ORIGINAL_ARTICLESImpact_of_the_Coordinated_Approach_to_ChildHealth

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, CNS 3

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 prev- alence of child overweight in low-income populations.

Keywords: early childhood; Head Start; nutrition; obesity prevention; physical activity; preschool

Introduction

T he 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

1Department 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. 2Data Science and Research Services Unit, University of Texas at Austin, Austin, TX. 3Department 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. 4USDA/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

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 ob- served 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 activ- ity, can prevent obesity and mitigate the risk of other chronic diseases later in life.4 The Texas Childhood Obe- sity 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 eval- uate 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 evidence- based 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 op- portunities 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, im- plemented among 3–5 years old children across Head Start centers (federally funded preschools for low-income fam- ilies) 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 re- sulting 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 se- lection of intervention and comparison catchment areas in Houston and Austin with demographic and socioeconomic characteristics that fit the target population: ethnically di- verse 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 Hous- ton 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 measure- ment (follow-up). While the CATCH EC program was implemented across all the participating intervention cen- ters across 2 school years, only those children whose par- ents 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 inter- vention; n = 319 comparison) and n = 848 parent-child dyads at follow-up (n = 365 intervention; n = 483 compar- ison). See Figure 1 for the Consolidated Standards of Re- porting 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 chil- dren.10,11 CATCH EC has three main components: (1) It’s Fun to be Healthy! a nutrition and gardening-based curricu- lum; (2) developmentally appropriate structured, indoor and outdoor physical activities; and (3) parent tip-sheets including recipes, meal plans, parent-child activities, and recommen- dations 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 con- ducted with the preschools in the intervention catchment ar- eas 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 z- scores.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 pre- vious 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 phys- ical activity

It’s fun to be healthy! Classroom curriculum

Weekly implementation (20–30 minutes). Lesson plans—interactive lesson plans in- cluding 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 with- out 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 min- utes 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, includ- ing 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 evaluation. Process evaluation was conducted using teacher and center director surveys documenting implementation of various CATCH EC program compo- nents. Furthermore, the surveys also asked questions re- garding 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 im- plementation of other non-CATCH health-related activi- ties since this could significantly influence the study outcomes. All intervention and comparison center direc- tors 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 im- plementation 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 pro- gram extension activities home (1 item), and (7) im- plementation 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 evaluation 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 imple- mentation of CATCH EC plus non-CATCH health-related activities. Implementation scores were first computed separately for teachers and center directors, and then av- eraged to compute mean CATCH EC implementation in- dex (CATCH EC II) and overall implementation index (Overall II) scores for each year of implementation. Sur- veys 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 avail- able 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 cen- ters, as outlined in the statistical analysis section below. Power analyses conducted assuming power = 0.80, al- pha = 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 interven- tion; n = 483 comparison). Missing data were excluded from the analysis. For descriptive analysis, means, stan- dard 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 school- year 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 ef- fects.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 in- clusion 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 com- parison 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 mem- bers 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 inter- vention 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 Assis- tance Program (SNAP; >60%), federal assistance pro- grams, and over 80% of the children were on Medicaid. Significantly more parents in the intervention centers re- ported 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). Approxi- mately 35% of the children were overweight or obese across years 1 and 2, with no significant differences be- tween the intervention and comparison centers. There were no significant differences in the average BMI z- scores 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% confi- dence 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). Re- sults 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 com- parison 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 Evaluation The process evaluation data showed high level of

CATCH EC program implementation among the inter- vention 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 im- plementation 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 pro- gram 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 ac- tivities 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 evaluation 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 evaluation across in- tervention 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)

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 assistance 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

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)

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 Assistance Program; WIC, Women,

Infants and Children.

