The importance of considering user requirements when designing mobile apps for mental healthcare. A presentation by Dr Mike Craven of NIHR MindTech www.mindtech.org.uk

1. Mobile Technology and Mental Health, Manchester 11/09/2013
User requirements for
smartphone applications
NIHR MindTech
Healthcare Technology Co-operative
Dr. Michael Craven – Senior Research Fellow

2. University of Nottingham Innovation Park
Institute of Mental Health
MindTech
NIHR HTC in Mental health & neurodevelopmental disorders
Since January 2013. Based in Nottingham.
Official launch 11th November 2013, London
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3. Clinical Landscape
• Mood Disorders
– Unipolar depression
– Bipolar depression
• Neurodevelopmental disorder
– Autism spectrum disorder (ASD)
– Tourette syndrome
– Attention Deficit Hyperactivity
Disorder (ADHD)
• Dementia
3
14.0
7.0
6.9
5.4
5.0
4.9
3.4
3.0
2.0
1.3
1.2
1.0
0.9
0.7
0.4
1.0
0 2 4 6 8 10 12 14
Anxiety disorders
Insomnia
Unipolar depression
Dementia
ADHD
Somatoform disorders
Alcohol dependence
Conduct disorder
PTSD
Personality dis.
Psychotic disorder
Cannabis dependence
Eating disorder
OCD
opiate dependence
Mental retardation
Wittchen et al. 2011 European
Neuropsychopharmacology
Current prevalence of mental
health disorders in Europe

4. Research Strategy
• Technology Innovation Pipeline
• High quality collaborative projects
• User-led design
• New partnerships
• National resource
• Transformation of mental health care and services

5. Bringing Partners Together
1: Institute of Mental Health
2: Technology Transfer Office
Academics
Patients & Carers
Clinicians
SMEs
University
NHS
Industry
HTC
IMH1
Medilink
TTO2
ADDISS
Tourettes
Action
Computer
Science
Nottinghamshire Healthcare Trust
Biomedical
Engineering
Business
School
BuddyApp
Qbtech Ltd
Buzz3D Ltd
Red Embedded
Ltd

6. Text messaging app to support therapy
Diary: SMS or
web
Analysis tool Goal
reminders
Appointment
prompts

7. QbTest: Objective Assessment of ADHD
• Computerised assessment of
attention and activity
• Supports clinical decision
making
• Provides patients with objective
reports on their condition
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8. ADHD measurement App
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• QbTest via smartphone application
– link with on-going work aimed at assessing
capacity of QbTest to inform clinical decision
making
• Specification:
– 1. continuous performance test delivered
via a mobile phone app
• provides measurement of attention and
impulsivity
– 2. in-built accelerometer and gyroscope to
assess levels of activity
• continuously
• during specific portions of the day
• while performing the cognitive task

9. A few new mental health Apps
• My Journey App – Early Intervention in
Psychosis Service for 14 - 35 year olds.
Surrey & Borders Partnership NHSFT
Graded self-assessment, mood management tips, emergency contacts,
information
• Actissist – personalised CBT treatment for
early stage psychosis. University of
Manchester
• Doc Ready – checklist for patient/GP
communication. Social Spider and others.
• CANTABmobile – Mobile app for memory
assessment using Paired Associates
Learning test. Cambridge Cognition Ltd.
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10. Earlier self-reporting App case studies
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Craven, M. P., Selvarajah, K., Miles,
R., Schnädelbach, H., Massey, A.,
Vedhara, K., Raine-Fenning, N.,
Crowe, J. User requirements for the
development of Smartphone self-
reporting applications in healthcare,
in Kurosu, M (Ed.): Human-Computer
Interaction, Part II, HCII 2013, LNCS
8005, 36-45, 2013.

11. The problem of user elicitation
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Perceptions
• Limited involvement of health professional involvement during App development
(Rosser et al. J. Telemed Telecare, 2011 - Pain Apps survey)
• Lack of end user involvement in the App design process (McCurdie et al. AAMI
Horizons 2012)
• Little good quality evidence for mHealth interventions used by ‘lay people’. Text
messaging services shown to increase adherence to anti-retroviral medication in
low cost setting, increase smoking cessation in high cost setting (Free et al. PLOS
Medicine, 2013)
Demands
• Regulatory (for ‘medical device’ Apps) e.g. HE75, IEC 62366:2007 - Medical devices
-- Application of usability
• Patient Public Involvement – imperative for NIHR research
• Implementation science (Brooks et al. 2011 – ‘conducive’ & ‘impeding’ conditions
for innovation in mental health services)
• Ethical (Wenze & Miller 2010 - ecological momentary assessment in mood
disorders research)

12. Ethical issues with Apps
• Security – data storage and communication. Apps vs. text messaging &
email (also a regulatory issue)
• Privacy - What do patients/participants expect or imagine might happen
with their data e.g. a trained professional monitoring it and acting upon it.
• Sensitive information - maybe better revealed face-to-face, in a group
situation …?
• Burden - what frequency of data collection is acceptable?
• Impact on clinical care - how to respond to results of data collection: do
nothing, give advice, treat as an emergency?
• Impact on health – stress, being reminded could cause exacerbation,
constant reminder of condition?
• Social – effect on family, carers etc.
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13. Case study 1 – IVF Stress App - design brief
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Ref: Quirin, M., Kazan, M., Kuhl, S.: When nonsense sounds happy or helpless: The implicit positive and negative affect test
(IPANAT). Journal of Personality and Social Psychology 3, 500–516 (2009)

14. Case study 1 – phone audit for IVF Stress App
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• 10 questions (76 users):
– What type/model of mobile phone do you
have?
– Is your mobile phone a smart phone?
– Which air time provider are you with?
– Is the phone on pay as you go or on
contract?
– Do you use email or internet access on your
phone?
– Is internet coverage included in your
contract?
– Do you use an alarm clock function?
– Are you familiar with the use of ‘Apps’ on
your phone?
– How regularly do you use an ‘App’ on your
phone?
– If you were to be asked to report your
distress levels throughout your treatment
which of the following methods would you
prefer?

