Posted: April 24th, 2023

For Kim Woods essay

ORIGINAL ARTICLES
Impact of the Coordinated Approach to Child
Health Early Childhood Program for Obesity
Prevention among Preschool Children:
The Texas Childhood Obesity Research
Demonstration Study
Shreela V. Sharma, PhD, RD, LD,1 Elizabeth Vandewater, PhD,2
Ru-Jye Chuang, DrPH,1 Courtney Byrd-Williams, PhD,3 Steven Kelder, PhD,1
Nancy Butte, PhD,4 and Deanna M. Hoelscher, PhD, RD, LD, CNS3
Abstract
Background: This study presents the impact of a 2-year implementation of Coordinated Approach to Child Health Early Childhood
(CATCH EC), a preschool-based healthy nutrition and physical activity program, on child BMI z-scores, BMI percentiles, diet, physical
activity, and sedentary behaviors among 3- to 5-year old children across Head Start centers in Houston and Austin, Texas.
Methods: We used a quasi-experimental study design with serial cross-sectional data collection (Intervention catchment area:
n = 12 centers, 353 parent-child dyads in Year 1; n = 12 centers, 365 parent-child dyads; Comparison catchment area: n = 13 centers
in year 1, 319 parent child dyads; and n = 12 centers, 483 parent-child dyads in year 2). Child height and weight were measured and
parent self-report surveys were conducted at year 1 (fall 2012) and year 2 (spring 2014).
Results: In year 1, 34.8% of the children were overweight or obese, 74% were Hispanic, and >80% reported an annual household
income of <$25,000. In year 2, 32.2% were overweight or obese, 72% were Hispanic, and 82.3% reported an annual income of
<$25,000. Results demonstrated significantly lower child BMI z-scores [b = -0.26 (95% confidence interval, CI: -0.50 to -0.01),
p = 0.041] and BMI percentiles [b = -6.5 (95% CI: -12.4 to -0.69), p = 0.028] from year 1 to 2 follow-up among those in intervention
Head Start centers, compared to those in the comparison centers. There were no significant between-group changes in child dietary,
physical activity, or screen time behaviors.
Conclusion: Implementation of a preschool-based obesity prevention program can be modestly effective in lowering the prevalence of child overweight in low-income populations.
Keywords: early childhood; Head Start; nutrition; obesity prevention; physical activity; preschool
Introduction
The United States continues to struggle with the
obesity epidemic. Even the nation’s youngest are
not spared with recent data from the 2015 to 2016
National Health Nutrition Examination Survey (NHANES)
reporting 13.9% of children ages 2–5 years of age being
obese with BMI percentile of ‡95.0.1 These rates increase
with increasing age; 18.4% of 6–11 years olds, 20.6% of
12–19 years old, and 39.6% of adults being classified as
1
Department of Epidemiology, Human Genetics, and Environmental Sciences, Michael & Susan Dell Center for Healthy Living, School of Public
Health, University of Texas Health Science Center at Houston, Houston, TX.
2
Data Science and Research Services Unit, University of Texas at Austin, Austin, TX.
3
Department of Health Promotion and Behavioral Sciences, Michael & Susan Dell Center for Healthy Living, School of Public Health, University of
Texas Health Science Center at Houston, Austin, TX.
4
USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX.
CHILDHOOD OBESITY
January 2019 j Volume 15, Number 1
ª Mary Ann Liebert, Inc.
DOI: 10.1089/chi.2018.0010
1
obese. Furthermore, the rates of extreme obesity (at or
above 120% of the 95th BMI percentile) in this age group
of 2–5 years old children has also been increasing over
time.1 Ethnic disparities in obesity rates are observed
among children with 22.4% and 20.2% of Hispanic and
Black children being obese, compared with 14.3% of their
White counterparts.2 Geographic differences are also observed with states in the southern part of the United States
being most impacted from the disease when compared with
other parts of the country.3
Research has established that creating healthy lifestyle
habits early on, including healthy diet and physical activity, can prevent obesity and mitigate the risk of other
chronic diseases later in life.4 The Texas Childhood Obesity Research Demonstration (TX CORD) study was
conducted to address this persistent childhood obesity
epidemic among those at highest risk. The overarching
goal of the TX CORD study was to implement and evaluate an integrated, systems-oriented model to incorporate
primary and secondary prevention approaches targeting
multiple sectors at the clinic, school, preschools, and
community organizations levels to mitigate obesity among
children ages 2–12 years from low-income populations in
Texas.5 Primary prevention in schools included evidencebased obesity prevention programs, including Coordinated
Approach to Child Health Early Childhood (CATCH EC)
in preschools.5 CATCH EC is a theoretically grounded,
preschool-based program with the goal of creating opportunities for the child to practice healthy eating and
physical activity behaviors in preschool and at home.6,7
The main outcome for the CATCH EC primary prevention
component was change in prevalence of obesity measured
using child BMI z-scores; secondary outcomes of interest
included changes in child BMI percentiles, diet, physical
activity, and screen time behaviors. The purpose of this
article is to present the primary outcome results of the TX
CORD primary prevention program, CATCH EC, implemented among 3–5 years old children across Head Start
centers (federally funded preschools for low-income families) in Houston and Austin, Texas.
Methods
Participants
TX CORD primary prevention component for children
ages 3–5 years used a quasi-experimental study design with
serial cross-sectional data collected from Head Start centers
in the intervention and comparison catchment areas in
Houston and Austin, TX (Intervention catchment area:
n = 12 centers in year 1 baseline, n = 12 centers in year 2
follow-up; Comparison catchment area: n = 13 centers in
year 1 baseline, and n = 12 centers in year 2 follow-up). The
Head Start sites at baseline and follow-up were the same. In
year 2 follow-up, 1 Head Start comparison site participating
at baseline closed and merged with another year 1 site resulting in 13 comparison Head Start centers at baseline, and
12 comparison Head Start centers at follow-up.
Sampling of the intervention and comparison catchment
areas and description of the study design for TX CORD are
presented elsewhere.5,8 Briefly, a three-stage Geographical
Information System (GIS) methodology resulted in the selection of intervention and comparison catchment areas in
Houston and Austin with demographic and socioeconomic
characteristics that fit the target population: ethnically diverse population; lower-median household income; and
lower home ownership rates.8 Intervention and comparison
catchment areas were assessed for comparability across
sociodemographic characteristics. Briefly, the TX CORD
study had multiple components including a secondary
prevention randomized control trial (RCT) embedded
within a community-based primary prevention CATCH
implementation across preschools and elementary schools.
