Association Between Childhood Obesity and Cerebral Palsy, Down Syndrome and Epilepsy or Seizure Disorder

Association Between Childhood Obesity and Cerebral

Palsy, Down Syndrome and Epilepsy or Seizure

Disorder

Section 1: Foundation of the Study and Literature Review

Introduction

Obesity has been identified as a nutrition-related chronic disease (NRCD) that has

increased over the decades and can lead to several other health issues such as diabetes

and coronary heart disease (Sahoo et al., 2015). According to the Centers for Disease

Control and Prevention (CDC, 2021), for children to maintain a healthy body weight,

they must consume nutritional foods and engage in physical activity. Lack of physical

activity, diet, and genetics may cause childhood obesity (Sahoo et al., 2015). Although

there is a plethora of research studies on childhood obesity and its relationship to physical

activity and nutrition, there is a lack of information on the relationship between

childhood obesity and some physical (PD) and intellectual disabilities (ID).

According to Segal et al. (2015), children with disabilities have a 13.4% higher

obesity prevalence rate than children without a disability. Furthermore, Segal et al.

(2015) suggested more research be conducted on childhood obesity and disabilities. This

study will utilize data focusing on childhood obesity archived from the National Survey

of Children’s Health (NSCH) to examine the association between childhood obesity and

the following intellectual and physical disabilities: cerebral palsy (CP), Down Syndrome

(DS), and epilepsy or seizure disorders.

CP is one of the most common childhood disabilities that affects an individual’s

ability to move around, interact, and maintain balance (CDC, 2022). According to Barja

et al. (2020), obesity is an emerging prognostic factor among individuals with CP. Over

the years, the number of overweight children diagnosed with cerebral palsy has increased

(Meyns et al., 2016). Moreover, overweight children diagnosed with CP have trouble

walking (Mudge et al., 2021), which is connected to a lack of physical activity (Mudge et

al., 2021).

Down Syndrome is defined as “a genetic disorder caused when abnormal cell

division results in an extra full or partial copy of chromosome 21” (Mayo Clinic, n.d.).

Children and adolescents diagnosed with Down Syndrome or trisomy 21 are at a higher

risk of being diagnosed as overweight or obese (Foerste et al., 2016). Considering the

BMI and shorter stature of a child with Down Syndrome, they are more likely to be

heavier than a child without Down Syndrome (Basil et al., 2016).

Epilepsy or seizure disorder is “any of various disorders marked by abnormal

electrical discharges in the brain and typically manifested by sudden, brief episodes of

altered or diminished consciousness, involuntary movements, or convulsions”

(MerriamWebster, n.d.). Researchers suggest that there be research conducted for

improved methods to identify the relationship between seizure severity and obesity (Ng

& Hodges, 2020). This study helps to close the gap on childhood obesity and its

association to intellectual and physical disabilities.

This research study aids in preventing childhood obesity among disabled children

whose parents are unaware of the relationship between this health issue and physical and

intellectual disabilities. This study gives health practitioners important information about

healthcare risk factors, disease trends, and functional abilities and disabilities in children.

Moreover, this study focused on disabilities that are associated with obesity that have not

yet been thoroughly studied. Lastly, this study’s potential social change impact is to

decrease morbidity and mortality associated with obesity among youth and children. To

begin this study, Section 1 includes a discussion of the problem of the study, the purpose

of the study, research questions and hypotheses, theoretical framework, nature of the

study, literature review related to key variables, definitions, limitations, the significance

of the research, and the summary and conclusion.

Problem Statement

According to the CDC, childhood obesity is an ongoing public health issue

causing poor health among children and adolescents (CDC, 2021). The prevalence rate

of childhood obesity in the U. S. was 49.7% amongst children ages 2-19 between

20112014 (CDC, 2021). Over 13.5 million children in the United States live with a

determining diagnosis of obesity or being overweight (Imoisili et al., 2019).

Based on the literature review and previous research, there is a relationship

between an overweight child and a physical or intellectual disability, specifically cerebral

palsy, Down Syndrome and epilepsy or seizure disorder. Socioeconomic factors, lack of

physical activity, environmental factors, and psychological factors are also associated

with childhood obesity. The many concerns for overweight children with a disability

include the inability to walk and or do things independently, increased risk of sleep

disturbances such as obstructive sleep apnea, heart conditions, and complications from

the medicine consumed for their disability. Other concerns are serious health issues that

could lead to death in overweight children with an ID or PD. Lastly, the problem is that

there are 49.7% of overweight children at risk for health conditions leading to mortality

(CDC, 2021).

Socioeconomic factors such as the parents’ income and education levels, and the

child’s age, sex, and race may influence a child’s weight status. According to researchers

Jin & Jones-Smith (2015), of the participants in their study, children living in a

household with lower income were at a higher rate of obesity than children living in a

household with higher income. Another researcher indicated that childhood obesity is

complex and may vary over time according to the child’s race, sex, and Hispanic origin

(Ogden, 2018). Ogden also stated that the head of household’s income and education

level had some effect on the child’s obesity status (2018). The lower the education and

income level, the higher the childhood obesity prevalence rate (Ogden, 2018). More

research should be conducted on the parents’ education level, and the race and age of the

child.

There is a lack of research on lifestyle factors and their relationship with

childhood obesity (O’Shea et al., 2018). Children with these disabilities could experience

a lack of social participation that affects their weight levels, as a lack of desire or

incapability to interact socially could be detrimental to a child’s weight. According to

McPherson et al. (2016), further research is needed to address the understanding of

childhood obesity findings and apply the findings to children who have physical

disabilities. Bertapelli et al. (2016) suggested that further research is needed to address

population-based research regarding the weight and BMI status of a child or youth with

Down Syndrome. Based on the literature, more research should be conducted on

intellectual and physical disabilities and their association with childhood obesity, race

and age of child, and the socioeconomic factors of the parents, such as income and

education level.

Purpose of Study

The purpose of this study is to generate knowledge of childhood obesity and its

relationship to cerebral palsy, Down Syndrome, and epilepsy or seizure disorders, based

on data from the National Survey of Children’s Health collected in 2018-2019. This

study also addressed the number of overweight, intellectually, and physically disabled

children at risk for developing other serious health issues and factors mentioned above.

This cross-sectional study examined the association between overweight children ages

017 and their diagnosis of the following physical and intellectual disabilities: cerebral

palsy, Down Syndrome, and epilepsy or seizure disorder. The confounding variables

explored were the education level and household income of the parents and the age, race,

and sex of the child.

The study provides insight on whether the physical and intellectual disabilities

and the parent’s socioeconomic status affect a child’s weight status. This study also

provides insight to other researchers and healthcare providers regarding if the age or race

of the disabled child affects the child’s weight status. Lastly, this study promotes

childhood obesity prevention in children with physical and intellectual disabilities.

Research Variables and Study Population

The research variables and study population for this cross-sectional research study

are as follows:

Dependent Variable (DV): Childhood Obesity as indicated by a doctor or other

healthcare provider.

Independent Variables (IV): cerebral palsy, Down Syndrome, and epilepsy or

seizure disorder

Confounding Variables (CV): socioeconomic status (SES) of the parent or

guardian, education level of the parent or guardian, income level of the parent or

guardian, as well as the age, race, and sex of the child.

Study Population: Children and adolescents up to age 17, nationwide, African

American, White, Hispanic, and Non-Hispanic children.

Research Questions and Hypotheses

RQ1: Is there an association between childhood obesity and cerebral palsy?

H01: There is no association between childhood obesity and cerebral palsy.

H11: There is an association between childhood obesity and cerebral palsy.

RQ2: Is there an association between childhood obesity and cerebral palsy when

controlling for socioeconomic status of parent, education level of parent, income level of

parent or guardian, and the age, race, and sex of child?

H02: There is no association between childhood obesity and cerebral palsy when

controlling for socioeconomic status of parent, education level of parent, income level of

parent or guardian, and the age, race, and sex of child.

H12: There is an association between childhood obesity and cerebral palsy when

controlling for socioeconomic status of parent, education level of parent, income level of

parent or guardian, and the age, race, and sex of child.

