Acceptability of Remote Monitoring Technologies for Early Warning of Major Depression

Abstract Recent research shows that 16% of individuals aged 16 and older are affected by depression, with Major Depressive Disorder (MDD) severely impairing daily life and potentially leading to suicide. Early intervention is vital, but current early warning methods of depression are time-consuming and not scalable. This study examines the acceptability of smartphone-based early warning systems using machine learning for depression intervention. Interviews were conducted with participants from the RADAR-MDD study, where smartphone sensors collected behavioural data. Three key themes emerged: designing remote monitoring technologies (RMTs) to enhance user engagement and wellbeing, RMTs as tools for comprehensive and empowering mental health support, and ethical and purpose-driven data utilization by RMTs. Whilst participants were open to RMTs, they expressed concerns about data commercialization. The study highlights the importance of prioritizing user experience, ethics, and personalization in designing effective early warning systems.