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 day1. ~25% of the calories from fat, low

nutritional value foods2. 1 CDC, 20032 Jacobsen, 1999

Physical Activity 50% are physically active on a

regular basis1

~ 25% in participation of regular PA from 9th-12th grade2

fewer girls than boys are physically active

1 CDC, 2004 2 USDHHS, 1999

Overweight & Obesity #1 nutritional disease1

Prevalence has by 182% (1971 to 2000).2

16.5% overweight; 31.5% at risk Minority adolescents at risk

South Florida estimates ~46% at risk4

Improved lifestyle habits prevent/delay development of chronic diseases

1. Dietz, 19972. Joliffe, 20043. CDC, 20034. 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 follow1.

Such programs have done little to elicit change in food/nutrient intake or PA2.

1 Hoelsher et al, 2002 2 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, 2003 2 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 ParticipantsDemographics for Study ParticipantsEthnicity CON CBI TDI Total p-value

n=87 n=84 n=83 n=254White 6 (6.9%) 11 (13.1%) 19 (22.9%) 36 (14.2%)Black 57 (65.5%) 55 (65.5%) 23 (27.7%) 135 (53.1%)Hispanic 16 (18.4%) 14 (16.7%) 29 (34.9%) 59 (23.2%)Other 8 (9.2%) 4 (4.8%) 12 (14.5%) 24 (9.4%)

Mean Grade

10.02a 10.84b 10.72b 10.52 p<0.01

Mean Age (+/-SD) 15.4+1.1a 15.9+1.1b 15.9+1.1b 15.8+1.1 p<0.01

Employment Status

(% Employed)Baseline 15c 33d*** 18c 66 (26%) p<0.001

Post 16c 36d*** 24c 76 (29.9%) p<0.001Follow-Up 24 34 29 87 (34.3%)

Parent's Ed (>High School Ed)

Mom 42.5%c 33.3%d 48.2%c p<0.001Dad 22.90% 25.10% 38.50%

a-dGroups with different letters are statistically different, p<0.05*** Indicates increase from baseline to post, p<0.001

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)

0

10

20

30

40

50

60

Pre Post Follow-Up

CONCBITDI

***

^p<0.001 a

bc

***

*** p<0.001, ^indicates over course of studya-c indicate difference between groups

BMI

21.5

22

22.5

23

23.5

24

24.5

25

25.5

Pre Post Follow-Up

CONCBITDI

*

^p<0.01

*** ^p<0.001

d

c,d

c

* p<0.05, *** p<0.001, ^indicates over course of studyc,d indicates difference between groups

LF Dairy (serv/day) 24-hr recall

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Pre Post Follow-Up

CONCBITDI

^p<0.01

^indicates over course of studya-d indicates difference between groups

c

d

da,b

a

b

Soda Intake 24-hr recall

0

5

10

15

20

25

Pre Post Follow-Up

CONCBITDI

**

* p<0.05, ** p<0.01

*

Meals Skipped 24-hr recall

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Pre Post Follow-Up

CONCBITDI

***

^p<0.001

*** p<0.001; ^indicates over course of studya,b indicates difference between groups

a

b

Physical Activity (PA score)

15

16

17

18

19

20

21

22

Pre Post Follow-Up

CONCBITDI

**

^p<0.001

*** p<0.001; ^indicates over course of studya,b indicates difference between groups

a

bb

8 - 15.9 sedentary

16 - 23.9 lightly active

24 - 31.9 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