RQ3: Is there an association between childhood obesity and Down Syndrome?

H03: There is no association between childhood obesity and Down Syndrome.

H13: There is an association between childhood obesity and Down Syndrome.

RQ4: Is there an association between childhood obesity and Down Syndrome

when controlling for socioeconomic status of parent, education level of parent, income

level of parent or guardian, and the age, race, and sex of child?

H04: There is no association between childhood obesity and Down Syndrome

when controlling for socioeconomic status of parent, education level of parent, income

level of parent or guardian, and the age, race, and sex of child.

H14: There is an association between childhood obesity and Down Syndrome

when controlling for socioeconomic status of parent, education level of parent, income

level of parent or guardian, and the age, race, and sex of child.

RQ5: Is there an association between childhood obesity and epilepsy or seizure

disorder?

H05: There is no association between childhood obesity and epilepsy or seizure

disorder.

H15: There is an association between childhood obesity and epilepsy or seizure

disorder.

RQ6: Is there an association between childhood obesity and epilepsy or seizure

disorder when controlling for socioeconomic status of parent, education level of parent,

income level of parent or guardian, and the age, race, and sex of child?

H06: There is no association between childhood obesity and epilepsy or seizure

disorder when controlling for socioeconomic status of parent, education level of parent,

income level of parent or guardian, and the age, race, and sex of child.

H16: There is an association between childhood obesity and epilepsy or seizure

disorder when controlling for socioeconomic status of parent, education level of parent,

income level of parent or guardian, and the age, race, and sex of child.

RQ7: Does the education level of the parent or guardian have a moderation effect

on the association between childhood obesity and cerebral palsy, Down Syndrome, and

epilepsy or seizure disorder?

H07: The education level of the parent or guardian does not have a moderation

effect on the association between childhood obesity and cerebral palsy, Down Syndrome,

and epilepsy or seizure disorder?

H17: The education level of the parent or guardian does have a moderation effect

on the association between childhood obesity and cerebral palsy, Down Syndrome, and

epilepsy or seizure disorder?

RQ8: Does the income level of the parent or guardian have a moderation effect on

the association between childhood obesity and cerebral palsy, Down Syndrome, and

epilepsy or seizure disorder?

H08: The income level of the parent or guardian does not have a moderation effect

on the association between childhood obesity and cerebral palsy, Down Syndrome, and

epilepsy or seizure disorder?

H18: The income level of the parent or guardian does have a moderation effect on

the association between childhood obesity and cerebral palsy, Down Syndrome, and

epilepsy or seizure disorder?

RQ9: Does the age of the child have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H09: The age of the child does not have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H19: The age of the child has a moderation effect on the association between

childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure disorder?

RQ10: Does the race of the child have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H010: The race of the child does not have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H110: The race of the child has a moderation effect on the association between

childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure disorder?

RQ11: Does the sex of the child have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H011: The sex of the child does not have a moderation effect on the association

between childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder?

H111: The sex of the child has a moderation effect on the association between

childhood obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure disorder?

Theoretical Framework

The theoretical framework used for this study was the Social Cognitive Theory

(SCT), formerly known as the Social Learning Theory (SLT) (LaMorte, 2019). SCT is a

causational theory that explains psychosocial functioning and includes three aspects:

belief strengthening (observation), improving goals (self-regulation), and competency

building (reciprocal determination) (Bandura, 1988). Since its inception in 1988, the

SCT has added other concepts to the framework, bringing the theory to six concepts. The

six constructs are listed below in Figure 1: reciprocal determination, behavioral

capability, observational learning, reinforcements, expectations, and self-efficacy

(LaMorte, 2019). The SLT began with the first five concepts, and the last concept as

added once the theory was renamed the SCT in 1986 (LaMorte, 2019). As the theory has

improved over the years, it has given more meaning and value to the framework and how

to utilize the theory in different aspects of life.

The SCT has been applied in other research areas such as clinical issues, health

issues, promotional programs, and environmental change (Kelder et al., 2015). The

application of this theory includes the following factors: personal cognitive factors,

socioenvironmental factors, and behavioral factors. The personal cognitive factors

involve the following constructs, self-efficacy, knowledge, and outcome expectations

(Kelder et al., 2015). The socio-environmental factors include observational learning,

normative beliefs, opportunities and barriers, and social support (Kelder et al., 2015). In

contrast, the behavioral factors include behavioral skills, intentions, and reinforcement &

punishment (Kelder et al., 2015). The SCT is the most used theory to promote childhood

obesity prevention (Alexander et al., 2021).

The purpose of using this theoretical framework in this study was to focus on the

environmental and socioeconomic factors of the child and parent or guardian. The SCT

shows how the child’s guardian’s socioeconomic factors, such as the environment,

finances, education level, personal beliefs, etc., can affect the child’s well-being

(Alexander et al., 2021). Bandura’s theory can help determine why people behave the

way they do, according to their age, education level, and other socioeconomic factors

(Knol et al., 2017). The theory could also provide the guardian with goal-setting support

to improve the child’s health. In other words, utilizing the SCT framework for this study

could promote child obesity prevention.

The SCT is a vital framework and is considered one of the most used theories for

published research (Kelder et al., 2015). Self-efficacy is a SCT construct that is essential

for human behavioral changes by recognizing a person’s confidence level (LaMorte,

2019). Due to the dependent variable, childhood obesity, each research question was

applied to the first and last concept of the social cognitive theory model: reciprocal

determination and self-efficacy. I operationalized the SCT in this study to examine how

intellectual or physical disability had the most significant effect on childhood obesity

rates in the United States. The SCT was the best framework to conduct my research

because it related to the quantitative data of the child’s environment, and the quantitative

data of the parents’ socioeconomic factors. Therefore, this model was an effective choice

for this study.

Figure 1

Social Cognitive Theory Model

Perceived

Susceptibility

Perceived

Severity

Perceived

Benefits

Perceived

Barriers

Cue to Action

Self-Efficacy

Note. (LaMorte, 2019)

Another model that was used for this study was the Health Belief Model (HBM).

The six constructs of the HBM are perceived susceptibility, perceived severity, perceived

benefits, perceived barriers, cue to action, and self-efficacy, as illustrated in Figure 2

(LaMorte, 2019). The HBM helps to explain why people fail to adopt disease prevention

strategies and responses to medical treatments and symptoms of an illness (LaMorte,

2019). The HBM explained the person’s feelings and perceptions of their current illness,

or the development of an illness (LaMorte, 2019). Lastly, the HBM stated that a person’s

beliefs and recommended health actions could determine whether the individual will

make the behavioral changes or not.

The HBM was useful for this current study because it identified whether there is

an association or relationship between the dependent variable, childhood obesity, and the

confounding variables, such as the parent or guardians’ income level and education level.

Obesity is a complex disease, and the HBM can help to understand why some of the

parents or guardians decided not to take the steps necessary to prevent this disease. I

operationalized the HBM in the current study by comparing the concepts of the model to

the beliefs or perceptions of the parent or guardian, which then leads to the decisions

made for the child. The HBM is associated with research questions 6 and 8.

Figure 2

Health Belief Model

Perceived

Susceptibility

Perceived

Severity

Perceived

Benefits

Perceived

Barriers

Cue to Action

Self-Efficacy

Note. (LaMorte, 2019)

Nature of the Study

The secondary data that were utilized in this study were archived from the

National Survey of Children’s Health (NSCH) with a cross-sectional research study

design, which was intended to identify whether there is an association between childhood

obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure disorder in children

ages 0-17. The NSCH data was collected electronically or on paper via a survey (Child

and Adolescent Health Measurement Initiative, 2020). The survey was compiled by the

Health Resources and Services Administration’s Maternal and Child Health Bureau

(HRSA MCHB) to provide data on children ages 0-17 (Child and Adolescent Health

Measurement Initiative, 2020). This data included the mental, physical, and emotional

health, risk factors of both the child and parent or guardian and environmental factors

related to the child’s well-being (Child and Adolescent Health Measurement Initiative,

2020).

