Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change

Challenges of Harnessing the Informatics Landscape to Promote

Health Behavior Change

David B. Abrams, PhD

Executive Director, The Schroeder Institute for Tobacco Research and Policy Studies

The Johns Hopkins Bloomberg School of Public HealthGeorgetown University Medical Center

KEYNOTE PRESENTED AT THE AMERICAN ACADEMY OF HEALTH BEHAVIOR

AUSTIN, TEXAS

MARCH 19, 2012

Source: Mendez, Warner. Tobacco control. Nicotine & Tobacco Research., August 11, 2010.

FDAact

Population Impact: The Example of Tobacco

Revisit Goal of Population Impact

Impact = Reach x Efficacy

Efficiency: Continuous optimization of quality of evidence-based intervention

delivery at scale, cost-effectively

RE-AIM: multi-level integration

SOURCES: (1) Abrams et al. (1996). Integrating individual and public health perspectives for treatment of tobacco: A combined stepped care matching model. Annals of BehMed,18,290-304. (2) Glasgow, Green, Klesges, Abrams et al. (2006). External validity: we need to do more. Ann Behav Med,31(2),105-108.

Back in 2005…

• Internet adoption in US: from 15% in 1995 to 75% in 2006

– More than 70 million adults go online each day

• ~ 80% of Internet users have searched online for health information at some point in their lives (Pew, 2005)

BUT…• In spite of a surge of technologic capability, research and

evaluation methodologies have not kept pace with rapid evolution & proliferation of communication technologies

• Nor has the dissemination of effective eHealth interventions achieved the level of penetration one might have hoped, given the number of people who now access the Internet

Source: Atienza, Hesse, Abrams, Rimer, et al. Critical Issues in eHealth Research. Am J Prev Med. 2007 May; 32(5 Suppl): S71–S74.

5+ Years Later: Where Are We Now?

Crounse commentary (2007):

“Even though robust communication and collaborationsolutions exist to speed scientific discovery and thedelivery of care, all too often our methodology fallsback on that which we know and have always donebefore… But we must not dig in our heels, resistchange, and continue to conduct business as we havealways done before just because it suits our comfortlevel. Others around the world will not indulge in ortolerate that luxury.”Source: Crounse B. The newspaper, the wristwatch, and the clinician. Am J Prev Med. 2007 May;32(5 Suppl):S134.

Assumptions

1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)

2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.

3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy

Source: Abrams, D (1999). Transdisciplinary paradigms for tobacco research. Nicotine & Tobacco Research, 1, S15.

The Individual Effectiveness to Population Impact Chasm

Assumption 1:Traditional Science

A New Definition of Translational Research

T1 T2 T3 T4

PotentialClinicalApplication

Evidence-Based

Guidelines

Clinical Careor

Intervention

Health of Communityor Population

Types of

Research

• Phase 1, 2 trials• Observational

• Phase 3 trials• Systematic reviews• Health services studies• Observational studies

• Phase 4 clinical trials• Implementation• Communication• Dissemination • Diffusion • Systematic reviews

•T3 type studies in community• Population / outcome studies• Cost-benefits, policy impact• Studies beyond clinical care

Potential Application Efficacy Effectiveness Population-Based

Basic Theoretical Efficacy Applied Public Health Knowledge Knowledge Knowledge Knowledge Knowledge

Basic ScienceDiscovery

Sources: 1) Szilagyi P. 2010: From Research to Dissemination Implementation:http://www.research-practice.org/presentations.aspx. 2) Khoury M, et al. Gen Med, 2007;9:665-674. 3) Glasgow et al., RE-AIM.

Outside the skin

Under the skin

Assumption 2:Single-level interventions

Assumption 3:Multi-level integration

Source: Lazer et al. (2009). Life in the network: the coming age of computational social science. Science. 323(5915): 721–723.

Dynamic model of research for multi-level impact: Theory to mechanisms to practice to policy loop

Iterative Continuous Improvement

Example: Multiphase Optimization

Strategy (MOST)

• Collins, Murphy, Strecher. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealthinterventions. Am J Prev Med. 2007 May;32(5 Suppl):S112-8. PMCID: PMC2062525.

