Bonander, J.Personally Tailored Health Information: A Health 2.0 Approach

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Personally Tailored Health Information: A Health 2.0 Approach

Jason Bonander, MA

Centers for Disease Control and PreventionNational Center for Public Health Informatics

Atlanta, Georgia, USASeptember 4, 2008

Outline

• Tailored health information and Web 2.0 thinking• Hypothesis and logic model• Methods• Findings / discussion• Next Steps

ScenariosJacob

20, lives in a suburb of San Francisco, CA; a student at the local community college, a social drinker and doesn’t consider himself a smoker (though he smokes socially); enjoys the outdoors (mountain biking, skate boarding) has many friends, and passionate about music and movies; uses multiple social networking sites (MySpace, Facebook, Ning)..What if tailored health information could be delivered to Jacob that addressed key health protection themes such as alcohol use, smoking related health issues, injury prevention, STD prevention, positive social and emotional health?

Sally36, working mom, married with children and living in St Paul, MN; a social drinker and non-smoker, but her husband smokes; shares family pictures and has a long list of favorite television shows and movies; uses social networking sites to keep in touch with current friends and to make new ones; also a member of specific health causes (e.g. fighting breast cancer).What if tailored health information could be delivered to Sally that addressed key health protection themes for herself and her family such as physical activity, chronic conditions, reproductive health, cancer, smoking-related health issues, social well being, immunizations?

Online social networking and health conceptual landscape

growth online social network use

and health infoseeking

Online health SNA

research

Christakis & Fowler

Moreno

BehaviorChange

Models

Tailoring

Informaticstools

NLP

Text analytics

Vocab/ontology

Chronic / infectiousdisease

prevalence

strongemergentnascent

Behavioraleconomics

TrustReciprocity

Groups

Tailoring and Changing Behavior• Increasing interest and focus in tailoring health

information to change behavior and improve health and wellbeing

– Effective with smoking cessation, weight loss, physical fitness, cancer screening, nutrition

• Challenges– High touch / low reach vs. low touch / high reach– Engagement over time– Time consuming questionnaires– Content development / availability

Recent work in SNS and Health

• Christakis and Fowler (NEJM 2007; 2008)

– Social distance over geographical distance risk influencer for obesity

– Collective interventions may be more effective than individual interventions

• Moreno, et al (MedGenMed 2007)

– Significant risk behavior demonstrated among teens in MySpace

• Sexual activity, alcohol, drug and cigarette use

• Mishra, et al (on going research at CDC)

– Riskbot• NLP and text analytics applied to online risk behavior

Hypothesis

• Part A– Enough information exists on an individual’s social

networking page(s) to be useful in generating meaningful, tailored health messages ......

• Part B– If so, could informatics tools be used to “discover”

such information• Part C

– If so, what would the context of engagement look like so as to not feel creepy, to stimulate behavior change and potentially even stimulate this through social networks

Logic Model

Knowledge garnered

and tailored information presented

Interest

TrustReciprocity

I

TR

I

TR

I

TR I

TR

I

TR

Altruism & sharing with public health

Social distanceCollective interventions

risk behavior

Improved healthand wellbeing

Informatics Tools

Theoreticalmodels

Context

• Focused solely on MySpace – Top social networking site– 69 million US users; 116.6 million worldwide

• Reach– Wide age range represented– Groups, forums, blogs– Relevance for health

• Health& Fitness, Food & Drink, Science, Sports, Travel & Vacations, Pets & Animals, Cities & Neighborhood, Family & Home, Fashion & Style

– Numbers of groups upwards of 153,000 and membership on the upper ends 15,000-35,000

Process and variables

0

5

10

15

20

25

Age Distribution

18-2021-2930-3940-4950-5960+

• Convenience sample– 100 publicly available profiles reviewed and coded

• 57 variables captured– Structured, unstructured, required and optional

• Gender– 43% male– 57% female

• Geography– 97% mention state (36

states represented)– 87% mention city

Findings: Structured Data

smoke12%

don't smoke41%

not reported47%

Smoking statusSmoking status

drink27%

don't drink27%

not reported46%

Drinking statusDrinking status

Findings: Structured Data

05

101520253035404550

Relationship Status

singlerelationshipmarrieddivorcedengagedno answer

01020304050607080

Orientation

straightbigaynot surenot reported

05

10152025303540

Ethnicity

AsianBlack/AfricanEast IndianLatino/HispanicMid EasternNative AmerPacific IslWhiteOtherNot reported

