27

Table 45. Odds ratio of factors evaluated for childhood overweight or obesity. (N=114)

Factors Overweight Obesity

OR p-value OR p-value

paternal BMI 0.977 0.769 1.053 0.511

maternal BMI 0.937 0.447 1.106 0.164

household income 0.799 0.259 1.116 0.510

birth order 0.657 0.524 1.075 0.893

breastfed 0.561 0.284 0.954 0.925

number of children in the family

1.08 0.745 1.167 0.484

exercise days 0.985 0.902 1.024 0.840

hours of sleep 0.710 0.710 1.602 0.628

weekday passive entertainment

1.23 0.270 0.999 0.994

weekend passive entertainment

0.931 0.636 1.026 0.844

carbonated drinks frequency 1.012 0.973 1.612 0.127

sweetened beverages frequency

1.526 0.240 2.140 0.048

fast food 1.133 0.687 1.039 0.889

chips 1.386 0.304 1.471 0.150

milk type

skim/nonfat 5188729.000

0.990 3.333 0.344

flavoured low fat or skim 2917679.000 0.990 1.563 0.712

low fat 3112269.000 0.990 3.500 0.297

flavoured 2% or whole 2121762.000 0.991 0.909 0.943

reduced fat - - 5.000 0.368

whole 389050.200 0.992 0.750 0.819

milk amount 0.982 0.928 1.062 0.705

vegetables 1.527 0.236 1.090 0.786

fruits 1.408 0.300 1.098 0.756

CEBQ food responsiveness 0.904 0.759 4.677 <0.0005

CEBQ enjoyment of food 1.871 0.169 7.313 <0.0005

CEBQ emotional over eating 0.533 0.122 2.47 0.025

CEBQ desire to drink 0.948 0.853 1.641 0.056

CEBQ slowness in eating 0.5663 0.123 0.579 0.073

CEBQ satiety responsiveness 0.715 0.480 0.298 0.007

CEBQ food fussiness 0.776 0.552 0.758 0.482

CEBQ emotional under eating 1.625 0.224 1.442 0.344

- significant with p-value <0.05

It was revealed thru multiple regression that only birthweight (0.004), FR (0.002), EF (0.003) and SE (<0.0005) were significant (p value<0.05) predictors of high BMI among elementary children in WVSU-ILS. Birthweight, FR and EF are therefore positive predictors while SE is a negative predictor of high BMI.

28

Table 46. Multiple regression analysis of factors as predictor for high BMI. (N=114) Factors Beta t-value Sig.

Parental factors

paternal BMI 0.101 1.318 0.190 maternal BMI 0.094 1.187 0.238

paternal education -0.069 -0.885 0.378

maternal education -0.019 -0.243 0.809

household monthly income -0.044 -0.577 0.565

number of children in the family -0.095 -1.231 0.221

order of birth -0.087 -1.110 0.269 Birth and prenatal factors

birth weight* 0.002 2.929 0.004

breastfeed 0.000 -0.002 0.999 Physical activity factors

exercise (days/week) -0.009 -0.121 0.904

exercise (hours/day) -0.009 -0.118 0.907

passive entertainment during weekdays (h) 0.099 1.292 0.199

passive entertainment during weekend (h) 0.077 0.999 0.320

hours of sleep 0.150 1.967 0.052 Food preference factors

frequency of drinking carbonated drinks 0.006 0.075 0.940

frequency of drinking sweetened beverage 0.076 0.966 0.336

eating at fast food -0.121 -1.521 0.131

chips -0.022 -0.291 0.771

type of milk -0.140 -1.854 0.066

amount of milk 0.052 0.679 0.498

vegetables 0.051 0.660 0.511

fruits 0.003 0.042 0.967

*significant at p – value <0.05

Table 47. Multiple regression analysis of CEBQ scores as predictors for high BMI (N=114) Beta t-value. Sig.