There are several reasons why the dataset was utilized for this research study.

The NSCH data are for public use of secondary data and are free of charge. Some data

were collected at the national level, and some were collected at the state level. The

independent and dependent variables that were utilized in this study were mainly

collected at the national level. I chose to use the national data because they provide a

broader range of data across the U.S. and because there were no state-level data available

for the selected research variables for this study.

Literature Search Strategy

I used the following databases and search engines for the literature review:

EBSCO, CINAHL, MEDLINE, Science Direct, Thoreau, and Google Scholar. Some key

search terms used when conducting research for this study were childhood obesity,

cerebral palsy, Down Syndrome, Downs Syndrome, epilepsy, seizure disorder, social

cognitive theory, health belief model, youth, obesity, overweight, children, pediatric, and

trisomy 21. The scope of the literature reviewed was between the years 2017-2022. The

majority of the articles and research found were from peer-reviewed research. There

were a few sources from textbooks, websites, and articles that either referenced Bandura

or were edited by Bandura.

Literature Review

Childhood Obesity and Health Outcomes

According to Reis et al. (2020), children living in a low-income household have a

2.31% higher chance of being obese than children living in a higher income household.

These researchers also found that there was no association between childhood obesity

and the parents’ level of education (Reis et al., 2020). In addition, children whose

parents’ income was less than $900 a month were at a higher risk of being overweight or

obese (Bazán et al., 2018). Similarly, an article indicated that children whose parents’

education level was low were at an increased risk of being overweight or obese (Bazán et

al., 2018).

Socioeconomic status, race, and age have an association with childhood obesity

(Banks et al., 2016). According to Banks et al.’s (2016) study of 12,674 participants in

grades kindergarten to eighth grade, Black males with a higher socioeconomic status had

a higher standardized body-mass index, also known as zBMI (six time points). In

contrast, the White male participants with a lower socioeconomic status had lower zBMI

scores. Therefore, the socioeconomic status, race, and sex of a child have a relationship

with childhood obesity.

As specific to children within intellectual and physical disabilities, youth who

have Down Syndrome were at a higher risk of being overweight or obese than youth

without intellectual disabilities, as concluded by Oulmane et al. (2021). Also, children

and adolescents who are newly diagnosed as epileptic and untreated have a higher risk of

being obese (Daniels et al., 2009). Epileptic obese children are considered a comorbidity,

and the relationship between the two are lack of physical activity, nutritional intake, and

the medicine that they consume due to their illness (Ladino & Téllez-Zenteno, 2019).

Meyns et al. (2016) conducted a study on the individualities of gait in children with

cerebral palsy and their effect on body weight increase. The study concluded that more

weight gain in a child is significant to the gait pattern of a child with cerebral palsy

(Meyns et al., 2016). Overall, these studies support the present research by identifying

some health concerns regarding childhood obesity.

Influential Factors of Childhood Obesity

Obesity has increased over the last four decades in the United States, with the

childhood obesity rate at 18.5% and the adult obesity rate at 39.8% (Anderson et al.,

2019). Childhood obesity is significant to discuss and study because it can lead to adult

obesity (Cohut, 2017). According to the CDC, a person’s weight status is determined by

their BMI ratio (2021). The BMI calculates a person’s weight in kilograms divided by

the height in meters (CDC, 2021). The BMI of a child is calculated based on their age, is

sex-specific, and is connected with direct body fat measurements (CDC, 2021). The

child’s percentile rate is how they are categorized. For example, an underweight child

has less than the 5th percentile, a healthy weight child has between the 5th to 85th

percentile, an overweight child has between the 85th to 95th percentile, and an obese child

is equal to or greater than the 95th percentile (CDC, 2021).

In a study conducted by Williams et al. (2018), the researchers examined the

families’ risk factors for obese children between the ages of 4-5. Some of the risk factors

examined were race and gender. In the study, 49.06% of the participants were females,

the remaining 51% were males, over 53% were White, and over 13% were Black

(Williams et al., 2018). Most of the children who had a higher prevalence rate of

childhood obesity were White males between four and five (Williams et al., 2018). In a

similar study, Assari (2018) examined the association between the parents’ income and

childhood obesity in Black and White families. The researchers used the NSCH data

from 2003-2004 to determine that the incidence rate of a family’s income and its

association with childhood obesity did not affect Black children more than White

children between the ages of 2-17 (Assari, 2018).

Lastly, Tylavsky et al. (2020) studied the environmental factors through the

Environmental Influences on Child Health Outcomes (ECHO) program and their effect

on childhood obesity in the United States. The ECHO program explores environmental

exposures to child health and development (Tylavsky et al., 2020). In the study, ECHO

was used to identify why childhood factors affect the risk of obesity (Tylavsky et al.,

2020). Several factors were utilized in this study, such as the child’s ethnicity, age, sex,

maternal demographics, and, most importantly, the child’s BMI. Children under two

who participated in the study were examined according to their high BMI scores

(Tylavsky et al., 2020). Children ages two-18 were examined according to their BMI,

based on the following categories: overweight, obese, and severe obesity (Tylavsky et al.,

2020). The researchers concluded that the child’s weight increased as their age

increased, while ethnicity varied (Tylavsky et al., 2020). Overall, there are several

environmental factors that affect a child’s weight status.

Childhood Obesity and other Countries

Childhood obesity is not only prevalent in the United States; it is also prevalent in

other countries. A study was conducted between the years 2010-2014 titled The

International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE).

This multi-national, observational study focused on the relationship between obesity and

lifestyle factors amongst 10-year-old children residing in 12 different countries and five

major regions such as North America, Africa and Eurasia, Europe, Latin America, and

the Pacific (Katzmarzyk et al., 2019). The researchers compared the BMI and body fat of

the children and found that the Indian boys had a more significant body fat percentage

than the boys in other countries (Katzmarzyk et al., 2019). On the other hand, girls from

Kenya had a lower body fat percentage than girls in other countries (Katzmarzyk et al.,

2019). The BMI of girls and boys in Columbia was low, and the boys’ body fat

percentage was higher than the boys' in other countries (Katzmarzyk et al., 2019).

The prevalence and incidence rate of childhood obesity varies depending on many

factors, including the country or region the child resides. Overall, there are several

research studies that show the relationship between childhood obesity and cerebral palsy,

Down Syndrome, epilepsy or seizure disorder, the child’s age, race, sex, lack of physical

activity and nutritional intake, environmental factors, and the education and income level

of the parent.

Social and Emotional Factors Associated with Childhood Obesity

Obesity could cause several complications, such as social, emotional, and physical

issues (National Institute of Diabetes and Digestive and Kidney Diseases, n.d.).

Obesity is also associated with gait disorder, obstructive sleep apnea, dyslipidemia, and

hyperinsulinemia (Bertapelli et al., 2016). Children with intellectual disabilities often

also suffer from anxiety and depression (Whitney et al., 2018). Additionally, childhood

obesity can also cause many health-related issues, such as diabetes, breathing problems,

high cholesterol, high blood pressure, and joint pain (Mayo Clinic, 2020). Current

research discusses how environmental status, family beliefs, child health influences, food

intake, lack of physical activity, school environment, and other related factors contributes

to childhood obesity (Anderson et al., 2019). Many child behavioral risk factors could

increase childhood obesity. Some factors include increased time spent watching

television, playing video games, lying down or sleeping, and decreased time interacting

in physical activity (Williams et al., 2018). Some studies discuss the association between

childbirth and childhood obesity. Parental risk factors are another vital topic regarding

childhood obesity. According to Williams et al. (2018), some parental risk factors are

maternal obesity, low education, smoking, lack of nutrition knowledge, African

American race, and perceived neighborhood safety.

In the study, kindergarten-aged children suffered from childhood obesity or being

overweight because their parent was a smoker, and the child did not eat dinner as a

family (Williams et al., 2018). Children with parents who smoked had a 40% higher

chance of being overweight, while children who ate dinner with their parents had a 4%

lower chance of becoming overweight (Williams et al., 2018). Overall, according to

previous studies, the socioeconomic status of the parent and child is a risk factor for the

child being diagnosed with childhood obesity.

