Transcript The COM-B Model of Behaviour - Centre for Research in Social

Exploring the role of sensors in
health behaviour
Dr. Naomi Klepacz
Food, Consumer Behaviour & Health Research Centre
School of Psychology
Faculty of Health and Medical Sciences
University of Surrey
[email protected]
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What do we mean by ‘behaviour’?
An ‘agreed’ social definition of behaviour is…
 “Anything a person does in response to internal (bodily) or external
(environmental) events.”
 “Behaviours are physical events that occur in the body and are controlled
by the brain.”
 Behaviours may be...
– Overt and directly measureable (e.g., motor or verbal responses)
– Covert and indirectly measureable (e.g., physiological responses)
– Simple/Specific actions (e.g, swallowing a pill)
– Complex sequences of actions
Hobbs et al., 2011
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Why capture health behaviour?
• Human behaviours, including tobacco and alcohol consumption, diet,
physical activity and sexual practice, area contributing factors to noncommunicable diseases (NCDs) and a leading cause of death in both
developed and developing countries.
• Even small changes in health behaviour can have a substantial effect on
population outcome.
• Understanding behaviours and the context in which they occur is essential for
developing effective evidence-based health behaviour change interventions
and policies.
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What is a health behaviour change intervention (HBCI)?
“An action or set of activities to get individual to behave differently from how they would
Psychological or physical ability to
act without such an action.”
enact in behaviour
 HOW they behave
Reflective and automatic
 HOW OFTEN they perform
a behaviour
mechanisms
that activate or inhibit
behaviour
 HOW LONG they would act for
Behaviour
The COM-B Model of
Behaviour
Michie et al (2011)
Physical and social environment
that enables the behaviour
Capability
Motivation
Opportunity
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Why are Smartphones good for delivering HBCIs?
Various features of smartphones make them good candidates for the delivery of HBCIs
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Portable and highly valued by individuals
Switched on and remain on throughout the day
Bring HBCIs into real life contexts
Cheap, convenient and less stigmatising that alternative interventions
Share health and behaviour actions with professionals (and peers)
Infer context through location, movement and engagement with social media.
Dennison et al., 2013
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There are now approximately 165,000 mobile health apps on the market, nearly
two thirds of which are wellness apps focusing on exercise, diet and lifestyle.
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The remainder focus on specific health conditions, pregnancy and medical
information/ reminders.
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Data Flow
User interactions with digital technology the possibility for researcher / health care provider individuation.
INPUT
DATA
Researcher
User
OUTPUT
HBCI
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Issues with user interactions
“it isn’t the information that matters, it’s what
you do with it”
People don’t know what to do with the data
• Information vs. Knowledge & Skills
User
How effective is this as a HBCI?
How useful is this data for research?
• Data quality
• Behaviour capture
User expectations
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App Quality
Evaluated 4 skin cancer apps and found
that 3/4 incorrectly classified at 30% of
melanomas as unconcerning. Only
accurate app send pics to dermatologist.
Wolf, J.A., Moreau, J.F., Akilov, O., Patton, T., English, J.C, Ho, J., & Ferris, L.K. (2013).
Examined 39 Skin Cancer apps and
found that none had been validated for
diagnostic accuracy or usefulness by any
established research methods.
Kassianos, A.P., Emery, J.D., Murchie, P., & Walter, F.M. (2015)
A study by Imperial College, London looked at 185 apps that focused on breast cancer information and awareness.
• Focused on Breast Cancer (n = 139)
• Educational (n = 94)
• Self-assessment tools (n = 30)
Findings: Only 14.2% were evidence based, and 12.8% had medical professional input when they were designed
and created. Suggestions: Need for regulation, full authorship disclosure and clinical trials.
Mobasheri, Johnston, King, Leff, Thiruchelvam & Darzi (2014)
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Apps based on theory: SMOKEFREE 28
SF28 focuses on Behaviour Change Theory (BCTs)
PRIME theory (Plans, Responses, Impulses, Motives, and Evaluations)
SF28 involves setting the target of becoming 28 days smoke-free and monitoring
progress towards the target using the app.
Includes a ‘toolbox’ of evidence-based BCT to help achieve goals
Advice on:
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the use of stop-smoking medications
Licensed nicotine products
Inspirational stories
Videos of smokers going through the process of quitting
A distraction game
Advice of reducing exposure to smoking cues.
Ubhi, H.K, Michie, S., Kotz, D., Wong, W.C., & West, R. (2015).
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SMOKEFREE 28
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From a total of 1170 participants, 997
(83%) set a quit date on the day of
registration.
226 (19.32%) used the app for 28+ days
Strong positive association between
number of times app was opened and 28
day abstinence
No significant difference between
genders for log-ins
Number of log-ins was highest for
individuals aged 30-40 years
Mean number of times users opened the
app was 8.5
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PD Manager
The objective of the project is to
build and evaluate a mHealth
ecosystem for Parkinson’s disease
(PD) management.
The mHealth Platform
Different devices and wearable
sensors will be used to carry out the
continuous monitoring of patient,
as well as enable the performance
of phone-based test and the
delivery of education and training.
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PD Manager
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Model the behaviour of patients,
caregivers, neurologists and other
health-care providers.
Educate patients, caregivers and
healthcare providers.
Use a set of mobile and wearable
devices that will be used for the
symptoms monitoring and collection of
adherence data.
Assess motor and non-motor
symptoms in PD patients.
Evaluate patients adherence to
medical prescriptions.
Conduct a dedicated nutritional study
and empower game-based
physiotherapy at home.
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RICHFIELDS: A research Infrastructure (RI)
“Designing a world-class infrastructure to facilitate research”.
“New ICT technological bring opportunities for researchers to monitor and collect information
on behaviours. Everyday, consumers and businesses generate “big data” – large volumes of
information, that offer detailed descriptions of behaviours, including time and place (e.g.,
using GPS). If these data-rich sources could be linked and analysed, they have the potential to
contribute greatly towards answering key questions to respond to societal challenges
regarding food and health (e.g., obesity, cardiovascular disease, sustainability).”
RICHFIELDS aims to design a consumer-data platform to collect and connect,
compare and share information about food behaviours, to revolutionize research
on every-day choices made across Europe.
www.richfields.eu
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RICHFIELDS: A research Infrastructure (RI)
Consumer Generated Data
Purchase
of Food
Planning &
Organization
Preparation
of Food
Knowledge
& Understanding
Consumption
of Food
Meal Prep
To what extent does it
capture ‘user’ behaviour?
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RICHFIELDS: A research Infrastructure (RI)
“Can data collected through apps be used in social science research?”
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Quality of Big Data
Behaviour capture
Ownership of Data
Data security (Where is the data stored? Who has access?)
Ethics (data privacy, confidentiality & consent)
The Ethics Committee
User/consumer expectations
Legal issues (country & international)
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What research questions can I answer with this data?
www.richfields.eu
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With Thanks to...
Prof Monique Raats (Scientific Advisor RICHFIELDS)
Dr Lada Timotijevic (PI PD Manager)
and members of the
Food, Consumer Behaviour & Health Research Centre.
Also, to our Partners…