Personally Tailored Health Information: a Health 2.0 Approach [4 Cr3 1100 Bonander]


Published on

Published in: Health & Medicine, Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Personally Tailored Health Information: a Health 2.0 Approach [4 Cr3 1100 Bonander]

    1. 1. Bonander, J. Personally Tailored Health Information: A Health 2.0 Approach <ul><li>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 </li></ul><ul><li>Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( ) </li></ul><ul><li>Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at </li></ul>
    2. 2. 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
    3. 3. Outline <ul><li>Tailored health information and Web 2.0 thinking </li></ul><ul><li>Hypothesis and logic model </li></ul><ul><li>Methods </li></ul><ul><li>Findings / discussion </li></ul><ul><li>Next Steps </li></ul>
    4. 4. Scenarios <ul><li>Jacob </li></ul><ul><ul><li>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).. </li></ul></ul><ul><ul><li>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? </li></ul></ul><ul><li>Sally </li></ul><ul><ul><li>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). </li></ul></ul><ul><ul><li>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? </li></ul></ul>
    5. 5. 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
    6. 6. Tailoring and Changing Behavior <ul><li>Increasing interest and focus in tailoring health information to change behavior and improve health and wellbeing </li></ul><ul><ul><li>Effective with smoking cessation, weight loss, physical fitness, cancer screening, nutrition </li></ul></ul><ul><li>Challenges </li></ul><ul><ul><li>High touch / low reach vs. low touch / high reach </li></ul></ul><ul><ul><li>Engagement over time </li></ul></ul><ul><ul><li>Time consuming questionnaires </li></ul></ul><ul><ul><li>Content development / availability </li></ul></ul>
    7. 7. Recent work in SNS and Health <ul><li>Christakis and Fowler (NEJM 2007; 2008) </li></ul><ul><ul><li>Social distance over geographical distance risk influencer for obesity </li></ul></ul><ul><ul><li>Collective interventions may be more effective than individual interventions </li></ul></ul><ul><li>Moreno, et al (MedGenMed 2007) </li></ul><ul><ul><li>Significant risk behavior demonstrated among teens in MySpace </li></ul></ul><ul><ul><ul><li>Sexual activity, alcohol, drug and cigarette use </li></ul></ul></ul><ul><li>Mishra, et al (on going research at CDC) </li></ul><ul><ul><li>Riskbot </li></ul></ul><ul><ul><ul><li>NLP and text analytics applied to online risk behavior </li></ul></ul></ul>
    8. 8. Hypothesis <ul><li>Part A </li></ul><ul><ul><li>Enough information exists on an individual’s social networking page(s) to be useful in generating meaningful, tailored health messages ...... </li></ul></ul><ul><li>Part B </li></ul><ul><ul><li>If so, could informatics tools be used to “discover” such information </li></ul></ul><ul><li>Part C </li></ul><ul><ul><li>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 </li></ul></ul>
    9. 9. 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
    10. 10. Context <ul><li>Focused solely on MySpace </li></ul><ul><ul><li>Top social networking site </li></ul></ul><ul><ul><li>69 million US users; 116.6 million worldwide </li></ul></ul><ul><li>Reach </li></ul><ul><ul><li>Wide age range represented </li></ul></ul><ul><ul><li>Groups, forums, blogs </li></ul></ul><ul><ul><li>Relevance for health </li></ul></ul><ul><ul><ul><li>Health& Fitness, Food & Drink, Science, Sports, Travel & Vacations, Pets & Animals, Cities & Neighborhood, Family & Home, Fashion & Style </li></ul></ul></ul><ul><ul><li>Numbers of groups upwards of 153,000 and membership on the upper ends 15,000-35,000 </li></ul></ul>
    11. 11. Process and variables <ul><li>Convenience sample </li></ul><ul><ul><li>100 publicly available profiles reviewed and coded </li></ul></ul><ul><li>57 variables captured </li></ul><ul><ul><li>Structured, unstructured, required and optional </li></ul></ul><ul><li>Gender </li></ul><ul><ul><li>43% male </li></ul></ul><ul><ul><li>57% female </li></ul></ul><ul><li>Geography </li></ul><ul><ul><li>97% mention state (36 states represented) </li></ul></ul><ul><ul><li>87% mention city </li></ul></ul>
    12. 12. Findings: Structured Data Smoking status Drinking status
    13. 13. Findings: Structured Data
    14. 14. Findings: Structured Data
    15. 15. Summary: Structured Data <ul><li>Significant rate of “not reported” across structured data elements </li></ul><ul><ul><li>Exceptions were relationship status, children, orientation, zodiac sign, mood, reason for being in MySpace </li></ul></ul><ul><li>Smoking and drinking status at ~50% reporting </li></ul><ul><li>High compliance specifying geographical information </li></ul>
    16. 16. Findings: Unstructured Data
    17. 17. Unstructured Data Sample <ul><li>Key words </li></ul><ul><ul><li>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 </li></ul></ul><ul><li>Pictures </li></ul><ul><ul><li>Drinking party girls, guns, money, sex, ultrasound pics, smoking pot/bongs, martini, Absolute bottle, seductive vampire women, sports teams, outdoor activities </li></ul></ul><ul><li>Blogs </li></ul><ul><ul><li>Goals for next year (lose baby weight), living through brain surgery, “I have AIDS bitch!” </li></ul></ul><ul><li>Language </li></ul><ul><ul><li>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…. </li></ul></ul>
    18. 18. Discussion <ul><li>Hypothesis, part A </li></ul><ul><ul><li>Possibly a viable medium for tailored health messaging – health ness is pervasive and infused throughout individual and group content </li></ul></ul><ul><ul><li>Structured data useful for targeting </li></ul></ul><ul><ul><li>Combined with unstructured content could rise to tailoring </li></ul></ul><ul><ul><ul><li>Dijkstra and Strecher have alluded to the possibility of high reach, low contact contexts being effective with “pre-contemplators” (following the transtheoretical model). </li></ul></ul></ul><ul><ul><ul><li>Bourgeois, et al recently found that tailored immunization information within an ePHR didn’t impact immunization rates, but significantly influenced KABs regarding flu immunization </li></ul></ul></ul>
    19. 19. Next Steps <ul><li>Apply informatics tools </li></ul><ul><ul><li>Working with existing corpus of MySpace data and refining Riskbot engine to surface intervention opportunities </li></ul></ul><ul><li>POC with University of Michigan </li></ul><ul><ul><li>What might a smart, reciprocal, trust building health tailoring engine/gadget/widget look like? </li></ul></ul><ul><li>Explore further public health possibilities </li></ul><ul><ul><li>Audience research </li></ul></ul><ul><ul><li>Sentinel citizens </li></ul></ul><ul><ul><li>Intervention modeling and delivery </li></ul></ul>
    20. 20. Thank you Jason Bonander [email_address]