1.
Personalized Mobile Medicine for Chronic Disease:
Towards a Theory of Sustained Health Behavior Change
5th Games for Health Europe November 2, 2015
Kenyon Crowley, MBA, MS, CPHIMS
Deputy Director of CHIDS, Robert H. Smith School of Business, University of Maryland
Managing Partner, DiaSocial
PhD Information Science Scholar, University of Maryland iSchool
@healthIT

2.
Team
2
European Collaborators (partial list):
© CHIDS

3.
Presentation Flow
 Background
 System
 Pilot Trial Structure and Results
 Takeaways
 Future
3 © CHIDS

4.
Problem
4 © CHIDS

5.
Problem
5 © CHIDS
Data and Image: American Diabetes Association

6.
Problem
6 © CHIDS
Data and Image: CDC, NIDDK, LA Times

7.
Problem
7 © CHIDS
Data and Image: IDF Diabetes Atlas

8.
8 © CHIDS
“For diabetes in particular, we know there's a relationship
between lack of glucose regulation and complications like
blindness and kidney failure. So if you were diabetic and you
knew that you could get your glucose in a tight, normal range
just by adjusting your lifestyle, wouldn't that be great.”
- Eric Topol, MD
Opportunity

9.
System
 Personalized Mobile Medicine System (“PM2Sys”) is a cloud-
based software system that integrates research from the
psychology, health behavior, information systems and medicine
domains  precision behavioral intervention
 Success algorithms and mobile application
 DiaSocial, 1st app on the platform, targets T2 diabetes with a
focus on older adults
 Testing hypotheses on role of social engagement types, tailoring
of intervention using personality and other data
 Initial pilot randomized clinical trial completed May 2015
 Prior: Several focus groups and co-design sessions in United States and
Germany
9 © CHIDS

10.
10 © CHIDS© CHIDS

11.
Pilot Trial
 A1C as primary metric with additional measures for clinical,
behavioral, usage and psychological measures
11
Control (5) A (5) B (5) C (6) D (6)
95
Usual care
February
2015
May
2015
 Among the 27 patients
 3 females, 24 males
 Age 67.5, [61, 86]
 Initial A1C: 9.0, [7.6, 11.6]
© CHIDS

12.
Psychological measures
 5 areas in pre-experiment questionnaire
 Regulatory Mode (Assessment, Locomotion), Need for Closure ,
Epistemic Authority (Self-ascribed and Physician), Individualism and
Collectivism
 6 areas in pre and post experiment questionnaire
 Mindset, Self-Efficacy, Regulatory Focus (Prevention, Promotion),
Commitment, Goal Progress, Setbacks
© CHIDS12

13.
Usage
13 © CHIDS
> 70% used
60 days of
trial
> 43% used
80 days of
trial

14.
Group Performance
14 © CHIDS

15.
Usage across teams
Average total scores: 5,057
> Comparing B and D: team does
increase usage
> Comparing C and D: Provider does
increase usage
> Might have complimentary effects
b/w team and provider.
> Caution: Baseline A has higher
psych measures than other teams.
> Interviews show that patients do not
make much use of team and
provider features.
© CHIDS15

16.
Effects
16 © CHIDS
Treatment group: -0.56%
Control group: -0.34%

17.
Effects
17 © CHIDS
Coefficient: -0.00028
P-Value: 0.0491
A1C v. Total Score
Coefficient: -0.0179
P-Value: 0.0381
A1C v. Days of App Use Correlation

18.
Results
 Across 22 intervention patients…
 73% saw an improvement in A1c
 4.9% average reduction in A1c less outliers.
 Full treatment group had 0.98 average drop in A1C. The top ½ of
patients showed 14.0% decrease in A1C
 Assessment and Prevention measures suggestive of success
 Many patients voiced improved understanding and perceived self-
efficacy in diabetes management
 Many indicated like the app, mixed perceptions re gaming aspects
 Features identified for V2.0 – better communication, analytics,
usability, game design, education
18 © CHIDS

19.
Tailoring and Personalization
Clinical
State
Personality
Usage
Behaviors
19
> Prescribing
> Message
> Content
> Framing
> Tone
> Teaming
> Timing
© CHIDS

20.
Takeaways
20
> Suggest mobile health precision behavioral interventions for diabetes can
be effective for some patients
> Interaction between personality and app prescribing + use offers applied
research targets for digital health solutions
> App
> Add automatic messages
> Simplify usage
> Experiment Process
> Measure A1C right before/after experiment
> Cluster patients before grouping (i.e. personality randomization)
> Add offline activities for team
> Monitor usage during the experiment
© CHIDS

21.
Next Steps
 Version 2.0 trial being planned for early 2016
 Communication upgrades, video-chat, provider dashboard updates
 Tailoring algorithms development
 Decision support
 Message library, games, visualization development
 Moving to smartphone as primary platform
 Commercialization explorations
 Future
 Additional data types
 Machine learning and self-adaptive
 More device integration
21 © CHIDS

22.
Dank u wel!
 Kenyon Crowley
 kcrowley@rhsmith.umd.edu
 @HealthIT
22 © CHIDS

23.
Problem
23 © CHIDS
Data and Images: ADA, CDC, LA Times, IDF Diabetes Atlas
“For diabetes in particular, we know there's a relationship
between lack of glucose regulation and complications like
blindness and kidney failure. So if you were diabetic and you
knew that you could get your glucose in a tight, normal range
just by adjusting your lifestyle, wouldn't that be great.”
- Eric Topol, MD