What goes up must come down? Using digital technology to understand the dynamic nature of mood in bipolar disorder

Project Code

MRCNMH24Ca Lewis

Research Theme

Neuroscience and Mental Health

Full Project Description

Please download the below document for a full project description and to see the full supervisory team.


People with bipolar disorder (BD) experience disabling episodes of high and low mood, but how mood fluctuates between them could help us better understand subtypes of BD and improve treatments. Using cutting-edge statistical methods and long-term digital mood tracking data from the largest cohort of people with BD in the world, you will derive novel measures of mood dynamics and link these with genetic factors and clinical features (e.g illness course, BD subtypes).

Lead Supervisor

Dr Katie Lewis

Lead Supervisor Email


University Affiliation