Food responsiveness (FR)* .280 3.193 0.002

Enjoyment of food (EF)* .271 3.084 0.003

Emotional Over Eating (EOE) -.012 -0.134 0.893

Desire to Drink (DD) -.052 -0.557 0.579

Slowness in Eating (SE)** -.308 -3.988 0.000

Satiety Responsiveness (SR) -.030 -0.333 0.740

Food Fussiness (FF) .048 0.593 0.555

Emotional Under Eating (EUE) -.054 -0.669 0.505

*significant at p-value <0.05 **significant at p-value<0.0001

29

DISCUSSION Prevalence of obesity The prevalence of overweight and obesity in developed countries is about double that in developing countries (11.7% and 6.1%, respectively) in which a vast majority of affected children (35 million) live in developing countries. In addition, the relative increase in the past 2 decades has been higher in developing countries (+65%) than in developed countries (+48%). Marked differences were observed across regions. In Africa, the prevalence of childhood overweight and obesity in 2010 is 8.5%, and it is expected to increase to 12.7% in 2020—a relative increase of 49%. In Asia, the estimated prevalence is lower than in Africa (4.9% in 2010, increasing to 6.8% in 2020); however, in absolute numbers, Asia has the highest number of overweight and obese children, because more than half (18 million in 2010) of the affected children from developing countries live in this region.36

According to the Regional Coordinator of the Asia-Pacific International Obesity

Taskforce of Australia, in the report in 2008, 1% of young children (0-10 years) and 3% of adolescents (11-17 years) were overweight in the Philippines. Among Chinese youth and young urban Thai children, the rate of overweight and obesity was as high as 23%. Also, in Taipei, Taiwan, 28% of boys, were either overweight or obese, as compared to only 21.3% of the girls between 12-15 years of age. Even Vietnam, which is just beginning the process of economic transition, 14-16% of the children were overweight.37

In the Philippines, a survey by the National Statistics Coordination Board (NSCB)

revealed that in 2008, the number of overweight Filipinos has increased to 26.6% from 16.6% in 1993.38 Overweight and obesity had affects 7 out of 10 women and about 1 out of 10 men, according to 7th National Nutrition Survey conducted by the Food and Nutrition Research Institute of the Department of Science and Technology (FNRI-DOST), thus this has become an increasing problem of the country.39 In this study, 35.7% of males were obese and 16.7% of males were overweight while 13.9% of females were obese and 15.3% of females were overweight.

The National Nutrition Survey (NNS) by the Food and Nutrition Research Institute of the

Department of Science and Technology (FNRI-DOST) revealed that 4.3 % or about 4 in every 100 of children, (newborns to 5 year olds) are overweight for their age. The survey results further revealed that 8 in 100 (7.5%) of school children 6-10 years old are overweight. Although the prevalence of overweight children belonging to this age group is still low, it has been steadily increasing since 1989.

Some of the regions with the highest prevalence of overweight children aged five years and below include Ilocos (or Region 1) with 6.3 %, the National Capital Region (NCR) with 6.2 percent, and Calabarzon (or Region IV-A) with 5.9 %. 40 Meanwhile the National Nutrition Council- Region 6 stated that 3 out of 10 persons in Western Visayas are obese based on records covering 1998 to 2011.41

In this study, the prevalence of obesity and overweight among elementary students in

WVSU – ILS are 21.9% and 15.8% respectively while 52.6% are normal. Theaverage BMI of the pupils is 19.99.A table below shows the comparison of the prevalence of obesity in this study with other studies with conducted in almost the same age group.42

30

Table 48. Prevalence of childhood obesity according to different studies.

Prevalence(%) Age range Country Year Study

35 6-12 Spain 199-1996 Moreno43

10 6-14 Japan 1993 Kotani44

20 7-11 UK 1998 Lobstein45

31.2 boys 26.5girls

6-12 Greece 2011 Tzotzas46

15 6-11 USA 1999-2000 NCHS47

18 6-11 USA 2009-2010 Ogden48

Studies have shown that the prevalence of overweight and obesity also varied between

the sexes. In a study conducted by Jackson from more than 1,700 sixth-grade students from 20 schools in Michigan between 2004 and 2011 revealed that more than 37% of boys and about 31% of the girls were overweight or obese.49A study on Greek children also revealed that there is higher percentage of boys that are obese.45A statistics report in Canada in 2009 to 2011 finds that 19.5% of boys aged 5 to 11 are obese, compared to 6.3 % of girls of the same ages. The same survey further stated that it‘s long been recognized that obesity tends to occur in higher rates in boys than girls, but the numbers were three times as much in boys than in girls.50

In this study, the total elementary student population including those who did not

participate in the study was 376. From the total population, 40.7% (153) were boys and 59.3% (223) were girls. Among those who participated in the study, 35.7% of the boys are obese while only 13.9% of the girls are obese. Among the boys, 52.4% are overweight or obese while only 29.2% are overweight or obese among the girls. The sample population that participated in this study was composed predominantly of females which could have affected the percent of obese and overweight among boys and girls when compared.