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 im- plementation of preschool-based programs,20–22 and adds to the current body of literature on evidence-based strate- gies 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 self- regulation 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 addi- tional work in this area to identify the biological, behav- ioral, 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 inter- ventions in child care settings have demonstrated suc- cessful 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 evaluation, measured using teacher and director surveys, demonstrate high im- plementation of the various CATCH EC program com- ponents (>80% of program components implemented). Interestingly, our process evaluation results also demon- strated similar high implementation of the CATCH EC program across four comparison centers. This was because the participating comparison centers were trained in im- plementing 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 signif- icantly 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, restric- tion of implementation of programs such as CATCH EC was not an option. These results also underscore the im- portance of process evaluation efforts across both inter- vention 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 sig- nificant improvements in child diet, activity, and sedentaryT

a b

le 3 .

W it

h in

a n

d B

e tw

e e n

-G r o

u p

C h

a n

g e s

in C

h il d

B M

I z -S

c o

r e s

a n

d P

e r c e n

ti le

s ,

T e x a s

C h

il d

h o

o d

O b

e s it

y R

e s e a r c h

D e m

o n

s tr

a ti

o n

S tu

d y

In te

r v e n

ti o

n g r o

u p

C o

m p

a r is

o n

g r o

u p

B a se

li n

e (y

e a r

1 )

m e a n

(S E

), n

5 3 5 3

F o

ll o

w -u

p (y

e a r

2 )

m e a n

(S E

), n

5 4 8 3

W it

h in

g r o

u p

c h

a n

g e sa

(9 5 %

C I)

, p

-v a lu

B a se

li n

e (y

e a r

1 )

m e a n

(S E

), n

5 3 1 9

F o

ll o

w -u

p (y

e a r

2 )

m e a n

(S E

), n

5 4 6 5

W it

h in

g r o

u p

c h

a n

g e sa

(9 5 %

C I)

, p

-v a lu

e N

e t

c h

a n

g e s

b (9

5 %

C I)

, p

-v a lu

B M

I z-

sc o re

0 .6

8 (0

.0 7 )

0 .6

0 (0

.0 6 )

-0 .0

8 (-

0 .2

4 to

0 .0

8 ),

0 .3

4 2

0 .4

6 (0

.0 7 )

0 .6

4 (0

.0 7 )

0 .1

7 (-

0 .0

1 to

0 .3

6 ),

0 .0

6 0

-0 .2

6 (-

0 .5

0 to

-0 .0

1 ),

0 .0

4 1 *

B M

I p e rc

e n ti le

6 7 .5

(1 .5

) 6 5 .5

(1 .3

) -2

.0 2

(- 5 .8

8 to

1 .8

3 ),

0 .3

0 4

6 3 .6

6 7 .1

4 .5

(0 .1

4 to

8 .9

), 0 .0

4 3 *

-6 .5

(- 1 2 .4

to -0

.6 9 ),

0 .0

2 8 *

a W

it h in

-g ro

u p

c h a n g e s

u si

n g

m ix

e d -m

o d e l re

g re

ss io

n a n a ly

si s

a d ju

st in

g fo

r sc

h o o l a s

a ra

n d o m

e ff e c t.

b B

e tw

e e n -g

ro u p

c h a n g e s

u si

n g

m ix

e d -m

o d e l re

g re

ss io

n a n a ly

si s

a d ju

st in

g fo

r sc

h o o l a s

a ra

n d o m

e ff e c t.

C o v a ri

a te

s a d ju

st e d

fo r

in th

e a n a ly

si s

in c lu

d e

c it y

(H o u st

o n

a n d

A u st

in ),

c h il d

a g e , e th

n ic

it y , g e n d e r,

a n d

p a re

n t

in c o m

e le

v e l.

*S ig

n ifi

c a n t

a t

p <

0 .0

5 .

C I,

c o n fi d e n c e

in te

rv a l;

S E . st

a n d a rd

e rr

o r.

CHILDHOOD OBESITY January 2019 9

T a b

le 4 .

