Bonander, J. Personally Tailored Health Information: A Health 2.0 Approach This slideshow, presented at Medicine 2.0’08 , Sept 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com ) Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php

This slideshow, presented at Medicine 2.0’08 , Sept 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team

Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com )

Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php

Personally Tailored Health Information: A Health 2.0 Approach Jason Bonander, MA Centers for Disease Control and Prevention National Center for Public Health Informatics Atlanta, Georgia, USA September 4, 2008

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

Tailored health information and Web 2.0 thinking

Hypothesis and logic model

Methods

Findings / discussion

Next Steps

Scenarios Jacob 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? Sally 36, 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?

Jacob

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?

Sally

36, 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 KEY growth online social network use and health info seeking Online health SNA research Christakis & Fowler Moreno Behavior Change Models Tailoring Informatics tools NLP Text analytics Vocab/ ontology Chronic / infectious disease prevalence strong emergent nascent Behavioral economics Trust Reciprocity 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

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

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

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 Altruism & sharing with public health Social distance Collective interventions risk behavior Improved health and wellbeing Informatics Tools Theoretical models Interest Trust Reciprocity I T R I T R I T R I T R I T R

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

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 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

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 Smoking status Drinking status

Findings: Structured Data

Findings: Structured Data

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

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

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….

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….

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

Hypothesis, part A

Possibly a viable medium for tailored health messaging – health ness 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 health possibilities Audience research Sentinel citizens Intervention modeling and delivery

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 health possibilities

Audience research

Sentinel citizens

Intervention modeling and delivery

Thank you Jason Bonander [email_address]