About the Project
Identify relevant habit formation theories and translate them into design requirements for home-based IoT systems for habit formation support.
Apply qualitative and quantitative methods to identify the effective components of home IoT systems and their impact on habit formation.
Build and test interactive IoT prototypes to identify key interaction methods (e.g. notifications, setup procedures) and wider system components (e.g. wearable devices, sensors).
Propose a theoretical framework for (re-)designing home-based IoT systems that can effectively support long-term behaviour change.
Every year, millions of people die because of cardiovascular disease, diabetes and other preventable conditions caused by bad and unhealthy habits. This health crisis could be prevented and addressed by helping people develop good habits such as walking, exercising, meditation, etc. However, forming new habits is difficult and current technological solutions are ineffective as they rely on continuous tracking and reminders, and disregard people’s daily routines and the environment – factors that are key in supporting habit formation. While Internet of Things (IoT) technologies could help to address this limitation, as they can be embedded directly in home environments, they tend to focus on monitoring and have not been used to support habit formation.
This PhD project will explore how IoT systems could be effectively used to support the formation of healthy habits at home, especially in multi-occupancy homes where people may be interested in different habits or some may not wish to change their behaviour.
The project will involve a mixed-methods approach, including controlled experiments and longitudinal studies to test different mechanisms supporting habit formation and to evaluate their effectiveness, and qualitative data collection to understand their effects on people’s everyday routines.
This studentship will contribute to the fundamental understanding of how home-based IoT systems can facilitate engagement with healthy behaviours. It will also help to understand how different components of such systems relate to behaviour change techniques and influence habit formation, providing a theoretical basis for building smart home environments that can be applied to a wide range of health and wellbeing contexts.
The student will be able to define specific habitual behaviours to focus on. They will work alongside, and will be able to collaborate with, members of Human-Centred Computing (HCC) group and Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) at Cardiff University.
Further information: email@example.com
Keywords: Digital Health, IoT, Internet of Things, home-based IoT, habits, behaviour change
This application is open to students worldwide.
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
How to apply:
In the funding field of your application, indicate “I am applying for 2021 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.
Interview - If the application meets the entrance requirements, you will be invited to an interview.
Please note that a PhD Scholarship is available for entry 2021/22. If you are interested in applying for a PhD Scholarship, please follow the instructions available on our website: View Website
In the Funding field of your application, insert "I am applying for 2021 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
Stawarz et al. (2016) Understanding the use of contextual cues: design implications for medication adherence technologies that support remembering. Digital Health
Lally et al (2010). How are habits formed: Modelling habit formation in the real world. Eur J Soc Psychol; 40(6):998–1009.
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