Marco J Haenssgen mHealth: Global Projects, Software & Critical Perspectives Icd4 London Meetup GSMA HQ, London, UK Tuesday 26th January 2016 For more info: http://www.meetup.com/London-ICT4D/events/227274734/

1. Before mHealth
How People’s Health-Related Mobile
Phone Use in Rural India and China
Challenges Common Assumptions in
Phone-Based Health Interventions
Dr Marco J Haenssgen
University of Oxford
Saïd Business School
Nuffield Department of Medicine
26 January 2016, GSMA
ICT4D Meetup – mHealth: Global Projects, Software & Critical Perspectives

2. 1. The Starting Point
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 2
Images under creative commons licence. Wikipedia (2016), user Adleos; Public Domain Pictures.net (2016), users George Hodan,
Karen Arnold; Simmer (2016); Stichting MedicalFacts (2012).

3. 1. The Starting Point
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 3
Images under creative commons licence. Wikipedia (2016), user Adleos; Public Domain Pictures.net (2016), users George Hodan,
Karen Arnold; Simmer (2016); Stichting MedicalFacts (2012).

4. 1. The Starting Point
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 4
Images under creative commons licence. Wikipedia (2016), user Adleos; Public Domain Pictures.net (2016), users George Hodan,
Karen Arnold; Simmer (2016); Stichting MedicalFacts (2012).

5. 2. Global Trends
Mobile Phone Subscriptions
Rapid diffusion of mobile phone subscriptions over the past decade.
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 5
-
100
200
300
400
500
600
700
2008 2009 2010 2011 2012 2013 2014*
MillionsofSubscriptions
Year-on-Year Subscription Increase in Developing Countries
Mobile Broadband Subscriptions Mobile Cellular Subscriptions
ITU (2015); * Estimate

6. 2. Global Trends
Mobile-Phone-Based Health Service Delivery (mHealth)
mHealth solutions follow the trend of increasing phone access.
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 6
Rank 2: South Africa – 81 Projects
Rank 3: India – 59 Projects
Rank 4: Kenya – 50 Projects
GSMA (2015) as of May 20, 2015; Apple Inc. (2016) as of Jan 5, 2016; research2guidance (2014)

7. 3. Mobile Phones and Health
Problematic Assumptions
Mobile phones are treated as…
Given
(everyone’s got access)
Static
(they do what they are expected to do)
Neutral
(people use them, but without healthcare implications)
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 7

8. 3. Mobile Phones and Health
Research Project
Exploring the linkages between mobile phone
diffusion and healthcare access.
Theory development
Qualitatively grounded, theoretical framework
Conceptualisation of mobile phone adoption
New methods
Measuring phone utilisation
Capturing sequential healthcare seeking
Satellite-aided survey sampling
Empirical research
Predictors of phone-aided health action
Impacts of phone use on health behaviour
Equity implications of health-related phone use
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 8

9. 3. Mobile Phones and Health
Hypotheses
We assume that mobile phones do more good than harm.
Mobile phone use during an illness leads to…
a) increased access to healthcare
b) more efficient healthcare-seeking processes
c) more desirable healthcare-seeking behaviour
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 9

10. 3. Mobile Phones and Health
Field Sites and Data
Qualitative (231 participants) and survey data (800 participants) was
gathered from two rural study sites.
Purposive, maximum-variance qualitative sample
Representative 3-stage systematic cluster random survey sampling
Single- and multilevel logistic, Poisson, and negative binomial regression
models with cluster-robust standard errors
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 10
Udaipur District
Rajsamand District
Jaipur (Capital)
Lanzhou District
Baiyin District
Lanzhou (Capital)
Dingxi District
0.74 subscriptions
per 100 people
Persistent health-care
challenges
3-tier public health
systems
Wikimedia Commons (2013), map copyright by Joowwww (Rajasthan) and TUBS (Gansu)

11. 4. Effects of Phone-Aided Health Action
Analysis
Mobile phone use is linked to more access to healthcare.
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 11
Positive changes correspond to increased access; holding 28 covariates constant at sample means.
-100%
-75%
-50%
-25%
0%
25%
50%
75%
100%
Formal Providers Public Providers Private Providers No Access to
Formal / Informal
ChangeinAccessDuringIllness
Predicted Relative Change in Healthcare Access for Phone Users
Rajasthan Gansu

12. 4. Effects of Phone-Aided Health Action
Analysis
Healthcare-seeking with phones is more complex than without.
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 12
Negative changes correspond to increased process efficiency; holding 28 covariates constant at sample means.
-50%
-25%
0%
25%
50%
75%
100%
125%
Formal Providers Public Providers Private Providers Informal Providers,
Family, Friends
ChangeinProcessComplexity
less|morecomplexity
Predicted Absolute Change in Complexity and Delays of Access
Rajasthan - Steps Rajasthan - Days Gansu - Steps Gansu - Days

13. 4. Effects of Phone-Aided Health Action
Analysis
Phone-aided behaviour is less desirable, due to system over-/ misuse.
Predicted Relative Change in Desirable Health Action
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 13
a1) Rajasthan a2) Gansu
Care Formal Formal Public Public Public Formal Formal Public Public Public
Penalty None None Non-Publ. Private Private None None Non-Publ. Private Private
Referral None Mild Illn. None None Mild Illn. None Mild Illn. None None Mild Illn.
Inclusion
Definition
1 – 50.7% – 58.6% – 56.4% – 51.8% – 61.5% – 47.2% – 53.9% – 49.0% – 44.7% – 53.0%
2 – 47.8% – 58.2% – 53.0% – 48.6% – 61.0% – 50.0% – 55.0% – 49.5% – 45.4% – 52.6%
3 – 14.7% + 17.3% – 39.2% – 35.3% – 56.8% – 25.0% – 41.6% – 36.9% – 33.4% – 49.1%
4 – 30.9% – 74.3% – 75.2% + 19.5% + 7.9% + 11.2%
Positive changes correspond to improved commensurability between people’s actions and their symptoms, i.e. more desirable behaviour;
holding 28 covariates constant at sample means.

14. 4. Effects of Phone-Aided Health Action
Summary
More access to healthcare
More complexity in healthcare seeking
More delays until healthcare access
Less desirable (“commensurate”) behaviour
Adverse equity implications for users and non-users
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 14

15. 5. Conclusion
Problematic outcomes of rapid phone diffusion.
Potential drivers
Behavioural disinhibition
Expanding activity spaces
Navigation of fragmented health systems
Consequences
Adverse health and financial consequences
Crowding out of most vulnerable non-users
Health system adaptation to increasing phone use
mHealth implications
Targeting – systematic exclusion from access
User acceptance – competing solutions
Sustainability – non-neutral platforms
Supply-sided policy responses?
26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 15

16. 26 January 2016Before mHealth: Effects of
Health-Related Phone Use Page 16
Thank you.
Questions?
marco.haenssgen@ndm.ox.ac.uk