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Panel: Biomedical and Healthcare Analytics
on Big Data
Self-Quantification Systems:
Big Data Prospects and Challenges

Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
&
Director, Health and Biomedical Informatics Centre (HABIC)
Why Self-Monitoring?

BIG DATA

Social
media

Quantified
Self

Participatory
health
Crowsourced
Clinical trials

Exposome
Participatory health

I.  Personal genome services (BYO)
II.  Personal diagnostic testing (BYO)
III.  Personal medical image management (DIY)
IV.  Personal sensing and monitoring (DIY)
V.  Personal health records (DIY)
VI.  Patient reading doctor’s notes
VII.  Patient initiating clinical trials
VIII.  Patient reporting outcomes
IX.  Patient accessing health information
X.  Shared decision making

Collecting
data

Participatory
health

Exchanging
information
Availability of new sensors for data collection
Exposome

Genome

Phenome

Environmental risk factors
(pollution, radiation, toxic agents, …)

Biomarkers (DNA sequence,
Epigenetics)

Anatomy, Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)

Social media / Integrated personal health record / Personal Health Systems
Exposome informatics (JAMIA Oct 2013)
Quantified Self: The concept
Quantified Self: The community
New market

Global annual wearable device
unit shipments crossing the 100
million milestone in 2014, and
reaching 300 million units five
years from now

Gartner hype cycle
Corporate health
plans – 13 Mill
The Quantified Self community

•  Quantified Self is a collaboration of users and tool
makers who share an interest in self knowledge through
self-tracking.

•  We exchange information about our personal projects,
the tools we use, tips we’ve gleaned, lessons we’ve
learned. We blog, meet face to face, and collaborate
online. There are three main “branches” to our work.
–  The Quantified Self blog and community site.
–  Show and Tell meetings (Meetup groups) - Melbourne
–  Quantified Self Conferences (US and Europe)

•  Groups 112, Members 17,893, Cities 89, Countries 31
Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms
The IBES SELF-OMICS Project

•  Addressing the information and communication needs of the
‘quantified individual’ for enabling participatory and
personalised medicine

•  Funded by IBES (Institute for a Broadband Enabled Society)
- 2012-2013

•  Resources:
http://www.broadband.unimelb.edu.au/health/monitoring/selfomics.html
http://www.scoop.it/t/selfomics
http://pinterest.com/hbir/self-omics-self-monitoring-quantified-self-omics/
Variety of self-monitoring devices, sensors and services

MoodPanda

Actipressure
Zeo Sleep Manager

Fitbit
23andMe

Sensaris Senspod

uBiome
iBGStar

12
13
QS Lab
White Paper

http://
www.broadband.unimelb.edu.au
Classification of self-quantification systems

•  Capture data directly from the user
(Primary or Secondary)
•  Sensor Location (Mobile or Fixed)
•  Involve skin pricking (In-contact or
On-body)
•  Data type (Environmental or
Touchless)
•  Location of data integration
(Software-based or Hardwarebased integration)
•  Location of data visualisation
16
(Standalone, etc.)
Data flow stages in Zeo Sleep Manager

Data Flow Stages
Data Collecting
Data Transmission
Data Saving (temporary
storage)
Data Storing (permanent
storage)
Data Analysis
Data Visualisation

Zeo Sleep Manager

Data Sharing

17
Second white paper – user guide
Second White Paper – Data integration methods

PCEHR
Individual analysis
Integrated
Analysis
Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms
Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms
Minimum Information about a
Self Monitoring Experiment (MISME)
EXPERIMENT

Who/Which part/
Where/When?

