Personalized Mobile Medicine for Chronic Disease: Towards a Theory of Sustained Health Behavior Change5th Games for Health Europe November 2, 2015

Kenyon Crowley, MBA, MS, CPHIMSDeputy Director of CHIDS, Robert H. Smith School of Business, University of MarylandManaging Partner, DiaSocialPhD Information Science Scholar, University of Maryland iSchool

@healthIT

Team

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European Collaborators (partial list):

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Presentation Flow Background System Pilot Trial Structure and Results Takeaways Future

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Problem

4 © CHIDSData 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

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

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Pilot Trial A1C as primary metric with additional measures for clinical,

behavioral, usage and psychological measures

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Control (5) A (5) B (5) C (6) D (6)

95

Usual care

February 2015

May2015

Among the 27 patients 3 females, 24 males Age 67.5, [61, 86] Initial A1C: 9.0, [7.6, 11.6]

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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

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Usage

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> 70% used 60 days of trial

> 43% used 80 days of trial

Group Performance

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Usage across teamsAverage 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.

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Effects

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Treatment group: -0.56 ptsControl group: -0.34 pts 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

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, but several did like gaming

Features identified for V2.0 – better communication, analytics, usability, game design, education

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Tailoring and Personalization

Clinical State

Personality

Usage

Behaviors

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> Prescribing> Message

> Content> Framing> Tone

> Teaming> Timing

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Takeaways

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> 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

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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

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Dank u wel! Kenyon Crowley kcrowley@rhsmith.umd.edu @HealthIT

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