Risk Factors for obesity

Parental BMI

A study conducted in the United Kingdom by Reilly et al identified parental obesity as one of the risk factors in early-life obesity in children.51 This was supported by another study from Hong Kong involving 6 and 7 year olds concluding that paternal and maternal obesity were independently associated with childhood overweight. A stronger association for maternal obesity than for paternal obesity was also found.52Another study also reported that a combination of independent risk factors including parental obesity resulted in the highest risk of overweight in 5-7 year old children.53

Parental obesity may increase the risk of obesity through genetic mechanisms or by

shared familial characteristics in the environment such as food preferences. 54 Even when only 1 of the parents was obese, the risk of obesity at age 7 was increased. The risk was higher when both parents were obese.50However, in our study, these associations were not found. Our data showed that both the paternal BMI and the maternal BMI have no significant relationship to

a child‘s BMI.

31

Parental educational level In this study, multiple regression analysis showed that the parental education was not a

significant predictor for obesity and overweight among elementary students. This was contrary to several studies which revealed that parental education was one of the significant predictors and has a negative or inverse relationship with childhood obesity.

Children of mothers with no school degree had an almost three times higher risk to be

obese than children of mothers with 13 years of school. Stratified analysis by BMI of the parents revealed that paternal and maternal education was particularly strongly associated with overweight in children in the subsample of both parents being overweight. 51

Nonetheless, 9 or less years of education of either of the parents were the highest risk

factor for childhood obesity. However, children of self-employed mothers, in spite of high educational levels, also had a high risk of being obese. Self-employed mothers who work more hours outside their homes might probably spend less time with their children and, hence, will have less control over food intake, eating habits, and physical activity levels of their children. 51

A representative cross-sectional survey on 1979 children and case–control study on 367

children in the German City of Aachen have also convincingly demonstrated that social status is inversely associated with childhood obesity as early as age 6. There was a significant relationship between parents‘ years of education and childhood obesity, and among the many other ascertained socioeconomic status (SES) variables, parental education was the most important SES variable that accounts for the SES-obesity association. Children of the lowest social status had a 3.3-fold higher risk to be obese than children of the highest social status.55

Differences in cultural and social norms between parents of high and low education

might be another reasonable explanation. Adverse economic circumstances, marital conflict and negative life events seem to be much more frequent in families with a lower SES. These parents might be less involved in the lives of their children, which might then lead to more overeating.54

Monthly family income

In this study, monthly income was not a significant factor in affecting the children‘s BMI. This is in contrast with the study by Danielzik which reported that low socioeconomic status, along with other independent risk factors, resulted in the highest risk for overweight in 5 to 7 year old children.52Children of the lowest social status had a 3.3-fold higher risk to be obese than children of the highest social status.54However in a study done byWang which gathered data from children of ages 6 to 18 and compared the effect of income on the risk for obesity. The study reported that in China and Russia, the risk for obesity is higher for high – income groups while; in the United States of America the low – income entails a higher risk for obesity.56

A systematic review of cross sectional studies looked into the association of income

indices and obesity. In the said study, 3 out of 11 showed no association, 4 of the studies showed an inverse association and the remaining 4 showed varying associations.57

Child birth weight A study by AtulSinghal et al have concluded that there was a positive correlation between birth weight indicative of fetal growth and lean body mass in adulthood as measured by

32

body mass index, implicating early factors for obesity and cardiovascular diseases.58 Hill et al have further indicated that birth weight has a positive correlation to the individual BMI as a measure of obesity in contrast to the body fat percentage.59

