Health statistics for physical activity, nutrition, and psychological wellbeing demonstrate the tenuous status of youth in the United States (US). These factors significantly affect growth and development during this critical period and indelibly influence adult health. Consequently, the successful utilization of multicomponent pediatric health promotion programs could improve current and future health, saving billions in health-care costs. The analysis of a literature review on this topic led to the development and completion of an evidence-based project. The project was guided by two conceptual frameworks, Pender’s Health Promotion Model and the Stetler Model for Evidence-based Practice. The project was completed in partnership with a local after-school youth program.

Methodology included a project intervention comprised of a single specialized training session. Data was collected using a pretest-posttest format with repeated measures from a survey adapted from the Organization Readiness to Change Assessment (ORCA) tool. Survey questions focused on participant’s knowledge, skills, attitudes, and use of the selected health promotion program. Descriptive Statistics, the Wilcoxon-Signed Rank Test, and the Friedman Test were completed for data analysis using IBM SPSS v25. Using a critical value p < .1, results from the data indicated improvement in median scores for participant’s knowledge and skills (p-value’s range = .05 - .082). Other changes were not statistically significant (p-value’s range = .135 - .317). The results indicate the project intervention’s efficacy. Future research may focus on optimal training formats, a comparison of repeat sessions versus supplemental web-accessible resources, and program sustainability via refresher sessions and/or designated management.

Background: 
Approximately 1 in 5 U.S. school-aged children are obese. There are many known health complications associated with obesity including premature death. Family-based obesity interventions that promote healthy lifestyle habits are effective at enabling children to make changes needed to avoid long-term health complications associated with obesity. The purpose of this evidence-based practice intervention was to evaluate the effectiveness of a family-based obesity intervention on familial lifestyle behaviors related to nutrition, physical activity, and screen time.

Methods:
Two overweight-obese children (according to CDC criteria) ages 8-12 years old visiting a pediatric primary care clinic in a suburban neighborhood located in the southwest region were recruited to participate in this evidence-based practice intervention based on inclusion and exclusion criteria. Familial lifestyle behaviors were assessed using the Family Health Behavior Scale (FHBS) prior to receiving an educational intervention addressing nutritional, physical activity, and screen time recommendations and again after following these recommendation for 6-weeks. Additionally, scheduled follow-up phone calls were made every 3 or 6-weeks addressing any parental questions that surfaced. Data was insufficient for statistical analysis, however, anecdotal recommendations for future implementation of this intervention resulted.

Results:
Of the two patients who participated, pre- and post-intervention data was only attainable from one patient. That patient did have improved scores within each of the 4 FHBS subscales (parent behaviors, physical activity, mealtime routines, and child behaviors). Overall, 11 of the 27 behaviors assessed improved, 12 behaviors resulted in no change, and 4 behaviors worsened. Recommendations related to a more successful implementation of this intervention in the future include improved provider participation (buy-in), utilization of broader inclusion criteria, consideration of the implementation time-frame, and application of the Health Belief Model for addressing existing barriers for each patient prior to implementing the intervention.

Conclusions:
In order to determine the effectiveness of this intervention a larger sample size and completed post-intervention data are needed. The small sample size and lack of post-intervention data inhibits proper data analyzation and significance from being determined.