1. Platform Technology for Neurodegenerative Diseases: Landscape and Roadmap development
Jude Bek
Computer Science/
Neuroscience & Experimental Psychology
University of Manchester

2. Centralising research initiatives in technology for ND
Objectives
Centralising research initiatives in technology for ND
- Across UoM faculties and schools/divisions
Strengthening funding bids & external collaborations
Working with
Support from Manchester Informatics

3. Connecting technological capability and clinical need
Diagnosis
Monitoring
Treatment
Communication
Tech:
Objective/quantitative measurement
Continuous/frequent data collection
Heterogeneous data
Algorithms/models
Solutions:
Biomarker identification
Profiling/stratification
Individualised treatment/Precision medicine
Cost-effective interventions
Living well

4. Technological solutions for neurodegeneration
Clinical assessments are typically:
Subjective – clinician/patient/carer
Categorical
Temporally constrained – clinic visits
Unrepresentative – stress/ecological validity
Insensitive to subtle change
Outdated?
Going beyond traditional clinical measures:
Scope/complexity of behaviours
Sensitivity to fluctuation/change

5. Technological solutions for neurodegeneration
Capitalising on increasing tech use in
older adults
Provision of detailed data – tailored for
clinician/patient
Efficiency
Cost effectiveness – increasingly stretched healthcare resources
Convenience
Psychological burden –confrontation with symptoms/disease progression (test 'failure'; reflecting on difficulties)

6. Clinical snapshots: motor and non-motor symptoms

7. Clinical snapshots: mood

8. Mapping the landscape Developing a roadmap Methods
Catalogue current research in ND
Identify synergies; cross-cutting themes
Developing a roadmap
Sharing of data and resources
Opportunities for collaboration
New tools and new applications
Ongoing communication/consultation

9. White paper: landscape/roadmap Integrated research community
Outputs
White paper: landscape/roadmap
Integrated research community
Web resource
Newsletter
Seminars
Workshops
…continued partnerships

10. CeHRes Roadmap

11. Landscape development
Surveying and cataloguing existing or potential applications for ND across schools
Identifying:
Themes
Synergies/overlaps
Gaps
Opportunities
Challenges

12. Landscape development
Bringing together:
Technologists with health researchers
Technologists with technologists
Technologists with clinicians
Identifying contributors:
9 schools across FSE
FBMH

13. Landscape development
Reaching out to technologists across FSE
Contacting schools
‘Pop-up’ information sessions
Follow-up (drop-in) sessions
Information gathering
Workshops:
Health + Tech: 31 Jan -1 Feb, Shrigley Hall Hotel
Tech + Tech: 7-8 March, MOSI
All – Roadmap development: TBC
Interviews

14. Landscape development
Pop-up sessions
February 2017
In-school advertising
Presentations, Q&A
Informal follow-up
Identification of school representatives

15. Landscape development
(1) Current projects in ND
Background /objectives
Partners/collaborators
Development/methods
Data
Results/interpretation
Next steps
(2) Potential projects
Re-purposing
Technologies in development
Potential collaborators?
Input needed?
Interview topics
Challenges
Technical/resources
Ethics/regulatory
Recruitment/compliance
Interpretation
Validation

16. Connecting technological capability and clinical need
Diagnosis
Monitoring
Treatment
Communication
Tech:
Objective/quantitative measurement
Continuous/frequent data collection
Heterogeneous data
Algorithms/models
Solutions:
Biomarker identification
Profiling/stratification
Individualised treatment/Precision medicine
Cost-effective interventions
Living well

17. Monitoring in Parkinson’s disease and dementia
Project examples
Monitoring in Parkinson’s disease and dementia
Collaborative projects (FSE/FBMH):
Symptom Knowledge in Parkinson’s (SKIP)
PI: Ellen Poliakoff (Manchester)
Objective: monitor symptoms across multiple domains in Parkinson’s
Software Architecture for Mental Health Self-Management (SAMS)
PI: Peter Sawyer (Lancaster)
Objective: detect early signs of cognitive decline

18. Examples: Parkinson’s disease and Dementia
Constructs measured
Cognition, speech, social, motor
Cognition (memory, planning…); functional behaviour
Feasibility/
Proof of concept
Directed tasks (laptop) x 30 days – detection of fluctuations
Video diary
Focus group
Semi-directed tasks (home PC) –cross-sectional; group differentiation (MCI/mild AD vs control)
Expert consultation
Continuous monitoring
-passive data collection
-proxy measures
-validation against clinical
Smartphone data: physical activity, communications, location, environment, web/ interactions
-e.g. UPDRS
Daily home PC use: mouse/keyboard, operating system, web/ , text analysis
-e.g. Addenbrooke’s Cognitive Examination

19. Parkinson’s disease

20. Parkinson’s disease

21. Dementia SAMS participant Layer I Layer II Layer III Layer IV
Computer-use behaviours
Raw text
Data mining
Text mining
Raw data
Cognitive/ functional change?

22. Keyboard behaviour Dementia r = .598, n = 41, p < .001*

23. Synergies and Challenges
Proof of concept
-Constructs to measure
-Cross-sectional analysis
-User/expert consultation
Retention/ compliance
Feasibility
-Small-scale data collection
-Patterns/fluctuations
Identification of suitable proxies
Longitudinal data collection
-Larger sample size
-Change detection
Recruitment
Acceptability

24. Synergies and Challenges
Raw data
-GPS
-Web
-Sensor
-Bluetooth
-Keyboard/mouse
Inferring complex behaviours
Analysis
-Identifying metrics
-Patterns
-Variation
Correlating with clinical measures
Interpretation
-Disease progression
-Functional decline
-Individual profiles
Hardware/ software limitations
Combining data sources
Missing data
Large volumes of data

25. Unobtrusive monitoring – everyday activity
Overlaps and themes
Unobtrusive monitoring – everyday activity
Utilising off-the shelf devices/apps
Home PC use
Smartphones – GPS, bluetooth…
Heterogeneous data
Pattern identification and interpretation
Change detection
Validation/mapping to clinical measures

26. Future opportunities and challenges
Next steps:
SKIP – 12 month longitudinal study planned
SAMS – longitudinal data analysis under way
Opportunities:
Application to other conditions?
Combining methods - PC use and sensors?
– cognitive, physical and social domains
Challenges:
Continued collaboration
Implementation pathway

27. Importance of collaborative working
Appropriate/timely research questions
Combined expertise needed to analyse/interpret data
User/patient input needed
Feasibility
Acceptability
Consultation at every stage
E.g. SKIP focus group:
-patients valued passive monitoring
-level of feedback important

28. Get in touch…

29. Acknowledgements Sam Couth Julio Vega Gemma Stringer SAMS and SKIP study teams

30. Jude Bek Judith.bek@manchester.ac.uk
Questions?
Jude Bek