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

Exposome Participatory health

Social media

Quantified Self

Crowsourced Clinical trials

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

Exchanging information

Participatory health

Genome Exposome

Phenome

Biomarkers (DNA sequence, Epigenetics)

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

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

Social media / Integrated personal health record / Personal Health Systems

Availability of new sensors for data collection

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

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/

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Zeo Sleep Manager Fitbit

Actipressure

iBGStar

Sensaris Senspod uBiome

MoodPanda

23andMe

Variety of self-monitoring devices, sensors and services

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QS Lab

White Paper

http://www.broadband.unimelb.edu.au

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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 Hardware-based integration)

•  Location of data visualisation(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 Data Sharing

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Zeo Sleep Manager

Second white paper – user guide

Second White Paper – Data integration methods

PCEHR

Integrated Analysis

Individual analysis

Device Sample

Data

Where is it stored

Units

Location

Time

Body part (FMA)

Method

Name Model

Manufacturer

Technical Specs

Taxonomy

Body structure Body function Around body

(based on WHO)

Who/Which part/Where/When?

What

How?

Processed

Raw

Minimum Information about a Self Monitoring Experiment (MISME)

Procedures

EXPERIMENT

Measurement

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

Benefits •  Motivation •  Deepening understanding

of their health •  Self-improvement •  Risk profiling •  Prevention •  Shift terciary à secondary à primary à home care

•  Data donors for research

Challenges •  Privacy •  Security •  Education •  Cyberchondria •  Equity •  Regulation, accreditation •  Role of the clinician •  Infrastructure needs •  Therapeutic gap (ethics)

Conclusion

• Almalki, M, Martin-Sanchez, F & Gray, K 2013, 'Self-Quantification: 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

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References

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)

© Copyright The University of Melbourne 2012

Thank you for your attention!