1. MobileHealth: Delivering Context-Appropriate Health Advice
Karin Forssell Chapin
Cory Lee

2. Problem Statement
Advice on weight management is pervasive in the media, but often times it is too generic to be useful
Doesn’t take into account person’s habits, body type, lifestyle.
Does delivering tailored health advice at relevant times help users learn more effectively about weight management?

3. Hypothesis
Users are more receptive to health advice if it is delivered in small, relevant doses.
…based on the theory that users will form a stronger connection between the recommended action and the conditions under which it applies.

4. Study
Created a one page passage on Resting Metabolic Rate (RMR) and its relevance to weight-management.
Example:
Eating regularly (every 3 hours at the least) maximizes the amount of energy burned by consuming and digesting food. Waiting too long between meals will signal the body to become more efficient on fewer calories, lowering the RMR.

5. The MobileHealth System
MobileHealth allows users to track their caloric intake and expenditures on their mobile phones.

6. Test 8 participants were given Motorola RAZR phones with application.
Planned for 10 days; 2 stopped early (tech difficulties)
4 were sent pieces of RMR passage during trial (Wizard-Of-Oz), 4 received entire passage at end.

7. Measures Pretest Posttest Delayed posttest
Background info (technology use, attitude, current weight management techniques)
Calorie Estimation
Posttest
Gave control group passage to read
Application/experience feedback
Tested on facts and inferences based on passage (10 questions)
Delayed posttest
Re-tested on facts and inferences based on passage (10 questions)

8. Example questions [fact] What is “resting metabolic rate”?
[inference] How is it that a 250-lb athlete can burn more calories watching TV than a 150-lb couch potato?

9. Findings No apparent benefit of “JIT” messages
Users receiving comprehensive “Passage” at the end scored higher in the post-test than those who received the passage in portions throughout their trial.

10. Post-test scores 2: complete 1: incomplete/partially correct
0: incorrect

11. Post-test scores 2: complete 1: incomplete/partially correct
0: incorrect

12. Post-test scores cont’d

13. Post-test scores cont’d

14. Caveats
Messages delivered by (possible issues with SPAM, length, not in-situ)
Passages too generic – participants reported low applicability to input
Delayed delivery of passage (due to upload lag)
Holiday week

15. More Findings
Less confident at start more likely to learn calorie estimation (=higher motivation?)
More frequent entries lead to estimation gains
More scared of tech at pretest on average higher confidence MH post than existing system

16. Conclusion and Future directions
Value in seeing “comprehensive” passage rather than short messages over time (users get a sense of the “bigger picture”)
Future Directions
Explore…
advantage of organized info at optimal intervals
length and delivery methods
how to better tailor health advice to the user.
Conduct longer user study.
Focus on motivated participants (who have most to gain)