mobile health for reducing disparities: does it work and how will we know?

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



FitBit


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



FitBit


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


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


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


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


Open Questions

• mHealth usage– going online/mobile for health– social media for health– participatory health/self-‐monitoring


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


• Minorities usecellphonefeatures athigher ratesthan Whites

mHealth Usage


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


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



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

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


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



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/


• 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)


Open Questions



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.

mobile health for reducing disparities: does it work and how will we know?

Health & Medicine

restated open

health participatory

social media

health social

social media

internet users

open architecture

health information