Transcript Slide 1

Mobile self-health applications for lasting behavior
change and health management
Eleni Stroulia, Blerina Bazelli, Shayna Fairbairn, Dylan Gibbs, Lili Liu, Robert Lederer
[email protected] - http://ssrg.cs.ualberta.ca
e-Health 2013: Accelerating Change
May 29, 2013 Ottawa - Canada
Today: Victor Guana – PhD. Student University of Alberta
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Faculty/Presenter Disclosure
• Faculty: Eleni Stroulia
• Relationships with commercial interests:
– Grants/Research Support: N/A
– Speakers Bureau/Honoraria: N/A
– Consulting Fees: N/A
– Other: N/A
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Mental Mashup
Self Monitoring Applications
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Mental Mashup
Self Monitoring Applications
Methodology
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Mental Mashup
Self Monitoring Applications
Methodology
Technologies
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Mental Mashup
Self Monitoring Applications
Methodology
Technologies
Our work so far:
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EASI
Physitivity
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Mental Mashup
Self Monitoring Applications
Methodology
Technologies
Our work so far:
EASI
Towards a
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Physitivity
General Method
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Self-monitoring applications
An emerging
family
of applications
for improving
dailydaily
life and
An emerging
family
of applications
for improving
life
and long-term health through
long-term health through
• Recording of data about personal state and activity
• Comparison against norms
• Contextual feedback
With
With common
common data
data model,
model, views
views and
and behaviors
behaviors
•
•
•
•
•
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User settings
Entries (to record)
History (to browse, search and filter)
Visualizations and feedback
Social sharing
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Methodology
Development of two 'quantified self' applications:
• Physitivity: physical activity monitoring
• EASI: blood glucose / diabetes management
Using human-behavioral theories to motivate the application
behavior and interaction design.
Reuse and analysis, to establish general and app-specific code
Creation of a set of reusable code components for efficient
creation of new 'quantified self' applications.
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Methodology
Theories Grounding Application Design
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Theory of Planned Behavior
Action performance is influenced by:
• Individual’s attitudes towards the action and its outcomes
• Subjective norms of the individual’s social group and his desire to
comply with them
• Individual’s perceived control
to achieve a given behavior
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Methodology
Theories Grounding Application Design
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The Intention-Behavior Gap
Intention for and actual performance of an action may differ because of:
- Intention variability:
Engage in a behavior but there may be no possible way to actually do so.
- Intention (in)activation:
Intention may not even be formulated (forgotten or perceived as low priority)
- Failure to act upon intentions:
Failure to act upon intentions: when there are not enough details for the individual
to engage in behavior
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Technologies
PhoneGap and Worklight
Prescribes the development architecture of mobile applications
by separating the platform-specific interaction concerns from
the general functionality.
Backbone.js
JavaScript framework for developing layered applications with
explicitly defined data models and a corresponding set of userinteraction widgets with declarative event handling.
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EASI & Physitivity:
Two Quantified-Self Applications
…
Technologies
Our work so far:
EASI
Towards a
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Physitivity
General Method
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EASI & Physitivity:
Two Quantified-Self Applications
Solution Commonalities
Entries (add / edit), entry types, entry list, dashboard, reusable
visualizations, users, settings
CSS / styling, list items, icons, types of visualizations used,
properties of models (entries, settings, entry types, etc.)
Physitivity
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EASI
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EASI & Physitivity:
Two Quantified-Self Applications
Physitivity
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EASI & Physitivity:
Two Quantified-Self Applications
Physitivity
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EASI & Physitivity:
Two Quantified-Self Applications
EASI
B
EASI is envisioned to encourage users who live with diabetes to regularly monitor
their blood glucose, food intake, insulin doses and physical activity.
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EASI & Physitivity:
Two Quantified-Self Applications
Lessons Learned
The two self-management applications share similar designs
and a codebase
New applications in this family can be generated, by reusing the
current resources
Some of the components (history, pattern-based
recommendation) can be reused
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Towards a General Method
Quantified-Self Applications Family
The data elements will be
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Structured entities consisting of multimedia attributes
Entity collections users.
Relations and their histories.
The services involved will be
Faceted search
Detailed entity view(s)
Entity-state manipulation
The applications will be contextually adaptive
Application usage history
Calendar
Social extensions
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Towards a General Method
Quantified-Self Applications Family
The data elements will be
Structured entities consisting of multimedia attributes
Entity collections users.
Relations and their histories.
The services involved will be
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Faceted search
Detailed entity view(s)
Entity-state manipulation
The applications will be contextually adaptive
Application usage history
Calendar
Social extensions
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Towards a General Method
Quantified-Self Applications Family
The data elements will be
Structured entities consisting of multimedia attributes
Entity collections users.
Relations and their histories.
The services involved will be
Faceted search
Detailed entity view(s)
Entity-state manipulation
The applications will be contextually adaptive
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Application usage history
Calendar
Social extensions
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Towards a General Method
Quantified-Self Applications Family
Design a collection of interactive widgets
For different media types with different size and layout properties
Develop a (wizard-style) process to guide the development of the
application
non-computer expert
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Towards a General Method
Quantified-Self Applications Family
Design a collection of interactive widgets
non-computer expert
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Define application data model
Identify possible widgets for simple data elements
Compose widgets for composite data elements
Define application behavior
Map application behavior to user interaction
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Towards a General Method
Conclusions
Smart phone based applications have the potential to affect meaningful
behavior change on a population level. The efficacy of mobile interventions
has been demonstrated albeit on a small scale.
Mobile applications offer a promising future for large scale population
level interventions because they have a large reach like web based
interventions but seem to have a better a adherence rate for engaging
with the application.
Our work argues (a) for systematicity in the development of mobile apps for
self monitoring, through reusable software components and (b) for a
theoretical grounding of the application design in psychology theories of
motivation and behavior.
[email protected]
http://ssrg.cs.ualberta.ca
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Thank you!
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