Phenotyping medication adherence to understand habit formation

Long-term conditions kill 33m people worldwide. Low medication adherence is common in long-term conditions, leading to 25%–90% of treatment failure. Effective adherence interventions are lacking, and reasons for low adherence are poorly understood. Understanding the determinants of adherence is important to enable the design of effective interventions. Studies in this area have tended to focus on conscious motivational factors but automatic processes, such as habit, may better predict treatment adherence.

Cystic fibrosis (CF) is an archetypal long-term condition in which we have been exploring adherence. We have developed a system, involving chipped nebulisers, cloud computing and a web platform. It is currently collecting data from ~1000 people with CF at 20 UK centres. Our empirical research using this platform has confirmed that high adherers report stronger habit than low adherers, making habit a promising target for self-management interventions.

We propose the PhD consists of a series of three inter-related projects with the overall aim of improving existing adherence interventions by analysing the data collected using artificial intelligence (AI) and machine learning techniques to identify people who have formed habits and to study the process of habit formation. Participants will be divided into derivation and evaluation cohorts for the purpose of model development and evaluation. For each participant, the behavioural data available includes one year of date and time stamped nebuliser use data and CFHealthHub engagement data which would be analysed in the first and second projects. Audio recordings of intervention sessions will be analysed in the third project. The overall output from the proposed projects can result in the development of more effective habit-forming adherence interventions. Given that CF is an archetypal long-term condition, the knowledge generated can also aid similar developments in other long-term conditions.

Candidates must have a first class undergraduate degree in Psychology and a distinction at masters level. Candidates must have strong quantitative skills and a professional track record in applied research.

Enquiries:
Interested candidates should in the first instance contact Professor Stephen Walters ([Email Address Removed])

How to apply:
Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply

Please clearly state the prospective main supervisor in the respective box and select “School of Health and Related Research” as the department.