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Sensor data in Patient Health: Lessons Learned

October 15, 2014

Geoff Low Lead Systems Architect

© 2014 Medidata Solutions, Inc.

Agenda

§  What is Quantified Self?

§  What is mHealth?

§  How can mHealth add value to Clinical Studies?

§  What are issues we need to consider?

§  Medidata MOVE 2014 – Medidata’s first mHealth study

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Medidata – Transforming clinical research

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Quantified Self

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The quantified self is a movement first named in 2006: “A collaboration of users and tool makers who share an interest in self knowledge through self-tracking." Extensive network of people collecting and sharing their data to get a better understanding of their disease.

This is the quantified self!

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What does a ‘self-quantifier’ capture? Any data that can be gathered and is aggregated together and can be linked to diary data The best way to get value is to look at the data longitudinally à context matters! Some data needs to be processed to give usable measures: •  Steps per day •  Average Blood Pressure •  Maximum Heart Rate

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•  Food consumed

•  Steps taken

•  Blood Pressure

•  Pulse

•  Mental State

•  Sleep

•  Exercise

•  …. many more metrics

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Websites •  patientslikeme.com •  BACtrack.com Devices •  Fitbit •  Garmin •  Withings Mobile Phones •  HealthKit by Apple •  Google Fit

How does a ‘self-quantifier’ capture data?

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Behaviour change à Better Health Outcomes Finding other individuals looking for same data à Shared experiences Managing ones condition à Patient networks Quantifying & rewarding good behaviour à Lower Insurance levies

ENGAGING INDIVIDUALS IN THEIR OWN HEALTH

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Why does a ‘self-quantifier’ capture data?

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Mobile Health (mHealth)

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Defined as “practice of medicine and public health supported by mobile devices”. Becoming more ubiquitous with increasing coverage of mobile technologies. Popular in areas where geographic location does not suit traditional approaches to medical oversight

mHealth applications •  Education/Awareness •  Helpline •  Diagnostic/Treatment support •  Communication/Training •  Disease/Epidemic outbreak •  Remote monitoring •  Remote data collection

What is mHealth?

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mHealth in Clinical Studies

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What is the value of mHealth?

Better data Improved Patient experience More efficient trials

Subject engagement Patient networks Remote access Disease Vigilance Patient recruitment ….

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World of data / informatics World of life sciences

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Patient identifying information (PII) Validation of algorithms Volumes of data Privacy

mHealth in clinical trials: issues to consider

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Medidata MOVE 2014

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Medidata’s mHEALTH study: MOVE 2014

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Look at the impact of Medidata Patient Cloud® and Fitbit® for Type II Diabetics Track •  Activity and Sleep using Fitbit •  Mental status using Patient Cloud®

Support •  Motivational Messages Evaluate •  HbA1c change from baseline

MOVE 2104 Study Design

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•  Designed app as interface between Fitbit® & Medidata Clinical Cloud®

•  Prioritised patient experience

•  Optimised onboarding •  Permission for subject’s data

•  Transfer data

•  Reconciliation

How we did it!

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•  Platform requirements

•  Issues with compliance

•  Issues with transfer scheduling

•  Familiarity with the software

•  Device ‘niggles’

Challenges!

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Conclusions

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Conclusions

§  Mobile technology well-suited to supporting & enhancing clinical trials

§  Mobile health empowers life science industry to: §  Expand the amount of data collected

§  Reach more study subjects – i.e. in emerging markets

§  Medidata MOVE 2014 study demonstrated Fitbit® data can be successfully integrated: §  Leveraged deep understanding of clinical trial

process and applied to mobile technology

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Thank You, any Questions?

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mHealth Data flows

Data collection •  Relay •  Portal/Direct •  Data processing •  Delivery to EDC •  Reconciliation

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