W it

h in

a n

d B

e tw

e e n

-G r o

u p

C h

a n

g e s

in C

h il d

D ie

ta r y

H a b

it s ,

P h

y s ic

a l

A c ti

v it

y ,

a n

d S

e d

e n

ta r y

B e h

a v io

r s ,

T e x a s

C h

il d

h o

o d

O b

e s it

y R

e s e a r c h

D e m

o n

s tr

a ti

o n

S tu

d y

V a r ia

b le

In te

r v e n

ti o

n g r o

u p

C o

m p

a r is

o n

g r o

u p

B a s e li n

e (y

e a r

1 )

m e a n

(S E

), n

5 3 5 3

F o

ll o

w -u

p (y

e a r

2 )

m e a n

(S E

), n

5 4 8 3

W it

h in

g r o

u p

c h

a n

g e s

(9 5 %

C I)

, p

-v a lu

B a s e li n

e (y

e a r

1 )

m e a n

(S E

), n

5 3 1 9

F o

ll o

w -u

p (y

e a r

2 )

m e a n

(S E

), n

5 4 6 5

W it

h in

g r o

u p

c h

a n

g e s

(9 5 %

C I)

, p

-v a lu

e N

e t

c h

a n

g e s

b (9

5 %

C I)

, p

-v a lu

C h il d

fr e q u e n c y

o f

in ta

k e

o f

(t im

e s

p e r

w e e k )

F ru

it 1 .6

9 (0

.0 5 )

1 .8

4 (0

.0 4 )

0 .1

5 (0

.0 4

to 0 .2

6 ),

0 .0

0 9 *

1 .7

3 (0

.0 5 )

1 .7

4 (0

.0 5 )

0 .0

0 5

(- 0 .1

3 to

0 .1

3 ),

0 .9

4 0

0 .1

5 (-

0 .0

3 to

0 .3

2 ),

0 .0

9 6

V e g e ta

b le

s 1 .3

5 (0

.0 5 )

1 .3

9 (0

.0 5 )

0 .0

4 (-

0 .0

7 to

0 .1

6 ),

0 .4

8 1

1 .3

7 (0

.0 6 )

1 .3

7 (0

.0 6 )

-0 .0

0 3

(- 0 .1

4 to

0 .1

4 ),

0 .9

9 6

0 .0

4 (-

0 .1

4 to

0 .2

3 ),

0 .6

4 4

F re

n c h

fr ie

s 0 .6

8 (0

.0 4 )

0 .7

5 (0

.0 4 )

0 .0

7 (-

0 .0

4 to

0 .1

7 ),

0 .2

1 3

0 .6

5 (0

.0 5 )

0 .8

9 (0

.0 4 )

0 .2

1 (0

.0 9

to 0 .3

3 ),

0 .0

0 0 *

-0 .1

4 (-

0 .3

to 0 .0

1 ),

0 .0

7 0

S p o rt

s d ri

n k s

0 .8

5 (0

.0 5 )

0 .9

1 (0

.0 5 )

0 .0

6 (-

0 .0

7 to

0 .1

8 ),

0 .3

7 4

0 .9

3 (0

.0 6 )

1 .0

6 (0

.0 5 )

0 .1

4 (-

0 .0

0 2

to 0 .2

9 ),

0 .0

5 4

-0 .0

8 (-

0 .2

2 to

0 .1

1 ),

0 .3

8 9

W a te

r 2 .1

9 (0

.0 5 )

2 .1

4 (0

.0 4 )

-0 .0

5 (-

0 .1

7 to

0 .0

8 ),

0 .4

4 2

2 .1

0 (0

.0 5 )

2 .0

8 (0

.0 5 )

-0 .0

2 (-

0 .1

6 to

0 .1

2 ),

0 .7

6 5

-0 .0

3 (-

0 .2

2 to

0 .1

6 ),

0 .7

7 5

S u g a r-

sw e e te

n e d

b e v e ra

g e s

1 .1

6 (0

.0 7 )

0 .6

5 (0

.0 6 )

