Mobile Health for Reducing HealthDisparities: Does it Work and How
Will We Know?
Ida Sim, MD, PhDDirector, Center for Clinical and Translational Informatics
University of California San FranciscoJune 7, 2011
A Phone in 73% of Pockets
50%
130%60%
90%
95%
75%
147%
93%
A Computer in 73% of Pockets
50%
130%60%
90%
95%
75%
147%
93%
mHealth
• using mobiletechnologies inconjunction withInternet and socialmedia forpreventive andmedical care
Haiku app, for Epic EHRAsthmaMD app
Corventis Piix EKG Monitor
No conflicts with any product mentioned
mHealth at Peak of Hype
Hype Cycle, Gartner Group
Outline
• Trends in mHealth Today• The Digital Divide, Restated• Open Questions• Does it Work?• Discussion
Text4Health
Devices
Participatory Health
Enterprise/Doctor Centric
FitBit
Aging-in-placehome monitors
AT&T ForHealth
WellDoc
1Society for Participatory Medicine
Text4Health
Devices
Participatory Health
Enterprise/Doctor Centric
FitBit
Aging-in-placehome monitors
AT&T ForHealth
WellDoc
self-monitoring and self-care using mobiledevices as “…networked patients shift frombeing mere passengers to responsible driversof their health, and in which providersencourage and value them as full partners.”1
1Society for Participatory Medicine
• “We can’t look at health in isolation. It’s notjust in the doctor’s office. It’s got to bewhere we live, we work, we play, we pray.”U.S. Surgeon General Regina Benjamin, LA Times
March 13, 2011
Global Impact of Chronic Disease
WHO | Facts related to Chronic Diseasehttp://www.who.int/dietphysicalactivity/publications/facts/chronic/en/
Text4Health
Devices
Participatory Health
Enterprise/Doctor Centric
FitBit
Aging-in-placehome monitors
AT&T ForHealth
WellDoc
LogFrog
mHealth Assumptions
• mHealth addresses “last mile” of health care– objective is behavior change
• Technology + User Experience -‐-‐> Change– “multi-‐touch” technology = sensors, phones, programs– user experience = emotional experience, leading to
motivation, ability, and triggers to change
• Behavior change will lead to improved healthoutcomes, reduced costs, etc.
Trends in Participatory mHealth
• Make it simple, fun, engaging, multi-‐touch– gaming and incentives (e.g., rewards at Home Depot)– package it like a consumer product
• Make it hyperlocal– location doesn’t matter: e.g., log your meals anytime
anywhere– location is everything: e.g., text reminder NOT to walk
into McDonalds
• Make it social– tie into Twitter, Facebook, etc.
Open Questions
• Technology reach (aka the Digital Divide)• mHealth usage
– going online/mobile for health– social media for health– participatory health/self-‐monitoring
• Sustainability of interventions
Outline
• Trends in mHealth Today• The Digital Divide, Restated• Open Questions• Does it Work?• Discussion
Data from Pew Internet and American Life Project, http://www.pewinternet.org/, unless otherwise stated.
1/25/2011 18Technology and People of Color
Gap between non-whites (black/Latino) & whites
Internet Access• 66% of Americans
have broadbandat home1
– growth is flat
• Internet accessdivide is shrinkingbut remains afteradjustment forincome andeducation2
1 Home Broadband Survey, Pew Internet, August 20102 http://www.esa.doc.gov/Reports/exploring-digital-nation-home-broadband-internet-adoption-united-states
Cell ownership, 2004-‐2011
4/28/2011 19Mobile Phone Trends
4/28/2011 20Mobile Phone Trends
Asian American: 90%(English-speaking only)
• 80% among whites;87% among Blacksand Latinos1
• Smartphoneownership 19%among Latinos; 23%in whites2
1Latinos Online, Pew, Sept 20102Scarborough Research, Dec 2010
Mobile-‐only Households
4/28/2011 21Mobile Phone Trends
High WirelessSubstitution:
• Young adults(esp. thoseages 24-‐29)
• Renters• Low income
(poverty line orbelow)
• Latino/Hispanic
“Reverse” Technology Divide
• Cell phone ownership as high as if not higher inBlacks and Latinos
• More low-‐income households are cellular only(no land line, no broadband)– where cellphone is main or only way to get on the web
• Overall trend is away from broadband/desktopcomputers so overall technology divide will likelynarrow
Digital Divide Still Exists
• But is in how technology is used, not whether it isavailable
• Language is strong predicator– foreign-‐born Latino much lower use of Internet, English-‐
speaking Latino equal to whites
• Also health literacy– low health literacy predicts lower e-‐health use (Sakar, J
Health Commun, 2010)
• Don’t automatically apply old assumptions/datafrom the “real” world to the virtual world
Outline
• Trends in mHealth Today• The Digital Divide, Restated• Open Questions• Does it Work?• Discussion
Open Questions
• mHealth usage– going online/mobile for health– social media for health– participatory health/self-‐monitoring
• Sustainability of interventions
Internet Health Usage
1 Social Life of Health Information, Pew, May 2011
13%18%Looked for other people withsimilar health concerns
59%80%Looked for health info
% of US Adults% InternetUsers
Associated withWhites (82% vs. low70s%)
Associated withmiddle ages (mid-80%vs. low 70s%)
Associated withhigher income
What Info/Actitivities Online?