In a recent article, researchers determined 10 family-related factors associated

with childhood obesity. Those factors are family history of diseases, parenting styles,

parental educational status, family structure, family perception about the child’s weight,

family meal frequency, parents’ weight, occupation status, feeding practices, and

parenting styles (Notara et al., 2020). These parental factors could affect the child’s

social, emotional, and physical well-being.

Social and emotional well-being is important in a child and young adolescent’s

life (Noonan & Fairclough, 2019). Their self-perception and ability to interact with other

children are critical in their young lives. If the social and emotional well-being of a child

is not taken care of while they are young, it could spiral into more challenging

experiences in their adult lives (Noonan & Fairclough, 2019). Peer relationship problems

can lead to social and emotional anxiety and later being overweight or obese in those

young children and adolescents (Noonan & Fairclough, 2019). Children who are less

active than other children are also more likely to develop social and emotional issues,

resulting in childhood obesity (Noonan & Fiarclough, 2019).

Public Health Impact of Childhood Obesity

Childhood obesity is a public health issue raising two prime concerns: the child’s

physiological and psychological health. Psychological health affects the child’s

selfesteem and social and emotional well-being (Sanyalou et al., 2019). Children who

have emotional issues such as anxiety, mood disorders, eating disorders, and somatoform

are most likely children who have problems with their weight (Sanyalou et al., 2019).

Sanyalou et al.’s (2019) study showed that 60% of girls and 35% of boys indicated that

they have an issue with binge eating and cannot control their eating habits.

Other public health concerns for childhood obesity are type 2 diabetes, possible

diagnosis of cancer, and pulmonary disease (Sanyalou et al., 2019). These health

problems are also known as chronic inflammation diseases. One or more of the listed

inflammation diseases can lead to heart disease in the child’s adulthood. According to

Sanyalou et al.’s (2019) study, these are just a few of the reasons why it is essential for

children to learn how to control their emotions and eating habits to avoid becoming

overweight or obese.

As of 2020, $14 billion is spent on childhood weight health issues each year (State

of Childhood Obesity, 2020). One place that we could help decrease the cost is in the

education system. School districts that participate in the National School Lunch Program

(NSLP) are required to provide free, drinkable, and clean water sources in their buildings

(Kenney et al., 2019). Some educational institutions have decided to install water

dispensers in the schools to assist with this (Kenney et al., 2019). Although the

installation cost of water dispensers is between $2.74- $5.79 per child, the water jets

could reach over 29 million children, prevent 179,550 childhood obesity cases, and save

$0.31 per dollar in health care costs by 2025 (Kenney et al., 2019). Childhood obesity

can lead to many cardiovascular and detrimental health issues that must be recognized to

prevent the issue and lower the cost of this public health concern.

Prevention Efforts for Childhood Obesity

Numerous interventions have been created and implemented to prevent childhood

obesity, including family-based, community-based, and school-based. Many

interventions have been practiced in several countries, such as the United States,

Australia, and Europe (Ash et al., 2017). Ash et al. (2017) analyzed family-based

childhood obesity interventions utilizing a quantitative approach. These researchers

reviewed the many family-based interventions conducted to prevent childhood obesity

and concluded that there were gaps within the intervention designs and methodology

(Ash et al., 2017). Their study concluded that most of the interventions conducted were

mainly geared toward children ages 2-10 and less likely geared toward children ages

1117 (Ash et al., 2017). They also concluded that more family-based interventions

should be suitable for children aged 0-17 (Ash et al., 2017).

Agaronoy et al. (2018) implemented a family-based sleep promotion intervention

to help prevent childhood obesity in the United States. Agaronoy et al. (2018) concluded

that sleep promotion interventions should be used as a best practice to prevent childhood

obesity and help infants and pre-school-aged children in high-income countries. Overall,

this intervention resulted that sleep promotion can have a positive impact on a child’s

behavior, such as physical activity and a healthy diet increase which can result in

childhood obesity prevention (Agaronoy et al., 2018).

In a recent study, researchers evaluated a new family-based intervention based in

Europe that will assist in preventing childhood obesity in school-age children (Homs et

al., 2021). Homs et al. (2021) used the FItness, VAlues, and Healthy LIfestyles

(FIVALIN) project for the intervention. The FIVALIN project was put in place to help

prevent childhood obesity in children ages 8-12 and follow them through adulthood

(Homs et al., 2021). This intervention will address several factors associated with

childhood obesity, and an evaluation will be conducted after following the child and their

family for 12 months (Homs et al., 2021).

As stated above, mental health is another health issue related to childhood obesity.

Narayanan et al. (2019) conducted a school-based intervention in the United States using

health mentors to address childhood obesity. This intervention was used to strengthen

the wellness policy in Title 1 schools by implementing a behavioral change model, Team

Kid POWER! (KiPOW!) and utilizing community resources to help manage childhood

obesity (Narayanan et al., 2019). The health mentors introduced the students to the

importance of healthy eating and daily exercise or physical activity while at recess

(Narayanan et al., 2019). After four years of implementing this intervention, researchers

resulted that the KiPOW behavior change model was helpful for the students, staff, and

administrators (Narayanan et al., 2019).

Also, Gadsby et al. (2020) conducted a community-based intervention on

childhood obesity as a whole system approach. The Go-Golborne intervention conducted

in London was created to address childhood obesity by promoting healthy lifestyles

among children and families throughout the communities (Gadsby et al., 2020). The

GoGolborne campaigns and events have been successful over the years (Gadsby et al.,

2020). The community believed that the children and families were beginning to make

better healthy eating choices and participate in daily physical activity to decrease or

prevent childhood obesity (Gadsby et al., 2020). The results showed that children who

participated in the Go-Golborne intervention showed positive change and increased

knowledge of healthy foods (Gadsby et al., 2020).

Another European study focused on a combination of school-based and

familybased interventions to prevent childhood obesity. The study targeted interventions

geared toward physical activity, dietary, and sedentary behaviors among younger

children (Lambrinou et al., 2020). Some programs focused on educational sessions for

parents, and others used incentives and social marketing techniques to help prevent

childhood obesity (Lambrinou et al., 2020). The parent sessions were not as successful

as the student incentive sessions (Lambrinou et al., 2020). After researching other

childhood obesity interventions, Lambrinou et al. took those researchers’

recommendations and created an intervention titled the Feel4Diabetes Intervention to

assist with designing similar childhood obesity prevention proposals (Lambrinou et al.,

2020). Overall, there are numerous articles about the prevention of childhood obesity

and the intervention tactics researchers develop to promote prevention or decrease

childhood obesity. Schoolbased, community-based, and family-based interventions are

public health tools to help prevent or decrease childhood obesity.

Childhood Obesity and Developmental Disability

Physical disability is a long-term condition that affects a specific area or area of

an individual’s body that limits physical functioning, dexterity, and stamina (Berg, 2020).

According to the Centers for Disease Control and Prevention, cerebral palsy is the most

common childhood motor disorder in the United States (CDC, 2022). Research has

shown that children with cerebral palsy usually lack physical activity and cannot

understand balanced food intake (Bandini et al., 2015). Bandini et al. (2015) believe that

a lack of physical activity and nutritional and proportional food intake could cause

children with cerebral palsy to become overweight or obese. Haegele et al. (2019)

conducted a study focused on the weight status among children and youth with chronic

diseases such as diabetes, intellectual disabilities, hearing impairments, Down Syndrome,

epilepsy, and more based on the 2016 NSCH data. These researchers found that children

with cerebral palsy were the least prevalent group considered overweight (Haegele et al.,

2019).

There are two types of seizures: generalized onset and focal onset (Epilepsy

Foundation, 2019). A few generalized onset seizures are atonic, absence, or tonic-clonic.

(Epilepsy Foundation, 2019). The two focal onset seizures are aware seizures and

impaired awareness seizures (Epilepsy Foundation, 2019). Some causes of epilepsy are

stroke, head injury, brain tumor, Alzheimer’s disease, brain infection, genetic factors, and

malformation of an area of the brain (Epilepsy Foundation, 2019).

