John Ainsworth, a Research Fellow at The University of Manchester, and member of Manchester mHealth ecosystem introduces m-health and how it has been successful in monitoring mental health patients.

1. mHealth
John Ainsworth
john.ainsworth@manchester.ac.uk
HI@M 9th July 2012

2. The Global Challenge: Ageing population and
management of long term conditions
Globally over 1 billion adults
and 155 million children are
overweight
700 million people are 60 or
older
Citizens - overweight & obesity effects
both small and large nations
•Britain- 25% men & women
Dramatic increase in people developing
•USA- 30% men & women
Asthma, Chronic Obstructive Pulmonary •Tonga- 47% men, 70% women
Disease (COPD), Diabetes and Hypertension •Samoa- 33% men, 63% women
Source WHO
New Innovation will be needed to help manage the challenges facing organisations
operating in this sector

3. Need to shift the Continuum of Care
Shift Left
Highest Quality of Life
Lowest Cost of Care
Quality of Life
Health and Wellness
Home Care
Residential Care
Acute Care
Cost of Care
Reproduced with permission of Intel™

4. mHealth
• Computing power
• Large display
• Usable
• Short range
connectivity
• Always on
• Always connected
• Always with you
• Familiar

6. mHealth Now…
• Lots of pilots, very few progress further
• Barriers to be overcome
– deployment at scale – system not individual
studies
– large, diverse, ‘instrumented’ study population
– health economics assessment
– access and equity
– regulatory environment EU 2007/47/EC

8. mHealth Ecosystem
• Multi-sector partnership of critical mass
– shared commitment to accelerate adoption
• Innovation factory
– co-develop innovative whole-system solutions
• Route from pilots to routine practice
– co-developed pilot-to-adoption business
plans, evidence
• Reduced barriers to new trials

9. The Manchester mHealth eco-system
• Manchester
– Social, ethnic, health and lifestyle diversity
– Only UK city in WHO network of age-friendly cities
• University of Manchester
– World-leading multidisciplinary research in health, particularly e-
health, informatics, social sciences, business models
– mHealth Innovation Centre (MHIC) founded in 2009 in partnership
with the GSM Association
• Partnership with NHS Trusts:
– Acute, specialist and primary care
– NW Exemplar clinical trials network
53 day trials set-up (UK av = 98 days)
• Partnerships with industry

10. Who is involved with the Manchester mHealth eco-
system?
Serves a population of > 3 million; delivers services to > 2 million patients p.a.
(3,700 beds); 8 Hospitals plus primary, community and social care; clinical
research network; c. 23,500NHS staff
University Hospital of South Manchester NHS Foundation Trust
J&J (Janssen Healthcare Innovation) The Christie NHS Foundation Trust
The University of Manchester
Intel
Manchester
Greater Manchester
MHealth
Comprehensive Local NWeHealth
Eco-system
Research Network
Manchester Mental Health &
Social Care Trust
Salford Royal NHS Foundation Trust
Central Manchester University Hospitals NHS Foundation Trust (comprising
Manchester Royal Infirmary, Manchester Royal Eye Hospital, Royal Manchester
Children’s Hospital, Saint Mary’s Hospital and University Dental Hospital)

11. m-Health Innovation Centre
Research
• Mental health
– Diagnosis & compliance with treatment
– psychological therapy via mobile
• Metabolic Health & Wellbeing
– bridging the gap: short-term decisions vs. long-term
outcomes
• Remote Monitoring for Post-operative
rehabilitation
– after knee replacement, cardiac surgery
• Intelligent Clothing
– wearer as mobile biosignal website
• Evaluation of long-term telecare interventions

12. Example projects
• Metabolic Health and Wellbeing (obesity,
diabetes)
• Assisted Living (including ICT and ageing, falls
prevention, self-care and remote monitoring)
• Mental Health & Wellbeing
• Process Optimisation
• Mobile Workforce

13. A new mobile assessment technology for psychosis
Jasper Palmier-Claus, PhD
The University of Manchester
Email: Jasper.Palmier-Claus@manchester.ac.uk
Tel: 01613067923

14. Summary
• Background
• Technology
• Phase one
• Phase two

15. Background
• Schizophrenia is one of the most prevalent forms of mental
illness.
• Associated cost of 6.7 billion pounds each year.
• Clinical outcome often poor despite treatment with 80% of
individuals relapsing within 5 years after the first episode.
• Major need for new forms of intervention and symptom
management.

