Who does iCBT work for and why: predicting and understanding treatment outcomes in depression

Abstract

At any given moment, depression directly affects 5% of the population, and indirectly impacts many more others. Effectively tackling this condition has remained at the forefront of psychiatry research. In the challenging landscape of soaring demands for mental health support and limited resources, digital interventions like internet-delivered cognitive behavioural therapy (iCBT) emerge as a accessible, scalable, and cost-effective solution. While iCBT has shown efficacy, it only works for 30-50% of depressed patients. At this time, we still do not have a clear understanding who it best benefits and why. Past endeavours dedicated to this research area have not been fruitful, mainly because of the inadequacy of the current methodologies to comprehensively address the intricate nature of depression. To this end, the thesis proposes a big data revolution to our current research infrastructure, by leveraging large, rich patient datasets with advanced statistical modelling to further elucidate the predictors and mechanisms of iCBT.