Body part
(FMA)

Sample

Device

How?
Name
Model
Manufacturer

Time

Technical
Specs

Location

Taxonomy

What

Measurement

Body structure
Body function
Around body
(based on WHO)

Method

Data

Raw

Where is
it stored
Procedures

Processed

Units
Self-Omics

•  QS as an interface to the Human
Body

•  How much information?
•  People-as-sensors
•  Making the personal public
•  From population surveillance to
individual surveillance

Infography:
Institute for Health Technology Transformation
Benefits

If 10% adults USA began a
regular walking program, an
estimated $5.6 Billion in heart
disease could be saved.
Self-monitoring

•  Project MUM-Size
–  Study of very obese pregnant women – risk of complications
due to anesthesia during labor
–  Using fitbit and social media support by research midwives in
the intervention group to prevent weight gain during
pregnancy
–  User guide (Aria scale not suitable for pregnant women, limit
of 140 Kgs of weight)
Convergence between personalised and
participatory medicine
Health Informatics aspects of QS

•  Integration of QS data with EHR/
• 
• 
• 
• 
• 
• 
• 

PHR
HIE of 1 - Blue Button and Blue
Button+
Meaningful use - V/D/T View/
Download/Transmit. Making sense
of data
Behaviour change
From QS early adopter to
mainstream motivated-self
Long-term or too-scary does not
work
Personalization
Designers or artists for data
presentation
Conclusion

Benefits

Challenges

•  Privacy
•  Security
•  Education
of their health
•  Cyberchondria
•  Self-improvement
•  Equity
•  Risk profiling
•  Regulation, accreditation
•  Prevention
•  Shift terciary à secondary •  Role of the clinician
•  Infrastructure needs
à primary à home care
•  Therapeutic gap (ethics)
•  Data donors for research
•  Motivation
•  Deepening understanding
References
• Almalki, M, Martin-Sanchez, F & Gray, K 2013, 'SelfQuantification: The Informatics of Personal Data Management
for Health and Fitness’, Institute for a Broadband-Enabled
Society (IBES).
• Almalki, M, Martin-Sanchez, F & Gray, K 2013. The Use of
Self-Quantification Systems: Big Data Prospects and
Challenges. Proceedings of HISA BIG DATA 2013 conference.
Accepted for publication at BMC Health Information Science
and Systems

29
UoM QS team

•  Dr. Kathleen Gray (HaBIC)
•  Manal Almalki (PhD candidate, HaBIC)
•  Pilar Cantero (RA - HaBIC)
•  Cecily Gibert (RA - HaBIC)
•  Dr. Bernd Ploderer (Computing and Information Systems)
•  Mark Whooley (MIS student)
•  Matthew McGavern (MIS student)
•  Prof. David Story (Chair of Anesthesia)
•  Prof. Mary Wlodek (Physiology)
Thank you for your attention!

© Copyright The University of Melbourne 2012

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Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms

