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 `information' via digital means (e.g., a web site) in order to support intents to change behaviour

AccelerometerMicrophoneCameraGPSCompassGyroscopeWi-FiBluetoothProximityNFCLight

“...each of these transactions leaves digital traces that can be compiled into comprehensive pictures of both individual and group behaviour...

“Computational Social Science” Lazer et. al

Monitor

Learn

Deliver

MobileIntervention

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

“...sampling to capture data from the sensors of the phone cannot be performed continuously, as this will drain the battery rapidly. However, conservative sampling leads to the loss of valuable behavioural data...”

K. Rachuri

“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

Monitor

Learn

Deliver

MobileIntervention

Design

Towards a framework...

Mobile Web App

Native Mobile App

ReconfgurableInterfaces

Dynamic Content

SensingNotifcations

{

“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

Part of the path so far...

Mostly measurement. (experience sampling)

Building from a subset of the functionality:

Emotion Sense

● Battery-friendly sensor data collection● Triggering notifcations● Data storage & transmission

“Reinventing the Wheel”

All smartphone-based research needs to begin by engineering solutions for:

● Pull Sensors– Accelerometer, Location, Microphone– Wi-Fi, Bluetooth, Camera– Active apps, SMS/Call Log Content

● Push Sensors– Battery, Connection State

– Proximity, Screen– Phone Calls/SMS Events

Everything as a 'Sensor'

Open Source Android Smartphone Libraries

http://emotionsense.orghttps://github.com/nlathia/SensorManager https://github.com/nlathia/TriggerManager https://github.com/nlathia/SensorDataManager

● 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 & Emotion Data

Valence vs. Sociability Self-Report:r = 0.0581

Valence vs. SMS Events:r = 0.2154

“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