A Mixed-Method Evaluation of a Rural Elementary School Implementing the Coordinated Approach to Child Health (CATCH) Program.
Carmen D Samuel-HodgeZiya GizliceAlexis R GuyKathryn BernsteinAurore Y VictorTyler GeorgeTrevor S HamlettLisa M HarrisonPublished in: Nutrients (2023)
Despite children living in rural US areas having 26% greater odds of being affected by obesity compared to those living in urban areas, the implementation of evidence-based programs in rural schools is rare. We collected quantitative data (weight and height) from 272 racially and ethnically diverse students at baseline, and qualitative data from students (4 focus groups), parents, and school staff (16 semi-structured interviews and 29 surveys) to evaluate program outcomes and perceptions. At the 2-year follow-up, paired data from 157 students, represented by racial/ethnic groups of 59% non-Hispanic White, 31% non-Hispanic Black, and 10% Hispanic, showed an overall mean change (SD) in BMI z-score of -0.04 (0.59), a decrease of -0.08 (0.69) in boys, and a significant -0.18 (0.33) decrease among Hispanic students. Boys had a mean decrease in obesity prevalence of 3 percentage points (from 17% to 14%), and Hispanic students had the largest mean decrease in BMI percentile. Qualitative data showed positive perceptions of the CATCH program and its implementation. This community-engaged research, with collaboration from an academic institution, a health department, a local wellness coalition, and a rural elementary school, demonstrated successful CATCH program implementation and showed promising outcomes in mean BMI changes.
Keyphrases
- high school
- quality improvement
- healthcare
- body mass index
- weight gain
- primary care
- mental health
- physical activity
- south africa
- electronic health record
- african american
- weight loss
- insulin resistance
- big data
- public health
- metabolic syndrome
- type diabetes
- risk assessment
- risk factors
- cross sectional
- high fat diet induced
- climate change
- deep learning
- adipose tissue
- young adults
- mass spectrometry
- machine learning