Teamscope; mHealth, a paradigm shift in clinical reseach. Presentation by Diego Mechaca during 'From Molecule to Business' event by SMB Life Sciences and Health Valley at NovioTechCampus, Nijmegen, The Netherlands on September 29, 2016.

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

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. 1. Not easy to use

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

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

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

13. teamscope.co

14. teamscope.co
1. Easy to use
2. Works offline
3. GCP compliant

15. Wajir, Kenya

16. Source: Capacities for Health

17. Source: Capacities for Health

18. Source: Capacities for Health

19. Bosaso, Somalia

20. Source: Capacities for Health

21. Source: Capacities for Health

22. Source: Capacities for Health

23. Concepts to understand the EDC
transition in developing countries3

24. Empowerment starts with usability
1

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

28. Rural healthcare is not static
2

32. last but not least

33. 3

36. Scalability

37. Examples of mobile scalability
in clinical research

39. mPower Parkinson Study
Participants in one week
8.000

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

41. 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

42. $30 USD
Cost of devices is drastically decreasing

43. +220mAfrica
+130mIndia
Estimated smartphone users by 2020

44. We think that its restrictions and limitations that we face
in low-resource settings what fuels our creativity.

45. Muchas gracias :-)
dmenchaca15
Diego Menchaca, CEO
teamscope.co