Another study by Salsberry et alhave elaborated further into other prenatal factors that have an effect on the child‘s early weights as having an influence towards overweight and obesity which increased as the child grew older. Such factors would include race, ethnicity, maternal smoking and prepregnancy obesity. This study suggests that overweight and obesity may well have its origins not only in childhood but also during pregnancy and prepregnancy periods.60

A systematic review and meta-analysis by Yu et al, comprising of 33 studies suggest

that high birth weight was associated with high risks of obesity and serves as a mediating factor between prenatal factors and obesity risk. The study showed that high birth weights (>4000 g) was associated with higher risks for obesity (odds ratio [OR], 2.07)compared with birthweights of ≤ 4000 g. Further, birth weights <2500 g were associated with decreased obesity risks (OR, 0.61) compared with to birth weights ≥ 2500 g. Also, a subgroup analysis of pre-school children, school children, and adolescents showed a positive association of high birth weights with high risks of obesity from childhood extending to early adulthood.61

A study in Buenos Aires, Argentina by Hirschler et al explored the relationship between

birth weight (BW) and childhood overweight and obesity (OW/OB) and metabolic syndrome (MS) in 10 elementary schools at 9 years of age. They concluded that low birth weights did not have an association with either obesity or metabolic syndrome in children; however, high birth weight was positively correlated with obesity and metabolic syndrome in children.62 High birth weight in combination with other independent risk factors such as parental obesity and low SES was also reported to high risk of overweight in 5 to 7 year old children.52In a cohort study of 7 year old children, birth weight was shown to be associated with the risk for obesity. 50In line with our study, multiple regression showed birth weight as one of the significant factors that

predicted the child‘s BMI. Breastfeeding

Though in this study, breast feeding has no association with obesity; it was recommended by the American Academy of Pediatrics that breast feeding mildly protects against later obesity. 63 A study among German children and adolescents also showed no significant association between obesity and breast feeding.64

Order of birth and Number of children A study on Portuguese children revealed that obesity and overweight was associated with being a single child in the family, belonging to large or small families, and being born later than other siblings.65

Physical activity

This study showed that physical activity had no significant relationship with childhood obesity. However, it has been hypothesized that a steady decline in physical activity among all age groups has heavily contributed to rising rates of obesity all around the world. Numerous

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studies have shown that sedentary behaviours like watching television and playing computer games are associated with increased prevalence of obesity. Increased proportions of children who are being driven to school,and low participation rates in sports and physical education, particularly among adolescent girls, were also associated with increased obesity prevalence.66It has also been cited by another study that children who watched at least 4 hours of television per day were also less likely to participate in vigorous physical activity, thus they also had greater BMIs and skinfold measurements than those who watched <2 hours of television per day. It also pointed out that restriction of television viewing resulted to improvement in BMI.67

Amount of sleep

There were 29 studies conducted in 16 countries about sleeping hours of children and its relation to being overweight or obese. Out of these studies, the researchers have found out that short sleep increases the risk of being overweight and obese. In addition, late bedtimes in children were also found to be a risk factor for overweight or obesity. The studies also suggested that changes in eating pathways may increase body fat.68 However, in our study, the results show that only there is no significant relationship between sleeping hours per day and risk of being overweight/obese evidenced by only a little percentage of the population having shorter sleeping hours. Passive entertainment

Of the total sample population, 27.2% watched TV or played computer games for <1-2

hours during the weekdays. On the other hand, during the weekends, 8.8% watched TV or played computer games for 4 hours, 14.0% for 3 hours and another 5.3% for 6 hours or more.

The Displacement theory hypothesizes that the time spent watching television and

playing computer games reduces the time allocated to physical activity. In a study done by Steele et al, results showed that more active children were less likely to be overweight or obese, but greater screen-time use alone did not significantly increased the risk of being overweight or obese.69In support to this, Marshall et al reported that the positive association between screen-time and excess in body weight was too small to be clinically significant. 70 However, in a study by Vanderwater et al, results have shown that there was a strong relationship between weight status of younger children and time spent playing electronic games, but not television use.71

In this study, 44% of children who spend 2 hours playing computer games or watching

television during weekends were obese and 33.3% were overweight. Only 4% were obese and 5.6% were overweight among children who spend none to less than an hour playing computer games or watching television during weekends.

