Identification of subtypes of depression using remote measurement technologies

Despite its heterogenous nature, it has proved difficult to identify sub-types of major depressive disorder (MDD). This has limited the development of novel therapeutics, that require more precise matching of treatments to specific patient presentations, while also hindering the identification of potential biomarkers (Baumeister & Parker, 2012). Distinguishing between atypical and typical depression is frequent in the literature to stratify patient profiles, however, research exploring disease trajectories across the two groups have found contradicting evidence (Lamers et al., 2016). Researchers have suggested that data mining methods on large scale prospective studies could be useful to better understand these subtypes (Chekroud et al. 2016 and van Loo et al. 2014). Yet, this requires long-term symptom tracking methodologies with high temporal resolution, to get a more defined picture of depression phenotypes across a wide variety of variables.

Remote measurement technologies (RMT) harvest data from smartphones and wearable devices and can provide a 360-degree picture of an individual's day-to-day life, including sleep, activity, heart rate, location, cognition, speech and mood/stressors. The Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study collected such data on 623 patients across three clinical sites (UK, Spain, Netherlands). The study utilized smartphone applications and wearable fitness devices to track MDD symptoms longitudinally over 2 years (for more detail see Matcham et al., 2019), providing fertile ground for further investigation into phenotypic clustering of depression outcomes and their trajectories. The proposed PhD project aims to fill this gap in the literature and design a unique study by utilizing the data collected throughout the RADAR-MDD study, to understand symptom clustering.

The supervisors for this project provide a partnership of clinical and epidemiological expertise (Hotopf) and AI/computer science (Cummins). Hotopf is the PI for the RADAR-MDD project, and therefore has a comprehensive understanding of data quality and can ensure data access.

The primary research question is to identify behavioural/physiological subtypes which are associated with different trajectories of depression. The research will combine a "top-down" data driven approach, utilizing machine learning tools to cluster symptom profiles, as well as hypothesis driven approaches to identify differences in phenotype across predetermined groups:

Identify whether depressive episodes within the same participants are phenotypically similar.

Determine whether pre-defined groups (mild, moderate and severe episodes; typical versus atypical patterns) can be distinguished using the RMT data.