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m-health technologies and mental health

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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.

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m-health technologies and mental health

  1. 1. mHealth John Ainsworthjohn.ainsworth@manchester.ac.uk HI@M 9th July 2012
  2. 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% womenSource WHO New Innovation will be needed to help manage the challenges facing organisations operating in this sector
  3. 3. Need to shift the Continuum of Care Shift Left Highest Quality of Life Lowest Cost of CareQuality of Life Health and Wellness Home Care Residential Care Acute Care Cost of Care Reproduced with permission of Intel™
  4. 4. mHealth • Computing power • Large display • Usable • Short range connectivity • Always on • Always connected • Always with you • Familiar
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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; clinicalresearch network; c. 23,500NHS staff University Hospital of South Manchester NHS Foundation Trust J&J (Janssen Healthcare Innovation) The Christie NHS Foundation TrustThe University of Manchester Intel ManchesterGreater Manchester MHealthComprehensive Local NWeHealth Eco-systemResearch NetworkManchester 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)
  9. 9. 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
  10. 10. 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
  11. 11. A new mobile assessment technology for psychosis Jasper Palmier-Claus, PhD The University of Manchester Email: Jasper.Palmier-Claus@manchester.ac.uk Tel: 01613067923
  12. 12. Summary• Background• Technology• Phase one• Phase two
  13. 13. Background• Schizophrenia is one of the most prevalent forms of mentalillness.• Associated cost of 6.7 billion pounds each year.• Clinical outcome often poor despite treatment with 80% ofindividuals relapsing within 5 years after the first episode.• Major need for new forms of intervention and symptommanagement.
  14. 14. 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
  15. 15. 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.
  16. 16. Why use mobile phones? Widespread and familiar interface• Monitor symptoms in real time. Alert clinician: Early intervention
  17. 17. The technology
  18. 18. Menus
  19. 19. Administrator page• Administrator configures participant details on the device.• Selected delusions influence questions presented to the user.
  20. 20. Question display• User responds on a touch-screen mobile phone.• Branching means that the questions change depending on an individual’s responses.
  21. 21. Phase one
  22. 22. 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).
  23. 23. 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.
  24. 24. 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)
  25. 25. Summary statistics Age, mean (SD) Males, n40 35.5 (8.0) 1235 33.8 (10.0) 10 1030 9 925 8 22.0 (4.4)20 615 410 5 2 0 0 Acute Remitted Ultra-high risk Acute Remitted Ultra-high risk
  26. 26. Medication, n 12 1212108 7 Antipsychotics 6 Antidepressants6 442 00 Acute Remitted Ultra-high risk
  27. 27. Living status, n Acute Remitted Ultra-high risk 12 2 Alone 1 3 Ward 1 Family 6 Partner Shared living 3 10 7 Supported living
  28. 28. Spearman’s correlations, rho0.90 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*0.400.30 0.26 0.250.200.10 0.06 -0.040.00-0.10 *p<.05
  29. 29. 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.
  30. 30. Phase two
  31. 31. 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.
  32. 32. Design No or or samplingWeek 1 Week 2 Week 3
  33. 33. 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.
  34. 34. 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.
  35. 35. 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
  36. 36. Future directions• Feasible over longer periods of time?• Can it be incorporated into clinical case management?• Is it effective at assessing other clinical phenomena?
  37. 37. 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 thepower to the robots just yet. I think it would be useful, but not to put all of our eggs in one basket’
  38. 38. 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
  39. 39. Thank you

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