-0 .5

2 (-

0 .6

7 to

-0 .3

6 ),

0 .0

0 0 *

1 .2

7 (0

.0 7 )

0 .7

5 (0

.0 7 )

-0 .5

2 (-

0 .7

0 to

-0 .3

5 ),

0 .0

0 0 *

0 .0

0 6

(- 0 .2

2 to

0 .2

4 ),

0 .9

6 1

C h il d

fr e q u e n c y

o f

(t im

e s

p e r

w e e k ):

E a ti n g

b re

a k fa

st 3 .2

7 (0

.0 6 )

3 .3

4 (0

.0 5 )

0 .0

8 (-

0 .0

9 to

0 .2

4 ),

0 .3

5 7

3 .3

0 (0

.0 7 )

3 .4

0 (0

.0 7 )

0 .0

9 (-

0 .0

9 to

0 .2

7 ),

0 .3

3 8

-0 .0

1 (-

0 .2

5 to

0 .2

3 ),

0 .9

1 7

E a ti n g

d in

n e r

w it h

fa m

il y

3 .1

5 (0

.0 6 )

3 .2

4 (0

.0 5 )

0 .0

9 (-

0 .0

7 to

0 .2

6 ),

0 .2

5 6

3 .2

8 (0

.0 7 )

3 .3

0 (0

.0 6 )

0 .0

3 (-

0 .1

5 to

0 .2

1 ),

0 .7

6 3

0 .0

6 (-

0 .1

8 to

0 .3

1 ),

0 .5

9 9

W a tc

h in

g T

V w

it h

d in

n e r

1 .1

1 (0

.0 8 )

1 .1

8 (0

.0 7 )

0 .0

7 (-

0 .1

1 to

0 .2

5 ),

0 .4

6 8

1 .2

0 (0

.0 8 )

1 .3

4 (0

.0 8 )

0 .1

4 (-

0 .0

7 to

0 .3

5 ),

0 .1

9 8

-0 .0

7 (-

0 .3

5 to

0 .2

0 ),

0 .6

2 1

E a ts

d in

n e r

fr o m

re st

a u ra

n t

0 .7

2 (0

.0 5 )

0 .7

8 (0

.0 5 )

0 .0

6 (-

0 .0

6 to

0 .1

8 ),

0 .3

1 3

0 .8

0 (0

.0 6 )

0 .9

9 (0

.0 6 )

