m-health technologies and mental health
DESCRIPTION
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.TRANSCRIPT
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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
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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™
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mHealth
• Computing power• Large display• Usable• Short range
connectivity• Always on• Always connected• Always with you• Familiar
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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
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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
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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
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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
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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
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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
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A new mobile assessment technology for psychosis
Jasper Palmier-Claus, PhDThe University of Manchester
Email: [email protected]: 01613067923
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• Background
• Technology
• Phase one
• Phase two
Summary
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• 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
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• 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
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• 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?
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Widespread and familiar interface
• Monitor symptoms in real time.
Why use mobile phones?
Alert clinician: Early intervention
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The technology
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Menus
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• Administrator configures participant details on the device.
• Selected delusions influence questions presented to the user.
Administrator page
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• User responds on a touch-screen mobile phone.
• Branching means that the questions change depending on an individual’s responses.
Question display
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Phase one
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• 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
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• 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
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• 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
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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
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Medication, n
Acute Remitted Ultra-high risk0
2
4
6
8
10
1212 12
0
7
6
4
Antipsychotics
Antidepressants
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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
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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
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• 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
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Phase two
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• 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
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Design
or
Week 1 Week 2 Week 3
orNo
sampling
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• 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
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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.
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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
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• Feasible over longer periods of time?
• Can it be incorporated into clinical case management?
• Is it effective at assessing other clinical phenomena?
Future directions
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‘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
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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
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Thank you