16. Momentary assessment
• Considerable evidence to suggest that patient self-report is valid.
• Momentary assessment common in research.
• Detailed view of individual’s symptoms in everyday settings.
• Different clinical populations.
– Anger
– Depression
– Pain
– Hyperactivity
– Psychosis

17. Why adapt for clinical use?
• Reduces need for averaging.
• Reduces retrospective recall bias.
• Contextual information.
• Temporal associations.
• Relapse-signatures.
• Treatment effects.
• Adjunct to psychosocial intervention.

18. Why use mobile phones?
 Widespread and familiar interface
• Monitor symptoms in real time.
Alert clinician:
Early intervention

19. The technology

20. Menus

21. Administrator page
• Administrator
configures participant
details on the device.
• Selected delusions
influence questions
presented to the user.

22. Question display
• User responds on a
touch-screen mobile
phone.
• Branching means that
the questions change
depending on an
individual’s responses.

23. Phase one

24. Aims
• To validate momentary assessment items against
corresponding gold standard interview scales.
• To ascertain levels of compliance and dropout in individuals
at different stages of psychosis (acute, remitted and ultra-
high risk).

25. Method
• Three groups:
– 12 acute patients.
– 12 remitted patients.
– 12 ultra-high risk individuals.
• Alerts 6 times per day for 1 week.
• PANSS and CDS performed before and after sampling
procedure by trained assessor.
• Telephone call during the week to encourage compliance.

26. Compliance
• Compliance = >33% of all possible entries.
• 44 individuals consented to take part.
• 8 individuals (6 acute, 2 remitted) failed to meet this
threshold and were excluded from later analysis (82%
compliance).
• Positive symptoms predicted non-compliance (OR = 0.68, p =
.033)

27. Summary statistics
Age, mean (SD) Males, n
40
35.5 (8.0) 12
35 33.8 (10.0)
10
10
30 9 9
25 8
22.0 (4.4)
20
6
15
4
10
5 2
0
0
Acute Remitted Ultra-high risk
Acute Remitted Ultra-high risk

28. Medication, n
12 12
12
10
8
7 Antipsychotics
6 Antidepressants
6
4
4
2
0
0
Acute Remitted Ultra-high risk

29. Living status, n
Acute Remitted Ultra-high risk
1
2 2 Alone
1 3
Ward
1
Family
6
Partner
Shared living
3
10
7 Supported living

30. Spearman’s correlations, rho
0.90
0.80*
0.80 0.74*
0.69* 0.68*
0.70
0.63*
0.60
0.53*
0.50 0.45* 0.44*
0.39*
0.40
0.30 0.26 0.25
0.20
0.10 0.06
-0.04
0.00
-0.10
*p<.05

31. Conclusions for phase one
• Mobile phone based momentary assessment is feasible in
individuals with different levels of psychosis.
• Positive symptom momentary assessment scales showed
strong correlations with the PANSS.
• PANSS subscales based on care coordinator reports and
behaviour during the interview showed more attenuated
correlations.

32. Phase two

33. Background
• Text messages may also effectively monitor psychotic
experiences in the real world.
• Texts may be advantageous in that individuals are familiar
with the technology.
• However, the ClinTouch application may show greater
functionality.
• Aim: To compare and contrast the new ClinTouch software
with a text based system.

34. Design
No
or or
sampling
Week 1 Week 2 Week 3

35. Design
• 24 community-based individuals with psychosis.
• Compare devices on:
– Number of completed data-points.
– Quantitative feedback scores.
– Length of time to complete each entry.
• Qualitative interviews:
– Benefits and limitations of both approaches.
– Perceptions of phone-usage and integration of technology into
everyday life and clinical case management.
– Ways of improving technology.