11%15%Consulted online rankingsor reviews of hospitals and
other facilities
18%24%Consulted online reviewsof drugs/treatments
% of USAdults
% InternetUsers
1 Chronic Disease and the Internet, Pew, Mar 2010
Associated withcaregiver status andrecent health crisis
Those with chronicdisease anddisabilities less likelyto look for health info• due to lower Internetaccess (62% vs.81%)1
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 ofsomeone they help take care of
• 38% say info affected decision whether to see adoctor
• Internet is first source of info, but doctors stillmore trusted (increasingly so)
Hesse, et al. NEJM, Mar 4, 2010
Cellphone Features Usage
1/25/2011 31Technology and People of Color
• Minorities usecellphonefeatures athigher ratesthan Whites
mHealth Usage
1 Social Life of Health Information, Pew, May 2011
7.5%9%Used health apps fortracking/managing their health
14%17%Looked for health info
% of US Adults% CellphoneUsers
Mobile in action – health appsand information
1/25/2011 33Technology and People of Color
Internet and mHealth Usage
• Increasingly a mainstream Internet activity• Somewhat minimal use on mobile devices
– trends would suggest increase as Internet usemigrates to “mobile web”
– early indications of greater uptake among minorities
• Digital divide exists, but is non-‐traditional– less broadband use among minorities– more cellphone owernship and use among minorities– greater interest in mHealth among those with chronic
diseases and disability, but have lower Internet access
Open Questions
• mHealth usage– going online/mobile for health– social media for health– participatory health/self-‐monitoring
• Sustainability of interventions
Social Media Usage in General
• 62% of adult internet users use social networksites– 46% of all US adults
• 13% of online Americans use Twitter (Pew, June 2011)
– up from 8% in Nov 2010– 18-‐29, urban, female, more likely to Twitter
1/25/2011 37Technology and People of Color
Daily Social Media Use
• Almost 50% ofblacks, 1/3 ofwhites
(Tech Trends in People of Color, Pew Jan. 2011)
Daily Twitter Use
Social Networks for Health
8%17%Memorialized someone with ahealth condition
7%15%Gotten health information fromsocial networks
11%23%Followed friend’s personalhealth or updates on a social site
% of US Adults% SocialNetwork Users
1 Social Life of Health Information, Pew, May 2011
Social Computing for Health
• Growing social media use by all Americans– especially among minorities– intensity of use higher in minorities
• Early use of social media for health,uncharted territory
Open Questions
• mHealth usage– going online/mobile for health– social media for health– participatory health/self-‐monitoring
• Sustainability of interventions
Self at the Center
• Participatory health, in league with clinicalcare team and other patients– http://www.c3nproject.org/
• Self-‐tracking, “data-‐driven lifestyle” for allareas of life, not just health– http://quantifiedself.com/
Participatory Health
• Started strongly for patients with rare diseases– e.g., http://www.patientslikeme.com/
• Now 18% of internet users find other patients– 25% of those with chronic health conditions– transitions in health: new diagnosis, pregnancy, wt.
gain/loss, quitting smoking– 29% (?!) have contributed health content
• Professionals still the go-‐to for technicalinformation
Peer-to-Peer Health, Pew Internet, Feb 2011
Self-‐Tracking
• 27% of internet users, or 20% of adults, havetracked their weight, diet, exercise routine orsome other health indicators or symptoms online– http://www.medhelp.org/health_tools
• Women more than men, more if recent lifechange (gain/lost wg, smoking, pregnancy)
1 Social Life of Health Information, Pew, May 2011
Open Questions
• mHealth usage– going online/mobile for health– social media for health– participatory health/self-‐monitoring
• Sustainability of interventions
mHealth Today
• Widespread use of Internet for health info• Early use of mobile tech for health info• Digital divide is with chronic health/disabled, low
health literacy– “reverse divide” with minorities on cellphone
ownership, usage and social media usage
• Mostly people doing their own thing with theirown social network– mostly not integrated with clinical care team, other
health professionals, community, public health,
“Full of sound and fury,signifying nothing”?