0 .1

8 (0

.0 5

to 0 .3

2 ),

0 .0

0 9 *

-0 .1

2 (-

0 .3

0 to

0 .0

6 ),

0 .1

8 5

T im

e sp

e n t

in se

d e n ta

ry b e h a v io

rs (m

in u te

M in

u te

s w

a tc

h e d

T V

— w

e e k e n d

1 5 6 .6

(7 .9

) 1 5 3 .7

(6 .7

) -2

.9 1

(- 2 3 .1

to 1 7 .2

), 0 .7

7 7

1 8 6 .5

(8 .6

) 1 8 6 .7

(7 .9

) 0 .2

6 (-

2 2 .7

to 2 3 .2

), 0 .9

8 2

-3 .1

7 (-

3 3 .7

to 2 7 .4

), 0 .8

3 9

M in

u te

s w

a tc

h e d

T V

— w

e e k

d a y

2 0 8 .9

(8 .9

) 2 1 0 .5

(7 .3

) 1 .5

9 (-

2 0 .8

to 2 4 .0

), 0 .8

8 9

2 4 3 .2

(9 .5

) 2 3 5 .9

(8 .7

) -7

.3 (-

3 2 .3

to 1 7 .8

), 0 .5

7 1

8 .8

4 (-

2 4 .8

to 4 2 .5

), 0 .6

0 6

M in

u te

s v id

e o

g a m

e —

w e e k e n d

6 3 .8

(6 .5

) 4 9 .3

(5 .2

) -1

4 .6

(- 3 0 .8

to 1 .7

1 ),

0 .0

8 0

6 4 .8

(7 .0

) 5 5 .9

(6 .3

) -8

.9 (-

2 7 .2

to 9 .3

3 ),

0 .3

3 8

-5 .6

(- 3 0 .1

to 1 8 .8

), 0 .6

5 1

M in

u te

s v id

e o

g a m

e – w

e e k

d a y

7 6 .3

(7 .6

) 7 7 .5

(6 .0

) 1 .2

(- 1 7 .6

to 2 0 .0

), 0 .9

0 1

7 9 .9

(8 .0

) 8 6 .9

(7 .2

) 7 .0

(- 1 3 .9

to 2 7 .9

), 0 .5

1 2

-5 .8

(- 3 3 .9

to 2 2 .3

), 0 .6

8 6

T im

e sp

e n t

in p h y si

c a l a c ti v it y

(d a y s

p e r

w e e k )

D a y s

p a rt

ic ip

a te

d in

6 0

m in

u te

s p h y si

c a l a c ti v it y

4 .9

(0 .1

4 )

4 .9

(0 .1

2 )

0 .0

4 (-

0 .2

6 to

0 .3

5 ),

0 .7

8 2

5 .1

(0 .1

5 )

5 .2

(0 .1

4 )

0 .0

9 (-

0 .2

6 to

0 .4

6 ),

0 .5

3 8

-0 .0

5 (-

0 .5

3 to

0 .4

2 ),

0 .8

2 4

D a y s

p la

y o u ts

id e

3 0

m in

u te

s/ d

0 .8

9 (0

.0 2 )

0 .8

7 (0

.0 2 )

-0 .0

1 (-

0 .0

6 to

0 .0

3 ),

0 .5

1 2

0 .9

0 (0

.0 2 )

0 .9

1 (0

.0 2 )

0 .1

2 (-

0 .0

4 to

0 .0

6 ),

0 .6

2 6

-0 .0

3 (-

0 .0

9 to

0 .0

4 ),

0 .4

3 5

a W

it h in

-g ro

u p

c h a n g e s

u si

n g

m ix

e d -m

o d e l re

g re

ss io

n a n a ly

si s

a d ju

st in

g fo

r sc

h o o l a s

a ra

n d o m

e ff e c t.

b B

e tw

e e n -g

ro u p

c h a n g e s

u si

n g

m ix

e d -m

o d e l re

g re

ss io

n a n a ly

si s

a d ju

st in

g fo

r sc

h o o l a s

a ra

n d o m

e ff e c t.

C o v a ri

a te

s a d ju

st e d

fo r

in th

e a n a ly

si s

in c lu

d e

c it y

(H o u st

o n

a n d

A u st

in ),

c h il d

a g e , e th

n ic

it y ,

g e n d e r,

a n d

p a re

n t

in c o m

e le

v e l.

*S ig

n ifi

c a n t

a t

p <

0 .0

5 .

T a b

le 5 .

P r o

c e s s

E v a lu

a ti

o n

fo r

P r im

a r y

P r e v e n

ti o

n In

te r v e n

ti o

n in

H e a d

S ta

r t

C e n

te r s ,

T e x a s

C h

il d

h o

o d

O b

e s it

y R

e s e a r c h

D e m

o n

s tr

a ti

o n

S tu

d y

P r o

c e s s

e v a lu

a ti

o n

s c a le

s N

o .

