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Personalized Mobile Medicine for Chronic Disease:
Towards a Theory of Sustained Health Behavior Change
5th Games for Healt...
Team
2
European Collaborators (partial list):
© CHIDS
Presentation Flow
 Background
 System
 Pilot Trial Structure and Results
 Takeaways
 Future
3 © CHIDS
Problem
4 © CHIDS
Problem
5 © CHIDS
Data and Image: American Diabetes Association
Problem
6 © CHIDS
Data and Image: CDC, NIDDK, LA Times
Problem
7 © CHIDS
Data and Image: IDF Diabetes Atlas
8 © CHIDS
“For diabetes in particular, we know there's a relationship
between lack of glucose regulation and complications...
System
 Personalized Mobile Medicine System (“PM2Sys”) is a cloud-
based software system that integrates research from th...
10 © CHIDS© CHIDS
Pilot Trial
 A1C as primary metric with additional measures for clinical,
behavioral, usage and psychological measures
11...
Psychological measures
 5 areas in pre-experiment questionnaire
 Regulatory Mode (Assessment, Locomotion), Need for Clos...
Usage
13 © CHIDS
> 70% used
60 days of
trial
> 43% used
80 days of
trial
Group Performance
14 © CHIDS
Usage across teams
Average total scores: 5,057
> Comparing B and D: team does
increase usage
> Comparing C and D: Provider...
Effects
16 © CHIDS
Treatment group: -0.56%
Control group: -0.34%
Effects
17 © CHIDS
Coefficient: -0.00028
P-Value: 0.0491
A1C v. Total Score
Coefficient: -0.0179
P-Value: 0.0381
A1C v. Da...
Results
 Across 22 intervention patients…
 73% saw an improvement in A1c
 4.9% average reduction in A1c less outliers.
...
Tailoring and Personalization
Clinical
State
Personality
Usage
Behaviors
19
> Prescribing
> Message
> Content
> Framing
> ...
Takeaways
20
> Suggest mobile health precision behavioral interventions for diabetes can
be effective for some patients
> ...
Next Steps
 Version 2.0 trial being planned for early 2016
 Communication upgrades, video-chat, provider dashboard updat...
Dank u wel!
 Kenyon Crowley
 kcrowley@rhsmith.umd.edu
 @HealthIT
22 © CHIDS
Problem
23 © CHIDS
Data and Images: ADA, CDC, LA Times, IDF Diabetes Atlas
“For diabetes in particular, we know there's a ...
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Personalized Mobile-Social Medicine for Chronic Disease: Pilot Clinical Trial Results Towards a Theory of Sustained Health Behavior Change

The findings from the implementation of a novel mobile health gaming application developed at the University of Maryland in partnership with Fraunhofer USA and tested at the Baltimore Veteran’s Administration Hospital will be discussed. The Personalized Mobile Medicine System (“PM2Sys”) is a cloud-based software system built on Google App Engine Components that integrates cutting-edge research from the psychology, health behavior, information systems and medicine domains in the form of a mobile device-based application targeted towards older adults suffering from chronic disease. DiaSocial is the first application built on PM2Sys and it is targeted towards type 2 diabetes. The technology is also designed to test research hypotheses on the role of social engagement types and tailoring of interventions using personality and other data. A pilot randomized control trial of DiaSocial was completed in May 2015. This 90-day trial included 29 older adults across four groups with varied intervention design and supporting processes. Participants were given a cellular-connected digital tablet, the application and an integrated wearable activity tracker. Clinical providers used the system to continuously monitor and communicate with some patients. In half the groups, patient teams competed for the best scores. The presentation will provide insights from the quantitative and qualitative analysis, which includes over 15,000 data points and interviews with 23 patients and the provider team. Design and usability lessons, and how applications may be more specifically tailored based on clinical, behavioral, app usage, and psychological dimensions of users will be featured.

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Personalized Mobile-Social Medicine for Chronic Disease: Pilot Clinical Trial Results Towards a Theory of Sustained Health Behavior Change

  1. 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. 2. Team 2 European Collaborators (partial list): © CHIDS
  3. 3. Presentation Flow  Background  System  Pilot Trial Structure and Results  Takeaways  Future 3 © CHIDS
  4. 4. Problem 4 © CHIDS
  5. 5. Problem 5 © CHIDS Data and Image: American Diabetes Association
  6. 6. Problem 6 © CHIDS Data and Image: CDC, NIDDK, LA Times
  7. 7. Problem 7 © CHIDS Data and Image: IDF Diabetes Atlas
  8. 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. 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. 10 © CHIDS© CHIDS
  11. 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. 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. 13. Usage 13 © CHIDS > 70% used 60 days of trial > 43% used 80 days of trial
  14. 14. Group Performance 14 © CHIDS
  15. 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. 16. Effects 16 © CHIDS Treatment group: -0.56% Control group: -0.34%
  17. 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. 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. 19. Tailoring and Personalization Clinical State Personality Usage Behaviors 19 > Prescribing > Message > Content > Framing > Tone > Teaming > Timing © CHIDS
  20. 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. 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. 22. Dank u wel!  Kenyon Crowley  kcrowley@rhsmith.umd.edu  @HealthIT 22 © CHIDS
  23. 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

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