SBM 2013 - Anthony Kouyate (presentation final) - WellDoc

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Delivering Chronic Illness Self-care

Behavioral and Clinical Support on a

Mobile Health Technology Platform Robin Anthony Kouyaté, Ph.D.

WellDoc, Director Behavioral Sciences

March 21, 2013

Society for Behavioral Medicine

San Francisco, CA

March 19, 2013

Disclosures

• Employee of WellDoc, Inc.

Overview

• Mobile Health Technology System

• Chronic Disease Program Development Framework

• Behavioral Design Framework

• Taxonomy of mHealth Behavioral Messages

• Application: Virtual Patient Coaching Intervention



Mobile Integrated Therapy System



WellDoc System

Virtual Patient Coach • Tailored and personalized, real-time

coaching • Monitoring and medications

reminders • Out of bounds alerts • Metabolic target ranges

Social Support • Family and community connectivity • Caregiver alerts and support

Clinical Decision Support • Outcomes-based support • EMR/EHR integration • Clinical analysis and trends • Patient stratification • Population management

Automated Expert Analytic System™

• Cloud-based analytics • Evidence-based guidelines • Predictive modeling • Longitudinal tracking • Event/alert tracking

Reference Study Characteristics Intervention Results

Quinn et al, 2008

Pilot Study

RCT

Adult type 2 diabetes patients

Community physician practices

(n=30)

3 months

Patient Coaching: •Real-time feedback on BG levels, nutrition, lifestyle and self-management •Guided compliance TM for BG checking Clinical decision support: •Displayed recommendations for medication regimens •View of patient logbook •Analysis of patient data and trends Analytics: •Hypo and hyperglycemia treatment algorithms

Mean A1C reduction = 2.03% (p>.02) 84% vs. 23% of physicians more likely to titrate/add drugs (p>.002)

Quinn et al, 2011

Cluster RCT

Adult type 2 diabetes patients

Community primary care practices

(n=163)

1 year

Patient Coaching: •1000+ automated real-time educational, behavioral, and motivational messaging •Virtual Case Manager messages based on longitudinal data trends •Action plan every 2.5 months •Patient web portal: Message center, learning library, Personal Health Record, logbook view Clinical decision support: Provider portal: Access patient data summary and relevant evidence-based guidelines Analytics: •Hypo and hyperglycemia treatment algorithms

Mean A1C reduction = 1.9% (P < 0.001)

Katz et al, 2012

Demonstration Project

Adult type 2 diabetes Medicaid patients

community clinic the primary care setting

(n=32)

1 year

Patient Coaching: •Real-time feedback on BG levels, nutrition, lifestyle and self-management •Weekly Case Manager messaging Clinical Decision Support Patient Summary: BG readings, SOC measure status, lab values and current medications in patient chart Analytics: •Hypo and hyperglycemia treatment algorithms

ER visits reduced by ~50%

Hospitalizations reduced by 100%

Demonstrated outcomes

Challenge

Develop a behavior design approach that:

• Supports multiple “service delivery models”

• Delivers integrated clinical and behavioral intervention development

• Covers behavioral support for 80% of chronic disease self-management

• Facilitates systematic translation and operationalization of research into products

• Provides a flexible, scalable, replicable process for behavior design

• Iterative

• Reviewed range of research to identify both the WHAT and the HOW

• Validated and refined the framework with new product development

Behavior Design

Framework

Pilot study, RCT, Demo

Project

Market research

Commercial Product

Feedback

Human Factors for

V.2 Commercial

Product

Voice of the Customer

Evidence and best practices

Process



WellDoc feature/messaging

WellDoc Scientific Framework

Value Drivers (improved outcomes,

reduced cost)

Outcomes

Outcome Metrics

Key stakeholder objectives

Essential behaviors and Supporting actions

Multi-dimensional framework filter

EBG-driven clinical and behavioral Interventions



Behavioral Design Framework

System has to be developed to deliver behavioral support via…

Lustria et al, 2009; Riley et al, 2011; Rimer & Kreuter, 2006; Mulcahy et al, 2003; Fogg, 2009; Murray et al, 1997; Favin, Naimoli, & Sherburne, 2004; NCI, 2001; O’Sullivan et al, 2003; Wolfers et al 2007; Klasnja & Pratt, 2012; Toscos and Kay Connelly, 2009; Schulz et al, 2010; Abrams & Michie, 2008; Heron & Smyth , 2010; Kreuter et al, 2000; Stretcher et al, 2005;

WellDoc Behavior Design Framework: Defining Scope of Behavioral Support

Ecological Approach

Patient behavioral

support

Provider clinical decision support

Supportive environment

Socio-ecological Model

Intervention Design

Dynamic, adaptive intervention

delivery

Adaptive design

One-time customized health

intervention

Intervention mapping

Behavior change techniques

Assessment delivery

Intervention delivery plan

Dynamic adaptive design

Patient Coaching Message Content

Tailored

Segmented

Generic

Tailoring strategies

•Clinical

•Behavioral

•Contextual

Tailoring Mechanisms

•Personalization

•Feedback

•Content matching

WellDoc Behavior Design Framework: Operationalization for Service Delivery Models