o f

it e m

Y e a r

1 in

te r v e n

ti o

n c e n

te r s

Y e a r

1 c o

m p

a r is

o n

c e n

te r s

Y e a r

2 in

te r v e n

ti o

n c e n

te r s

Y e a r

2 c o

m p

a r is

o n

c e n

te r s

n M

e a n

% 6

S D

n M

e a n

% 6

S D

n M

e a n

% 6

S D

n M

e a n

% 6

S D

C e n te

r D

ir e c to

C A

T C

H E C

P ro

g ra

m a c c e ss

b 4

1 2

9 8 .0

– 7 .2

1 0

4 3 .2

– 5 0 .1

1 2

9 8 .0

– 7 .2

1 0

3 4 .1

– 4 7 .8

C A

T C

H E C

P ro

g ra

m u sa

g e

b 4

1 2

9 3 .8

– 1 1 .3

1 0

3 1 .8

– 4 4 .8

1 2

9 5 .8

– 9 .7

1 0

3 6 .4

– 5 0 .5

C A

T C

H E C

su p p o rt

b 4

1 1

6 6 .7

– 2 0 .1

8 6 2 .5

– 2 8 .9

1 2

7 4 .0

– 1 8 .4

6 5 8 .0

– 3 3 .6

D ir

e c to

r le

v e l im

p le

m e n ta

ti o n

in d e x

c 1 2

1 1

8 6 .1

– 1 0 .7

8 5 5 .2

– 3 4 .7

1 2

8 9 .4

– 9 .3

6 5 3 .4

– 3 6 .6

T e a c h e r

C A

T C

H E C

P ro

g ra

m a c c e ss

b 4

6 1

8 9 .6

– 2 1 .2

4 2

3 8 .9

– 4 0 .6

4 1

8 6 .0

– 2 8 .0

2 9

3 6 .2

– 4 4 .6

C A

T C

H E C

P ro

g ra

m u sa

g e

b 4

6 1

8 4 .8

– 2 4 .7

4 4

3 3 .0

– 3 7 .7

4 2

8 5 .1

– 2 7 .6

2 9

3 5 .3

– 4 4 .1

C A

T C

H E C

e n jo

y m

e n td

3 6 2

8 6 .7

– 1 9 .8

4 4

3 9 .2

– 4 1 .0

4 2

7 5 .0

– 2 9 .8

2 9

3 7 .1

– 4 6 .2

C A

T C

H E C

su p p o rt

b 4

6 2

7 8 .2

– 1 8 .6

3 0

6 2 .1

– 2 7 .7

4 2

7 6 .2

– 1 8 .6

1 8

6 0 .5

– 3 8 .1

C A

T C

H E C

p a re

n t

ti p -s

h e e ts

se n t

h o m

e d

1 6 2

5 3 .2

– 5 0 .3

4 4

2 9 .6

– 4 6 .2

4 3

6 7 .4

– 4 7 .4

2 9

2 4 .1

– 4 3 .5

C A

T C

H E C

a c ti v it ie

s se

n t

h o m

e d

1 6 1

5 4 .1

– 5 0 .2

4 4

2 7 .3

– 4 5 .1

4 3

7 4 .4

– 4 4 .1

3 0

2 3 .3

– 4 3 .0

T e a c h e r

le v e l im

p le

m e n ta

ti o n

in d e x

e 1 7

6 0

8 0 .7

– 1 7 .4

3 0

5 2 .8

– 2 9 .4

4 1

6 8 .8

– 2 9 .2

1 8

4 9 .2

– 3 6 .9

A v e ra

g e

C A

T C

H E C

im p le

m e n ta

ti o n

in d e x

% sc

o re

m e a n

(S E )

1 2

8 1 .3

5 (2

.9 2 )

8 5 1 .0

1 (1

0 .8

0 )

0 .0

0 3 *

1 2

8 4 .5

2 (2

.9 4 )

9 3 9 .0

1 (1

1 .9

5 )

0 .0

0 2 *

N o n -C

A T

C H

h e a lt h

a c ti v it ie

D ir

e c to

r m

e a n

% sc

o re

2 5

1 1

8 0 .7

– 9 .8

1 0

6 8 .5

– 1 5 .7

1 2

7 2 .2

– 9 .9

1 0

5 4 .7

– 1 4 .5

T e a c h e r

m e a n

% sc

o re

5 5 9

7 3 .3

– 1 6 .3

3 9

4 3 .9

– 2 9 .9

4 3

6 2 .4

– 2 8 .3

2 7

3 6 .5

– 3 0 .8

O v e ra

ll im

p le

m e n ta

ti o n

% (C

A T

C H

+ n o n -C

A T

C H

) sc

o re

m e a n

(S E )f

1 2

7 4 .7

0 (2

.3 1 )