While the RCT employed a longitudinal design, the primary
prevention component employed a serial cross-sectional
design primarily for ease of recruitment and measurement.
The CATCH EC program was implemented across Head
Start centers in the intervention catchment areas in Houston and Austin. Project staff met with participating Head
Start center directors to present the goals of the study and
discuss recruitment strategies. Bilingual consent forms in
English and Spanish along with parent surveys were sent
home to the parents through their children in fall 2012 for
the first cross-sectional measurement (baseline), and again
in spring 2014 for the second cross-sectional measurement (follow-up). While the CATCH EC program was
implemented across all the participating intervention centers across 2 school years, only those children whose parents consented to the study were measured. CATCH EC
program was implemented across 2 school years starting
fall 2012. Child and parent measurements were conducted
in fall 2012 before implementation of CATCH EC
(baseline) and spring 2014 (follow-up) after 2 school years
of implementation. The final sample size for the analysis
was n = 672 parent-child dyads at baseline (n = 353 intervention; n = 319 comparison) and n = 848 parent-child
dyads at follow-up (n = 365 intervention; n = 483 comparison). See Figure 1 for the Consolidated Standards of Reporting Trials (CONSORT) diagram depicting study flow.
Description of the CATCH EC program. CATCH EC is a
preschool-based program modeled after CATCH. CATCH is
a behaviorally based school health promotion program based
on Social Cognitive Theory9 constructs to improve the school
environment for healthy nutrition and physical activity.
CATCH has been most effective in reducing obesity among
very low income, African American and Hispanic children.10,11 CATCH EC has three main components: (1) It’s
Fun to be Healthy! a nutrition and gardening-based curriculum; (2) developmentally appropriate structured, indoor and
outdoor physical activities; and (3) parent tip-sheets including
recipes, meal plans, parent-child activities, and recommendations for preschoolers’ diet, physical activity, and screen
time. CATCH EC has been found to be highly acceptable and
feasible among preschoolers from low-income, minority
2 SHARMA ET AL.
populations.6 CATCH EC uses a train-the-trainer model
whereby the preschool staff is trained over a 4- to 6-hour
training period. Primary prevention approaches, such as
CATCH EC, are complex with multiple intervention targets
aimed to change multiple psychosocial and behavioral factors
among children and their caregivers (e.g., teachers, parents).
For the TX CORD study, CATCH EC trainings were conducted with the preschools in the intervention catchment areas in fall 2012 at the start of the study. A total of 92 Head
Start teachers/directors were trained in the 12 intervention
centers in year 1. At the start of year 2, another full training
was conducted across the intervention centers for all teaching
staff. Furthermore, across both years of implementation,
program staff conducted technical support in the form of
booster trainings, monthly messages, and email reminders.
See Table 1 describing CATCH EC program components.
Data Collection Measures
Demographics. Parent and child demographics were
collected at baseline and follow-up using parent surveys
including child age, gender, race, and ethnicity. Parent
Figure 1. Study flow using CONSORT diagram, TX CORD study. TX CORD, Texas Childhood Obesity Research Demonstration.
CHILDHOOD OBESITY January 2019 3
demographics included parent age, gender, race, ethnicity,
primary language spoken at home, income, and education
level.
Child anthropometrics. Child height and weight were
measured using stadiometers (Perspective Enterprises) and
digital scales (Tanita). All measurements were conducted
by trained project staff using standard protocols in fall
2012 and spring 2014. Height and weight were used to
compute age and gender-specific BMI percentiles and zscores.12
Parent surveys. Parent surveys were sent home to the
two cohorts of consenting parents at baseline (fall 2012)
and follow-up (spring 2014). All surveys were offered in
English or Spanish. Parents were requested to send the
completed surveys back to their child’s Head Start center
in sealed envelopes provided, which were then collected by
project staff. Surveys included items developed from previous survey instruments, including the School Physical
Activity and Nutrition (SPAN) survey and other items
including CORD common measures13–16 measuring child
frequency of consumption of various foods including fruit,
vegetables, French fries, sports drinks, water, and other
sugar-sweetened beverages (e.g., sodas). For example:
‘‘Yesterday, did your child eat fruit? Do not count fruit
juice. Please think about all forms of fruits, including
cooked or raw, fresh, frozen, or canned.’’ Response options
ranged on a 4-point scale from 0 times to 3 or more times.
Parents were also asked about their child’s frequency
Table 1. Coordinated Approach to Child Health Early Childhood Program Components
Program components Description Constructs
Center staff training Annual 6-hour training of center teaching staff,
center directors, Head Start organization level
staff including wellness manager and nutrition
manager.
Booster trainings conducted twice a year.
Teacher level:
Nutrition knowledge and skills
Self-efficacy toward teaching the CATCH EC program
Outcome expectations
Social support
Communication around healthy eating and physical activity
Modeling of healthy behaviors
Center level:
Environment toward promoting healthy eating and physical activity
It’s fun to be healthy!
Classroom curriculum
Weekly implementation (20–30 minutes).
Lesson plans—interactive lesson plans including stories, games, and songs to facilitate
learning.
Extension activities—activities linking the
center and the home.
Curriculum connectors—activities linking
multiple areas of learning including language,
arts, math, and science to reinforce concepts.
Child level:
Nutrition knowledge and skills
Knowledge of food and its relationship to health, healthy
vs. unhealthy food choices
Gardening knowledge and skills
Self-efficacy toward making healthy eating choices
Communication of healthy and unhealthy food
Observational learning
Reinforcement
Physical activities Daily implementation (30 minutes).
500+ Structured activities to promote MVPA.
Indoor and outdoor activities; with or without equipment.
Developmentally appropriate.
Adapted for children with disabilities.
Child level:
Behavioral capability toward being physically active
Self-efficacy toward being physically active
Peer modeling and teacher modeling of physical activity
Observational learning
Parent tip-sheets Complementary to the classroom curriculum.
Sent home monthly to parents.
Nine bilingual parent tip-sheets focusing on
recommendations for preschool-age children
on nutrition (fruits, vegetables, dairy, whole
grain foods, healthy snacks and beverages,
recipes, menu planning), physical activity, and
alternatives to screen time
Parent level:
Knowledge and skills to promote healthy eating, activity,
and reduce sedentary behaviors for their preschooler
Self-efficacy toward providing opportunities for their child
to practice healthy behaviors
Knowledge and self-efficacy toward creating a healthy
home environment.
Communication around healthy eating and physical
activity
Coordination kit Six themed activities at the center and
classroom-level conducted through the
school year.
