Depression is a common and often recurring mental health condition. Many people who experience depression go through periods of varying depression symptoms. Traditional ways to monitor depression focus on questionnaires and interviews during clinic visits. These methods are useful but have clear limits. They rely on memory, give only a snapshot of someone’s condition, and may miss small but important changes in how a person feels or behaves outside of clinical assessments.

Today, wearable devices such as smartwatches and smartphones make it possible to track behaviours continuously in daily life. These behaviours include how much someone moves, how they sleep, and how their heart rate changes. These measurements are called digital biomarkers. They can be collected with minimal effort from the person and provide an objective picture of what happens in their body and behaviour from day to day. This method of using digital devices to measure behaviour and physical signals in real life is called digital phenotyping. It offers a new way to observe how people function outside the clinic, in their everyday environment, and helps detect subtle changes that may not be visible through traditional interviews or questionnaires.

This thesis explores how digital biomarkers interact with each other over time in individuals with varying levels of depression symptoms. The main idea is based on a network approach. In this approach, depression is not seen as one fixed condition, but as a system where different behaviours and symptoms can influence each other. A network illustrates which signals are connected and how changes in one signal can lead to changes in others. This makes it possible to see not only what changes, but how change happens....

A considerable portion of the analyses is presented in a confidential appendix, which is not publicly accessible