The UL-AiD depression project: Exploring depression, residual symptoms, and stress using network models and wearable technology

Background: Depression is the leading cause of disability worldwide, often due to the ‘relapse-remitting’ course normatively displayed by the disorder. Consistent evidence suggests residual symptoms and stress are the most reliable predictors of depression relapse. Yet, little is known about day-to-day interactions between depression, residual symptoms, and stress. This is due to prevailing methodologies often not recognising the complexity of symptom interactions or enabling continuous stress measures.

 Aim: Thus, the overarching aim of this thesis is to explore interactions between depression, residual symptoms, and stress using psychometric network models - data models representing psychological constructs as systems of autonomous interacting components and/or wearable technology - capable of continuous stress measures. Four empirical studies are presented: Chapter 2 systematically reviewed, estimated, and compared cross-sectional residual symptom networks following different depression treatments of 25 from 663 eligible samples (N =1,389), providing individual patient data. Depressed mood and anhedonia were central post-treatments, and anxiety and fatigue complaints were only central post-cognitive behavioural therapy. Chapter 3 integrated physiological stress as a risk factor alongside residual symptoms and tested a wearable electrodermal activity (EDA) sensor for monitoring stress over 1-week in 13 remitted depressed participants. Wearable EDA sensors showed high acceptability, engagement, and accuracy. Preliminary results showed stress predicts increased residual depression symptoms. Chapter 4 estimated longitudinal networks of residual symptoms and stress over 3-weeks in 22 remitted depressed participants. Residual depressed mood, anhedonia, energy loss, suicide, concentration, sleep, appetite problems, and stress were complex in their centralities and co-occurrences. Increased stress predicteddecreased energy loss. Chapter 5 explored a broader view of depression and stress by estimating cross-sectional emotions networks across depression levels of 26,034 participants during a global stressor - COVID-19. Negative (vs. positive) emotions dominated emotional life across depression levels, gratitude was central across depression levels, and increased love distinguished those with low (vs. high) depression. 

Conclusion: Contributions of this thesis are four-fold. First, residual symptoms, stress, and emotions showed complex interactions and centralities - supporting and building on complexity-based network approaches for depression. Second, stress and residual depression symptoms showed bi-directional interactions – emphasising methodologies must be capable of monitoring these dynamic interactions. Third, wearable EDA sensors showed high acceptability and reliability – supporting EDA as a potentially pioneering signal for stress detection. Finally, residual depressed mood, anhedonia, loss of energy, sleep, and concentration problems were consistently central throughout –constituting potentially important intervention and relapse prevention targets.