Center environment to support healthy eating and physical
activity.
Ongoing technical support Monthly emails, booster trainings, in-person
visits to the centers.
Support program implementation fidelity
Sharing best practices
CATCH EC, Coordinated Approach to Child Health Early Childhood; MVPA, moderate-to-vigorous physical activity.
4 SHARMA ET AL.
(times per week) of eating breakfast, eating dinner with the
family, watching TV while eating dinner, and eating dinner
from a restaurant. Parents were also asked about their
child’s time spent in sedentary behaviors, including minutes spent during the weekday and weekend day watching
TV, playing video games. Finally, parents were asked
about their child’s time spent in physical activity, including number of days per week participated in more than 60
minutes of physical activity, and the number of days per
week played outside for 30 minutes.
Process Assessment. Process Assessment was conducted
using teacher and center director surveys documenting
implementation of various CATCH EC program components. Furthermore, the surveys also asked questions regarding implementation of other health-related activities at
the center. These data on other non-CATCH related health
events were collected because Head Start performance
standards17,18 mandate the implementation of nutrition and
health education in their centers. Data were collected
across both, intervention and comparison centers because
the CATCH EC program has been available for purchase
nationwide since 2010. Also, we wanted to monitor implementation of other non-CATCH health-related activities since this could significantly influence the study
outcomes. All intervention and comparison center directors and teachers completed the surveys at the end of years
1 and 2 of implementation (Spring 2013 and Spring 2014,
respectively). A total of 122 teachers in year 1, and 105
teachers in year 2 across 12 intervention Head Start centers
completed the survey. In the comparison catchment area,
a total of 120 teachers in year 1 and 102 teachers in year
2 across 13 Head Start centers completed the surveys.
The survey included 23 questions on CATCH EC implementation including (1) access to materials (4 items),
(2) usage (4 items), (3) support for implementation (4
items), (4) child enjoyment of the program (3 items), (5)
sending parent tip-sheets home (1 item), (6) sending program extension activities home (1 item), and (7) implementation of non-CATCH preschool health-related
events and activities (6 items). Additionally, the center
director survey measured center policies and practices and
staff opportunities around nutrition and physical activity
(11 items). Response options were Yes/No on a Likert-type
scale. Details regarding the TX CORD process Assessment
methodology and results are presented elsewhere. Briefly,
two composite implementation indices were developed.
The CATCH EC implementation index measured the
implementation of the CATCH EC program, while the
overall implementation index measured the implementation of CATCH EC plus non-CATCH health-related
activities. Implementation scores were first computed
separately for teachers and center directors, and then averaged to compute mean CATCH EC implementation index (CATCH EC II) and overall implementation index
(Overall II) scores for each year of implementation. Surveys with <80% completion were excluded from the
analysis (<5% exclusion). Percent implementation score
was computed to standardize scores for CATCH EC II and
Overall II: Percent Score = [(observed score)/(total available score)] · 100.
Power analysis. For the TX CORD project, the primary
prevention assessments of a representative sample of
children from the target and comparison catchment areas
were compared at baseline and year 2 of implementation to
examine the effect of the primary prevention program on
child BMI z-scores as the primary outcome. Our analyses
accounts for clustering of children within child care centers, as outlined in the statistical analysis section below.
Power analyses conducted assuming power = 0.80, alpha = 0.05 two-tailed, effect size = 0.15, and one random
effect to account for school-based sampling indicates that a
total sample of 538 children (n = 269 per intervention and
comparison group) at each of the two time points needed to
compare the intervention and comparison catchment areas.
Statistical Analysis
STATA version 15.0 statistical software was used for all
analysis (STATA Corp, College Station, TX). The final
sample size for the analysis was n = 672 parent-child dyads
at baseline (n = 353 intervention; n = 319 comparison) and
n = 848 parent-child dyads at follow-up (n = 365 intervention; n = 483 comparison). Missing data were excluded
from the analysis. For descriptive analysis, means, standard deviations, and frequencies were computed for the
sociodemographic and BMI variables that were primary
outcomes of interest for this study. Pearson’s chi-square
test and student’s t-test were used to examine the baseline
differences in years 1 and 2 between the intervention and
comparison groups for sociodemographic characteristics
and parent and child BMI.
This study examined the effects of whether the 2 schoolyear implementation of the CATCH EC program had an
impact on BMI z-scores and percentiles among children
enrolled in the Head Start centers in the intervention
catchment areas compared with those in the comparison
catchment areas. Secondary outcomes of interest included
changes in frequency of child intake of various healthy and
unhealthy foods, time spent in physical activity, and screen
time behaviors. Multilevel linear regression analysis was
used to compare the serial cross-sectional pre-to-post
changes in the outcome variables in the intervention and
control groups. This study adjusted for the variation among
Head Start centers and the variation among children nested
within Head Start centers, thus controlling for cluster effects.19 We employed a random-intercept regression model
with Head Start center as a random effect in the analysis.
Various known confounders that were considered for inclusion into each of the regression models included city
(Houston and Austin), child race/ethnicity and gender,
parental race, and education level. Collinearity among the
variables was assessed. Variance inflation factor for all of
the variables was <10. Backward selection technique was
CHILDHOOD OBESITY January 2019 5
used for variables selection. Any covariate that resulted in
a change of the point estimate by >10% were included in
the final model. Significance level for all analyses was set
at 0.05.
Results
Results of the demographic data presented in Table 2
outline the household, parent, and child characteristics of
the study sample stratified by the intervention and comparison Head Start centers in years 1 (baseline) and 2 (2-
year follow-up) measurements. On average, across both
years of measurement, the household size was 4.6 members per household with 2.6 children. Approximately 80%
of the sample across both years had an annual household
income of £$25,000 with no differences between intervention and comparison centers across both years. Also,
across years 1 and 2, a significant proportion of the sample
reported being on Women, Infants and Children’s (WIC)
program (>50%) and Supplementation Nutrition Helpance Program (SNAP; >60%), federal Helpance programs, and over 80% of the children were on Medicaid.
Significantly more parents in the intervention centers reported receiving SNAP benefits compared with those in
the comparison centers across both years of measurement
( p < 0.05). Also, across years 1 and 2, a majority of the
parents were reportedly Hispanic (>70%), married (>59%),
employed (>50%), and had a high school diploma or less
(>70%). There were no differences by intervention and
control group across all these variables. For year 1,
children in the comparison centers (mean age: 4.2 years)
were slightly younger than those in the intervention
centers (mean age: 4.3 years, p = 0.0215). A majority of
the sample was Hispanic (>70%), and >20% were Black
across both years. In year 2, a greater proportion of the
children in the intervention centers were Hispanic and
smaller proportion were Black ( p < 0.001). Approximately 35% of the children were overweight or obese
across years 1 and 2, with no significant differences between the intervention and comparison centers. There
were no significant differences in the average BMI zscores across the BMI z-scores or percentiles across years
1 and 2.