15. Example 1 – phone audit
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IVF stress self-reporting App study: Craven MP et al. (2013) User requirements for the
development of Smartphone self-reporting applications in healthcare, in Kurosu, M (Ed.):
Human-Computer Interaction, Part II, HCII 2013, LNCS 8005, 36-45
Phones &
functions
Yes %
Frequency
of App use %
Communication
preference %
Is your mobile
phone a smart
phone?
75
Every day 53 App 58
Do you use email or
internet access on
your phone?
80
Weekly 17 Text message 30
Is internet coverage
included in your
contract?
82
Monthly 4
Telephone
conversation
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Do you use an
alarm clock function?
92
Not at all
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(Paper)
Questionnaire 1
Are you familiar with
the use of ‘Apps’ on
your phone?
80 Other (including
email)
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16. Case study 2 – mild asthma study
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• Week 1 – diary only • Week 2 – diary + physiological
measures
Each weekday evening:
•complete diary entry on
smartphone
(questionnaire modified
from Juniper et al. 1992)
Each weekday morning & evening:
•Take 3 PEF measurements and enter data
•Record 5 mins of pulse oximeter data
Each weekday evening: complete diary entry

17. Case study 2 – mild asthma – diary adherence
Participant Days with diary
entries, week 1 (of 5)
Days with diary
entries, week 2 (of 5)
Days with diary
entries, total (of 10)
Days with full diary
data (of 10)
1 5 3 8 6
2 3 0 3 3
3 1 1 2 2
4 2 2 4 3
5 4 5 9 8
6 1 5 6 4
7 4 1 5 4
8 2 0 2 2
9 3 3 6 4
10 4 4 8 4
11 2 1 3 3
Average (%) 56 45 51 39
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18. Case study 2 – mild asthma – physiological data adherence
Participant Mornings with
oximeter data (of 5)
Afternoons with
oximeter data (of 5)
Days with some
phys. data entry, total
(of 10)
Days with full phys.
data (of 10)
1 4 4 5 1
2 0 0 0 0
3 2 2 5 2
4 3 4 4 1
5 4 5 5 4
6 3 4 5 3
7 4 3 5 0
8 2 2 2 0
9 3 3 4 1
10 1 1 5 0
11 3 3 4 1
Average (%) 53 56 80 24
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19. Case study 2 – mild asthma - user experience (results)
Mild asthma self-reporting App study (with BlueTooth pulse oximeter device)
• 5/11 participants - technology ‘nice’ or ‘easy to use’.
– 2 ‘interesting’
– 6 minor technical problem (internet/Wi-Fi, Bluetooth, battery)
– 1 not confident data upload succeeded
– 1 ‘sometimes a bit of a hassle … overkill for mild asthma’
• 5/11 participants - no effect on lifestyle or ‘got used to it’
– 1 more cautious about remembering inhaler
– 1 needed to plan when going out
– 1 interference with daily activities
– 2 difficulty or annoyance scheduling the recordings correctly
– 1 inconvenience of sitting down to take measurements
• 11/11 - no effect of technology on condition
– 1 reported exacerbation during the study.
• 7/11 - more aware of condition whilst taking part.
– 2 ‘a good thing’.
– 1 ‘thinking about a cough exacerbates it’.
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Mild asthma self-reporting App study: Craven MP et al. (2013) User requirements for the development of Smartphone self-
reporting applications in healthcare, in Kurosu, M (Ed.): Human-Computer Interaction, Part II, HCII 2013, LNCS 8005, 36-45

20. Towards a protocol
• Conduct a phone audit before commencing a research study
– Discover range of prior experiences & preferences for phone functions amongst
participants (e.g. alarm clock)
– Detect potential for conflict between normal daily use & research study use of phone
(since functions may mix or conflict)
• Investigate design tolerance to real-world phone use amongst user group
– Not keeping devices turned on or charged up
– Effect of missing or ignoring prompts
• Ensure secure collection and storage of data
– Pre-empt ethical approval issues
• Determine patient burden and adherence
– Frequency of self-monitoring prompts
– Pilot studies aimed at measuring adherence
– Could more passive monitoring be preferable?
Early stage user involvement and/or a participatory design process
helps reveal needs which may not initially be apparent
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21. MindTech contacts:
Principal Investigator
Prof Chris Hollis chris.hollis@nottingham.ac.uk
Technology Theme Lead:
Prof John Crowe john.crowe@nottingham.ac.uk
Senior Research Fellow (Technology)
Dr Michael Craven michael.craven@nottingham.ac.uk
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NIHR MindTech Healthcare Technology Co-operative
Thank you for listening!