Health social networks are created to allow patients to interact online. In this presentation i cover some of the topics related to online health social networks: patient characteristics, criteria for user segmentation, and actual behaviour. I present a series of results related to actual search behaviour, user characteristics, self-tracking and patient quantified-self status, emotional content vs data, behavioural modification status, and comparability of online patient populations and offline populations. Presented in the context of Vitanect.com activity.

1. Patients and online networks –
characteristics, motivators, behaviour!
Ricardo Sousa!
Vitanect co-founder and General Manager !
February 2014!
1

2. Synopsis!
• Goal: To share insights about patient
online behaviour!
• Audience: all those interested in online
patient networks!
• Date: Feb 2014!

3. Why do patients go online?!
Seek
informa-on
(on
drugs,
diseases,
diagnosis)
Seek
treatment
(which
doctor,
which
hospital)
Seek
advice
(how
to
live
with,
family
issues,
psychological
support)

4. Who is there to help?!
Seek
informa-on
(on
drugs,
dWikipedia
iseases,
WebMD
diagnosis)
Pa-ent
groups
Pharma
Disease
portals
Seek
treatment
(which
doctor,
sites
Hospital
and
clinic
which
hospital)
Health
systems
sites
Review
sites
Seek
advice
(how
to
live
with,
family
Facebook??
issues,
psychological
Pa-ent
support)
Forums
Groups

5. Patient-to-patient support: A gap in
online information!
• There are no reference websites
worldwide!
• Facebook is not a great alternative due to
privacy concerns!
• Missing vertical social network… but:
confidentiality ≠ virality?!

6. Perceived risks (by patients)!
• Privacy!
– Employers and colleagues!
– Insurers, Banks!
– Friends, family and neighbours!
– Exposing their children!
• Fake doctors!
• Bad advice!
• Business people making money out of them!

7. Caveat: what do we mean when we
say “ online patients”?!

8. N x N population!
• There is no “patients”: thousands of
diseases and multiple stages by disease.
People are different!
– A Parkinson’s disease caregiver has little in
common with a schizophrenia caregiver!
– An early stage diabetes patient has little in
common with an insulin-dependent diabetes
patient!

9. No global village!
• There is no “global village” for most patients!
– NHS is in England. Germany uses another word
and another system. Language, processes,
patient experiences are different!
– Specialist referral paths are different by country!
– Drugs have different names…!
– … and sometimes different indications!!
– Clinical practice is often very different!
– Language is a barrier even inside the same
country!

10. Disease type and stage strongly
influence online behaviour!
Acute disease! Searching diagnosis and treatment, occasional!
Chronic degenerative disease! Continuous online presence!
Accident/Impairment! Event-based online presence, sometimes continuous!

11. Disease stage and patient
concerns/information needs!
Pre-care: Prevention/
Wellness!
§ Wellness
solutions!
§ Selfdiagnosis /
disease
information!
Point-of care:
Diagnosis/
Consultation/
Procedure!
§ Provider
search /
matching!
§ Telemedicine!
§ eDiagnosis!
§ Remote care /
patient-doctor
link!
Post-care:
Medication!
Post-care:
Management/
Optimization!
§ Rx fulfillment! § Comprehensiv
§ Adherence!
e disease
§ Vigilance!
mgmt!
§ Follow-up /
§ Treatment
Monitor!
optimization!
§ Alerts!
§ Follow-up /
Monitor!
Further reading: IMS, Pa-ent
Apps
for
Improved
Healthcare,
October
2013
http://ow.ly/tY0Xe and Swan, Emerging PatientDriven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and
Quantified Self-Tracking, Int J Environ Res Public Health. 2009 February; 6(2): 492–525. http://ow.ly/tYhKG !

