Depression during pregnancy or after childbirth (peripartum depression (PPD)) is a common (ca 10%), serious and potentially life-threatening disorder with high societal costs. Preventive efforts targeting PPD are thus of paramount importance, especially among high-risk groups, but our current ability to predict PPD is poor. We propose a novel, transdisciplinary project to design, develop and evaluate effective and user-centered methods for the prediction of PPD. We will use self-reported measures, voice recordings and digital phenotyping (passively-registered data) through the Mom2b mobile application to predict the development of PPD in the third pregnancy trimester, as well as during the early and late postpartum period.