Haegele et al. (2019) found that being overweight was the least prevalent among

children with epilepsy. Parents of children who have epilepsy and responded to the

NCHS 2016 survey were concerned that their child’s weight was too high (Haegele et al.,

2019). According to Haegele et al. (2019), 13.9% of children with epilepsy were

overweight but fell under the least prevalent groups to be considered overweight,

according to their parents. Intellectual disabilities are significant limitations in the

adaptive behavior and intellectual functioning of a person’s mental capacity that usually

begins before 22 (American Association on Intellectual and Developmental Disabilities,

2021).

Physical disabilities are considered temporary or permanent limitations or

disabilities that affect at least one limb in a person’s body (Rutgers School of Arts and

Sciences, n.d.). CP has four types: spastic cerebral palsy, spastic diplegia, spastic

hemiplegia, and spastic quadriplegia (Rutgers School of Arts and Sciences, n.d.).

Cerebral palsy can be considered a physical and intellectual disability because it impacts

a person’s ability to move around and it causes brain abnormalities (Rutgers School of

Arts and Sciences, n.d.). Also, spastic quadriplegia CP affects facial features, limbs, and

walking capabilities and can lead to other intellectual disabilities, such as seizures

(Rutgers School of Arts and Sciences, n.d.).

Children and youth with Down Syndrome could be at a higher risk for obesity

based on behavioral or physiological factors (O’Shea et al., 2018). Some behavioral

factors are dietary and physical activity (O’Shea et al., 2018). Some physiological

factors are low mastication, low basal metabolic rate, and hypothyroidism (O’Shea et al.,

2018). O’Shea et al. (2018) used a cross-sectional approach to their study, which will

also be used in my research study. According to Haegele et al. (2019), intellectual

disabilities were one of the most prevalent reasons children were overweight. Because

Down

Syndrome was a part of the intellectual disability group in Haegele et al. (2019) study,

Down Syndrome accounted for 53.6% of the children identified as overweight. The

current study helps to fill the gap in the literature and spur further research in this area of

obesity.

Studies Using the Proposed Methodology

The method used in this proposal is observational and quantitative. There were

several studies conducted using this methodology for childhood obesity prevention. In a

most recent meta-regression quantitative study, researchers studied the relationship

between the outcome and dose (duration and how many sessions) of behavioral

intervention trials and its association with childhood obesity (Heerman et al., 2017). The

behavioral interventions were conducted in a controlled study over time between 1990

and June 2017, and the target population was children ages 2-18 years old. This study

was a secondary randomized control study that identified 258 studies and used 133

studies in their meta-regression analysis (Heerman et al., 2017). The results showed no

significant association between the study’s covariates (Heerman et al., 2017). The results

of this quantitative study did not show an association between the dose and weightrelated

outcomes. The researchers believed that more interventions should be conducted to

prevent childhood obesity and improve healthy childhood growth (Heerman et al., 2017).

Another study observed the association between community programs and

policies on a child’s dietary intake to provide suggestions on facing childhood obesity. In

previous research studies, nutrition was a huge factor in childhood obesity causes and

prevention. Ritchie et al. (2018) examined the relationship between community policies

and child nutrition in 5,138 children grades kindergarten to eighth. The conclusion of the

quantitative observational study showed that if there are additional strategies included in

the community program policies and more interventions conducted to control a child’s

limit to sugars, sweetened beverages, and energy-dense foods, it will improve children’s

diets (Ritchie et al., 2018). Ritchie et al. (2018) used secondary data from the

observational study conducted by the Healthy Communities Study (HCS) and funded by

the National Institutes of Health (NIH). This study was conducted for ten years, and

interviews collected additional data. For the data to be gathered by the minor children,

parents were asked to sign a consent form.

Furthermore, nutrition is important to childhood obesity awareness and prevention

in children ages 5-19. Carrero-González et al. (2021) conducted a study that focused on

the dietary intake habits of school-aged children who suffer from malnutrition and are

considered overweight or obese. Of the 82 children who participated in the study, 21.9%

of girls and 10.1% of boys were overweight, whereas obesity was more prevalent in boys

at 24.5% and girls at 9.7% (Carrero-González et al., 2021). The correlational study

concluded that there was a relationship between school-age children and their eating

habits and weight status (Carrero-González et al., 2021). The researchers used a

descriptive, correlational, and quantitative approach, which is the method that I am using

in my study (Carrero-González et al., 2021). Carrero-González et al. (2021) measured

the child’s BMI and created a form to capture the child’s date of birth, sex, weight,

height, and chronological age. My study focuses on the child’s race, age, sex, and weight

status. Participants were provided a questionnaire to capture their food consumption.

The data used in my study were also collected via a questionnaire by the primary

researchers. The participants were measured based on anthropometric evaluation and

classification using the nutritional diagnosis of normal weight, overweight, and obese

(Carrero-González et al., 2021). Lastly, the researchers analyzed that children ages 10-12

had overweight and obese rates of 13.89% and 74.38%, whereas children ages 12-14 had

overweight and obese rates of 12.23% and 77.21% (Carrero-González et al., 2021). Each

of these researchers used the quantitative method in their study. Still, they used other

analyses to test their hypothesis, test if there was or was not an association between the

variables, and answer their research questions.

Definitions

Body Mass Index (BMI). Calculates a person’s weight in kilograms divided by the

height in meters (Centers for Disease Control and Prevention, 2021).

Cerebral Palsy (CP). “A disability resulting from damage to the brain before,

during, or shortly after birth and outwardly manifested by muscular incoordination and

speech disturbances” (Merriam-Webster, n.d.).

Childhood Obesity. Weight over the average height and weight ratio for a child

(Childhood Obesity Foundation, 2019).

Downs Syndrome (DS). “A genetic disorder caused when abnormal cell division

results in an extra full or partial copy of chromosome 21. (trisomy-21)” (Mayo Clinic,

n.d.).

Epilepsy. is “any of various disorders marked by abnormal electrical discharges in

the brain and typically manifested by sudden, brief episodes of altered or diminished

consciousness, involuntary movements, or convulsions” (Merriam-Webster, n.d.).

Socioeconomic (SES). “Relating to or involving a combination of social and

economic factors” (Merriam-Webster, n.d.).

Assumptions

I assume that the process of collecting the data for the 2018-2019 National Survey

of Children’s Health was extensive. It is assumed that the guardians or parents of the

children understood the questions from the questionnaire and responded to surveys

honestly and truthfully. Another assumption is that all the children were between the

ages of 0-17. I assume that the process for collecting the data was rigorous, resulting in

valid data. Lastly, I assume that the original study participants received and signed an

informed consent document.

Scope and Delimitations

The data used for this research study was from the 2018-2019 National Survey of

Children’s Health. There were 59,963 completed surveys between the two years

combined, with 2018 collecting 30,530 completed surveys and 29,433 completed surveys

in 2019 (Child and Adolescent Health Measurement Initiative, 2020). The population

was weighted because the two years were combined and consisted of noninstitutionalized

children aged 0-17 nationwide and statewide (Child and Adolescent Health Measurement

Initiative, 2020). For the variables used in this research study, there were no state data

available under the 2018-2019 NSCH survey. Therefore, national data were used. When

finding the dataset for the selected variables, the indicators used were if the parent was

“ever told that child is overweight” and the “prevalence of current or lifelong conditions”

of the child.

The Social Cognitive Theory was a good model to use for the current study. The

SCT was used to promote childhood obesity prevention and help identify the outcome of

the study. Another model used in the current research study is the Health Belief Model.

The HBM was used to discuss the relationship between the variables.

The theory considered but excluded from this study was the Theory of Planned

Behavior (TPB). The TPB includes the motivational factors that consist of the reasons

and actions why individuals perform certain behaviors (Kelder et al., 2015). It does not

necessarily promote behavioral change through the several constructs of the framework

(Kelder et al., 2015). The constructs of the SCT and HBM are fundamental in facing

childhood obesity. Several other aspects of childhood obesity are essential but will not

be discussed in this study, including diabetes, heart problems, autism, attention deficit

hyperactivity disorder (ADHD), asthma, and more.