  • 1. Panel: Biomedical and Healthcare Analytics on Big Data Self-Quantification Systems: Big Data Prospects and Challenges Fernando J. Martin-Sanchez Professor and Chair of Health Informatics Melbourne Medical School & Director, Health and Biomedical Informatics Centre (HABIC)
  • 3. Participatory health I.  Personal genome services (BYO) II.  Personal diagnostic testing (BYO) III.  Personal medical image management (DIY) IV.  Personal sensing and monitoring (DIY) V.  Personal health records (DIY) VI.  Patient reading doctor’s notes VII.  Patient initiating clinical trials VIII.  Patient reporting outcomes IX.  Patient accessing health information X.  Shared decision making Collecting data Participatory health Exchanging information
  • 4. Availability of new sensors for data collection Exposome Genome Phenome Environmental risk factors (pollution, radiation, toxic agents, …) Biomarkers (DNA sequence, Epigenetics) Anatomy, Physiological, biochemical parameters (cholesterol, temperature, glucose, heart rate…) Social media / Integrated personal health record / Personal Health Systems
  • 8. New market Global annual wearable device unit shipments crossing the 100 million milestone in 2014, and reaching 300 million units five years from now Gartner hype cycle Corporate health plans – 13 Mill
  • 9. The Quantified Self community •  Quantified Self is a collaboration of users and tool makers who share an interest in self knowledge through self-tracking. •  We exchange information about our personal projects, the tools we use, tips we’ve gleaned, lessons we’ve learned. We blog, meet face to face, and collaborate online. There are three main “branches” to our work. –  The Quantified Self blog and community site. –  Show and Tell meetings (Meetup groups) - Melbourne –  Quantified Self Conferences (US and Europe) •  Groups 112, Members 17,893, Cities 89, Countries 31
  • 11. The IBES SELF-OMICS Project •  Addressing the information and communication needs of the ‘quantified individual’ for enabling participatory and personalised medicine •  Funded by IBES (Institute for a Broadband Enabled Society) - 2012-2013 •  Resources: http://www.broadband.unimelb.edu.au/health/monitoring/selfomics.html http://www.scoop.it/t/selfomics http://pinterest.com/hbir/self-omics-self-monitoring-quantified-self-omics/
  • 12. Variety of self-monitoring devices, sensors and services MoodPanda Actipressure Zeo Sleep Manager Fitbit 23andMe Sensaris Senspod uBiome iBGStar 12
  • 13. 13
  • 16. Classification of self-quantification systems •  Capture data directly from the user (Primary or Secondary) •  Sensor Location (Mobile or Fixed) •  Involve skin pricking (In-contact or On-body) •  Data type (Environmental or Touchless) •  Location of data integration (Software-based or Hardwarebased integration) •  Location of data visualisation 16 (Standalone, etc.)
  • 17. Data flow stages in Zeo Sleep Manager Data Flow Stages Data Collecting Data Transmission Data Saving (temporary storage) Data Storing (permanent storage) Data Analysis Data Visualisation Zeo Sleep Manager Data Sharing 17
  • 18. Second white paper – user guide
  • 19. Second White Paper – Data integration methods PCEHR Individual analysis Integrated Analysis
  • 22. Minimum Information about a Self Monitoring Experiment (MISME) EXPERIMENT Who/Which part/ Where/When? Body part (FMA) Sample Device How? Name Model Manufacturer Time Technical Specs Location Taxonomy What Measurement Body structure Body function Around body (based on WHO) Method Data Raw Where is it stored Procedures Processed Units
  • 23. Self-Omics •  QS as an interface to the Human Body •  How much information? •  People-as-sensors •  Making the personal public •  From population surveillance to individual surveillance Infography: Institute for Health Technology Transformation
  • 24. Benefits If 10% adults USA began a regular walking program, an estimated $5.6 Billion in heart disease could be saved.
  • 25. Self-monitoring •  Project MUM-Size –  Study of very obese pregnant women – risk of complications due to anesthesia during labor –  Using fitbit and social media support by research midwives in the intervention group to prevent weight gain during pregnancy –  User guide (Aria scale not suitable for pregnant women, limit of 140 Kgs of weight)
  • 26. Convergence between personalised and participatory medicine
  • 27. Health Informatics aspects of QS •  Integration of QS data with EHR/ •  •  •  •  •  •  •  PHR HIE of 1 - Blue Button and Blue Button+ Meaningful use - V/D/T View/ Download/Transmit. Making sense of data Behaviour change From QS early adopter to mainstream motivated-self Long-term or too-scary does not work Personalization Designers or artists for data presentation
  • 28. Conclusion Benefits Challenges •  Privacy •  Security •  Education of their health •  Cyberchondria •  Self-improvement •  Equity •  Risk profiling •  Regulation, accreditation •  Prevention •  Shift terciary à secondary •  Role of the clinician •  Infrastructure needs à primary à home care •  Therapeutic gap (ethics) •  Data donors for research •  Motivation •  Deepening understanding
  • 29. References • Almalki, M, Martin-Sanchez, F & Gray, K 2013, 'SelfQuantification: The Informatics of Personal Data Management for Health and Fitness’, Institute for a Broadband-Enabled Society (IBES). • Almalki, M, Martin-Sanchez, F & Gray, K 2013. The Use of Self-Quantification Systems: Big Data Prospects and Challenges. Proceedings of HISA BIG DATA 2013 conference. Accepted for publication at BMC Health Information Science and Systems 29
  • 30. UoM QS team •  Dr. Kathleen Gray (HaBIC) •  Manal Almalki (PhD candidate, HaBIC) •  Pilar Cantero (RA - HaBIC) •  Cecily Gibert (RA - HaBIC) •  Dr. Bernd Ploderer (Computing and Information Systems) •  Mark Whooley (MIS student) •  Matthew McGavern (MIS student) •  Prof. David Story (Chair of Anesthesia) •  Prof. Mary Wlodek (Physiology)
  • 31. Thank you for your attention! © Copyright The University of Melbourne 2012