The primary outcome of the study was change in obesity
prevalence using child BMI z-scores. Results of the 2-year
implementation of the CATCH EC program demonstrated
significantly lower BMI z-scores: [b = -0.26 (95% confidence interval CI: -0.50 to -0.01), p = 0.041], and BMI
percentiles [BMI percentiles: b = -6.5 (95% CI: -12.4 to
-0.69), p = 0.028] among children at the 2-year follow-up
compared with those in year 1 in the intervention centers
when compared with those in the comparison centers.
Within-group changes demonstrate that the child BMI
percentiles and z-scores were lower at year 2 follow-up
compared with baseline among children in the intervention
centers, whereas they concurrently were higher among
those in the comparison centers (Table 3).
The secondary outcomes of the study were child diet,
physical activity, and sedentary behaviors (Table 4). Results showed that there were no statistically significant
between-group changes at year 2 follow-up. There were
several noteworthy within-group changes. There was a
significantly higher frequency of intake of fruit among
children in intervention centers ( p = 0.009) in year 2 when
compared with year 1, but not among those in the comparison centers. There was a significantly higher intake of
French fries among children in the comparison centers in
year 2 compared with year 1 ( p = 0.000), but not in the
intervention centers. Similarly, there was a significantly
higher frequency of eating dinner at a restaurant in year 2
compared with year 1 among those in the comparison
centers ( p = 0.009), with no significant changes among
those in the intervention centers.
Process Assessment
The process Assessment data showed high level of
CATCH EC program implementation among the intervention centers for both year 1 (mean score: 81.4% – 2.9%)
and year 2 (mean score: 84.52% – 2.9%) indicating that
across both years, there was over 80% implementation
across the various CATCH EC components (Table 5).
Interestingly, our data showed some CATCH EC implementation across four comparison centers (mean scores:
51.01% – 10.8 at year 1; 39.01% – 11.6 at year 2). Among
the intervention centers, the highest scores across both,
center directors and teachers were for CATCH EC program access and usage (mean score >85% across both
years), followed by CATCH EC enjoyment and support
(mean score >75% across both years). The lowest scores
were for CATCH EC parent tip-sheets and extension activities sent home to parents with mean scores ranging
from 53% to 74% across both years of implementation.
The overall implementation index (CATCH + non-CATCH
health activities) showed overall higher implementation
scores across both years in the intervention centers (mean
score: 74.7% in year 1; 72.0% in year 2) compared with
those in the comparison centers (mean score: 45.5% in year
1; 44.2% in year 2).
Discussion
Significantly lower BMI z-scores and BMI percentiles
were seen in year 2 compared with year 1 among children
across Head Start centers implementing CATCH EC in the
TX CORD intervention catchment areas compared with
those in the comparison catchment areas. Furthermore,
process Assessment demonstrated high implementation of
CATCH EC across the intervention sites, and even though
the cohorts of children in years and 1 and 2 were different
the participating Head Start centers were the same. Finally,
we conducted outcome and process Assessment across intervention and comparison sites, and our outcome analysis
assessed for between and within-group changes in years 1
and 2 adjusting for baseline differences, city, center, and
6 SHARMA ET AL.
Table 2. Participant Demographics at Baseline and Follow-Up Stratified by Intervention
and Comparison Groups, Texas Childhood Obesity Research Demonstration Study
Year 1 (baseline) Year 2 (follow-up)
Overall
(n 5 672)
Comparison
(n 5 319)
Intervention
(n 5 353) p
Overall
(n 5 848)
Comparison
(n 5 365)
Intervention
(n 5 483) p
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
Household characteristics
No. of people living in
the household
4.57 (1.5) 4.64 (1.6) 4.51 (1.4) 0.2849 4.62 (1.6) 4.67 (1.6) 4.58 (1.5) 0.3875
No. of children under 18
in the household
2.56 (1.3) 2.62 (1.4) 2.51 (1.3) 0.2670 2.61 (1.3) 2.66 (1.3) 2.58 (1.3) 0.4304
Annual household income
Less than $10,000 34.2 32.3 36.0 0.774 34.0 35.3 33.1 0.688
$10,001–$15,000 20.4 20.5 20.2 20.6 18.6 22.2
$15,001–$20,000 15.8 16.5 15.2 16.3 17.0 15.9
$20,001–$25,000 15.5 16.8 14.3 11.2 9.4 12.5
$25,001–$35,000 9.5 9.8 9.3 9.9 10.6 9.3
$35,001–$50,000 3.4 2.7 4.0 6.5 7.3 5.9
$50,001–$75,000 1.0 1.4 0.6 1.2 1.5 0.9
$75,001 or more 0.2 0.0 0.3 0.3 0.3 0.2
Government Helpance
received (%)
WIC 50.0 53.4 47.0 0.102 56.2 56.0 56.4 0.911
Food stamps (SNAP) 63.0 58.3 67.2 0.018* 66.4 61.8 69.8 0.019*
Free/reduced price school
meals
44.2 45.3 43.1 0.564 74.0 74.2 73.8 0.917
Medicaid or Texas Health
Steps
84.0 81.9 85.9 0.186 79.9 78.0 91.4 0.253
Medicare 17.6 16.8 18.3 0.662 18.3 21.4 15.9 0.063
CHIP 16.6 15.5 17.7 0.503 18.9 21.5 17.0 0.126
Parent characteristics
Age in years 31.2 (6.9) 31.2 (7.2) 31.3 (6.7) 0.8726 31.33 (7.1) 31.11 (7.3) 31.50 (6.9) 0.4491
% Female 94.5 96.0 93.1 0.108 92.3 93.6 91.4 0.237
Race/ethnicity (%)
Hispanic or Latino 73.0 73.4 72.7 0.059 72.6 69.3 75.2 0.001*
Black 23.0 24.4 21.6 22.8 28.0 18.8
Other 4.0 2.2 5.7 4.6 2.7 6.0
Primary language (%)
Only English 27.5 31.8 23.4 0.094 29.5 38.5 22.6 <0.001*