• Collins et al. The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions. Ann Behav Med. 2011 Apr;41(2):208-26. PMCID: PMC3053423.

From Gene Chip Arrays To Population Arrays

Multi-level tailoring at: • biological level• individual level• proximal socio-behavioral level• community level • population level

GENOMICS TO POPULOMICS

Source: Murray et al. (2006). Eight Americas: Investigating Mortality Disparities across Races, Counties, and Race-Counties in the United States. PLoS Medicine: Vol 3, 15139, e260.

1. The iQUITT Study - Internet (Graham, PI)

2. Facebook (Cobb, PI)

3. POSSE (Kirchner, PI)

4. Adaptive designs in clinical trials (Niaura)

Illustrative Examples from the Schroeder Institute

Assumptions




Internet and Telephone Treatment for Smoking CessationAmanda L. Graham, PhD (PI)

National Cancer Institute5 R01 CA104836

2004 – 2010

Initial Evaluation ofQuitNet

• Observational study in December 2002

• Total # surveyed = 1,501

• Responders: 25.6% (N=385)

Least conservative

Most conservative

ADHERENCE SAMPLE (N=223): 30.0%– Respondents only

INTENTION TO TREAT (N=1,024): 7.0%– Counts all non-responders as smokers

• Used site ≥ 2x (N=336): 13.1%

• Used site >1x (N=488): 9.8%

• Excluding bounced (N=892): 8.0%

Initial Evaluation ofQuitNet

2005 participants

Recruited online

Randomized to “real world”Internet or phone treatments

~ 70% follow-up rates 3-18 months

Source: Graham AL, Bock BC, Cobb NK, Niaura R, Abrams DB. Characteristics of smokers reached and recruited to an internet smoking cessation trial: a case of denominators. Nicotine Tob Res. 2006 Dec;8 Suppl 1:S43-8.

Control Condition

Static site designed by research team

“look and feel” of QuitNet

Extracted content from QuitNet

No interactive features

No online community

Recruitment Approach

“Active User Interception Sampling”

Google, AOL, MSN, Yahoo!

Quit smoking Stop smoking Quitting smoking Stopping smoking

Informed Consent

3 explicit steps:

“Digital signature”

Contact information

Do you give informed consent?

1. Denominator, denominator, wherefore art thou denominator

2. Generalizability

Recruitment Results

Research Questions

1. Informed Consent: For low-risk, population-based studies focused on dissemination and implementation research (i.e., evaluating interventions as they are used in the “real world”), what is the appropriate and optimal level of informed consent? How might informed consent be a barrier that actually limits the reach and understanding of the target population in fundamental ways?

2. Control/Comparison Group: What is the appropriate control condition or comparison condition? Is one needed at all? How can we move away from traditional RCTs and consider SMART/adaptive designs, practical & comparative efficacy trials, and other approaches?

30 day abstin

ence

Population Impact

Impact = Reach x Efficacy

Efficiency: Continuous optimization of quality of evidence-based intervention

delivery at scale, cost-effectively

RE-AIM: multi-level integration

SOURCES: (1) Abrams et al. (1996). Integrating individual and public health perspectives for treatment of tobacco: A combined stepped care matching model. Annals of BehMed,18,290-304. (2) Glasgow, Green, Klesges, Abrams et al. (2006). External validity: we need to do more. Ann Behav Med,31(2),105-108.

Population Impact

IMPACT:Secondary Analyses

• Of funnels and tunnels and rabbit holes…

• From community newspaper to Internet tx seekers…

• From 10+ million to 99,900 to 2,005…

• Who do we have here, who is NOT here, and how much implementation dissemination, generalizability and scalability do we REALLY have here?

• Oh (nearest and dearest) denominator wherefore art thou?

IMPACT:Utilization & Outcomes

Pilot study 2002: • Use of any social support and 2-month continuous abstinence: OR = 4.03

• Intensity of website use and 2-month continuous abstinence: OR = 6.07

iQUITT Study 2011:Compared to no treatment:• 3+ logins were 1.9x more likely to quit (p < .05)• 3+ calls were 2.4x more likely to quit (p < .01)

NOTE: to date we can’t explain the growth of the static minimal Internet comparison (control) group

User Engagement & Outcomes

Engagement:Social Networks & Cessation

NEXT STUDY

Sequential Multiple Assignment Randomized

Trial (SMART)

Assumptions




J Med Internet Res. 2011 Dec 19;13(4):e119.

