March 9, 2010: I. SimMobile and Internet-Based Research Epi 206 – Medical Informatics Ida Sim, MD, PhD March 9, 2010 Division of General Internal Medicine,

March 9, 2010: I. Sim Mobile and Internet-Based ResearchEpi 206 – Medical Informatics

Ida Sim, MD, PhD

March 9, 2010

Division of General Internal Medicine, and Center for Clinical and Translational Informatics

UCSF

Mobile and Internet-Based Health and Research

Copyright Ida Sim, 2010. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


“Pilot Funding” Homework

• Remember to email me your project idea to verify that your project idea is appropriate for this exercise

• Due: Thurs. March 25, 5 pm


Outline

• eHealth– “Internet-based” applications

– mobile/ubiquitous health

– telemedicine/sensing

• Methodological considerations• Summary


Internet vs. Web

itsa

medicine

ucsf.edu

nci.nih.gov

“the cloud”

myhome.com

Main Trunk Cables

local trunk cablethrough Berkeley

amazon.com

at homedial-in to itsa.ucsf.edu via modem

pacbell.net

aol.com

Internet Service Provider (ISP)via DSLor cable

LAN


Internet vs. Web

• Internet = network of networks– computers and cables all linked to one another

and talking to one another using protocols

– supports lots of different internet protocols• e.g., http, ftp, smtp, https, rdf, doi, etc. etc.

• Web is the internet traffic that uses http– servers send out information in HTML

• Hypertext Markup Language

– web browsers can decode HTML and display it


Clients and Servers

itsa

medicine

ucsf.edu

nci.nih.gov

“the cloud”

myhome.com

Main Trunk Cables

local trunk cablethrough Berkeley

amazon.com

at homedial-in to itsa.ucsf.edu via modem

pacbell.net

aol.com

Internet Service Provider (ISP)via DSLor cable

LAN

Server

Client


Research IT on Internet/Web

• Research IT using Internet (e.g., CTMS over internet)– uses Internet network of networks to send data and

commands back and forth– servers and clients do the storage, query, retrieval,

computation, reporting– may have nothing to do with a web browser

• Research IT using Web– web servers send HTML content over the Internet using

HTTP– web browsers and other “clients” receive that content for

display or computation• Wireless: same as above without wires/cables


Outline






4 Classes Internet Interventions

• Web-based interventions• Online counseling and therapy• Internet-operated therapeutic software• Other

Barak, et al. Ann Behav Med. (2009) 38:4–17


Web-Based Interventions

• Purposes: therapeutic, educational, preventive– primarily self-guided prescriptive online program

– used by consumers seeking health-related assistance

– program attempts to create positive change and/or improve/enhance knowledge, awareness, and understanding via the provision of sound health-related material and use of interactive web-based components


SubTypes and AxesProgram content

Multimedia use/choices

Interactive activities

Guidance and

supportive feedback

Educational

Self-guided therapeutic

Human-supported therapeutic

provides information, therapeutically “inactive”

designed to create positive cognitive, behavioral, or emotional change


SubTypes and AxesProgram content

Multimedia use/choices

Interactive activities

Guidance and

supportive feedback

Educational

Self-guided therapeutic

Human-supported therapeutic

e.g

., te

xt, v

ideo

, gra

phi

cs

e.g

., su

rve

ys, s

elf-

ass

ess

me

nt

i.e.,

alg

ori

thm

ic o

r h

uma

n (s

ynch

or

asyn

ch)


U-Can-Poop-Too Study

• http://ucp2.bht.virginia.edu/interest/study


Websites for Surveys• e.g., Survey Monkey www.surveymonkey.com

– free for 10 questions, 100 responses per survey

– professional subscription $19.95/mo, or $200/yr unlimited• up to 1000 responses per month, $0.05 per additional response

• DatStat’s Illume – high-end survey system with advanced logic, real-time data access,

complex query capabilities, exports to SPSS, etc.

– Internet World Health Research Center is beta user• $25,000/yr for 250,000 surveys ($10/survey)

• RedCAP– open source from Vanderbilt, UCSF budgeting $40,000/yr

– free for now

Disclosure: I have no ties to SurveyMonkey or DatStat


Online Counselling and Therapy

• Mode– text, chat, video, whiteboard

• Who’s involved– professional counsellors, peers

• How many– one-on-one, group

• Timing– synchronous (at the same time), asynchronous (at

different times)


Online Care

• http://www.liveperson.com/experts/professional-counseling/

• https://platform.hellohealth.com/PublicPortalServlet/DoctorsList.jsp

http://www.liveperson.com/experts/professional-counseling/

http://www.liveperson.com/experts/professional-counseling/


Internet-operated Therapeutic Software

• decision support systems– rule-based and others

• simulation and virtual reality– http://secondlife.com/?v=1.1

• gaming for health– http://www.humanagames.com/#/home/

– http://www.gamesforhealth.org/


Other

• Blogging, twitter• Online support groups and networks

– http://www.patientslikeme.com/

• etc. etc.