More English than Spanish 11.6 12.5 10.8 7.4 8.1 7.0
Both English and Spanish 15.6 14.5 16.6 16.9 16.1 17.6
More Spanish than English 26.4 25.1 27.7 24.9 23.3 26.1
Only Spanish 18.9 16.1 21.5 21.3 14.1 26.7
% Married 64.6 62.6 66.5 0.359 59.3 56.2 61.4 0.180
continued on page 8
7
Table 2. Participant Demographics at Baseline and Follow-Up Stratified by Intervention
and Comparison Groups, Texas Childhood Obesity Research Demonstration Study continued
Year 1 (baseline) Year 2 (follow-up)
Overall
(n 5 672)
Comparison
(n 5 319)
Intervention
(n 5 353) p
Overall
(n 5 848)
Comparison
(n 5 365)
Intervention
(n 5 483) p
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
% or
Mean (SD)
Employment status (%)
Currently employed
(including seasonal)
53.0 48.7 57.0 0.182 53.7 56.0 52.0 0.307
Currently unemployed 15.9 16.8 15.0 18.1 17.4 18.6
Homemaker 31.1 34.5 28.0 28.2 26.6 29.4
Employment status (%)
Currently employed
(not including seasonal)
50.0 46.8 52.9 0.182 51.2 53.5 49.5 0.307
Currently unemployed 18.9 18.7 19.1 20.6 19.9 21.1
Homemaker 31.1 34.5 28.0 28.2 26.6 29.4
Education level (%)
None/kindergarten only 1.5 0.6 2.3 0.059 1.6 1.1 1.9 0.782
Elementary-middle school 13.1 9.8 16.0 11.7 10.6 12.6
Some high school 18.8 18.1 19.5 20.6 19.5 21.3
High school diploma 36.3 38.7 34.2 35.2 36.8 34.1
Some college or technical
school
25.4 27.9 23.0 25.2 26.2 24.5
College diploma 4.9 4.8 5.0 5.7 5.8 5.6
Child characteristics
Age in years, mean (SD) 4.25 (0.68) 4.2 (0.7) 4.3 (0.6) 0.0215* 4.13 (0.7) 4.16 (0.7) 4.11 (0.7) 0.2430
% Female 46.9 49.8 44.2 0.143 47.9 47.4 48.2 0.808
Race/ethnicity (%)
Hispanic or Latino 72.9 72.7 73.1 0.462 70.8 69.0 72.3 <0.001*
Black 22.0 23.2 21.0 23.0 28.0 19.2
Other 5.1 4.1 5.9 6.2 3.0 8.5
Primary language at home (%)
Only English 31.8 38.8 25.6 0.007* 31.2 40.7 23.9 <0.001*
More English than Spanish 12.5 10.9 14.2 11.7 12.5 11.1
Both English and Spanish 21.5 20.5 22.2 16.8 15.7 17.6
More Spanish than English 23.4 21.2 25.0 25.5 23.1 27.4
Only Spanish 10.8 8.7 13.0 14.8 8.0 20.0
Child weight status
Normal weight 65.2 64.6 65.7 0.769 67.8 66.6 68.8 0.720
Overweight 17.4 16.9 17.9 16.4 17.5 15.5
Obese 17.4 18.5 16.4 15.8 15.9 15.7
Child BMI z-scores 0.62 (1.3) 0.54 (1.4) 0.68 (1.1) 0.1617 0.62 (1.1) 0.64 (1.1) 0.60 (1.1) 0.6340
Child BMI percentiles 65.8 (28.4) 63.6 (29.7) 67.7 (27.0) 0.0629 66.3 (27.6) 67.4 (27.0) 65.5 (28.0) 0.3224
CHIP, Children’s Health Insurance Program; SD, standard deviation; SNAP, Supplementation Nutrition Helpance Program; WIC, Women,
Infants and Children.
8
individual-level covariates, indicating that the outcomes
seen in our study were likely due to the implementation of
CATCH EC. The literature on testing interventions in
Head Start settings is sparse. Our results concur with the
findings of other studies that have demonstrated significant
improvements in BMI of children following the implementation of preschool-based programs,20–22 and adds
to the current body of literature on evidence-based strategies to implement in a preschool environment, specifically
Head Start. However, in contrast to our study, a recent
study published by Lumeng et al.,23 of a cluster-randomized
intervention trial in Head Start classrooms in Michigan
demonstrated significant improvements in child selfregulation but no impact on child prevalence of obesity. To
our knowledge, there was one other randomized controlled
trial of a preschool-based intervention among Head Start
children in Illinois that reported significant reductions in
child BMI from baseline to 2-year follow-up.24 These
varying results on child BMI across studies in Head Start
settings may suggest that subsets of children may respond
differently to interventions warranting the need for additional work in this area to identify the biological, behavioral, environmental, or other predisposing moderators of
these interventions in future studies.25 Our results also
concur with a recent systematic review of the literature26
that reported that while a few obesity prevention interventions in child care settings have demonstrated successful changes in the child BMI levels, these interventions
may or may not demonstrate favorable improvements in
the obesity-related behaviors such as diet and physical
activity.
Furthermore, results of the process Assessment, measured
using teacher and director surveys, demonstrate high implementation of the various CATCH EC program components (>80% of program components implemented).
Interestingly, our process Assessment results also demonstrated similar high implementation of the CATCH EC
program across four comparison centers. This was because
the participating comparison centers were trained in implementing the CATCH EC program before the TX CORD
study as part of their regular ‘‘standard of care’’ (Head
Start director, personnel communication). However, it is
important to note that, this implementation of the CATCH
EC program in the comparison centers could have significantly attenuated the findings of our study. However, our
study was a ‘‘real-life’’ effectiveness study and since the
comparison centers were implementing CATCH EC before
the TX CORD study, from an ethical perspective, restriction of implementation of programs such as CATCH EC
was not an option. These results also underscore the importance of process Assessment efforts across both intervention and comparison groups in experimental studies.