12. Online behaviour depends on the patient
type (attitudinal segmentation is needed)!
Self-­‐managers
• Core
quan-fied
self-­‐users
• App
users
• Data-­‐tracking
pioneers
• Con-nuous
engagement
Find-­‐me-­‐a-­‐
solu-on
• Hospital
/
Doctor
seeking
• Rx
advice
seeking
• Temporary
engagement
Give-­‐me-­‐the-­‐
good-­‐news
• Occasional
ac-vity
• Low
engagement
Source: Vitanect research, 2013. For an alternative view on online patient segmentation, see mHealth
in an mWorld - How mobile technology is transforming health care, Deloitte 2012 (link - page 6).
“Segment ‘Online and Onboard’ corresponds to % of total population)!

13. Empirical observation – online
patient behaviour in social networks!

14. Search behaviour: What are common
searches today?!
4%
2%
%
of
searches
16%
Disease
informa-on
Symptoms
Treatments
55%
23%
Source: Vitanect internal data, Jan-Feb 2014!
Medica-on
informa-on
Other

15. Motivations of patients seeking health
information online via social health networks!
Online survey, N=605 online social health site users. Cluster analysis:!
Cluster:
main
mo3va3on
User
characteris3cs
Acquiring
informa-on
and
support
over
age
55
years,
women,
those
with
lower
income,
chronic
pain,
obesity
and
depression
Communica-ng
men,
those
20–34
years
old,
those
with
less
educa-on,
or
an
ea-ng
disorder
Networking
mul-ple
sclerosis
or
depression
Browsing
mul-ple
sclerosis
Source: Magnezi R1, Grosberg D, Novikov I, Ziv A, Shani M, Freedman LS Characteristics of patients seeking health
information online via social health networks versus general Internet sites: a comparative study, Inform Health Soc
Care. 2014 Jan 29.!
http://informahealthcare.com/doi/abs/10.3109/17538157.2013.879147 !

16. Patient self-tracking: how much is taking
place?!
• 70% of doctors report that at least one patient is
sharing health-measurement data with them!
• Most common methods:!
– Hand writing the data!
– Printout of the information!
• ¾ of physicians agree that self-tracking leads to
better outcomes!
Source: Manhattan Research’s Taking the Pulse, US 2013, N=2950!

17. Are patients interested in self-tracking?!
• Depends. A majority is not – they are interested in living a
normal life. Patient segments:!
– Self-managers: they carefully follow their disease, medication,
progress. Interested in self-tracking. Minority!
– Help-me-if-you-can: they want solutions and seek them. But
diagnosis and treatment is the job of the health professionals.
Big group!
– Give me the good news: they intend to live normally and avoid
actions that reminds them they are sick. Disengage. Big group.!
• Test – which group do you belong?:!
– Do you read the results of your blood tests carefully or do you
hand it over to your doctor? !
– Do you forget taking your pills after 3 days?!
– A doctor is someone you visited once in your life, or less!
Further reading: See some additional data here by @susannahfox !

18. Empirical results: health-information seeking
behaviour and social networking use!
Online survey, N=1,745 online health information users. Results:!
Factor
Used
online
health
rankings/reviews
or
health
social
networks
Has
chronic
disease?
Twice
as
likely
[OR
2.09,
P<.001).
Formal
educa-on
Lower
odds
for
less
formal
educa-on
(OR
0.49,
P<.001)
Male
/
Female
Lower
odds
for
male
(OR
0.71,
P<.001)
Income
1.5x
as
likely
when
higher
incomes
(OR
1.49,
P=.05)
Age
Older
respondents
were
less
likely
to
use
SNS
(OR
0.96,
P<.001
Regular
health
care
provider
1.9x
odds
for
users
with
a
regular
provider
(OR
1.89,
P<.001)
Source: Thackeray R1, Crookston BT, West JH. Correlates of health-related social media use among adults. IJ Med
Internet Res. 2013 Jan 30 http://www.jmir.org/2013/1/e21/ !

