Seminar given at the Medical Effectiveness Research Center, UCSF, June 2011.

1. Mobile
Health
for
Reducing
Health
Disparities:
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

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

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

6. 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

7. mHealth
at
Peak
of
Hype
Hype Cycle, Gartner Group

8. Outline
• Trends
in
mHealth
Today
• The
Digital
Divide,
Restated
• Open
Questions
• Does
it
Work?
• Discussion

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

10. Aging-in-place
home monitors
Text4Health
Devices
Enterprise/Doctor Centric
self-monitoring and self-care using mobile
devices as “…networked patients AT&T from
shift For
Health
being mere passengers to responsible drivers
WellDoc
of their health, and in which providers
FitBit
encourage and value them as full partners.”1
Participatory Health
1Society for Participatory Medicine

11. • “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

12. Global
Impact
of
Chronic
Disease
WHO | Facts related to Chronic Disease
http://www.who.int/dietphysicalactivity/publications/facts/chronic/en/

13. Aging-in-place
home monitors
Text4Health
Devices
Enterprise/Doctor Centric
AT&T For
Health
WellDoc
FitBit
Participatory Health
LogFrog

14. 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.

15. 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.

16. 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

17. 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.

18. 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
education2
1 Home Broadband Survey, Pew Internet, August 2010
2 http://www.esa.doc.gov/Reports/exploring-digital-nation-home-broadband-internet-adoption-united-states
Technology and People of Color 1/25/2011 18

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

20. 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
2010
Mobile Phone Trends 4/28/2011 20

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

22. “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

23. 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

24. Outline
• Trends
in
mHealth
Today
• The
Digital
Divide,
Restated
• Open
Questions
• Does
it
Work?
• Discussion

25. Open
Questions
• mHealth
usage
– going
online/mobile
for
health
– social
media
for
health
– participatory
health/self-­‐monitoring
• Sustainability
of
interventions

26. 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

27. Associated with
Whites (82% vs. low
70s%)
Associated with
middle ages (mid-80%
vs. low 70s%)
Associated with
higher income

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

29. 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%)1
1
Chronic
Disease
and
the
Internet,
Pew,
Mar
2010

30. 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

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

32. 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

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

34. 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

35. Open
Questions
• mHealth
usage
– going
online/mobile
for
health
– social
media
for
health
– participatory
health/self-­‐monitoring
• Sustainability
of
interventions

36. 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

37. Technology and People of Color 1/25/2011 37

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

39. 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

40. 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

41. Open
Questions
• mHealth
usage
– going
online/mobile
for
health
– social
media
for
health
– participatory
health/self-­‐monitoring
• Sustainability
of
interventions

42. 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/

43. 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

44. 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

45. Open
Questions
• mHealth
usage
– going
online/mobile
for
health
– social
media
for
health
– participatory
health/self-­‐monitoring
• Sustainability
of
interventions

46. 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,

47. “Full
of
sound
and
fury,
signifying
nothing”?
Hype Cycle, Gartner Group

48. 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/

49. 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…

50. Outline
• Trends
in
mHealth
Today
• The
Digital
Divide,
Restated
• Open
Questions
• Does
it
Work?
How
and
when
will
we
know??
• Discussion

51. Rephrasing
“Does
it
Work?”
(Complexes of)
Outcome
Exposures strength of association? Increased
Text4Baby individual breastfeeding
population
1With
thanks
to
Rich
Kravitz
MD,
UC
Davis
and
Naihua
Duan,
Columbia

52. 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

53. 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)

54. 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

55. 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

56. Evidence
Strip
Mining

57. Evidence
Farming
Hay, et al. J Eval Clin Prac 14(2008):707-713.

58. Rooting
for
Evidence

59. Industrial
Evidence
Farming
Asthma App ER visits at 1 year
50 people
100 people
Usual Care ER visits at 1 year
50 people population

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

61. Personal
Evidence
Gardens
Flovent Flovent PRN Flovent
dancing dancing
Flovent PRN Flovent Flovent PRN
individual

62. 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?

63. Evidence
Macrosystem
Rooting for Industrial Evidence Personal Evidence
Evidence Farming Gardens

64. How
can
we
scale
evaluation?

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

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

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

68. OpenmHealth.org
• The
waist
should
support
the
evidence
macrosystem

69. 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.

70. 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.

71. 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

72. Outline
• Trends
in
mHealth
Today
• The
Digital
Divide,
Restated
• Challenges/Open
Questions
• Does
it
Work?
• Discussion

73. • 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.