Carbonated drinks and Sweetened beverages Results from our study says that frequency of intake of sweetened beverages was a risk factor for childhood obesity.

Ludwig et al examined the relationship between the consumption of sugar-sweetened

drinks and childhood obesity in 548 ethnically diverse children, over a period of 19 months. They reported that with each increased serving of sugar-sweetened carbonated drink, body mass index and frequency of obesity increased. The odds ratio of becoming obese increasing

34

1.6 times for each additional sugar-sweetened drink consumed each day. 72 A final cross-sectional survey of 385 school children in Santa Barbara County, CA assessed BMI and body fat directly, and diet and lifestyle by questionnaire. The odds of being overweight were 46%

higher (95% CI=2–110%) among those students (n=49) who reported consuming 3 SSB

(sugar sweetened drinks) servings/day compared to those consuming lower amounts, after adjustment for age, gender, ethnicity and television viewing, but many other lifestyle and dietary factors were not considered in this study.73

Obesity and overweight were attributed to the increase in intake of high-energy foods that are high in fat, salt and sugars but low in vitamins and minerals. Sugary beverages in kindergarten at least weekly were associated with more than double the odds of severe kindergarten obesity. One study correlated the high consumption of sugar-sweetened beverages or SSBs is associated with development of metabolic syndrome and type 2 diabetes. These data provide empirical evidence that intake of SSBs should be limited to reduce obesity-related risk of chronic metabolic diseases.74Our study also had similar results of other studies.

Fast foods

In a study by Currie et al, they found out that there was an increase in the number of children who became obese (5.27%) is associated with one-mile proximity with a fast food chain. 75In our study, eating fast foods is not a predictor for overweight or obesity though there were fast food chains in the proximity of the school.

Milk type and amount

Factors involved in childhood obesity begin by reviewing the child‘s energy intake, energy expenditure, and "energy balance‖. This gives us the notion that children who eat more "empty calories" and expend fewer calories through physical activity are more likely to be obese than other children. Included also were the changes in the child's environment to upset this energy balance equation. In particular, they examine changes in the food market, in the built environment, in schools and child care settings, and in the role of parents—paying attention to the timing of these changes.76

Among the changes that affect children's energy intake are the increasing availability of energy-dense, high-calorie foods and drinks through schools. Despite studies claiming that the intake of these energy dense high calorie food and drinks are of signif icant value to ones predisposition to obesity, our results revealed that frequency of milk consumption was not found to be a risk factor to childhood obesity, neither was the frequency of carbonated drink consumption.

This was in contrast to a cohort study from the Framingham Children‘s study involving 92 children. They found out that children who consumed least dairy during pre-school acquired significantly more body fat during childhood than those who consumed the most. Preschool children with the lowest dairy intake (<1.25 servings/day for girls and <1.7 serving/day for boys) have significantly greater gains in body fat during childhood(an extra 25mm subcutaneous fat by early adolescence).77In a study by Gianvincenzo et al in 2005, an investigation among 1087 children showed that milk consumption was still significantly and inversely associated with BMI z scores in the whole milk consumers when controlling for age and the frequency of various food.78

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Chips Chips are energy dense foods which are said to contribute to childhood obesity. There have been studies that linked the rise in obesity incidence with increasing consumption of snacks, fast foods, and soft drinks and with the consumption of high-energy-density diets. 79 However this study showed no significant association between consumption of chips and overweight or obesity.

Vegetables and Fruits

In this study, the amount of consumption of fruits and vegetables had no significance in

predicting the occurrence of obesity and overweight in elementary children. Although the WHO in their Global Strategy on Diet, Physical Activity and Health (2002) cited a low intake of fruits and vegetables as among the top 10 risk factors for obesity and other preventable noncommunicable diseases80, a study by Ledoux and Hingle in 2011 showed that the inverse relationship between the consumption of fruits and vegetables and weight gain appeared to be significant only among the adult population. No association was shown among children as

well.81

Child Eating and Behaviour Questionnaire

CEBQ describes the development of a multi – dimensional questionnaire generally regarded as one of the most comprehensive instruments in assessing children‘s eating behaviour based on parents‘ reports of their child‘s behaviour. Individual differences in eating behaviour were conceptualized as having several dimensions.

