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, PhD Director, Center for Clinical and Translational Informatics University of California San Francisco June 7, 2011

A Phone in 73% of Pockets 147% 130% 90% 60% 75% 50% 95% 93%

A Computer in 73% of Pockets 147% 130% 90% 60% 75% 50% 95% 93%

mHealth• using mobile technologies in conjunction with Internet and social media for preventive and medical care Corventis Piix EKG Monitor Haiku app, for Epic EHR AsthmaMD app 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

Aging-in-placehome monitors Text4Health Devices Enterprise/Doctor Centric AT&T For Health WellDoc FitBit Participatory Health 1Society for Participatory Medicine

Aging-in-placehome monitors Text4Health Devices Enterprise/Doctor Centricself-monitoring and self-care using mobiledevices as “…networked patients AT&T from shift For Healthbeing mere passengers to responsible drivers WellDocof their health, and in which providers FitBitencourage and value them as full partners.”1 Participatory Health 1Society for Participatory Medicine

• “We can’t look at health in isolation. It’s not just in the doctor’s oﬃce. It’s got to be where 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 Disease http://www.who.int/dietphysicalactivity/publications/facts/chronic/en/

Aging-in-placehome monitors Text4Health Devices Enterprise/Doctor Centric AT&T For Health WellDoc FitBit Participatory Health 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 health outcomes, 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?• DiscussionData from Pew Internet and American Life Project, http://www.pewinternet.org/, unless otherwise stated.

Internet Access Gap between non-whites (black/Latino) & whites • 66% of Americans have broadband at home1 – growth is ﬂat • Internet access divide is shrinking but remains after adjustment for income and education21 Home Broadband Survey, Pew Internet, August 20102 http://www.esa.doc.gov/Reports/exploring-digital-nation-home-broadband-internet-adoption-united-states Technology and People of Color 1/25/2011 18

Cell ownership, 2004-‐2011Mobile Phone Trends 4/28/2011 19

Asian American: 90% (English-speaking only) • 80% among whites; 87% among Blacks and Latinos1 • Smartphone ownership 19% among Latinos; 23% in whites2 1Latinos Online, Pew, Sept 2010 2Scarborough Research, Dec 2010Mobile Phone Trends 4/28/2011 20

Mobile-‐only Households High Wireless Substitution: • Young adults (esp. those ages 24-‐29) • Renters • Low income (poverty line or below) • Latino/HispanicMobile Phone Trends 4/28/2011 21

“Reverse” Technology Divide• Cell phone ownership as high as if not higher in Blacks 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/desktop computers so overall technology divide will likely narrow

Digital Divide Still Exists• But is in how technology is used, not whether it is available• 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/data from 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 % Internet % of US Adults Users Looked for health info 80% 59%Looked for other people with 18% 13% similar health concerns 1 Social Life of Health Information, Pew, May 2011

Associated withWhites (82% vs. low70s%)Associated withmiddle ages (mid-80%vs. low 70s%)Associated withhigher income

What Info/Actitivities Online? % Internet % of US Users Adults Consulted online reviews 24% 18% of drugs/treatmentsConsulted online rankings 15% 11%or reviews of hospitals and other facilities

Associated with caregiver status and recent health crisis Those with chronic disease and disabilities less likely to look for health info • due to lower Internet access (62% vs. 81%)11 Chronic Disease and the Internet, Pew, Mar 2010

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, et al. NEJM, Mar 4, 2010

Cellphone Features Usage • Minorities use cellphone features at higher rates than WhitesTechnology and People of Color 1/25/2011 31

mHealth Usage % Cellphone % of US Adults Users Looked for health info 17% 14% Used health apps for 9% 7.5%tracking/managing their health 1 Social Life of Health Information, Pew, May 2011

Mobile in action – health apps and information Technology and People of Color 1/25/2011 33

Internet and mHealth Usage• Increasingly a mainstream Internet activity• Somewhat minimal use on mobile devices – trends would suggest increase as Internet use migrates 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

Social Media Usage in General• 62% of adult internet users use social network sites – 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

Technology and People of Color 1/25/2011 37

Daily Social Media Use• Almost 50% of blacks, 1/3 of whites Daily Twitter Use (Tech Trends in People of Color, Pew Jan. 2011)

Social Networks for Health % Social % of US Adults Network Users Followed friend’s personal 23% 11%health or updates on a social site Gotten health information from 15% 7% social networks Memorialized someone with a 17% 8% health condition 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

Self at the Center• Participatory health, in league with clinical care team and other patients – http://www.c3nproject.org/• Self-‐tracking, “data-‐driven lifestyle” for all areas 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 technical information Peer-to-Peer Health, Pew Internet, Feb 2011

Self-‐Tracking• 27% of internet users, or 20% of adults, have tracked their weight, diet, exercise routine or some other health indicators or symptoms online – http://www.medhelp.org/health_tools• Women more than men, more if recent life change (gain/lost wg, smoking, pregnancy) 1 Social Life of Health Information, Pew, May 2011

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 their own 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 only once• Most (48%) used fewer than 10 times• Little data on sustained use, sustained beneﬁt 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) mothers brief, free, evidence-‐based text messages for prenatal and postpartum care• A multi-‐million $ public-‐private partnership of 500 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 we know??• Discussion

Rephrasing “Does it Work?”(Complexes of) Outcome Exposures strength of association? Increased Text4Baby individual breastfeeding population1With thanks to Rich Kravitz MD, UC Davis and Naihua Duan, Columbia

Current Approaches: RCT Asthma App ER visits at 1 year 50 people 100 people Usual Care ER visits at 1 year 50 people population• 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

Current Approaches: Data Mining EHR Exposures Outcomes ? Apps population• 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)

Current Approaches: N-‐of-‐1 Studies Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual• 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

Evidence Extraction Attitude• Evidence is something to be extracted from the care process – mining it from the data – directly manipulating the care process with rigid 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 Asthma App ER visits at 1 year 50 people100 people Usual Care ER visits at 1 year 50 people population

Personal Evidence Gardens Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual

Personal Evidence Gardens Flovent Flovent PRN Flovent dancing dancing Flovent PRN Flovent Flovent PRN individual

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 for Industrial Evidence Personal Evidence Evidence Farming Gardens

How can we scale evaluation?

Stovepiped mHealth• Health apps built independently – little data sharing and interoperability• Limits eﬃciency and impact of quality mHealth

Internet Hourglass Model• Standardize and make open the “narrow waist”• Reduces duplication, spurs community innovation, supports commercial and non-‐ proﬁt uses

OpenmHealth.org Estrin DE, Sim I. Science; 330: 759-60. 2010.

OpenmHealth.org• The waist should support the evidence macrosystem

Open Architecture for an Evidence 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 an Evidence Macrosystem• Social media for discovery of exposures and outcomes that matter• Shared libraries of validated measures and instruments (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 for improving 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? Is behavior 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.

http://www.scoop.it	580
http://profiles.ucsf.edu	65
http://www.mhimss.org	21
http://www.linkedin.com	4
http://www.twylah.com	3

Mobile Health for Reducing Disparities: Does it Work and How Will we Know?

by Ida Sim on Jun 07, 2011

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Mobile Health for Reducing Disparities: Does it Work and How Will we Know? Presentation Transcript