Am J Public Health. 2010 Jul;100(7):1282-9.

QuitNet By the Numbers

• Website overview 2007

– 1.17 million unique visitors to the web site

– 76.45 million “page views”

– 123,927 unique registered users

– 160,000 active users

• Internal communications 2007

– 1.36 million internal email (“Qmail”) messages

– 815,070 forum posts, ~ equal numbers in “Clubs”

37

QuitNet Scope

• One of the 1st examples of large-scale, web-based therapeutic social network• > 750,00 members – approx. 30-50K are active in any given month• Growth rates of up to 22,000 members in a month.

QuitNet Data Applications

A: Longitudinal Social Network Analysis

– 5+ years of detailed network data

B: Content Analysis

– 10+ years of forum postings, chat logs, private message history, blog posts, personal profiles and testimonials.

C: Agent Based Modeling

– Recreation of QuitNet as a dynamic, synthetic network that can be manipulated.

Source: http://instagr.am/p/nm695/

Example: Facebook

• 65 M users/month (US alone)– Covers over 50% of

people aged 15-24• Age:

– 45% of the population is over 25

– Over 35 population doubling every 2 months

• Gender:– Women are fastest

growing segment

Why Online Networks?

• For Interventions:

– Faster intervention development

– Better diffusion and dissemination

• For Evaluation:

– Faster recruitment

– Fewer barriers to enrollment

– Fewer barriers to follow-up

– Broader conceptualization of impact

Network Impact

Network Impact

“Impact 2.0”

• Traditional View:

Impact = Reach X Efficacy

• Network View:

Impact = (Initial Reach X R) X Effectiveness

Where R is the reproductive ratio or viral spread of an intervention or behavior.

Network Impact

“Impact 2.0+”

Impact = (Initial Reach X R) X Effectiveness + Externalities

Source: Christakis NA. Social networks and collateral health effects. BMJ 2004, Jul 24;329(7459):184-5329.

Bringing the “mountain to Mohammed”

Example: Facebook R01

• Nate Cobb, PI (2012 – 2015)

• Planned >12,000 participants in factorial design

• Outcome is R - diffusion of the application from one member to another. Not effect!

• Answers question of whatdrives diffusion and spread?

• Entire process is automated from enrollment to tracking of diffusion.

Diffusion Model

Assumptions




Ecological Momentary Tobacco Control

Thomas R. Kirchner, PhD (PI)National Institute on Drug Abuse / DC Department

of HealthRC1 DA028710 / CDC CPPW Contract

2009 – 2012

Real-time Exposure

IVRMMSSMSEmailGPS

Ecological Momentary “Surveillance”

Amazon Mechanical Turk

Amazon Mechanical Turk

Socio-economic POST Variation

Average pack price: Newport M = $7.75 block-group whiteM = $7.29 block-group non-white p = 0.004

Low pack price: All cigarette brandsM = $6.73

Average pack price: LCCM = $3.71

Low cost LCCs more prevalent in non-white block-groups

(2 = 4.31, p=0.04).

Jan 6 – Jan 9, 2012: M = 2.3 touches, 6 outletsM Newport $7.13 LCC $3.53

Real-time Exposure

Relapse Dynamics

SOURCE: Kirchner et al. Relapse dynamics during smoking cessation: Recurrent abstinence violation effects and lapse-relapse progression. J Abn Psych; 2012: 121(1).

SOURCE: Shiyko MP, Lanza ST, Tan X, Li R, Shiffman S. Using the Time-Varying Effect Model (TVEM) to Examine Dynamic Associations between Negative Affect and Self Confidence on Smoking Urges: Differences between Successful Quitters and Relapsers. Prev Sci. 2012 Jan 14. [Epub ahead of print].

