chris hollis - big data in mental health - 23rd july 2014 - 2

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MindTech: who’s who 1

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Organised by the Bioinformatics group at the BRCMH, IoP, SLaM and Maudsley Digital, this symposium showcased talks regarding the important roles of big data in mental health biomedical research and treatments.

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Page 1: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

MindTech: who’s who

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Page 2: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

What we can offer ?

• Clinical & User Perspective– knowledge and expertise in mental health and dementia– access to patients & PPI– MindTech patient reference group

• Research – communicating user and clinical needs into design briefs– collaboration & consultation with SMEs, academics, NHS Trusts– protocol development for technology trials, early stage health economics– expertise in automated facial analysis, patient ambient monitoring (PAM), app development,

serious games, implementation science and human factors

• Governance & funding advice– Intellectual property (IP) and regulatory issues– NICE & MHRA assessment procedures – Knowledge of healthtech funding streams

• NHS Policy & Implementation– Access to AHSNs, NHS England

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Page 3: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Research Approach

Identify the clinical problem/ unmet need

Develop/ identify a technological solution

Evaluate clinical/cost effectiveness

Adopt and disseminate in NHS

Page 4: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Research Approach

Identify the clinical problem/ unmet need

Develop/ identify a technological solution

Evaluate clinical/cost effectiveness

Adopt and disseminate in NHS

User perspectives

Page 5: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2
Page 6: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Academics

Technology Innovation Pipeline

ImplementationDevelopmentIdentifying Need

QbTest

BuddyApp

Patients, Clinicians, NHS Trusts

SMEs

Facial affect recognition

Personalised ambient monitoring

&

HTA/ i4i CLAHRC & AHSN

Page 7: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

MindTech

Automated objective

behavioural analysis

Big data & machine learning

E-mental health

Apps

On-line therapy

Personalised ambient

monitoring (PAM)

Social media & networks

Neuromodulation Serious Games

Facial expression, voice & eye gaze

Real-time behavioural signatures

Movement, activity & cognition

Avatar therapy

Neurofeedback & gaze control

Virtual reality

Apps, SMS text messaging

rTMS, tDCS, VNS

machine learning (SVML) for

neuroimaging

Page 8: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Automated objective

behavioural analysis

Big data & machine learning

E-mental health

Apps

On-line therapy

Personalised ambient

monitoring (PAM)

Neuromodulation Serious Games

Activity, mood & cognition

Avatar therapy

Virtual reality

Apps, SMS text messaging

rTMS, tDCS, VNS

The Mental Health Technology Innovation Landscape

• Widening access• Increasing adherence• Self-management

• Objective assessment• Real-time monitoring• Early relapse

detection

Page 9: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

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Clinical problem/ unmet need

Potential technology solution Target conditions

Poor engagement, treatment adherence/ missed appointments

• Apps• SMS text messaging • remote video-consultation• On-line therapy

• All conditions

Time consuming, delayed & inaccurate assessment, diagnosis and prediction

• Objective computerised assessment of mood, attention/cognition & movement

• Machine learning/ data mining (CRIS)

• ADHD, ASD• Depression• Dementia

Relapse preventionTreatment optimisationMaintaining independence

• Real-time personalised ambient monitoring (PAM)

• Bipolar disorder• ADHD• Dementia

Limited efficacy of non-pharmacological interventions

• Serious Games: computerised cognitive and social communication training

• Virtual reality • Avatar therapy• Neuromodulation

• PTSD, phobia• ADHD, ASD• Depression, psychosis

Page 10: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

2 key questions:• Do people use the

tools?• Do they work i.e.

support young people’s mental health?

Contact: [email protected]

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QbTest: Objective Assessment of ADHD

• Computerised assessment of attention and activity

• Supports clinical decision making• Provides patients with objective

reports on their condition

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AQUA Trial Design

Assessment for ADHD

Consent

Randomise

Qb Test Qb Test

QbO: Report disclosed

QbB: Report withheld

Clinical management decisions recorded: – diagnosis & treatment

Baseline

QbTest disclosed6 months

Assessment of QbTest Utility in ADHD

Page 13: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Automated objective behavioural analysis:mood and depression

Valstar et al. (technology theme)

Page 14: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Personalised Ambient Monitoring (PAM)

GPS module

XYZaccelerometer

Internal accelerometer

GSM location

User input:• General health

questionnaires • Mood self-

assessment

Wearable Node•Acceleration•General light level•Artificial light level•Ambient sound properties

BluetoothEncounters*

- Bluetooth - 3G / GPRS - User input -Internal

Page 15: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

CASAConnecting Assistive Solutions to Aspirations

• 9 month partnership project supported by government grant (Technology Strategy Board)

• aims:– create independent living packages for 2 groups

1. older people inc. with dementia

2. younger people with learning disability– link to a person’s aspirations – what they like doing

or want to achieve in life– conduct test of example packages– develop business model for packages

New Mind EPSRC Network for MH Technologies,

Manchester

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Remote / Video TherapyApplicable to multiple conditions:• Depression• Anxiety• Self-harm• Tourette’s• Eating Disorders, etc

• On-line therapies via text, voice, video• Real-time and asynchronous• 24/7 access• Evidence-based content/ CBT therapy

• High reported user satisfaction & improvement• But few independent evaluations……• Research evidence for cost-effectiveness still needed• Potential platform for NHS clinicians

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The Digital Health Explosion

• 2010: 5,000 health apps available• 2013: Global m-Health market worth £2bn• 2014: 97,000 mobile health apps in 62 app stores• 2017: Global m-Health market worth £20bn

• Where’s the research evidence?Safety?Efficacy?

Page 18: Chris Hollis - Big Data in Mental Health - 23rd July 2014 - 2

Key Challenges

• Earlier user involvement in design and development• Grow the research evidence-base for user acceptability,

improved mental health outcomes and cost effectiveness• Regulation, industry standards and quality control for

mHealth apps • Scalability and implementation: from local to national• Privacy, trust, consent and data security• Interoperability – connectivity of apps and m-health with

NHS N3, EPR, clinician decision support systems• Collaboration across the NHS, industry and academia

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Mind The Gap – How can evidence keep pace with new technologies in mental healthcare?

MindTech 2014 National Symposium

Date: Monday 24th November 2014Venue: The Royal College of Physicians,

London

SAVE THE DATE!• Avatar therapy for voices: From a clinical trial to routine practice• Big White Wall and PsychologyOnline: The ‘big’ challenges for eTherapy evaluation• ClinTouch: Mobile support for people with psychosis• Video-conferencing for psychological interventions: How can innovative practice inform

research?• Mental health apps: How do we know what works? • My Health Locker: Empowering patients with personal electronic health records • User-led evaluation: Why it matters

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MindTech Contacts:

DirectorProf Chris Hollis [email protected]

Technology LeadProf John [email protected]

Programme ManagerDr Jen [email protected]

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