1 2

4 5 .5

2 (6

.9 5 )

0 .0

0 2 *

1 2

7 2 .0

4 (3

.0 1 )

1 1

4 4 .2

3 (6

.9 2 )

0 .0

0 7 *

a %

S c o re

is c a lc

u la

te d : S c o re

= (o

ri g in

a l sc

o re

)/ (o

ri g in

a l p o te

n ti a l sc

o re

ra n g e ) ·

1 0 0 . H

ig h e r

sc o re

in d ic

a te

s h ig

h e r

le v e l o f

im p le

m e n ta

ti o n .

b T

e a c h e r

a n d

c e n te

r d ir

e c to

r re

sp o n se

s. c C

A T

C H

E C

im p le

m e n ta

ti o n

in d e x

fo r

c e n te

r is

c a lc

u la

te d

b a se

d o n

1 2

it e m

s fr

o m

th e

d ir

e c to

r- le

v e l su

rv e y .

d T

e a c h e r

re sp

o n se

s o n ly

. e C

A T

C H

E C

im p le

m e n ta

ti o n

in d e x

fo r

te a c h e r

is c a lc

u la

te d

b a se

d o n

1 7

it e m

s fr

o m

th e

te a c h e r-

le v e l su

rv e y .

f O v e ra

ll im

p le

m e n ta

ti o n

in d e x

fo r

c e n te

r is

c a lc

u la

te d

b a se

d o n

3 7

it e m

s fr

o m

th e

d ir

e c to

r- le

v e l su

rv e y .

g O

v e ra

ll im

p le

m e n ta

ti o n

in d e x

fo r

te a c h e r

is c a lc

u la

te d

b a se

d o n

2 2

it e m

s fr

o m

th e

te a c h e r-

le v e l su

rv e y .

*R e su

lt fr

o m

M a n n – W

h it n e y

U te

st to

a ss

e ss

d if fe

re n c e s

b e tw

e e n

in te

rv e n ti o n

a n d

c o m

p a ri

so n

c e n te

rs in

y e a rs

1 a n d

2 , p

< 0 .0

5 .

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 base- line 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 Cen- ters with many competing priorities and challenges; use of validated surveys, achieving adequate sample size, and participation rates in a population of low-income, ethni- cally 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 parent- reported 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 longi- tudinal 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 improve-

ments 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 nec- essarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organi- zations 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 heal- thy 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.

http://www.cdc.gov/diabetes/data/county.html

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 obe- sity 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 epi- demic 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 fourth- grade 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 question- naire. 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 evaluation 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 pro- gram 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 ac- tivity 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 self- regulation 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 pre- vention interventions in early care and education settings: A sys- tematic 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: [email protected]

CHILDHOOD OBESITY January 2019 13

https://eclkc.ohs.acf.hhs.gov/sites/default/files/pdf/preamble-part1.pdf

http://www.acf.hhs.gov/ohs/policy

Reproduced with permission of copyright owner. Further reproduction prohibited without permission.

Student Laboratory Guide Chapter 16: Neurologic System

With your lab partner assuming the role of a client, conduct a focused history and examination. Your “student client” may role-play a client with a particular neurologic symptom.