Usage?

• 61% of US adults look online for health info• 52% of health inquiries are for someone other

than the searcher• What are they doing?

– 41% read someone else's health commentary/blog

– 24% consulted rankings/reviews of doctors

– 24% rankings or reviews/reviews of hospitals

– 13% listened to a health/medical podcast

– ~6% post any contentPew Internet and American Life, The Social Life of Health Information, Jun 2009


Effect of Online Health Info?

• 60% say info affected a real-life medical decision

• 56% say info changed their overall approach to maintaining their health or the health of someone they help take care of

• 38% say info affected decision whether to see a doctor

• Internet is first source of info, but doctors still more trusted (increasingly so) [Hesse, NEJM, Mar 4, 2010]


Outline






Wireless Med Future

• http://www.ted.com/talks/eric_topol_the_wireless_future_of_medicine.html– 00:45 to 7:15

http://www.ted.com/talks/eric_topol_the_wireless_future_of_medicine.html

http://www.ted.com/talks/eric_topol_the_wireless_future_of_medicine.html


24/7/anywhere/everyone Medicine

• Ubiquitous/pervasive computing– small, cheap, highly networked devices, distributed

everywhere doing everything (everyware)

• Allows ubiq data collection and interventions– passive data collection

• e.g., in home to monitor gait, falls

• activity classification

– active• personal: ecological momentary assessments (EMA) of health

data

• group: participatory sensing– pictures of invasive plants in national parks


Outline






Telemedicine

• Provision of clinical/research services from a distance– i.e., geographically separated synchronous traditional

care/research

– can be far (e.g., Singapore) or near (e.g., Walmart)

• Prop 1D 2006 approved $200m for telemedicine infrastructure (ie wires) statewide– “With Proposition 1D funds we will eventually be able to connect

our best hospitals and our best medical schools with clinics in remote areas all over the state of California.” (Schwarzenegger)

– wiring up SFGH, Parnassus, Central Valley, etc.


Sensing for Research

• Ubiquitous sensing– Eric Topol’s TED talk

• Embedded sensing in the home– Kaiser Garfield Innovation Center

http://xnet.kp.org/innovationcenter/index.htm

• Combining passive and prompted “sensing”

– CENS slides 1-8

• To get data we’ve never been able to get before...


CRC Without Walls

• Traditional clinical research centers (CRCs)– patients came in to campus for monitoring

• CRC without walls– monitoring at home, in ambient living

– need to use data interchange standards (e.g., Continua Alliance) to hook into CTMS, etc.

• UCSF “Distance Medicine” initiative...


Outline






Methodological Considerations

• eHealth research is very new field– http://www.isrii.org/ and http://www.jmir.org/

• Intervention design– measurement error and bias

– choosing a comparison group

• Participant recruitment– selection bias and the digital divide

– sampling error

• Sample size


Intervention Design• Usual survey design issues apply, PLUS• Technical design

– platform (e.g., Mac) and browser (e.g., Safari) incompatibilities

– use Flash, Java, etc requiring plug-ins or version compatibility

– readiblity (font too small), need to scroll, confusing navigation, bugs

• What technology does respondent group use?– check some browser statistics sources

• e.g., http://www.w3schools.com/browsers/browsers_stats.asp

– need to test and double-test in various platforms and browsers used, various versions of HTML, Java, Flash, etc.

http://www.w3schools.com/browsers/browsers_stats.asp


Measurement Error/Bias

• What you designed may not be what respondent sees

• Client’s browser displays the intervention based on – platform, browser, monitor, screen/window size

– different users see different survey, e.g., • small screen/window size makes “Next” button not visible

• text doesn’t fit on small window, or requires scrolling for some respondents and not others

• colors, graphics (e.g., visual analog scales) may appear differently


Non-Completion Bias

• Influenced by– respondent familiarity with web (e.g., click on link)– technical design of survey– bandwidth– convenience (can interrupt survey?)

• Can use mixed-mode surveys to address– e.g., combined web/phone, web/mail


Choosing a Comparison Group

• Need to– distinguish marginal effect of intervention over

natural history

– intervention effects not caused by participant expectations

– give generalizable evidence• to other patients in a care practice: comparison could be

usual care (no web intervention)

• to all patients: comparison could be a simple vs. more full-featured web intervention


Web-Based Comparators

• Since both interventions web-based, more control for patient selection, participant expectations, etc.