Notably, while we did see significantly lower BMI at 2-
year follow-up compared with baseline among children
in the intervention centers, when compared with those in
the comparison centers, we did not see concurrent significant improvements in child diet, activity, and sedentary
Table 3. Within and Between-Group Changes in Child BMI z-Scores and Percentiles, Texas Childhood Obesity
Research Demonstration Study
Intervention group Comparison group
Baseline (year 1)
mean (SE),
n 5 353
Follow-up (year 2)
mean (SE), n 5 483
Within group changesa
(95% CI),
p-value
Baseline (year 1)
mean (SE),
n 5 319
Follow-up
(year 2)
mean (SE),
n 5 465
Within group changesa
(95% CI),
p-value
Net changesb
b (95% CI),
p-value
BMI z-score 0.68 (0.07) 0.60 (0.06)
-0.08 (
-0.24 to 0.08), 0.342 0.46 (0.07) 0.64 (0.07) 0.17 (
-0.01 to 0.36), 0.060
-0.26 (
-0.50 to
-0.01), 0.041*
BMI percentile 67.5 (1.5) 65.5 (1.3)
-2.02 (
-5.88 to 1.83), 0.304 63.6 67.1 4.5 (0.14 to 8.9), 0.043*
-6.5 (
-12.4 to
-0.69), 0.028*
aWithin-group changes using mixed-model regression analysis adjusting for school as a random effect.
bBetween-group changes using mixed-model regression analysis adjusting for school as a random effect. Covariates adjusted for in the analysis include city (Houston and Austin), child
age, ethnicity, gender, and parent income level.
*Significant at p < 0.05.
CI, confidence interval; SE. standard error.
CHILDHOOD OBESITY January 2019 9
Table 4. Within and Between-Group Changes in Child Dietary Habits, Physical Activity, and Sedentary Behaviors,
Texas Childhood Obesity Research Demonstration Study
Variable
Intervention group Comparison group
Baseline
(year 1)
mean (SE),
n 5 353
Follow-up
(year 2)
mean (SE),
n 5 483
Within group changesa
(95% CI),
p-value
Baseline
(year 1)
mean (SE),
n 5 319
Follow-up
(year 2)
mean (SE),
n 5 465
Within group changesa
(95% CI),
p-value
Net changesb
b (95% CI),
p-value
Child frequency of intake of (times per week)
Fruit 1.69 (0.05) 1.84 (0.04) 0.15 (0.04 to 0.26), 0.009* 1.73 (0.05) 1.74 (0.05) 0.005 (
-0.13 to 0.13), 0.940 0.15 (
-0.03 to 0.32), 0.096
Vegetables 1.35 (0.05) 1.39 (0.05) 0.04 (
-0.07 to 0.16), 0.481 1.37 (0.06) 1.37 (0.06)
-0.003 (
-0.14 to 0.14), 0.996 0.04 (
-0.14 to 0.23), 0.644
French fries 0.68 (0.04) 0.75 (0.04) 0.07 (
-0.04 to 0.17), 0.213 0.65 (0.05) 0.89 (0.04) 0.21 (0.09 to 0.33), 0.000*
-0.14 (
-0.3 to 0.01), 0.070
Sports drinks 0.85 (0.05) 0.91 (0.05) 0.06 (
-0.07 to 0.18), 0.374 0.93 (0.06) 1.06 (0.05) 0.14 (
-0.002 to 0.29), 0.054
-0.08 (
-0.22 to 0.11), 0.389
Water 2.19 (0.05) 2.14 (0.04)
-0.05 (
-0.17 to 0.08), 0.442 2.10 (0.05) 2.08 (0.05)
-0.02 (
-0.16 to 0.12), 0.765
-0.03 (
-0.22 to 0.16), 0.775
Sugar-sweetened beverages 1.16 (0.07) 0.65 (0.06)
-0.52 (
-0.67 to
-0.36), 0.000* 1.27 (0.07) 0.75 (0.07)
-0.52 (
-0.70 to
-0.35), 0.000* 0.006 (
-0.22 to 0.24), 0.961
Child frequency of (times per week):
Eating breakfast 3.27 (0.06) 3.34 (0.05) 0.08 (
-0.09 to 0.24), 0.357 3.30 (0.07) 3.40 (0.07) 0.09 (
-0.09 to 0.27), 0.338
-0.01 (
-0.25 to 0.23), 0.917
Eating dinner with family 3.15 (0.06) 3.24 (0.05) 0.09 (
-0.07 to 0.26), 0.256 3.28 (0.07) 3.30 (0.06) 0.03 (
-0.15 to 0.21), 0.763 0.06 (
-0.18 to 0.31), 0.599
Watching TV with dinner 1.11 (0.08) 1.18 (0.07) 0.07 (
-0.11 to 0.25), 0.468 1.20 (0.08) 1.34 (0.08) 0.14 (
-0.07 to 0.35), 0.198
-0.07 (
-0.35 to 0.20), 0.621
Eats dinner from restaurant 0.72 (0.05) 0.78 (0.05) 0.06 (
-0.06 to 0.18), 0.313 0.80 (0.06) 0.99 (0.06) 0.18 (0.05 to 0.32), 0.009*
-0.12 (
-0.30 to 0.06), 0.185
Time spent in sedentary behaviors (minutes)
Minutes watched TV—weekend 156.6 (7.9) 153.7 (6.7)
-2.91 (
-23.1 to 17.2), 0.777 186.5 (8.6) 186.7 (7.9) 0.26 (
-22.7 to 23.2), 0.982
-3.17 (
-33.7 to 27.4), 0.839
Minutes watched TV—week day 208.9 (8.9) 210.5 (7.3) 1.59 (
-20.8 to 24.0), 0.889 243.2 (9.5) 235.9 (8.7)
-7.3 (
-32.3 to 17.8), 0.571 8.84 (
-24.8 to 42.5), 0.606
Minutes video game—weekend 63.8 (6.5) 49.3 (5.2)
-14.6 (
-30.8 to 1.71), 0.080 64.8 (7.0) 55.9 (6.3)
-8.9 (
-27.2 to 9.33), 0.338
-5.6 (
-30.1 to 18.8), 0.651
Minutes video game–week day 76.3 (7.6) 77.5 (6.0) 1.2 (
-17.6 to 20.0), 0.901 79.9 (8.0) 86.9 (7.2) 7.0 (
-13.9 to 27.9), 0.512
-5.8 (
-33.9 to 22.3), 0.686
Time spent in physical activity
(days per week)
Days participated in 60 minutes
physical activity
4.9 (0.14) 4.9 (0.12) 0.04 (
-0.26 to 0.35), 0.782 5.1 (0.15) 5.2 (0.14) 0.09 (
-0.26 to 0.46), 0.538
-0.05 (
-0.53 to 0.42), 0.824
Days play outside 30 minutes/d 0.89 (0.02) 0.87 (0.02)
-0.01 (
-0.06 to 0.03), 0.512 0.90 (0.02) 0.91 (0.02) 0.12 (
-0.04 to 0.06), 0.626
-0.03 (
-0.09 to 0.04), 0.435
aWithin-group changes using mixed-model regression analysis adjusting for school as a random effect.
bBetween-group changes using mixed-model regression analysis adjusting for school as a random effect. Covariates adjusted for in the analysis include city (Houston and Austin), child age, ethnicity,
gender, and parent income level.