Limitations

Because this study is a secondary analysis of archived data, I am bound by the

original study’s goals, objectives, and design. I did not partake in the planning and

original data collection process. I worked on the parameters of the existing database, and

my analysis was limited to national data as state-level data were not collected in the

original study. Another limitation is that the self-reported data were collected from the

adult parents or guardians of the children. Lastly, I did not have any input on the ethical

considerations for collecting, analyzing, and storing the data. There could be bias in the

responses from the guardians, as some of them could have made false responses to one or

more of the questions regarding the variables used for this research study. The bias could

have influenced the outcomes of the study.

Significance

By studying these factors and gaining more knowledge about childhood obesity,

my study might assist with a better understanding of the factors that impact childhood

obesity. Children with cerebral palsy, Down Syndrome, and epilepsy or seizure disorder

disabilities could lead to lack of social participation, affecting their weight levels. The

lack of desire or incapability to interact socially could be detrimental to the child’s

weight. It is with the hope that the findings from this study help to promote positive

social change in children with these specific physical and intellectual disabilities and

control their weight status. Another potential social change impact of this study will be

to eventually decrease morbidity and mortality associated with obesity among youth and

children, especially those with physical disabilities.

Summary and Conclusion

Childhood obesity significantly affects a child’s physical, social, emotional,

selfesteem, and mental health (Sahoo et al., 2015). When one considers the factors of a

child who has physical or intellectual disabilities, one may not connect their disability

with their weight status. This study is a secondary analysis of archived data using the

National Children’s Health Survey database conducted between 2018 and 2019. Using

the given data, I conducted a correlational study to examine the association between

childhood obesity and cerebral palsy, childhood obesity and Down Syndrome, and

childhood obesity and epilepsy or seizure disorder. There has been very little research

conducted on some of the physical and intellectual disabilities used in this study, cerebral

palsy, Down

Syndrome, and epilepsy or seizure disorder. There is a gap in research on physical

disabilities (cerebral palsy) and their relationship to childhood obesity. There is also a

gap in research on the association between epilepsy or seizure disorder and childhood

obesity. Previous researchers recommended more research on lifestyle factors to address

the risk of childhood obesity in children with intellectual disabilities (O’Shea et al.,

2018).

This study has the potential to fill the gap of the researchers’ recommendation.

The present study filled the gap by providing data on intellectual and physical disabilities

in children ages 0-17. This study identified the relationship between the dependent and

independent variables using the NSCH 2018-2019 data. This study presented

information to better understand some physical and intellectual disability factors

associated with childhood obesity versus the other factors previously studied among

youth ages 0-17. Childhood obesity is a major public health concern that will continue to

be an ongoing research topic in further studies. This study opened research for future

studies regarding these and other physical and intellectual disabilities and their

relationship with childhood obesity or other public health issues. In section 2 of this

proposal, I will explain the methodological approach thoroughly, including a

comprehensive plan for data analysis.

Section 2: Research Design and Data Collection

Introduction

This cross-sectional study examined the association between obesity amongst

U.S. children ages 0-17 and their diagnosis of the following physical and intellectual

disabilities: cerebral palsy, Down Syndrome, and epilepsy or seizure disorder. I used a

quantitative approach. The dependent variable is childhood obesity, and the independent

variables are cerebral palsy, Down Syndrome, and epilepsy and/or seizure disorder. The

confounding variables are the socioeconomic status of the parent (education level and

income level) and the age, race, and sex of the child. Past researchers have not combined

and thoroughly studied the dependent and independent variables used in this research

study together. As such, this research study filled that gap in research. Lastly, the

variables utilized in this study were collected on a national level using data from the

NSCH’s database. No state or regional data were provided or analyzed throughout this

study.

Section 2 includes a description of the research design and rationale,

methodology, threats to validity, ethical procedures, and a summary of the information

under the subtopics. The research design states the variables used in the study, the types

of research designs used in the study, how they relate to the research questions, why the

design was selected, and why choosing a design is essential to this research study. The

methodology section explains the target population, sample strategy, inclusion and

exclusion data information, study source and access to the dataset, power analysis for

sample size, instrumentation of constructs, operationalization of the selected variables,

and the data analysis plan. The threats of validity section cover the internal and external

threats. The ethical procedures section includes details about the anonymity and security

of the dataset, along with any other ethical processes. Lastly, the summary concludes the

key points of each section.

Research Design and Rationale

The research variables are childhood obesity (dependent variable), cerebral palsy

(independent variable), Down Syndrome (independent variable), epilepsy and/or seizure

disorder (independent variable), education level of parent or guardian (confounding

variable), income level of parent or guardian (confounding variable), age of the child

(confounding variable), race of the child (confounding variable), and sex of the child

(confounding variable). The original data were collected and analyzed in numerical

format. Therefore, this study was quantitative.

A correlational study using the cross-sectional study design examines the

association between two or more variables (Seeram, 2019). The study design also

identifies the outcome and exposure of the participants (Seeram, 2019). A cross-

sectional study design will focus on both the internal and external data from the dataset

(Seeram, 2019). Cross-sectional studies are used to study a population-based survey,

calculate odds ratios (OR), and the prevalence of clinic-based studies (Seeram, 2019).

The current cross-sectional study measured the OR of the archived data. An example of

the order of operations for a cross-sectional study design is in Figure 3.

Figure 3

Cross-Sectional Study Design

Included &

Excluded

Participants

Exposure & Outcome Prevalence

& Odds

Ratio

Calculation

Because a secondary dataset was used, the cross-sectional study design was the

best study design option since there was no indication of temporality. As stated above, a

cross-sectional study design is used for population-based surveys, and the NSCH is a

population survey-designed database. Cross-sectional studies are observational study

designs that help measure the outcome and exposure of the population and their

association with the variables (Setia, 2016). A cross-sectional study design was used to

test the research questions and determine if there was an association between childhood

obesity and cerebral palsy, Down Syndrome, and epilepsy or seizure disorder. I used a

sample of the data to test my hypothesis.

A cross-sectional study is also cost-efficient, but it has a lower response rate

(Wang & Cheng, 2020). Another advantage of this type of study is that it is

timeefficient, as the primary researchers do not follow the participants over time

(Seeram, 2019; Wang & Cheng, 2020). Overall, the design choice was vital when

conducting a research study like this because it allowed me to understand their study,

interpret the data’s outcome and exposure, and answer the research questions. It also

allowed me to measure the association between the variables using the dataset.

Methodology

Data Sampling

This section describes the methodology used for this quantitative study. This

section provides a brief synopsis of the original study’s data collection. The NSCH’s

2018-2019 survey’s original data collection used a random sampling strategy to identify

households with children by using a screener questionnaire. The questionnaire was

mailed to the homes, and an adult who is the caretaker of the children in the household or

the person who knew most about the children’s health completed the survey. The adult

was then given the option to complete the short screening via electronic or paper form.

From the completed surveys, if there were more than four children in the household, the

screeners asked additional questions about the four youngest children (Child and

Adolescent Health Measurement Initiative, 2019). Once that information was collected,

a child-level topical questionnaire was administered. The topical questionnaire was

divided into three age groups: 0-5, 6-11, and 12-17. The adult who completed the survey

for the child had to be 18 years or older, and preferably the parent of the child whose

health information was being shared. Excluded participants were those 18 and above (U.

S. Census Bureau, 2020).

Original Data Sampling

The National Center for Health Statistics (NCHS) is a branch of the Centers for

Disease Control and Prevention (Data Resource Center for Child & Adolescent Health,

n.d.). They help conduct and collect the data from the surveys (Data Resource Center for

Child & Adolescent Health, n.d.). Since the NSCH began providing data on mental

health, health care needs, and well-being of children ages 0-17 in the United States in

2003, the data has been used by multiple researchers (Data Resource Center for Child &

Adolescent Health, n.d.). The NSCH survey was initially only conducted in 2003, 2007,

and 2011/2012, but four years later, in 2016, it began to be conducted every year (Data

Resource Center for Child & Adolescent Health, n.d.). Because the CDC and the Census

Bureau are involved, the NSCH is a reliable source for data collection and research. The

Data Resource Center for Child and Adolescent Health is responsible for cleaning and

labeling the data for public exploration and use (Child and Adolescent Health

Measurement Initiative, 2020).

