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Ubiquitous and Social Computing for
Positive Behaviour Change
UBHave's
...aim is to investigate the power and
challenges of using mobile phones and
social networking for Digital Behavi...
Digital Behaviour Change Interventions
...focus on delivering relevant
information via digital means (e.g., a
web site) in...
Monitor
Learn
Deliver
Mobile
Intervention
“Smartphones for Large-Scale Behaviour Change
Interventions”. IEEE Pervasive 201...
“ Study fndings suggested that young, currently
healthy adults have some interest in apps that
attempt to support health-r...
“...They predicted that context-triggered advice
and suggestions would produce
counterproductive effects by drawing attent...
Monitor
Learn
Deliver
Mobile
Intervention
Design
● User Interface and interaction:
– Diaries
– Menus
– Information
– Questionnaires
– Feedback
● Sensor data collection
● C...
{
“intervention_id”:”my_intervention”,
“questions”: [ … ]
“diary”: [ …]
“sensors”: [ …],
“trigger”:[
{“accelerometer”:”mov...
● Questionnaires
● Feedback
● Sensor data collection & management
– “Open Source Smartphone Libraries for
Computational So...
Emotion Sense
● How can we keep users engaged in a
seemingly repetitive task?
– Diversify and sample from the questions as a
“journey” o...
“sensor duty cycling”
Sensor & Emotion Data
“Can I run an ESM study
like Emotion Sense?”
Generalise sensor-
enhanced experience
sampling tool. Currently in
alpha test...
Smartphone Libraries:
Sensing, Triggers, Data
Management
Emotion Sense
Easy M
Sensing
Apps &
ESM
Research
towards ubhave's...
The Ubhave Project (Part 1/2)
The Ubhave Project (Part 1/2)
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The Ubhave Project (Part 1/2)

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Invited talk at the 2nd ACM Conference on Mobile Systems for Computational Social Science #ubicom13

Published in: Technology, Business

Transcript of "The Ubhave Project (Part 1/2)"

  1. 1. Ubiquitous and Social Computing for Positive Behaviour Change
  2. 2. UBHave's ...aim is to investigate the power and challenges of using mobile phones and social networking for Digital Behaviour Change Interventions (DBCIs), and to contribute to creating a scientifc foundation for digitally supported behaviour change.
  3. 3. Digital Behaviour Change Interventions ...focus on delivering relevant information via digital means (e.g., a web site) in order to support intents to change behaviour
  4. 4. Monitor Learn Deliver Mobile Intervention “Smartphones for Large-Scale Behaviour Change Interventions”. IEEE Pervasive 2013.
  5. 5. “ Study fndings suggested that young, currently healthy adults have some interest in apps that attempt to support health-related behaviour change [...] The ability to record and track behaviour and goals and the ability to acquire advice and information “on the go” were valued. Context-sensing capabilities and social media features tended to be considered unnecessary and off-putting.” “Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study” Dennison et. al
  6. 6. “...They predicted that context-triggered advice and suggestions would produce counterproductive effects by drawing attention towards unhealthy but attractive behaviour choices.” “Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study” Dennison et. al
  7. 7. Monitor Learn Deliver Mobile Intervention Design
  8. 8. ● User Interface and interaction: – Diaries – Menus – Information – Questionnaires – Feedback ● Sensor data collection ● Context triggering Towards a framework... Generic components for mobile apps:
  9. 9. { “intervention_id”:”my_intervention”, “questions”: [ … ] “diary”: [ …] “sensors”: [ …], “trigger”:[ {“accelerometer”:”moving”, “survey”:”physical_activity”} ] } ...that can be 'authored' Using well-known mobile app design patterns Native app's benefts, web apps' benefts:
  10. 10. ● Questionnaires ● Feedback ● Sensor data collection & management – “Open Source Smartphone Libraries for Computational Social Science” MCSS 2013. Part of the path so far... Mostly measurement. (experience sampling) Building from a subset of the functionality:
  11. 11. Emotion Sense
  12. 12. ● How can we keep users engaged in a seemingly repetitive task? – Diversify and sample from the questions as a “journey” of unlocking feedback – User needs vs. research needs ● How can we effciently collect sensor data? – First deployment took a naïve approach – Current implementation focuses on CPU time rather than sensor strategy Design Challenges
  13. 13. “sensor duty cycling”
  14. 14. Sensor & Emotion Data
  15. 15. “Can I run an ESM study like Emotion Sense?” Generalise sensor- enhanced experience sampling tool. Currently in alpha testing.
  16. 16. Smartphone Libraries: Sensing, Triggers, Data Management Emotion Sense Easy M Sensing Apps & ESM Research towards ubhave's intervention framework Research
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