Ecological Approach

Patient

Patient coaching

Provider

Case manager decision support

Supportive environment

Caregiver portal

Patient Coaching Message Content

Tailoring strategy:

•Clinical – Tailored by reading type, BG value

•Behaviorally – Targeted by reading type

Tailoring Mechanism:

•Personalized: first name, BG value

•Evaluative and motivational feedback

Behavioral Guidance

Targeted behaviors based

on clinical treatment plan

Multiple health behavior domains

One health behavior domain

Behavioral Guidance

Behavioral Domains

Behavior Types

Essential Behaviors

Supporting actions

Behavioral Determinants

Explanatory Theory

Behavioral Guidance

Multiple health behavioral domains

•BG Monitoring •Medication •Lifestyle •Sign and symptom management •Treatment plan adherence

Provider

HCP clinical decision support

Targeted and integrated clinical/behavioral self-management support for:

•Healthy eating •Physical activity •BG Monitoring •Medication taking •Problem in solving •Reducing risks of diabetes complications •Coping

Tailoring strategy:

•Clinical – medication regimen segments, reading type, BG value

• Behavioral – Targeted by reading type; tailored

•Educational –Curriculum level

Tailoring Mechanism:

•Personalized: first name, BG value

•Evaluative and motivational feedback

Intervention Design

One-time 6 months – 1 year customized intervention Based on registration data

Lifetime long-term dynamic, adaptive intervention

•Based on ongoing data collected through assessments

•Automated Expert Analytics System analysis



Taxonomy of mHealth Behavioral Messages

Message Type Message Attributes

Prompts Automated Real-time Feedback

Trending Messages (“Just in time”)

Interpersonal messaging

Trigger Reminder set Lack of data

Single data point Longitudinal data analysis

Stakeholder

Feature delivery (illustrative)

Calendar Journal/Logbook Message Center Message Center

System source Rules Mobile Algorithm

Automated expert analytics system TM

Stakeholders (e.g. providers)

Content Cue to action Educational Behavioral support Safety

Comments on data patterns

Based on provider professional licensing/protocol

Timing Event-based Time-based

Immediate Event-based Based on protocol

Frequency Per event Per data entry point Rule-based Based on protocol

Message cycle None Rotation based on intervention duration

None Based on protocol

Behavior Change/Support Strategy

Outcome: Improve glycemic control

Outcome metric: A1c

Stakeholder/Objective: Patient self-management

Essential Behavior: Take medications as prescribed

Supporting actions:

• Take the right medication dose

• Perform appropriate self assessment

Intervention: Virtual Patient Coaching

MDF: Medication regimen segmentation

Feature: Journal with real-time feedback messaging

Application: Virtual Patient Coaching Intervention

114

Behavior Design

Ecological approach: Patient, provider, caregiver

Behavioral Guidance: Targeted and integrated clinical/behavioral self-management support

Intervention design: Adaptive based on medication regimen changes

Message Type: Automated Real-time Feedback

Tailoring strategy: •Clinical – medication regimen segment, reading type, BG value

•Behavioral – Targeted by reading type

Tailoring Mechanism: Motivational feedback

Behavior Design Framework supports

• Systems approach

• Multiple service delivery models

• Clinical-behavioral integration

• Range of behavioral support

• Research to product design

• Iterative design process

Acknowledgements

WellDoc Teams

• Clinical

• Behavioral

• Analytics

• Technical

• Analysis

• Commercial and Product Marketing

• Operations

Selected References

Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health psychology: official journal of the Division of Health Psychology, American Psychological Association, 27(3), 379–387. Favin, M, Naimoli, G, and Sherburne, S. (2004). Improving health behavior change: A Process guide on hygiene promotion. Kreuter, M. W., Farrell, D. W., Olevitch, L. R., & Brennan, L. K. (1999). Tailoring Health Messages: Customizing Communication With Computer Technology. Routledge. Lustria, M. L. A., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer-tailored health interventions delivered over the Web: review and analysis of key components. Patient education and counseling, 74(2), 156–173. Mulcahy, K., Maryniuk, M., Peeples, M., Peyrot, M., Tomky, D., Weaver, T., & Yarborough, P. (2003). Diabetes self-management education core outcomes measures. The Diabetes educator, 29(5), 768–770, 773–784, 787–788. Peeples, M., Iyer, A. , Cohen, J. (in pres/under review). Integration of a Mobile Integrated Therapy (MIT) with Electronic Health Records: Lessons Learned. Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., & Mermelstein, R. (2011). Health behavior models in the age of mobile interventions: are our theories up to the task? Translational behavioral medicine, 1(1), 53–71. Schulz, R., Czaja, S. J., McKay, J. R., Ory, M. G., & Belle, S. H. (2010). Intervention taxonomy (ITAX): describing essential features of interventions. American journal of health behavior, 34(6), 811–821. Strecher, V. J., Shiffman, S., & West, R. (2005). Randomized controlled trial of a web-based computer-tailored smoking cessation program as a supplement to nicotine patch therapy. Addiction (Abingdon, England), 100(5), 682–688. U. S. Dept of Health and Human Services. (1989). Making health communication programs work : a planner’s guide. U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, Office of Cancer Communications, National Cancer Institute.



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SBM 2013 - Anthony Kouyate (presentation final) - WellDoc