• Can isolate parts of intervention to randomly assign and evaluate (e.g., video vs. pictures in explanations)







– sampling error

• Sample size


Subject Recruitment

• Recruitment is biggest bottleneck of clinical research– 30-40% of clinical trial costs – >80% of trials have recruitment delays– 1/20 recruited patients actually enroll

• Web-based recruitment can be international, cheap, fast– e.g., www.stopsmoking.ucsf.edu Dec 05 - Feb 07

• 350,000 hits, 60,000 entered data, 20,000 enrolled• 2/3 Spanish-speaking, 1/3 English• 131,517 visits from 121 countries Jan 12, 05 to April 5,

06

http://www.stopsmoking.ucsf.edu/


Visits0=>1=>100=>1,000=>10,000

Distribution of Visits to www.stopsmoking.ucsf.edu Jan 12, 2005 to April 5, 2006

(131,517 visits from 121 countries)

http://www.stopsmoking.ucsf.edu/

• Digital divide– Internet use

– data from Pew Internet, Dec 2009

• Digital divide– Broadband use

– data from Pew Internet, Dec 2009


Broadband Use

• Overall, 65% of adults have home broadband• Others are

– Digitally Distant: 10%, median age = 63. Half say internet is not relevant or lack digital literacy.

– Digital Hopefuls: 8%, low-income, heavily Hispanic and African American, lack resources.

– Digitally Uncomfortable: 7%, lack skills and interest in taking advantage of internet.

– Near Converts: 10%, median age is 45. Cost is the biggest barrier to having broadband at home.


Digital Health Divide

• Broadband more available to higher-income white households with children– uneven potential access to Flash, tele-consultation,

etc.

• Most of divide attributable to income, not to race


Wireless Divide

• 56% of Americans have accessed Internet using a mobile device (Pew Internet, Wireless Internet Use, Jul 2009)

– on a typical day, 19% of Americans access Internet wirelessly

• Large minority of households are cellular only– 9% of households in CA, 26.2% in OK in 2007-8

(CDC, Wireless Substitution, NHS Reports, Mar 2009)


Reducing Sampling Error

• Social sciences and marketing are most advanced in web survey methodology– e.g., Joint Statistical Meetings of the American

Statistical Association

– http://www.knowledgenetworks.com/

• Recruit a representative sample• Use a pre-assembled representative cohort

Disclosure: I have no relationship with KnowledgeNetworks


Recruit Representative Sample

• Random digit dialing (RDD) with web supplementation equally representative as (land-line) telephone RDD– RDD sampling (landline and cellular)

– if respondent agrees, provide them with free Internet access (via WebTV) or other necessary hardware for duration of participation

– e.g.,http://knowledgenetworks.com/

http://knowledgenetworks.com/




Representative Cohorts

• Maintained by e.g., large survey and marketing firms– http://www.knowledgenetworks.com/knpanel/

index.html • KnowledgePanel is representative of US• can target specific respondents, “response rates of 65-

75%, abandonment rate <2%”

– www.surveysampling.com• ~4 m people from 17 countries, incl. health-specific panels

– http://experimentcentral.org/ • NSF-funded representative panel for social science

research

http://www.surveysampling.com/

http://experimentcentral.org/


Enrollment Rates

• Response rates typically 30-60%• Affected by

– number of (pre) contacts, whether personalized• most influential factors

– incentives (e.g., Amazon certificate)

– population surveyed, nature of topic, official sponsorship, etc.


Other Recruitment Methods

• With higher risk of sampling bias– search engines, with search engine optimzation

(SEO) techniques• e.g., webrings, Google adwords

– links from related pages– email lists, social networking sites, chat rooms,

newsgroups• friends, twittering, etc.

• Can blend traditional and web– give website on radio, TV, print, brochures



Search Engine Ranking

• Search engines have their own (secret) algorithm for ranking pages– Google uses >100 factors, esp. how many pages

link into a page• Google AdWords

– put in your keywords, see cost-per-click• https://adwords.google.com/select/KeywordToolExternal

?defaultView=3

– pay only if someone clicks, more if the keyword historically brings more traffic

https://adwords.google.com/select/KeywordToolExternal?defaultView=3

https://adwords.google.com/select/KeywordToolExternal?defaultView=3







– sampling error

• Sample size


Note on Sample Size

• Estimating sample size– e.g., Google provides traffic history for various

keywords (adwords.google.com)

• Since incremental cost often negligible, less pressure to minimize sample size– not unusal to get large samples (>10,000)

• But high sample size = high accuracy!– may be precise but inaccurate if sample is non-

representative


Outline






Summary

• Definitely a brave new world• Fusion of ubiquity, social computing, participatory

medicine• May exacerbate health disparities• Infrastructure needs are high

– for easy to develop applications

– for standards of meaning and exchange

• Methodological challenges are real– a new field of evaluative research

• To be continued...

Documents

March 9, 2010: I. SimMobile and Internet-Based Research Epi 206 – Medical Informatics Ida Sim, MD, PhD March 9, 2010 Division of General Internal Medicine,