19. Are online patient populations comparable
with populations in the clinical practice?!
• Comparison between patientslikeme MS
population (N=10,255) and MS center (N=4,039)!
– Online population is younger (45 vs 48) !
– Higher % of females (80% vs 75%)!
– Higher % of high education (26% completed college
vs 12%)!
– Good correlations between patient-reported MSRS
composite and physician-derived measures!
Source: Bove R, Secor E, Healy BC, Musallam A, Vaughan T, et al. (2013) Evaluation of an Online Platform for
Multiple Sclerosis Research: Patient Description, Validation of Severity Scale, and Exploration of BMI Effects on
Disease Course. PLoS ONE 8(3): e59707. doi:10.1371/journal.pone.0059707!

20. Emotion and data!
• Disease is emotional. It is not a game, a
contest, a cool new “space”: Emotional
message is important!
• The average patient is not a 25 year-old
hispter!
• Medicine and healthcare are often
scientific areas with precise language!

21. Some empirical evidence on emotion and
online behaviour – emotions matter!
Online survey, N=525 posts by 116 participants in a cancer social network. Results:!
Message
content
Result
Higher
word
count
More
likely
to
receive
a
reply
(OR
1.3
P=0.001)
Fewer
2nd
person
pronouns
(you,
your,
etc)
More
likely
to
receive
a
reply
(OR
0.92
P=0.04)
High
level
of
posi-ve
emo-on
Less
likely
to
receive
a
reply
(OR
0.94,
P=0.03)
Topics
with
higher
likelihood
Self-­‐disclosure
(p < 0.001)
of
a
reply
Medical
experiences
(p = 0.01)
Rela-onship
issues
(p = 0.05)
Introductory
posts
(p < 0.01).
Source: Lewallen AC1, Owen JE, Bantum EO, Stanton AL.. (2014) How language affects peer responsiveness in an
online cancer support group: implications for treatment design and facilitation.
http://www.ncbi.nlm.nih.gov/pubmed/24519856 !

22. Some empirical evidence on emotion and online
behaviour – emotions matter, and can have
detrimental impact for some patients!
Online survey, N=18,064 posts by 236 patients in a breast cancer social network. Results:!
Type
of
pa3ent
Impact
of
giving
and
receiving
emo3onal
support
in
CMSS
groups
With
higher
emo-onal
communica-on
competence
Posi-ve
effects
on
emo-onal
well-­‐being
With
lower
emo-onal
communica-on
competence
Detrimental
impacts
on
emo-onal
well-­‐being
Source: Yoo W1, Namkoong K, Choi M, Shah DV, Tsang S, Hong Y, Aguilar M, Gustafson DH.. (2014) Giving and
Receiving Emotional Support Online: Communication Competence as a Moderator of Psychosocial Benefits for
Women with Breast Cancer, omput Human Behav. 2014 Jan;30:13-22. http://www.ncbi.nlm.nih.gov/pubmed/24058261 !

23. Behaviour modification – are we close to affecting
outcomes with online/mobile applications?!
• Systematic review of 2,040 studies (2014) assessing the
current level of evidence regarding the effectiveness of
online social network health behaviour interventions.!
– 10 studies met inclusion criteria!
– 9 out of 10 reported significant improvements in some
aspect of health behaviour change!
– Effect sizes for behaviour change in general were
small in magnitude and statistically non-significant!
Source: Maher CA1, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. (2014) Are Health Behavior
Change Interventions That Use Online Social Networks Effective? A Systematic Review. J Med Internet Res. 2014
Feb 14;16(2):e40.!

24. Takeaways!
• Don’t treat patients as a single entity – there is wide variability!
– Geography!
– Disease, disease type and stage!
– Patient behavioural segment!
• Takeaways on current behaviour!
–
–
–
–
Information and support seeking is main motivation. Gap is real.!
Women more than men, younger, better educated!
Self-tracking is still done by a minority!
Online populations are comparable to offline, slight bias to
young, more educated!
– Emotional content matters, but impact differs by user!
– Early stage, little evidence on behaviour modification claims!

25. Questions?!
contact@vitanect.com!
!
About Vitanect!
!
Vitanect is an online social network for patients and
caregivers, focusing on healthcare research!
!
Visit us at www.vitanect.com !