Implementing technology for clinical
assessment: opportunities and challenges
Gemma Stringer
Manchester Dementia Clinical Research Group
Division of Neuroscience and Experimental Psychology
Manchester Dementia Clinical Research Group
Clinical Research Portfolio
Finding the problems….
• Deep & Frequent
Phenotyping
• CYGNUS
• SENSE-Cog
• MODEM
• SAMS
• Disease modification
in prodromal
Alzheimer
• Mild-moderate AD
(MADE)
• Behaviour (SYMBADD)
Detection
and
monitoring
Phase llb/III
medication
trials
Public
engagement
Psychosocial
interventions
Interventions
• Dementia Matters
Toolkit (NOWGEN)
• Memory drop in events
(NIHR)
And treating them…
• SENSE-Cog Trial
• INVEST
• GREAT
• ATTILA
• iCST
Manchester Dementia Clinical Research Group
Clinical Research Portfolio
Finding the problems….
• Deep & Frequent
Phenotyping
• CYGNUS
• SENSE-Cog
• MODEM
• SAMS
• Disease modification
in prodromal
Alzheimer
• Mild-moderate AD
(MADE)
• Behaviour (SYMBADD)
Detection
and
monitoring
Phase llb/III
medication
trials
Public
engagement
Psychosocial
interventions
Interventions
• Dementia Matters
Toolkit (NOWGEN)
• Memory drop in events
(NIHR)
And treating them…
• SENSE-Cog Trial
• INVEST
• GREAT
• ATTILA
• iCST
Deep and Frequent Phenotyping Study
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Collaborators – UoM, Cambridge, Oxford, Edinburgh, Newcastle, KCL
Funding: NIHR and MRC
Dementias Platform UK
250 participants (some at risk of developing AD) over 12 months
The tests will involve:
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Movement and walking (gait) assessments using wearable devices
Ophthalmological assessments
Magnetic Resonance Imaging (MRI)
Magnetoencephalography (MEG) and electroencephalogram (EEG)
Positron emission tomography (PET)
Thinking and memory (cognitive) assessments
Clinical assessments including blood and urine samples and cerebrospinal
fluid.
• Development of a multi-modal marker set for measurement of change and
its prevention or modification in AD.
http://www.dementiasplatform.uk/the-deep-and-frequent-phenotyping-study/
• Multidisciplinary collaborative project
• Funding: EPSRC
• Objective: To investigate the potential of measuring computer
use as a marker of change in cognitive and functional ability
Which computer-use
behaviours are
indicative of
cognitive decline?
Which cognitive
domains are
implicated?
Cross –
sectional
How do computer
use behaviours
relate to cognitive
and functional
ability?
Sample: MCI/mild
AD (n=19) and
healthy controls
(n=24)
Study 3
Expert
Reference
Group
Study 2
Study 1
Methodology
Longitudinal
Can the SAMS
software detect
subtle changes in
computer-use
behaviours over
time?
Does this relate to a
change in cognitive
functioning over
time?
Sample: 33 (SCI and
MCI)
Results
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A number of computer use behaviours (keystrokes, mouse clicks, pixel count etc) were shown to be different
between MCI/mild AD and HCs
Computer use behaviours were related to scores on cognitive tests and measures of functional capacity
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Graph 1: Relationship between ACE
total and total duration
Graph 2: Relationship between ECog
memory scores and keystrokes
70
Keystrokes (per min)
Total duration (mins)
50.00
40.00
30.00
20.00
10.00
60
50
40
30
20
10
0
0.00
70
80
90
ACE score
100
1
2
3
ECog Memory
4
NB. Hierarchical regression models showed that participants age and number of years of computer use could not
account for these effects.
Results
– e.g. ‘Text’ keystrokes per
min 91% correct
classification of cognitive
impairment (90%
sensitivity and 78%
specificity)
0.8
0.6
Sensitivity
• Combined computer-use
behaviours (pauses,
keystrokes, mouse clicks)
predict functional
abilities
• High sensitivity and
specificity for cognitive
impairment
1.0
0.4
Pauses/min
0.2
'Text' keystrokes/min
Total mouse clicks
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1 - Specificity
ROC analysis for pauses (per min), ‘Text’ keystrokes (per min) and total mouse clicks.
Next steps for SAMS
• Complete the longitudinal data analysis
• Apply for further funding to develop the
software and validate in a large sample
• Opportunities:
– Implement SAMS in other clinical trials
• Henry Brodaty - Centre for Healthy Brain Aging, Sydney
• Deep and Frequent Phenotyping
Challenges (Valley of Death)
• Ensuring that the academic aspirations of all collaborative
teams are translated into the research objectives
• Clinical motivation = positive outcomes for patients
• How to develop a proof of concept software for routine
clinical practice/the home
• Finding funding for implementation
• Bridging the gap between proof of concept and
implementation with patient group
• Finding an industry partner to develop a proof of concept
Possible solutions
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Create solutions to problems that are patient-led rather than tech-led
Think about the next steps (next grant) at the earliest stage
Consider the academic priorities of all collaborators and integrate this into the
objectives
Design software that can be developed after proof of concept
Involve industrial partners who want an active role in proof of concept studies
Maintain communication between collaborators to keep momentum after the end
of the project
Utilise the CeHRes Roadmap (or new NeuroD roadmap) when designing the study
CeHRes Roadmap
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Contextual Inquiry: understanding prospective users and their context, analyze strong and weak
points of current provision of care.
Value Specification: determine which values the different stakeholders deem important and
translate into user requirements.
Design: develop prototype technology based on requirements, cooperative design with prospective
users and stakeholders.
Operationalization: launch technology, marketing plans, organizational working procedures.
Summative Evaluation: evaluation: how is it being used? what is its effect on patients and
healthcare?
http://www.ehealthresearchcenter.org/wiki/index.php/Main_Page