36. MRC DPFS Mobile Assessment Technology for Schizophrenia (ClinTouch) Study
Milestone 3 Preliminary Results
• Demographics (n=24)
• Male, n =19
• White British, n =17
• Age = mean 33.0, SD 9.5, min 18, max 49
• Recruited through Community Mental Health
Teams (N=15), Early Intervention Services (N=8)
and supported living staff (N=1).
• Four individuals owned a touch-screen
SmartPhone at the time of taking part.

37. MRC DPFS Mobile Assessment Technology for Schizophrenia (ClinTouch) Study
Milestone 3 Preliminary Results
Table X: Quantative feedback scores for the SmartPhone devices and text-based system.
Smartphone Text messages
Mean SD Min Max Mean SD Min Max β
Time taken to complete questions (seconds) 68.4 39.5 18.8 179.7 325.5 145.6 118.8 686.9 0.78**
Number of entries completed 16.5 5.5 4.0 24.0 13.5 6.6 0.0 24.0 -0.25*
Did answering the questions take a lot of work? 1.8 1.1 1 5 2.3 1.6 1 6 0.16
Were there times when you felt like not answering? 2.3 1.3 1 5 3.0 2.1 1 7 0.22.073
Did answering the questions take up a lot of time? 1.7 0.9 1 4 2.3 1.6 1 7 0.24
Were there times where you had to stop doing something in order to answer the questions? 3.4 1.7 1 7 4.1 1.7 1 7 0.200.97
Was it difficult to keep track of what the questions were asking you? 1.6 1.2 1 7 1.9 1.7 1 7 0.11
Were you familiar with using this type of technology? 4.7 2.3 1 7 5.3 2.2 1 7 0.14
Was it difficult to keep the device with you or carry it around? 1.9 1.4 1 6 2.4 1.8 1 6 0.16
Did you ever lose or forget the device? 1.7 0.9 1 4 1.8 1.4 1 6 0.06
Was using the key pad/touch screen difficult to use? 2.0 1.3 1 5 1.8 1.4 1 6 -0.08
Do you think other people would find the software easy to use? 5.3 1.8 2 7 5.9 1.4 3 7 0.19
Do you think you could make use of this approach in your everyday life? 4.0 1.8 1 7 3.9 2.2 1 7 -0.02
Do you think that this approach could help you or other service users? 5.3 1.9 1 7 5.6 1.2 3 7 0.11
Overall, this experience was stressful. 1.8 1.1 1 5 1.8 1.3 1 6 -0.04
Overall, this experience was challenging. 2.2 1.6 1 7 2.7 1.7 1 6 0.16
Overall, this experience was pleasing. 3.7 2.0 1 7 3.7 1.7 1 7 0.01
Did filling in the questions make you feel worse? 1.8 1.1 1 5 2.1 1.4 1 5 0.14
Did filling in the questions make you feel better? 2.8 1.5 1 6 3.0 1.6 1 7 0.08
Did you find the questions intrusive? 2.2 1.2 1 4 2.6 1.8 1 7 0.23
Was filling in the questions inconvenient? 2.0 1.0 1 4 2.5 1.4 1 5 0.01
Did you enjoy filling in the questions? 3.6 2.0 1 7 3.7 1.6 1 7 0.01
NB β represents the extent to which device type predicted the difference outcomes when controlling for order effect.
*p <.05 **p <.001

38. Future directions
• Feasible over longer periods of time?
• Can it be incorporated into clinical case
management?
• Is it effective at assessing other clinical
phenomena?

39. Quote
‘This is like quantitative stuff isn’t it? So as long
as it was balanced with interviews, however
often that person needs them then yeah [it
would be useful], but I wouldn’t give all the
power to the robots just yet. I think it would be
useful, but not to put all of our eggs in one
basket’

40. Acknowledgements
Manchester
• Prof Shon Lewis
• Mr John Ainsworth
• Mr Matt Machin
• Prof Christine Barrowclough
• Prof Graham Dunn
• Prof Anne Rogers
• Mrs Christine Day
Institute of Psychiatry
• Prof Til Wykes
• Prof Shitij Kapur

41. Thank you