*Significant at p < 0.05.
10
Table 5. Process Assessment for Primary Prevention Intervention in Head Start Centers, Texas Childhood Obesity
Research Demonstration Study
Process Assessment scales
No. of
items
Year 1
intervention
centersa
Year 1
comparison
centersa
Year 2
intervention
centersa
Year 2
comparison
centersa
n
Mean
%
6 SD n
Mean
%
6 SD n
Mean
%
6 SD n
Mean
%
6 SD
Center Director
CATCH EC Program accessb 4 12 98.0
– 7.2 10 43.2
– 50.1 12 98.0
– 7.2 10 34.1
– 47.8
CATCH EC Program usageb 4 12 93.8
– 11.3 10 31.8
– 44.8 12 95.8
– 9.7 10 36.4
– 50.5
CATCH EC supportb 4 11 66.7
– 20.1 8 62.5
– 28.9 12 74.0
– 18.4 6 58.0
– 33.6
Director level implementation indexc 12 11 86.1
– 10.7 8 55.2
– 34.7 12 89.4
– 9.3 6 53.4
– 36.6
Teacher
CATCH EC Program accessb 4 61 89.6
– 21.2 42 38.9
– 40.6 41 86.0
– 28.0 29 36.2
– 44.6
CATCH EC Program usageb 4 61 84.8
– 24.7 44 33.0
– 37.7 42 85.1
– 27.6 29 35.3
– 44.1
CATCH EC enjoymentd 3 62 86.7
– 19.8 44 39.2
– 41.0 42 75.0
– 29.8 29 37.1
– 46.2
CATCH EC supportb 4 62 78.2
– 18.6 30 62.1
– 27.7 42 76.2
– 18.6 18 60.5
– 38.1
CATCH EC parent tip-sheets sent homed 1 62 53.2
– 50.3 44 29.6
– 46.2 43 67.4
– 47.4 29 24.1
– 43.5
CATCH EC activities sent homed 1 61 54.1
– 50.2 44 27.3
– 45.1 43 74.4
– 44.1 30 23.3
– 43.0
Teacher level implementation indexe 17 60 80.7
– 17.4 30 52.8
– 29.4 41 68.8
– 29.2 18 49.2
– 36.9
Average CATCH EC implementation index % score mean (SE) 12 81.35 (2.92) 8 51.01 (10.80) 0.003* 12 84.52 (2.94) 9 39.01 (11.95) 0.002*
Non-CATCH health activities
Director mean % score 25 11 80.7
– 9.8 10 68.5
– 15.7 12 72.2
– 9.9 10 54.7
– 14.5
Teacher mean % score 5 59 73.3
– 16.3 39 43.9
– 29.9 43 62.4
– 28.3 27 36.5
– 30.8
Overall implementation %
(CATCH
+ non-CATCH) score mean (SE)f,g
12 74.70 (2.31) 12 45.52 (6.95) 0.002* 12 72.04 (3.01) 11 44.23 (6.92) 0.007*
a% Score is calculated: Score
= (original score)/(original potential score range)
· 100. Higher score indicates higher level of implementation.
bTeacher and center director responses.
cCATCH EC implementation index for center is calculated based on 12 items from the director-level survey.
dTeacher responses only.
eCATCH EC implementation index for teacher is calculated based on 17 items from the teacher-level survey.
fOverall implementation index for center is calculated based on 37 items from the director-level survey.
gOverall implementation index for teacher is calculated based on 22 items from the teacher-level survey.
*Result from Mann–Whitney U test to assess differences between intervention and comparison centers in years 1 and 2, p < 0.05.
11
behaviors (Table 4). One reason for the lack of significant
between-group differences in diet and activity could be
because these were secondary outcomes and we lacked
sufficient power, and also the serial cross-sectional study
design used in our study. Thus, children enrolled at baseline were not necessarily those enrolled at the 2-year
follow-up. Longitudinal repeated measures studies are
stronger in design and warranted in future studies but
difficult in Head Start centers in which annual enrollment
changes substantially. Finally, the CATCH EC program is
a teacher-led preschool-based program, and while there are
parent engagement components to the program, a large
portion of the program components are preschool based.
Also, in Head Start centers, children may receive their
breakfast, lunch, and two snacks while at school, thus
eating a majority of their meals outside of home away from
the parents. Similarly, given that children are spending a
majority of their day at school, a significant amount of their
time spent in activity is also away from home. Given that
the child diet and activity behaviors were measured using
parent-reported surveys, parents may not have complete
knowledge of these behaviors as they pertain to the time
their child spends away from home that can significantly
influence findings. Our next step is to assess results of
the teacher and center director surveys to evaluate the
impact of the CATCH EC program on Head Start center
environment.
Strengths of the study include training and execution of
the CATCH EC program in a number of Head Start Centers with many competing priorities and challenges; use of
validated surveys, achieving adequate sample size, and
participation rates in a population of low-income, ethnically diverse children and their parents. Finally, child
height and weight were objectively measured across the
two time points. These strengths notwithstanding, our
study has some limitations. This includes use of parentreported survey data for child diet and activity that could
result in social desirability bias, and potential recall bias in
knowing what the child behaviors were at school away
from home. Furthermore, the survey items, even though
previously validated, were not again validated as part of
this study. The serial cross-sectional design (vs. a longitudinal cohort design) limits causality. However, even
though the cohorts of children were different across both
years of measurement, it is important to note that the Head
Start centers measured were the same. Finally, we had a
convenience sample (vs. random sample) of Head Start
centers located within the intervention and comparison
catchment areas that were invited to participate in the
study. Lack of random sampling and randomization can
limit internal validity of the findings.
Conclusion
In conclusion, the results of our study demonstrate that
implementation of a primary prevention program over 2
years was successful and demonstrated modest improvements in BMI z-scores and percentiles among children
enrolled in Head Start centers participating in the TX
CORD study. These results have significant implications in
the promise of such programs using train-the-trainer model
for Head Start providers to create healthy environments in
the preschool and promote obesity prevention behaviors
among preschoolers.
Acknowledgments
This research was supported by cooperative agreement
RFA-DP-11-007 from the CDC. The content is solely the
responsibility of the authors and does not necessarily
represent the official views of the CDC. Additional support
was provided by the Michael and Susan Dell Foundation
through the Michael & Susan Dell Center for Healthy
Living. This work is a publication of the USDA (USDA/
ARS) Children’s Nutrition Research Center, Department
of Pediatrics, Baylor College of Medicine, Houston, Texas,
and has been funded, in part, with federal funds from the
USDA/ARS under Cooperative Agreement number 58-
6250-0-008. The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does
mention of trade names, commercial products, or organizations imply endorsement from the U.S. government.