History Date: Name:

Gender: M F

Age: Race: LMP:

Occupation:

Source of Data:

Immunizations:

Allergies/Reactions:

Presenting Problem (check all that apply):

Headaches Dizziness Seizures Loss of consciousness

Changes in movement

Changes in sensation

Difficulty swallowing

Difficulty communicating

Other:______

Symptom Analysis of Presenting Problem (location, quality, quantity, chronology, setting, associated manifestations, aggravating and alleviating factors) Present Health Status (include medications, dose, and frequency) Past Medical and Surgical History (include description and dates) Family History

Chapter 16 Neurologic System Page 2

Examination

Examination Technique Findings (document findings below)

Routine Assessment ASSESS Mental Status.

o Level of consciousness o Speech for articulation and voice

quality and conversation of verbal communication

NOTICE cranial nerve functions. o CN I (olfactory)—smell o CN II (optic nerve)—ability to

move in environment and see chair to sit

o CN III (oculomotor), IV (trochlear), VI (abducens)—eye movement

o CN V (trigeminal)—eye blink o CN VII (facial)—face is

symmetric during talking or smiling

o CN VIII (acoustic)—ability to hear

o CN IX (glossopharyngeal), CN X (vagus)—swallowing and ability to handle saliva

o CN X (vagus)—guttural speech sounds

o CN XI (spinal accessory)—shrug shoulders or turn head

OBSERVE gait for balance and symmetry.

EVALUATE extremities for muscle strength.

Chapter 16 Neurologic System Page 3

Examination Technique Findings (document findings below)

Special Circumstances and Advanced Practice ASSESS individual cranial nerves.

o TEST nose for smell. o TEST eyes for visual acuity. o TEST eyes for peripheral vision. o OBSERVE eyes for extraocular

muscle movement. o OBSERVE eyes for papillary size,

shape, equality, constriction, and accommodation.

o EVALUATE face for movement and sensation.

o TEST ears for hearing. o TEST tongue for taste. o INSPECT orophyarynx for gag

reflex and movement of soft palate.

o TEST tongue for movement, symmetry, strength, and absence of tumors; test for muscle strength.

o TEST shoulders and neck muscles for strength and movement.

TEST cerebellar function for balance and coordination.

o TEST for balance. - Romberg test - With eyes closed, stand on one

foot, then the other - Heel-to-toe walking - Hop on one foot, then the other - Deep knee bends - Walk on toes, then on heels

o EVALUATE upper extremity. - Alternately tap hands to thighs - With eyes closed and

outstretched arms, touch finger to own nose

- Touch each finger to thumb in rapid sequence

- Rapidly move finger between nose and nurse’s finger

Chapter 16 Neurologic System Page 4

Examination Technique Findings (document findings below)

o EVALUATE lower extremity. - Lying supine, slide heel down

opposite shin ASSESS peripheral nerves.

o ASSESS for sensation—close eyes and test for sensation identification on upper and lower extremities.

o ASSESS sharp and dull sensation. o ASSESS peripheral sensation with

monofilament (advanced practice). o ASSESS vibratory sense using

tuning fork (advanced practice). o ASSESS kinesthetic sensation.

(advanced practice).

o TEST stereognosis (advanced practice).

o TEST two-point discrimination. (advanced practice).

o EVALUATE graphesthesia (advanced practice).

EVALUATE extremities for deep tendon reflexes.

o Triceps reflex o Biceps reflex o Brachioradial reflex o Patellar reflex o Archilles tendon

EVALUATE plantar reflex (advanced practice).

EVALUATE ankle clonus (advanced practice).

EVALUATE for superficial reflexes (abdominal).

Chapter 16 Neurologic System Page 5

Nursing Diagnoses and Collaborative Problems Based on the subjective and objective data collected above, identify applicable nursing diagnoses and collaborative problems.

Nursing Diagnoses Collaborative Problems

Text1:
Text2:
Radio Button1: 1
Text3:
Text4:
Text5:
Text6:
Text7:
Text8:
Text9:
Check Box1: Off
Check Box2: Off
Check Box3: Off
Check Box4: Off
Check Box5: Off
Check Box6: Off
Check Box7: Off
Check Box8: Off
Check Box9: Off
Text10:
Text11:
Text12:
Text13:
Text14:
Text15:
Text16:
Text17:
Text18:
Text19:

blog2

Get help from top-rated tutors in any subject.