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mHealth: A paradigm shift in clinical research
Diego Menchaca, CEO
teamscope.co
Source: Flickr/Robert Guerra
Early clinical research data management
Electronic Data Capture (EDC) Systems
Was replaced with
CRFweb
Then came the cloud
crfweb.com
Automatic data validation
Monitor progress per patient
Quicker access to data
Study compliance: EU, FDA, GCP, etc.
Benefits
of EDC
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
Although the benefits of EDC are evident,
developing countries have been to slow adopt it
1. Not easy to use
Source : karuna-shechen.org
2. Infrastructure can be limited
Source: Dimagi
3. On-site training and support is needed
If only we could eradicate pen & paper
clinical research in developing countries
teamscope.co
teamscope.co
1. Easy to use
2. Works offline
3. GCP compliant
Wajir, Kenya
Source: Capacities for Health
Source: Capacities for Health
Source: Capacities for Health
Bosaso, Somalia
Source: Capacities for Health
Source: Capacities for Health
Source: Capacities for Health
Concepts to understand the EDC
transition in developing countries3
Empowerment starts with usability
1
Do them a favor, don’t train them…
Apps have a shorter learning curve than software
Rural healthcare is not static
2
last but not least
3
Scalability
Examples of mobile scalability
in clinical research
mPower Parkinson Study
Participants in one week
8.000
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
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
$30 USD
Cost of devices is drastically decreasing
+220mAfrica
+130mIndia
Estimated smartphone users by 2020
We think that its restrictions and limitations that we face
in low-resource settings what fuels our creativity.
Muchas gracias :-)
dmenchaca15
Diego Menchaca, CEO
teamscope.co

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160929 teamscope presentation molecule to business

  • 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
  • 12.
  • 14. teamscope.co 1. Easy to use 2. Works offline 3. GCP compliant
  • 23. Concepts to understand the EDC transition in developing countries3
  • 24. Empowerment starts with usability 1
  • 25.
  • 26.
  • 27. Do them a favor, don’t train them… Apps have a shorter learning curve than software
  • 28. Rural healthcare is not static 2
  • 29.
  • 30.
  • 31.
  • 32. last but not least
  • 33. 3
  • 34.
  • 35.
  • 37. Examples of mobile scalability in clinical research
  • 38.
  • 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
  • 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