Simulation Modeling

Summary & Conclusions

Solutions & Future Directions

Crounse commentary (2007):

“all too often our methodology falls back on that which we know and have always done before....But we must...not dig in our heels, resist change and continue to conduct business as we’ve always done so before just because it suits our comfort level. Others around the world will not indulge in or tolerate that luxury”

Source: Crounse B. The newspaper, the wristwatch, and the clinician. Am J Prev Med. 2007 May;32(5 Suppl):S134.

Iterative Continuous Improvement

Dynamic model of research for multi-level impact: Theory to mechanisms to practice to policy loop

Assumptions




Promises Promises…

Bio + behavioral + social + population - based sciences MAYfinally make the dream of efficient population behavior change a reality if and only if:

• Rapid innovation across: platforms, modes, capacity in near or in real time, will overcome prior barriers to:

– reach

– engagement

– utilization of efficient tailored behavior change interventions

– and their seamless proximal and distal integration into contexts (i.e. traditional and new -- social media, Internet, community, low SES subgroups, health and public health delivery systems and aligned policy at scale)

• “Today, the hurricane and earthquake do not pose the greatest danger.

• It is the unanticipated effects of our own actions, effects created by our inability to understand the complex systems we have created and in which we are embedded.

• Creating a healthy, sustainable future requires a fundamental shift in the way we generate, learn from, and act on evidence about the delayed and distal effects of our technologies, policies, and institutions.”

Source: Sterman JD. Learning from evidence in a complex world. Am J Public Health. 2006 Mar;96(3):505-14. Epub 2006 Jan 31.

Embrace Complexity

• The world is complex, contextual, dynamic, multi-causal (causal loops), multi-level, multiply determined…

– For every complex problem there is a simple solution….and it is usually wrong

• Research designs, methods and measures should take this into account and capitalize on advances in computer sciences, technology, informatics, imaging, knowledge management, networking and communications

• Vertical integration: cells to society across varying time units (seconds to centuries)

• Solid basic behavioral and social and population science is needed as a firm foundation to build systems within systems models

• Aligned incentives at every level of the system can change populations

75

WE NEED EVIDENCE IN T2-T4 THAT…

IS MORE IS LESS

Contextual Isolated, de-contextualized

Practical, efficient Abstract, intensive

Robust, generalizable Singular (Setting, staff, population)

Comparative Academic

Comprehensive Single outcome

Representative From ideal settings

www.re-aim.org

Individuals Eligiblen and %

RE-AIM Issue Content CriticalConsiderations

Total number potential settings

Settings Eligiblen and %

Excluded by Investigatorn, %, and reasons

Setting and AgentsWho Participate

n and %

Setting and AgentsWho Decline

n, %, and reasons

Othern and %

Total PotentialParticipants, n

Excluded by InvestigatorN, %, and reasons

ADOPTION

REACH

CharacteristicsOf Adopters vs Non

EXTENDED CONSORT DIAGRAM

Extent Tx DeliveredBy Different Agents

as in Protocol

Present at Follow-up(n and %) and Amount of Change or Relapse

(By Condition)

Lost to Follow-upN, %, and Reasons

Amount of change orRelapse (By Condition)

Component A = XX%Component B = YY%

Etc.

Complete Tx(n and % and

Amount of Change(By Condition)

Drop out of TXN,%, and Reasons;

And Amount of change(By Condition)

Settings in which Program is Continued And/or Modified after

Research is Over (n, %, and reasons)

Settings in whichProgram notMaintained

(n, %, and reasons)

IMPLEMENTATION

EFFICACY

MAINTENANCEa) Individual

Level

b) SettingLevel

Extent TxDelivered as

Intended

Characteristics of Drop-outs vs.

Completers

Characteristics of Drop-outs vs

Completers

Characteristics of Settings that

Continue vsDo Not

*At each step, record qualitative and quantitative information and factors affecting each RE-AIM dimension and step in flowchart

Individuals EnrollN and %

IndividualsDecline

N, %, and reasons

Not Contacted/Other

N and %

CharacteristicsOf Enrolles vs.

Decliners

The Challenge: If we have it all, then will they really come?

• Impact = Efficacy x Reach /cost + externalities

Not nearly as much as we

could be!

[email protected]

Health & Medicine

Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change