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 Behaviour Change Interventions (DBCIs), and to contribute to creating a scientifc foundation for digitally supported behaviour change.

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

Monitor

Learn

Deliver

MobileIntervention

“Smartphones for Large-Scale Behaviour Change Interventions”. IEEE Pervasive 2013.

“ 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

“...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

Monitor

Learn

Deliver

MobileIntervention

Design

● User Interface and interaction:– Diaries

– Menus

– Information

– Questionnaires– Feedback

● Sensor data collection● Context triggering

Towards a framework...

Generic components for mobile apps:

{

“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:

● 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:

Emotion Sense

● 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

“sensor duty cycling”

Sensor & Emotion Data

“Can I run an ESM study like Emotion Sense?”

Generalise sensor-enhanced experience sampling tool. Currently in alpha testing.

Smartphone Libraries:

Sensing, Triggers, Data Management

Emotion Sense

Easy M

Sensing

Apps &

ESM

Research

towards ubhave's

intervention framework

Research