Several instruments were used for the original data collection: web questionnaire,

email questionnaire, paper questionnaire, telephone questionnaire, and Spanish language

translations (U. S. Census Bureau, 2020). The U.S. Census Bureau conducted the

surveys on behalf of the HRSA MCHB (U.S. Census Bureau, 2019). Once the data from

the current study was cleaned, the target population sample size was determined.

Original Study Data and Target Population

In the 2018 original dataset, there were initially 176,000 households selected, but

only 71,000 surveys were completed (U.S. Census Bureau, 2019). A follow-up survey

was generated, and only 38,140 surveys were completed (U.S. Census Bureau, 2019). Of

the 38,140 completed follow-up surveys, 30,530 households completed the interviews,

which was concluded in the dataset of 30,530 participating households (U.S. Census

Bureau, 2019). Table 1 illustrates the 2018 unweighted data of the original study

population in the order of operation.

In the 2019 original dataset, there were initially 184,000 households selected, but

only 68,500 surveys were completed (U.S. Census Bureau, 2020). Of the completed

surveys, 35,760 were eligible for the follow-up questionnaire (U.S. Census Bureau,

2020). Of the eligible households, only 29,433 completed the interviews, which gave the

final number of surveys that could be used for the dataset (U.S. Census Bureau, 2020).

Table 2 illustrates the 2019 unweighted data of the original study population in the order

of operation.

Table 1

2018 Unweighted Primary Data in Sequential Order

Data Listed in Sequential Order Unweighted Numbers

Completed Screeners/Surveys 71,000

Screeners with Children 38,140

Completed Screeners with Children 30,530

Total Cases 176,000

Table 2

2019 Unweighted Primary Data in Sequential Order

Data Listed in Sequential Order Unweighted Numbers

Completed Screeners/Surveys 68,500

Screeners with Children 36,196

Completed Screeners with Children 29,433

Total Cases 180,000

The target population of the original data collected was households with children

ages 0-17. The total estimated population size of the NSCH 2018-2019 dataset was

356,000 cases. Of the estimated possible cases, only 286,000 were occupied households.

There were 161,000 estimated responses from the targeted audience with children

residing in the home. After identifying the houses that had children, the data collectors

checked for completed screeners, which were a total of 139,000 cases. Once that data

was collected, the number of completed screeners that included children were identified

at 74,336 cases. A topical survey was sent out to 74,336 participants, and 59,963 of the

topical surveys were completed. A randomized sample was conducted for this dataset,

and one child from each household was included in the data. All screeners and surveys

were completed by an adult in the home that knew the child best.

Access to the Dataset

The NSCH database is reputable because it has been used in many successful

studies. Another reason that the NSCH data is reputable is because of its sponsors. The

sponsors are as follows: The Health Resources and Services Administration’s Maternal

and Child Health Bureau (HRSA MCHB), the United States (US) Census Bureau, US

Department of Health and Human Services, CDC, National Center on Birth Defects and

Developmental Disabilities (NCBDDD), United States Department of Agriculture

(USDA), Food and Nutrition Service, and the United States Environmental Protection

Agency (EPA) (2018 only) (Child and Adolescent Health Measurement Initiative, 2020).

Each of these organizations is respected and heavily utilized in most health-related

research. The NSCH database has a track record of collecting data. The institution has

an excellent reputation, and the data is viable and collected scientifically.

The Data Resource Center for Child and Adolescent Health (DRC) has a website

that provides the procedure for gaining access to the 2018-2019 NSCH survey dataset.

The data query provides interactive access to the data at the national and state levels. The

data query also breaks down the topics and subtopics of the children’s health and

demographics. The questionnaire collected all the necessary information (Child and

Adolescent Health Measurement Initiative, 2020).

Because the NSCH dataset is for public use, permission to receive access to the

needed codebook data was easy to obtain. The codebook was available on the Data

Resource Center for Child and Adolescent Health website under the CAHMI section

(Child and Adolescent Health Measurement Initiative, 2020). The Child and Adolescent

Health Measurement Initiative (CAHMI) leaders ask that all who use the data keep them

abreast of the publications and presentations. This data set represented the best source

for my study because it provided information regarding my dependent and independent

variables. Professionals and reliable organizations collected the data, and it is available

for secondary use, which was important for the study.

Present Study Data Sampling and Target Population

I used all available cases in the dataset for this study (N = 59,963). The target

population for the present study included children ages 0-17 who had been told they were

overweight and children with cerebral palsy, Down Syndrome, and epilepsy or seizure

disorder. In 2018, there were 30,402 children between the ages of 0-17 whose guardians

answered questions regarding cerebral palsy. There were 30,445 completed surveys with

responses to Down Syndrome, and 30,427 completed surveys that were responded to

regarding epilepsy or seizure disorder. In 2019, there were 29,323 children between the

ages of 0-17 whose guardians answered questions regarding cerebral palsy. There were

29,373 completed surveys with responses to Down Syndrome, and 29,342 completed

surveys that responded to epilepsy or seizure disorder. The sample size for 2018-2019

estimated totals are 59,725 for cerebral palsy, 59,818 for Down Syndrome, and 59,769

for epilepsy or seizure disorder. Table 3 illustrates the present total study population by

the independent variables and year(s).

Inclusion and Exclusion

The inclusion of cases in the current study were children ages 0-17 whose parent

or guardian responded to the following questions: ever being told by a healthcare

professional that their child was overweight (indicator 1.4b) if the child currently has

cerebral palsy (indicator 1.9a), ever told by a healthcare professional that their child has

Down Syndrome (indicator 1.9b), and if the child currently has epilepsy or seizure

disorder (indicator 1.9c). There were 59,719 responses to whether they were or were not

ever told that their child was overweight. There were 59,707 parents or guardians that

answered either yes or no on whether their child currently has cerebral palsy. There were

59,818 responses to whether their child currently has or was never told their child has

Down Syndrome. There were 59,511 responses to whether their child has or does not

have epilepsy or seizure disorder.

The current study focused only on the children who were told they were

overweight and children who have cerebral palsy, Down Syndrome, and epilepsy or

seizure disorder. Children were excluded based on the criteria of the research questions

and the purpose of the study. They were also excluded if there were responses based on

ever told but do not currently have the condition. The final study sample was identified

after the data was cleaned and prepared for analysis.

Table 3

Sample Size for Years (Individually and Combined) by Variables

Independent Variable 2018 Sample Size 2019 Sample Size 2018-2019 Sample Size

Cerebral Palsy 30,402 29,323 59,725

Down Syndrome 30,445 29,373 59,818

Epilepsy/Seizure Disorder 30,427 29,342 59,769

Totals 91,274 88,038 179,312

Note: Sample sizes are based on the total U.S. population and households; data includes “has,” “does not

have,” and “never told child has condition.”

To break down the current study target population, in the NSCH 2018-2019

combined data, there is a sample count of 4,076 children who were told they were

overweight. There is a sample count of 174 children who currently have cerebral palsy.

There is a sample size of 104 responses stating they were told their child has Down

Syndrome. Lastly, there is a sample size of 377 children who were told they currently

have epilepsy or seizure disorder.