Findings: Structured Data

05

101520253035404550

Body Type

slimathleticavglittle xtramore 2 luvbody bldrnot reported

05

10152025303540

Children

no prefdon't wantsomedayundecidedgreat, not for meproud parentexpectingnot reported

0

5

10

15

20

25

30

35

Education

high schoolsome collegein collegecollege gradgrad schoolpost gradnot reported

0

20

40

60

80

100

Additional

MoodZodiac signReason

Summary: Structured Data

• Significant rate of “not reported” across structured data elements– Exceptions were relationship status, children,

orientation, zodiac sign, mood, reason for being in MySpace

• Smoking and drinking status at ~50% reporting

• High compliance specifying geographical information

15

0102030405060708090

personal interestsabout meblogspicturesvideos

Findings: Unstructured Data

Unstructured Data Sample• Key words

– Playin ball, working out, jogging, booze, sports, cancer, tumor, mother, baby, pregnant, sick, intensive care, impaired vision, preggers again, clubber, blood sugar, diabetes, colestral, diet

• Pictures– Drinking party girls, guns, money, sex, ultrasound pics, smoking

pot/bongs, martini, Absolute bottle, seductive vampire women, sports teams, outdoor activities

• Blogs– Goals for next year (lose baby weight), living through brain

surgery, “I have AIDS bitch!”• Language

– ThE Shit ThaT I RiP is C^6 DoWn All DaY Cuz. The SkOOl I Go toO i$ AuStin EaSt WeRe AlL ThE ReAl Ni66a$ C. I Play FooT6All n 6aSkEt6all….

17

Discussion

• Hypothesis, part A– Possibly a viable medium for tailored health messaging –

healthness is pervasive and infused throughout individual and group content

– Structured data useful for targeting– Combined with unstructured content could rise to tailoring

• Dijkstra and Strecher have alluded to the possibility of high reach, low contact contexts being effective with “pre-contemplators” (following the transtheoretical model).

• Bourgeois, et al recently found that tailored immunization information within an ePHR didn’t impact immunization rates, but significantly influenced KABs regarding flu immunization

Next Steps

• Apply informatics tools– Working with existing corpus of MySpace data and refining

Riskbot engine to surface intervention opportunities• POC with University of Michigan

– What might a smart, reciprocal,trust building health tailoring engine/gadget/widget look like?

• Explore further public healthpossibilities

– Audience research– Sentinel citizens– Intervention modeling and

delivery

EncounterParameters

Protection

Role

Enhancements

Numberof Contacts

PenetrationMode

Experience

Demographics

VirtualEnvironment

YesNo

Receptive

Dominant

Drug 1Drug 2

Drug 3

Etc.

ChatRooms

Blogs

Mobiles

PDAs

Oral

Anal

Vaginal

Spouse / Partner

Curious Discretion

Race

Age

Sex

SexualOrientation

MMMW

WW

Multiple M

Multiple W

MixedMultiple

HIV/STD Risk Behavior Category Diagram(Based on Internet Communications

Disease Disclosure- Self

HIV +ve

HIV -ve

DDF

Disease Disclosure- Partner

HIV +ve

HIV -veDDF

MeetingLocation

CircuitParty

HomePartner'shome

PublicplacesClubs Hotels

BathHousesBarsRest

Areas

Created By: Asha Krishnaswamy, DKMS, NCPHI; April 3, 2007 RiskBot Project

Not a riskcontributor

RadicallyDifferent

Parametersto study

RiskAreas

Legend

MaritalStatus

SexualIdentity

EncounterLengthSought

SpecialInterests

B&D

D&S

S&M

Thank you

Jason Bonanderzjz2@cdc.gov