mHealthJohn Ainsworth

john.ainsworth@manchester.ac.ukHI@M 9th July 2012

The Global Challenge: Ageing population andmanagement of long term conditions

Dramatic increase in people developing Asthma, Chronic Obstructive Pulmonary

Disease (COPD), Diabetes and Hypertension

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• USA- 30% men & women

• Tonga- 47% men, 70% women• Samoa- 33% men, 63% women

Source WHONew Innovation will be needed to help manage the challenges facing organisations

operating in this sector

Need to shift the Continuum of CareQ

ualit

y of

Life

Shift LeftHighest Quality of Life

Lowest Cost of Care

Health and Wellness

Home Care

Residential Care

Acute Care

Cost of Care

Reproduced with permission of Intel™

mHealth

• Computing power• Large display• Usable• Short range

connectivity• Always on• Always connected• Always with you• Familiar

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

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

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

Who is involved with the Manchester mHealth eco-system?

Manchester MHealth

Eco-system

Manchester Mental Health & Social Care 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)

The University of Manchester

NWeHealth

Salford Royal NHS Foundation Trust

The Christie NHS Foundation Trust

University Hospital of South Manchester NHS Foundation Trust

Intel

J&J (Janssen Healthcare Innovation)

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

Greater Manchester Comprehensive Local Research Network

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

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

A new mobile assessment technology for psychosis

Jasper Palmier-Claus, PhDThe University of Manchester

Email: Jasper.Palmier-Claus@manchester.ac.ukTel: 01613067923

• Background

• Technology

• Phase one

• Phase two

Summary

• 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.

Background

• 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

Momentary assessment

• Reduces need for averaging.

• Reduces retrospective recall bias.

• Contextual information.

• Temporal associations.

• Relapse-signatures.

• Treatment effects.

• Adjunct to psychosocial intervention.

Why adapt for clinical use?

Widespread and familiar interface

• Monitor symptoms in real time.

Why use mobile phones?

Alert clinician: Early intervention

The technology

Menus

• Administrator configures participant details on the device.

• Selected delusions influence questions presented to the user.

Administrator page

• User responds on a touch-screen mobile phone.

• Branching means that the questions change depending on an individual’s responses.

Question display

Phase one

• 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).

Aims

• 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.

Method

• 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)

Compliance

Summary statistics

Acute Remitted Ultra-high risk0

5

10

15

20

25

30

35

40

33.8 (10.0)35.5 (8.0)

22.0 (4.4)

Age, mean (SD)

Acute Remitted Ultra-high risk0

2

4

6

8

10

12

9 9

10

Males, n

Medication, n

Acute Remitted Ultra-high risk0

2

4

6

8

10

1212 12

0

7

6

4

Antipsychotics

Antidepressants

Living status, n

6

3

1

1

1

Remitted

10

2

Acute

2

7

3

Ultra-high risk

Alone

Ward

Family

Partner

Shared living

Supported living

Spearman’s correlations, rho

Hopelessn

ess

Delusio

ns

Anxiety

Hallucin

ations

Susp

iciousn

ess

Grandiosit

y

Depres

sion

Guilt

Somati

c concer

n

Socia

l with

drawal

Hostility

Excit

emen

t

Disorga

nisation

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.80*0.74*

0.69* 0.68*0.63*

0.53*

0.45* 0.44*0.39*

0.26 0.25

0.06

-0.04

*p<.05

• 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.

Conclusions for phase one

Phase two

• 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.

Background

Design

or

Week 1 Week 2 Week 3

orNo

sampling

• 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.

Design

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.

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.

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 0.16Were 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 diffi cult to keep track of what the questions were asking you? 1.6 1.2 1 7 1.9 1.7 1 7 0.11Were you familiar with using this type of technology? 4.7 2.3 1 7 5.3 2.2 1 7 0.14Was it diffi cult to keep the device with you or carry it around? 1.9 1.4 1 6 2.4 1.8 1 6 0.16Did you ever lose or forget the device? 1.7 0.9 1 4 1.8 1.4 1 6 0.06Was using the key pad/touch screen diffi cult to use? 2.0 1.3 1 5 1.8 1.4 1 6 -0.08Do you think other people would find the software easy to use? 5.3 1.8 2 7 5.9 1.4 3 7 0.19Do 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 -0.02Do you think that this approach could help you or other service users? 5.3 1.9 1 7 5.6 1.2 3 7 0.11Overall, this experience was stressful. 1.8 1.1 1 5 1.8 1.3 1 6 -0.04Overall, this experience was challenging. 2.2 1.6 1 7 2.7 1.7 1 6 0.16Overall, this experience was pleasing. 3.7 2.0 1 7 3.7 1.7 1 7 0.01Did filling in the questions make you feel worse? 1.8 1.1 1 5 2.1 1.4 1 5 0.14Did filling in the questions make you feel better? 2.8 1.5 1 6 3.0 1.6 1 7 0.08Did you find the questions intrusive? 2.2 1.2 1 4 2.6 1.8 1 7 0.23Was filling in the questions inconvenient? 2.0 1.0 1 4 2.5 1.4 1 5 0.01Did 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

Smartphone Text messages

• Feasible over longer periods of time?

• Can it be incorporated into clinical case management?

• Is it effective at assessing other clinical phenomena?

Future directions

‘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’

Quote

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

Acknowledgements

Thank you