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

1. Ubiquitous and Social Computing for
Positive Behaviour Change

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. 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. Monitor
Learn
Deliver
Mobile
Intervention
“Smartphones for Large-Scale Behaviour Change
Interventions”. IEEE Pervasive 2013.

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. “...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. Monitor
Learn
Deliver
Mobile
Intervention
Design

8. ● User Interface and interaction:
– Diaries
– Menus
– Information
– Questionnaires
– Feedback
● Sensor data collection
● Context triggering
Towards a framework...
Generic components for mobile apps:

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. ● 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. Emotion Sense

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. “sensor duty cycling”

15. Sensor & Emotion Data

17. “Can I run an ESM study
like Emotion Sense?”
Generalise sensor-
enhanced experience
sampling tool. Currently in
alpha testing.

18. Smartphone Libraries:
Sensing, Triggers, Data
Management
Emotion Sense
Easy M
Sensing
Apps &
ESM
Research
towards ubhave's
intervention framework
Research