Hype Cycle, Gartner Group
App Usage
• 26% of downloaded apps are used onlyonce
• Most (48%) used fewer than 10 times• Little data on sustained use, sustainedbenefit
http://www.localytics.com/blog/2011/first-‐impressions-‐matter-‐26-‐percent-‐of-‐apps-‐downloaded-‐used-‐just-‐once/
Case Study: Text4Baby
• Text4Baby sends new (mostly Medicaid) mothersbrief, free, evidence-‐based text messages forprenatal and postpartum care
• A multi-‐million $ public-‐private partnership of500 partners (HHS, wireless carriers, Voxiva, etc.)– launched Feb 2010, now over 157,000 enrollees– spinning off into Text4Baby Russia, Text4Health,…
• 6 ongoing evaluations– “96% would recommend Text4Baby”– no outcomes data so far…
Outline
• Trends in mHealth Today• The Digital Divide, Restated• Open Questions• Does it Work? How and when will weknow??
• Discussion
Rephrasing “Does it Work?”
(Complexes of)Exposures Outcome
strength of association?
individual
population
IncreasedbreastfeedingText4Baby
1With thanks to Rich Kravitz MD, UC Davis and Naihua Duan, Columbia
Current Approaches: RCT
• Tests prespecified interventions and outcomes• To confirm a hypothesis at the population level• Strong internal validity• Problems: slow to set-‐up, expensive, short-‐term, lack
relevance to the real world
ER visits at 1 year50 people population
100 people
ER visits at 1 year50 people
Asthma App
Usual Care
Exposures Outcomes?
population
Current Approaches: Data Mining
• Exposures and outcomes from care process systems• To generate hypotheses at the population level• Problems: limited to data collected, weak internal
validity (data not complete or systematic)
EHR
Apps
Current Approaches:N-‐of-‐1 Studies
• Within-‐subject multiple crossover• Only formal method for determining individual
treatment effectiveness• Problems: complicated to set up, analysis is
difficult, little known, not widely used
individual
peak flowpeak flowUsual Care
Asthma app
Asthma app
Usual Care
Asthma app
Usual Care
Evidence Extraction Attitude
• Evidence is something to be extractedfrom the care process– mining it from the data– directly manipulating the care process withrigid and pre-‐defined protocols
Evidence Strip Mining
Evidence Farming
Hay, et al. J Eval Clin Prac 14(2008):707-713.
Rooting for Evidence
Industrial Evidence Farming
ER visits at 1 year50 people population
100 people
ER visits at 1 year50 people
Asthma App
Usual Care
Personal Evidence Gardens
individual
peak flowpeak flowUsual Care
Asthma app
Asthma app
Usual Care
Asthma app
Usual Care
Personal Evidence Gardens
individual
dancingFlovent PRN
Flovent
Flovent
Flovent PRN
Flovent
Flovent PRN
dancing
Crowdsourcing What Matters
• (Complexes of) Exposures– does chocolate trigger (my) asthma?– testing common regimens (ACEI, statin, b-‐blocker),
complementary medicines
• (Complexes of) Outcomes– what outcomes do patients care about?
Evidence MacrosystemRooting forEvidence
Industrial EvidenceFarming
Personal EvidenceGardens
How can we scale evaluation?
StovepipedmHealth
• Health apps builtindependently– little data sharing and
interoperability
• Limits efficiency andimpact of qualitymHealth
Internet Hourglass Model
• Standardize andmake open the“narrow waist”
• Reduces duplication,spurs communityinnovation, supportscommercial and non-‐profit uses
OpenmHealth.org
Estrin DE, Sim I. Science; 330: 759-60. 2010.
• The waist should supportthe evidence macrosystem
OpenmHealth.org
Open Architecture for anEvidence Macrosystem
• Modules for usage analytics– # of text messages, # of sessions, etc.
• Rooting for (glocal) evidence– data sharing with shared syntax and semantics
• Industrial farming, e.g., with RCTs– modules for informed consent, randomization, adaptive
treatment strategy, mixed methods, etc.
• Personal evidence gardening, e.g., N-‐of-‐1– modules for scripting and analyzing individualized N-‐of-‐
1 protocols, etc.
Open Architecture for anEvidence Macrosystem
• Social media for discovery of exposures andoutcomes that matter
• Shared libraries of validated measures andinstruments (e.g., PROMIS)– measures that get at finer-‐grained mechanisms based
on theoretical models of change, etc.
Goal for mHealth Ecosystem• Becomes a learning community enabled by an open
architecture, to more effectively innovate, share,and deploy best technology and best practices forimproving individual and population health
Outline
• Trends in mHealth Today• The Digital Divide, Restated• Challenges/Open Questions• Does it Work?• Discussion
• Will people really use mobile tech to manage their health? Isbehavior change the target?
• Is self-‐tracking only for uber-‐geeks?• How much integration with traditional care system is
needed? public health? consumer world?• What will be the role of social media?• Are there fundamentally different approaches needed for
different population segments?• How can we learn as much and as fast as possible about
what works?• Any interest in establishing a trusted tester community in SF
minority populations?• etc. etc.