September 29, 2016 @ Novio Tech Campus. Nijmegen, the Netherlands.

1. mHealth: A paradigm shift in clinical research
Diego Menchaca, CEO
teamscope.co
September 29, 2016

2. Source: Flickr/Robert Guerra
Early clinical research data management

3. Electronic Data Capture (EDC) Systems
Was replaced with

4. CRFweb
Then came the cloud
crfweb.com

5. Automatic data validation
Monitor progress per patient
Quicker access to data
Study compliance: EU, FDA, GCP, etc.
Benefits
of EDC

6. Automatic data validation
Monitor progress per patient
Quicker access to data
Study compliance: EU, FDA, GCP, etc.
Benefits
of EDC
“Reduces study cost by 5x
Cuts study time by 30%
“Comparison of Electronic Data Capture with Paper Data Collection – Is There Really an Advantage?” by Dr Thomas Bart
‘‘

7. Although the benefits of EDC are evident,
developing countries have been to slow adopt it

8. Although the benefits of EDC are evident,
developing countries have been to slow adopt it
?

9. 1. Not easy to use

10. Source : karuna-shechen.org
2. Infrastructure can be limited

11. Source: Dimagi
3. On-site training and support is needed

12. If only we could eradicate pen & paper
clinical research in developing countries

14. Available on

15. Available on
1. Easy to use
2. Works offline
3. GCP compliant

16. Wajir, Kenya

17. Source: Capacities for Health

18. Bosaso, Somalia

19. Source: Capacities for Health

20. Source: Capacities for Health

21. Source: Capacities for Health

22. Understanding the EDC transition
in developing countries
Three concepts

23. Empowerment starts with usability
1

26. Do them a favor, don’t train them…
Apps have a shorter learning curve than software

27. Rural healthcare is not static
2

31. last but not least

32. 3

35. Scalability!

36. Examples of mobile scalability in clinical research

37. ResearchKit

38. mPower Parkinson Study
Participants in one week
8.000

39. The mPower study, Parkinson
disease mobile data collected using
ResearchKit
Brian M. Bot1
, Christine Suver1
, Elias Chaibub Neto1
, Michael Kellen1
, Arno Klein1
,
Christopher Bare1
, Megan Doerr1
, Abhishek Pratap1
, John Wilbanks1
, E. Ray Dorsey2
,
Stephen H. Friend1
& Andrew D. Trister1
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these
measures generally are not designed to provide meaningful feedback to individuals. The impact of high-
resolution activity data collected from mobile phones is only beginning to be explored. Here we present
data from mPower, a clinical observational study about Parkinson disease conducted purely through an
iPhone app interface. The study interrogated aspects of this movement disorder through surveys and
frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from
large enrollment and repeated measurements on many individuals, these data may help establish baseline
variability of real-world activity measurement collected via mobile phones, and ultimately may lead to
quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection
modules are available through an open source license for use in studies of other conditions. We hope that
releasing data contributed by engaged research participants will seed a new community of analysts working
collaboratively on understanding mobile health data to advance human health.
Design Type(s) observation design • time series design • repeated measure design
OPEN
SUBJECT CATEGORIES
» Research data
» Neurology
» Parkinson’s disease
» Medical research
Received: 07 December 2015
Accepted: 02 February 2016
Published: 3 March 2016
www.nature.com/scientificdata

40. The mPower study, Parkinson
disease mobile data collected using
ResearchKit
Brian M. Bot1
, Christine Suver1
, Elias Chaibub Neto1
, Michael Kellen1
, Arno Klein1
,
Christopher Bare1
, Megan Doerr1
, Abhishek Pratap1
, John Wilbanks1
, E. Ray Dorsey2
,
Stephen H. Friend1
& Andrew D. Trister1
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these
measures generally are not designed to provide meaningful feedback to individuals. The impact of high-
resolution activity data collected from mobile phones is only beginning to be explored. Here we present
data from mPower, a clinical observational study about Parkinson disease conducted purely through an
iPhone app interface. The study interrogated aspects of this movement disorder through surveys and
frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from
large enrollment and repeated measurements on many individuals, these data may help establish baseline
variability of real-world activity measurement collected via mobile phones, and ultimately may lead to
quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection
modules are available through an open source license for use in studies of other conditions. We hope that
releasing data contributed by engaged research participants will seed a new community of analysts working
collaboratively on understanding mobile health data to advance human health.
Design Type(s) observation design • time series design • repeated measure design
OPEN
SUBJECT CATEGORIES
» Research data
» Neurology
» Parkinson’s disease
» Medical research
Received: 07 December 2015
Accepted: 02 February 2016
Published: 3 March 2016
www.nature.com/scientificdata
This is just the tip of the iceberg

41. $30 USD
Cost of devices is drastically decreasing

42. +220mAfrica
+130mIndia
Estimated smartphone users by 2020

43. We think that its the restrictions
that we face in low-resource settings
what drives our creativity.
When less is more

44. Muchas gracias
dmenchaca15
Diego Menchaca, CEO
teamscope.co