Author Disclosure Statement
The authors do not have any conflicts to disclose.
References
1. Ogden CL, Carroll MD, Lawman HG, et al. Trends in obesity
prevalence among children and adolescents in the United States,
1988–1994 through 2013–2014. JAMA 2016;315:2292–2299.
2. Wang Y, Beydoun MA. The obesity epidemic in the United
States—Gender, age, socioeconomic, racial/ethnic, and geographic
characteristics: A systematic review and meta-regression analysis.
Epidemiol Rev 2007;29:6–28.
3. Centers for Disease Control and Prevention. Diabetes home:
County data. 2013. Updated May 2, 2017. Available at www
.cdc.gov/diabetes/data/county.html Last accessed November 29,
2017.
4. Wang Y, Cai L, Wu Y, et al. What childhood obesity prevention
programmes work? A systematic review and meta-analysis. Obes
Rev 2015;16:547–565.
5. Hoelscher DM, Butte NF, Barlow S, et al. Incorporating primary
and secondary prevention approaches to address childhood obesity
prevention and treatment in a low-income, ethnically diverse
population: Study design and demographic data from the Texas
Childhood Obesity Research Demonstration (TX CORD) study.
Child Obes 2015;11:71–91.
6. Sharma SV, Chuang R, Hedberg AM. Pilot testing CATCH Early
Childhood: A preschool-based program aimed at promoting healthy nutrition and physical activity among 3 to 5 year old children
enrolled in Head Start. Am J Health Educ 2011;42:12–23.
7. Chuang RJ, Sharma SV, Perry CL, Diamond P. Does the CATCH
Early Childhood program increase vigorous physical activity
12 SHARMA ET AL.
among low-income preschoolers?—Results from a pilot study. Am
J Health Promot 2017;32:344–348.
8. Oluyomi AO, Byars A, Byrd-Williams C, et al. The utility of
geographical information systems (GIS) in systems-oriented obesity intervention projects: The selection of comparable study sites
for a quasi-experimental intervention design—TX CORD. Child
Obes 2015;11:58–70.
9. Bandura A. Social Foundations of Thought and Action: A Social
Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ, 1986.
10. Hoelscher DM, Springer AE, Ranjit N, et al. Reductions in child
obesity among disadvantaged school children with community
involvement: The Travis county CATCH trial. Obesity 2010;18:
S36–S44.
11. Coleman KJ, Tiller CL, Sanchez J, et al. Prevention of the epidemic increase in child risk of overweight in low-income schools
the El Paso coordinated approach to child health. Arch Pediatr
Adolesc Med 2005;159:217–224.
12. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth
charts for the United States: Methods and development. Vital
Health Stat 11 2002;(246)1–190.
13. Hoelscher DM, Day RS, Kelder SH, Ward JL. Reproducibility and
validity of the secondary level school-based nutrition monitoring
student questionnaire. J Am Diet Assoc 2003;103:186–194.
14. Penkilo M, George GC, Hoelscher DM. Reproducibility of the
school-based nutrition monitoring questionnaire among fourthgrade students in Texas. J Nutr Educ Behav 2008;40:20–27.
15. Thiagarajah K, Fly AD, Hoelscher DM, et al. Validating the food
behavior questions from the elementary school SPAN questionnaire. J Nutr Educ Behav 2008;40:305–310.
16. O’connor DP, Lee RE, Mehta P, et al. Childhood obesity research
demonstration project: Cross-site Assessment methods. Child Obes
2015;11:92–103.
17. U.S. Department of Health and Human Services Administration
for Children and Families. Head Start program performance
standards: Preamble—Part 1. Updated 2016. Available at https://
eclkc.ohs.acf.hhs.gov/sites/default/files/pdf/preamble-part1.pdf Last
accessed November 29, 2017.
18. U.S. Department of Health and Human Services Administration for
Children and Families. Policy & regulations—The Head Start program performance standards. Updated February 5, 2018. Available
at www.acf.hhs.gov/ohs/policy Last accessed June 20, 2018.
19. Murray DM. Design and Analysis of Group-Randomized Trials.
Oxford University Press, Oxford, United Kingdom, 1998.
20. Alkon A, Crowley AA, Neelon SEB, et al. Nutrition and physical
activity randomized control trial in child care centers improves
knowledge, policies, and children’s body mass index. BMC Public
Health 2014;14:215.
21. Annesi JJ, Smith AE, Tennant GA. Reducing high BMI in African
American preschoolers: Effects of a behavior-based physical activity intervention on caloric expenditure. South Med J 2013;106:
456–459.
22. Herman A, Nelson BB, Teutsch C, Chung PJ. ‘‘Eat healthy, stay
active!’’: A coordinated intervention to improve nutrition and
physical activity among Head Start parents, staff, and children. Am
J Health Promot 2012;27:e27–e36.
23. Lumeng JC, Miller AL, Horodynski MA, et al. Improving selfregulation for obesity prevention in Head Start: A randomized
controlled trial. Pediatrics 2017;5:139.
24. Fitzgibbon ML, Stolley MR, Schiffer L, et al. Two-year follow-up
results for Hip-Hop to Health Jr.: A randomized controlled trial for
overweight prevention in preschool minority children. J Pediatr
2005;146:618–625.
25. Baranowski T, Taveras EM. Childhood obesity prevention:

Changing the focus. Child Obes 2018;14:1–3.
26. Ward DS, Welker E, Choate A, et al. Strength of obesity prevention interventions in early care and education settings: A systematic review. Prev Med 2017;95:S37–S52.
Address correspondence to:
Shreela V. Sharma, PhD, RD, LD
Department of Epidemiology, Human Genetics,
and Environmental Sciences
Michael & Susan Dell Center for Healthy Living
School of Public Health
University of Texas Health Science Center at Houston
1200 Pressler, RAS E603
Houston, TX 77030
E-mail: shreela.v.sharma@uth.tmc.edu
CHILDHOOD OBESITY January 2019 13
Reproduced with permission of copyright owner. Further reproduction
prohibited without permission.
-research paper writing service

Order | Check Discount

Tags: #1 Assignment Help Online Service for Students in the USA, AI Plagiarism free essay writing tool, Australian best tutors, best trans tutors, buy essay uk