The literature in eating behaviour suggested eight areas for consideration: Food

Responsiveness (FR), Enjoyment of Food (EF), Emotional Over–Eating (EOE), Desire to Drink (DD), Slowness in Eating (SE), Satiety Responsiveness (SR), Food Fussiness (FF) and Emotional Under–Eating (EUE).

EF and FR reflect different aspects of excessive responsiveness to external food cues. EOE and EUE measure an increase or a decrease in eating response to a range of negative emotions such as anger, loneliness or anxiety. DD reflects the inclination of children to drink frequently, sometimes associated with an increased intake of sugar – sweetened drinks. SR represents the ability of a child to reduce food intake after eating to regulate its energy intake. High scores of SE meant a reduction in eating rate as a consequence of lack of enjoyment and interest in food. FF is related with a rejection of a substantial amount of novel and common foods, narrowing the range of the variety of consumed foods.

The first four subscales (EF, FR. EOE and DD) are ‗food–approach‘ subscales that

indicate positive inclinations for eating while the other four subscales (SR, SE, FF and EUE) are considered as ‗food–avoidant‘ subscales related to negative inclinations to food intake.

This study provided information of association between young grade school children‘s eating behaviour and their BMI. The present study showed the presence of 3 positive subscales (FR, EF and EOE) as predictors of obesity and one negative subscale predictor (SR). The results were similar to previous studies showing that children with increased BMI are highly responsive to environmental food cues. The inverse association between BMI and SR subscale

36

are similar to other studies. The CEBQ subscales DD, SE, FF and EUE showed no association with childhood obesity.

Higher levels of children‘s Food responsiveness (FR), Enjoyment of Food (EF) and Emotional Over Eating (EOE) were positively associated with obesity while Satiety Responsiveness (SR) was negatively related to obesity. Food responsiveness (FR) is measured behaviourally by seeing whether food intake is reduced to compensate for a prior increase in consumption. Enjoyment of food (EF) assessed behaviourally on the basis of the amount of good–tasting versus less–good–tasting food consumed in standard conditions. Emotional overeating (EOE) is characterized by either an increase in eating in response to a range of negative emotions, such as anger and anxiety. Satiety responsiveness (SR) represents the ability of a child to reduce food intake after eating to regulate its energy intake.

Individual differences in eating style contribute both to overweight and underweight. Many different eating styles have been implicated in the etiology of overweight or obesity. Eating behaviour is susceptible to modification through interventions to prevent childhood obesity is important in focusing on behavioural traits.82

It was revealed thru multiple regression that only FR (0.002), EF (0.003) and SE

(<0.0005) are significant (p value<0.05) predictors of increased BMI among elementary children in WVSU-ILS. FR and EF are both positive predictors of increased BMI while SE is a negative predictor of high BMI. More specifically, multiple regression of CEBQ scores to determine the odds for overweight and obesity revealed that there is a positively strong association of obesity to FR and EF. There was a weak positive association of obesity to EOE. The SR is also a weakly negative predictor for risk of obesity.

37

CONCLUSIONS

The prevalence of obesity and overweight among elementary students of WVSU ILS are

21.9% and 15.8% respectively. For the males, 35.7% are obese, while 21.9% are overweight. For the females, 13.9% are obese, while 15.3% are overweight. As to age, 64.3% of 9 years old students, 28.6% of 8 year old students and 26.1% of 11 years old students are obese. While 27.8% of 10 year old students, 26.1% of 11 years old students, 16.7% of 12 years old students are overweight. Significant positive associations existed only between obesity and consumption of sweetened beverages (p=0.048), high CEBQ score for food responsiveness (p<0.0005), enjoyment of food (p<0.0005), emotional over eating (p=0.025), and low CEBQ score for satiety responsiveness (p=0.007). There are no significant associations between the predetermined risk factors and being overweight.

Appendix West Visayas State University

College of Medicine

La Paz, Iloilo City

12 July 2013

Emellie G. Palomo, Ph.D

Director WVSU - ILS

Dear Dr. Palomo

We are a group of thirteen third year medicine students from the West Visayas State University - College

of Medicine. As part of academic requirement, we are proposing to conduct a study entitled, "Prevalence

and Risk Factors for Obesity and Overweight Elementary Children at WVSU-ILS.” In this study, we

would compute the BMI of students and ask questions to assess the risk factors.

