A Computer Based Approach to Improve Dietary and Physical Activity Patterns of a Diverse Group of Adolescents Krista Casazza, PhD

Population Adolescents at  risk of developing DM, CVD, and other chronic diseases in adulthood.

Diet Quality 60% eat  fat and sat fat 1/3 consume 5 a day 1. ~25% of the calories from  fat, low nutritional value foods 2. 1 CDC, Jacobsen, 1999

Physical Activity 50% are physically active on a regular basis 1 ~ 25%  in participation of regular PA from 9 th -12 th grade 2 fewer girls than boys are physically active 1 CDC, USDHHS, 1999

Overweight & Obesity #1 nutritional disease 1 Prevalence has  by 182% (1971 to 2000) % overweight; 31.5% at risk Minority adolescents at  risk South Florida estimates ~46% at risk 4 Improved lifestyle habits  prevent/delay development of chronic diseases 1. Dietz, Joliffe, CDC, BRFSS, 2004

Purpose of this study Compare effectiveness of two methods of delivery for health messages Computer based (CBI) Traditional didactic (TDI) which program elicits greater change in Diet Daily PA BMI Nutrition Knowledge Psychosocial Variables

Intervention Programs Traditional nutrition education models emphasize  knowledge. Rationale: behavior changes will follow 1. Such programs have done little to elicit change in food/nutrient intake or  PA 2. 1 Hoelsher et al, Hoelsher et al, 2002; Sallis et al, 2003; Reynolds et al, 1998

Computer Based Learning Changes occurring in nutrition, health care, education, and technology. Opportunities to engage youth in computer-based health programs. 1 Computer-mediated communications are assuming a large role in the future of behavioral health care. 2 1 Skinner, Probst & Tapsell, 2005; Brug et al, 2003; Oenema et al, 2005

Research Question Will a behaviorally oriented computer-based nutrition education intervention (CBI) result in more positive lifestyle habits (diet and PA) for HS students compared to a traditional didactic intervention (TDI) program?

Hypotheses Compared to the TDI and control groups, the CBI group will show post & follow-up): Hypothesis 1: a greater  in knowledge Hypothesis 2: greater strides toward achieving a healthy BMI Hypothesis 3: a  in fat intake, an  intake of f/v, fiber, and low-fat dairy pdts. Hypothesis 4:  PA.

Methods Preliminary Studies Qualitative Quantitative Development of CD-ROM Reviewers Recruitment of Schools Recruitment of Students  n=254 Statistical Analysis:SPSS Repeated Measures ANOVA (=0.05)

Intervention Sites 3 schools in Broward County, FL CON = Control CBI = Computer based instruction TDI = Traditional didactic instruction

Study Participants

Intervention Behaviorally focused; designed for adolescents 5 sessions – 45 minutes each CBI and TDI received same info via different media CBI template Session Topics Intro to Adolescent Nutrition Overview of Nutrients Nutrition for Life Taking Responsibility for Your Health Using What You’ve Learned LAUNCH CD-ROM

Data Collection Collection points Baseline Post (month 3) Follow-up (month 6) Measures Nutrition Knowledge Questionnaire Ht/Wt Non-consecutive 24 hour recall (2 per period) Food Frequency Questionnaire (FFQ) Physical Activity Questionnaire for Adolescents (PAQ-a)

Nutrition Knowledge (% Correct) *** ^ p<0.001 a b c *** *** p<0.001, ^indicates over course of study a-c indicate difference between groups

BMI * ^ p<0.01 *** ^ p<0.001 d c,d c * p<0.05, *** p<0.001, ^indicates over course of study c,d indicates difference between groups

LF Dairy (serv/day) 24-hr recall ^ p<0.01 ^indicates over course of study a-d indicates difference between groups c d d a,b a b

Soda Intake 24-hr recall ** * p<0.05, ** p<0.01 *

Meals Skipped 24-hr recall *** ^ p<0.001 *** p<0.001; ^indicates over course of study a,b indicates difference between groups a b

Physical Activity (PA score) ** ^ p<0.001 *** p<0.001; ^indicates over course of study a,b indicates difference between groups a b b sedentary lightly active mod. active 32+ vig. active

Intake Avg daily intake (n=254): ~ 1850 kcal ~ 69 g fat ~ 24 g (34%) sat fat ~ 10.7 g fiber ~ 1.4 serv of f&v/day No difference between groups at any time point

Discussion CBI Was more effective than TDI BMI PA Knowledge Dairy Intake Meals Skipped Soda Intake Changes maintained at follow-up CBI Was not more effective than TDI Kilocalories, fat, saturated fat, fruits, vegetables, fiber

Conclusions Compared to traditional didactic teaching, computer-based nutrition and health education has greater potential to: elicit changes in knowledge and behavior promote maintenance of the behavior change over time

Limitations include Treatments nested within school Self-selection bias Convenience sample Time limitations Self administered questionnaires Environment Generalizability

Applications Innovative techniques needed mirror learning style of adolescents address patterns of adolescents One approach does not fit all Various methods of delivery needed Several factors affect the lifestyle habits of adolescents Reason for change in PA and BMI, but not diet quality? Lack of Availability

Acknowledgements Financial Support Florence Bayuk Graduate Fellowship in Health from the Florence Bayuk Foundation Dissertation Year Fellowship, University Graduate School Dissertation Fellowship, FIU