HUMAN-COMPUTER INTERACTIONRisk Info Based on Food

log Wenjun Sun & Xuan Zhang

Department of ISyE & EEUniversity of Wisconsin–Madison

CS/Psych-770 Human-Computer Interaction

• Risk info plays significant role in decision making, especially for diabetic patients (Mühlhauser & Berger, 2000; Adrian Edwards, 2006; Bernard D Frijling, 2004 )

• Numerous diet and calorie tracking applications & websites http://www.fitday.com/ http://www.my-calorie counter.com/calorie_counter.asp

https://www.supertracker.usda.gov/default.aspx

• No application provide risk relation information in the market through our research.

Motivation & Related work

Mühlhauser, I., & Berger, M. (2000). Evidence‐based patient information in diabetes. Diabetic medicine, 17(12), 823-829.Edwards, Adrian, et al. "Presenting risk information to people with diabetes: evaluating effects and preferences for different formats by a web-based randomised controlled trial." Patient education and counseling 63.3 (2006): 336-349.Frijling, Bernard D., et al. "Perceptions of cardiovascular risk among patients with hypertension or diabetes." Patient education and counseling 52.1 (2004): 47-53.

Research Question

The effects of risk info on decision making of eating habit based on logging and tracking summaries (among diabetic

patients).

Instrument

Smartphone App

Take food pictures-calculate the risk-feedback

Wizard-of-OZ Method

Take food pictures- send to us

-we calculate the risk

-send feedback via email

Video

Risk calculationStep1: Estimate main nutrition compositions through

crowdsourcing. Mechanical turk+link to google doc.

Step2: Calculate the numerical risk value using food healthfulness metric, then convert negative health scale into risk value scale from1.0 to 10.0

Budget: $0.02 per HIT × 10 HIT ×10 participant × 7 days = $ 14.00

Martin, J. M., Beshears, J., Milkman, K. L., Bazerman, M. H., & Sutherland, L. A. (2009). Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric.Journal of the American Dietetic Association, 109(6), 1088-1091

Hypotheses • Condition1: Pictures with descriptive risk info

• Condition2: Picture with numeric risk info

• Condition3: Meal pictures only

Change/Decrease of the risk food taken:

Condition1>Conditon2>Condition3

"A picture is worth a thousand words"

Risk Calculation based on food healthfulness metric

Martin, J. M., Beshears, J., Milkman, K. L., Bazerman, M. H., & Sutherland, L. A. (2009). Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric.Journal of the American Dietetic Association, 109(6), 1088-1091.

Low Risk

7.8

 Study design• Variables:

Demographic variable: age, gender Dependent variable: Risk value of the food

• Task

Take pictures of food they eat, get risk feedback via email

• Participant population: convenient sample-- 15 friends5 participants in each group9 males, 6 females

All graduate students Average Age 24.6 (1.85)

Data analysis methods• Calculate the mean risk for three meals each day. Get one risk scale for each day.

• Fit linear mixed model for each group, get slope of risk scale change.

mod3<-lmer(risk~Day+ (Day| ID), data = dsub1)

• Testing using contrast to see if the slope of three groups

POCList = list(c(1,0,-1), c(1,-2,1)), Labels = c("c1group", "c2group")) mod2<- lm(abs(k)~ c1group+c2group+age+sex

Day 1

4.4 7.2 6.8

day1

6.13

 Results

• No significant difference between three groups

(b=-0.022, t=-0.414, p=0.6874)

• Compare the change of two intervention groups with thechange of control group

Marginal significant

(b= -0.067, t=-1.890,

p= 0.0853 < 0.1

Holm-Bonferroni adjustment)

Results• Pair Comparison

All adjusted by Holm-Bonferroni adjustment

• No significant difference

Group 1 V.S. Group 2

Group 1 V.S. Group 3

Group 2 V.S. Group 3

p=0.547 p= 0.547 p=0.160

 Discussion• Based on the trend of the data, participants who received risk information feedback are more willing to consume low risk food, even though no significant effect was found.

• It is promising to provide risk information as intervention to change users’ behavior.

• Difficulties of risk value estimate.

• Limitation of sample size and time.

QUESTIONS?Wenjun Sun & Xuan Zhang

Department of ISyE&EEUniversity of Wisconsin–Madison

CS/Psych-770 Human-Computer Interaction