In connection with this, we would like to ask for a consultation with your good office regarding the

feasibility of our study.

We hope for your favorable response on this matter.

Thank you very much and God bless.

Respectfully yours,

Bryan Atas

Group Representative

Noted:

Prof. Ma. Pilar Charmaine S. Malata,

Research Coordinator

Fred P. Guillergan, M.D.

Head, Office of Research

West Visayas State University College of Medicine La Paz, Iloilo City

July 26, 2013 Atty. Paulino Salmon President, Parent-Teachers Association West Visayas State University- Integrated Laboratory School Cc: Emellie G. Palomo, Ph.D Director, West Visayas State University- Integrated Laboratory School Dear Attorney Salmon; Greetings! We are a group of third year medical students of West Visayas State University who are presently conducting a research study entitled ―Prevalence and Risk Factors of Overweight and Obese Grade School Students of WVSU-ILS” as a requirement in our college’s curriculum.

In line with this, we would like to request permission from the Parents -Teachers‘ Association to allow us to conduct this study involving the students and their parents. We plan to weigh the elementary pupils and give out questionnaires pertaining to the children‘s habits and lifestyles. Your positive response on this matter will be highly appreciated. Respectfully yours, Bryan Atas Group Representative Noted: Prof. Ma. Pilar Charmaine S. Malata, Research Coordinator Fred P. Guillergan, M.D. Head, Office of Research

Schedule of Activities

TASKS SPECIFICS DATES

Plan for Pilot testing and

Check probable population Finalize Paper works Schedule Testing

October 21-26 October 28-31

Pilot Testing Proper

November 4-8

Evaluation of Pilot testing and Validation of Questionnaire

November 11-15

Finalize paper works ILS Finalize permission to access records(birth certificate/list of students/schedule of classes) Accomplish Final Consent form signed by:

- Director ILS

- Guidance Counselor

- PTA president

- Class Advisers

- Research Adviser

- Head Office Research,

COM

- Dean, COM

October 21-26 October 21-26

Meeting of each Class Advisers concerning:

Background and

Objective of Study

Roles of Class Advisers

in the study

Arrange Schedule for

Anthropometric

Measurements

November 4-8

Giving of Consent Forms with Questionnaires attached

Short talk during classes November 18-22

Finalizing Sample Population upon collection of returned questionnaires with consent forms

Finalize schedule of anthropometric measurements

November 25-29

Anthropometric Measurements

December 2-6

Organize data collected Christmas Break

Data analysis January 6-10

Start chapter 4-5 January 13-24

Revise Chap 1-3 January 13-24

Revise whole paper 1st and 2nd week February

Editing final research paper 3rd and 4th week February

Budget Proposal

EXPENSES ALLOTTED BUDGET(PHP)

Printing Photocopying Binding Snacks for Students Honorarium Token

Statistician School Advisers Principal Guidance Counselor TOTAL

1000 2000 1000 3000 500 2000 10,500

38

RECOMMENDATIONS

It is recommended that: 1. a similar study be performed in both private and public schools to look into factors may differ between the private and public schools, 2. to look into other factors which may be implicated in childhood obesity such as weight gain in infancy, advanced maternal age at pregnancy, self-perception of obesity or race, and 3. a prospective study on the BMI of children into adolescents or adults to monitor how many obese or overweight children actually become obese or overweight adults

39

ACKNOWLEDGMENT

The researchers would like to thank the people whose contributions and participation made this study possible. Truly, they are indebted to the following:

Jerusha Comuelo, MD, research adviser, for advise and guide in the formulation and

writing of this study Prof. Malata, research coordinator, for the invaluable critic and guide in the writing of this

manuscript Integrated Laboratory School faculty, administrators, advisers and student teachers for

facilitating the return of the questionnaires and assisting in the anthropometric measurement of the children

Ms.Geneveve Parreño and Mr. Roderick Napulan for the statistical advice and

calculations The staff of the WVSU Infirmary for allowing us to use the weigh beam scale. Dr. Jane Wardle for the permission to use the Child Eating and Behaviour Questionnaire

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