Sample Size

I used the SPSS system to analyze the uncleaned data. I sampled all available

cases in the dataset. G*power was used to calculate the sample size and power analysis

(Kang, 2021). When calculating the sample size using G*power, the following was used:

t-test (test family), means difference between two independent means, two groups

(statistical test), and a priori compute required sample size given α, power, and effect size

(type of power analysis). The input parameters consisted of two tails, effect size d at 0.5

(medium), α at 0.05, power at .80, and allocation ratio N2/N1 at 1. The outcome

parameters were computed to the following: Df at 126, sample size group 1 at 64, sample

size group 2 at 64, total sample size at 128, and actual power at 0.8014596. Therefore,

the sample size for the independent variables must be around 128 for the power to be

80%. Figure 4 illustrates the results. Given the original dataset’s large sample size and

the G*power test results comparing the means of two independent variables, I was

confident that I would have a medium study sample after data preparation. However,

after the data were cleaned, my study resulted in a small study sample size.

Figure 4

G*power Analysis

Post Hoc Power Analysis

The post hoc analysis is conducted after I have the sample size for the current

study (Kang, 2021). I conducted a post hoc power analysis to determine the actual power

of the study. Using the sample size (N), effect size, and the given α, the power level can

be determined (Kang, 2021). The post hoc formula is the alpha level (α) divided by the

number of tests. Post hoc analysis is known as a “statistical power 1-β and is figured as a

function of significance level α, sample size, and population effect size” (Faul et al.,

2009). After determination of the sample size, I reached a medium effect size (OR=3.5),

an alpha level of .05, and a power of .80 to produce reliable estimates.

Data Analysis Plan

Data Preparation

There are six steps to data preparation: access the data, ingest the data, cleanse the

data, format the data, combine the data, and analyze the data (Bhanot, 2021). I accessed

the data from the NSCH website. I also accessed the data by reviewing other research

studies that have utilized or collected data on childhood obesity, cerebral palsy, Down

Syndrome, and epilepsy or seizure disorder. Once I gained access to the necessary data, I

ingested the data by using software to review the data. Although the latest package is the

28th version, the software used to analyze the data was the Statistical Package for the

Social Sciences (SPSS) 27th version (IBM, n.d.). I only used the data necessary to answer

my research questions.

Recoding was another helpful tool for breaking down the categories into smaller

categories (Babbie, 2006). Recoding is complicated because several steps should be

taken to recode the data (Babbie, 2006). If there is a random sample, the validity will be

high due to there not being a specific population being targeted. Random sampling can

limit the data and makes room for mistakes and additional tests. The NSCH stated that

the DRC takes the results, codes them, and makes the data available to the public, making

the original data collection rigorous (2022).

Data Cleaning

I used the SPSS software to clean and screen the needed data and identify the

missing data and cases during the data cleaning and screening process. I checked for

spelling, errors, blank cells, duplicated cells, spacing, formatting, missing cells, and

number or word conversions (Bhanot, 2021). There were some missing data that were

excluded from the study. The missing data and outliers depended on the values of the

data. Data cleaning assisted with re-labeled cases. There were no duplicated cells within

the data. Therefore, I did not highlight the cells to indicate the duplicated values. Each

variable was cleaned so that I could identify the necessary data from the unnecessary

data. Once I cleaned the data, I formatted the data. There were no duplicated dates,

inconsistent abbreviations, or unnecessary data. After the data formation was completed,

I checked for missing data and identified the missing data and cases (Bhanot, 2021).

After those cases were identified as missing, I excluded the cases from the study (Bhanot,

2021). By using the preparation steps named above, I was able to analyze the data and

perform tests with the cleaned dataset. The data-cleaning process was a key instrument

for my research study.

Operationalization of Dependent and Independent Variables

The dependent variable, childhood obesity, was measured using the question,

“Has a doctor or other health care provider ever told you that this child is overweight?”

and solely on the memory of the parent or guardian. The weight status of the child was

not confirmed by checking the child’s weight or BMI for age (Child and Adolescent

Health Measurement Initiative, 2021). In the current study, I recoded the dependent

variable as 0=no and 1=yes. The independent variable cerebral palsy, was based on

parent recollection and measured using the question, “Does this child currently have

cerebral palsy?”; therefore, it was defined as self-reported guardian responses. I identified

the responses for cerebral palsy as 1= does not have the condition, 2= ever told but do not

currently have the condition, and 3= currently have the condition. The independent

variable Down Syndrome was measured using the following question, “Does this child

have Down Syndrome?” and it was also based on parent recollection; therefore, it was

defined as self-reported responses. I categorized the responses for Down Syndrome as 1=

never had condition and 2= has condition. The independent variable, epilepsy or seizure

disorder, was measured using the question, “Does this child currently have epilepsy or a

seizure disorder?” and was based on the knowledge of the parent: therefore, it was

defined as self-reported responses. I identified the responses for epilepsy or seizure

disorder as 1= does not have the condition, 2= ever told but do not currently have the

condition, and 3= currently have the condition.

Operationalization of Confounding Variables

The confounding variables were the sex, age, and race of the child as well as the

education and income level of the parent. Sex was measured using the question “What is

the child’s sex?” and was based on the parent’s recollection. The variable was identified

as 1= male and 2= female. Age was measured using the question “How old is this child?”

with a note explaining to the parent that if the child is less than 1 month, round their age

up to 1 month. The responses were given based on the parent’s recollections. I

categorized the responses to the age as 1= 0-5 years, 2= 6-11 years, and 3= 12-17 years.

Race was measured using the question “What is the child’s race?” with a pro question

inquiring whether the child was of Hispanic, Latino, or Spanish origin. The responses

were based on the knowledge of the guardian. I identified the responses for race of the

child as 1= Hispanic, 2= White, Non-Hispanic, 3= Black, Non-Hispanic, and 4= Other

Multiracial, Non-Hispanic. The education level of the parent or guardian was measured

using the question “What is the highest grade or level of school you have completed?”

and was based on the parent's knowledge. I categorized the parent’s education level as 1=

less than high school, 2= high school or GED, 3= some college or technical school, and

4= college degree or higher. The final confounding variable, the income level of the

parent, was measured by asking the parent what their income for the previous year was.

The responses were based solely on the guardian’s recollection. Based on the federal

poverty level (FPL) of 2018 and 2019, the responses were categorized as 1= 0-99% FPL,

2= 100-199% FPL, 3= 200-399% FPL, and 4= 400% FPL or greater. Table 37 in

Appendix T shows a visual of the breakdown of the operationalization for the dependent,

independent, and confounding variables.

Research Questions

According to the NSCH 2018-2019 codebook, the child’s weight status (DV) was

measured by an individual item based on the parents’ knowledge (Child and Adolescent

Health Measurement Initiative (CAHMI) (2021). The data on the dependent variable was

collected based on whether the parent or guardian was ever told by a doctor or health care

professional that their child was overweight. The codebook also indicated that the child’s

overweight status is not verified by the child’s weight or BMI per the child’s age. The

parent either responded yes or no.

According to the NSCH 2018-2019 codebook, the independent variables cerebral

palsy, Down Syndrome, and epilepsy or seizure disorder were measured and originated

from responses by the parent or guardian to the health condition question regarding 1 or

more current or lifelong health conditions (Child and Adolescent Health Measurement

Initiative (CAHMI) (2021). The survey question further asked if the parent or guardian

was ever told by a healthcare professional or educator that the child has the condition or

whether the child currently has the condition (Child and Adolescent Health Measurement

Initiative (CAHMI) (2021). Additional options were, the child does not have condition,

parent was told, but the child does not currently have condition and the child currently

has condition (Child and Adolescent Health Measurement Initiative (CAHMI) (2021).

Each independent variable was compared with the dependent variable to see if there was

an association between each independent variable and the dependent variable. For the

current study, the variables were measured using the SPSS 27th version. The eleven

research questions and their null hypotheses (H0) and alternative hypotheses (H1) are as

follows:

RQ1: Is there an association between childhood obesity and cerebral palsy?

H01: There is no association between childhood obesity and cerebral palsy.

H11: There is an association between childhood obesity and cerebral palsy.

RQ2: Is there an association between childhood obesity and cerebral palsy when

controlling for socioeconomic status of parent, education level of parent, income level of

parent or guardian, and the age, race, and sex of child?

H02: There is no association between childhood obesity and cerebral palsy when

controlling for socioeconomic status of parent, education level of parent, income level of

parent or guardian, and the age, race, and sex of child.