  Digital health applications are becoming increasingly important for assessing
and monitoring the wellbeing of people suffering from mental health conditions
like depression. A common target of said applications is to predict the results
of self-assessed Patient-Health-Questionnaires (PHQ), indicating current
symptom severity of depressive individuals. In this work, we explore the
potential of using actively-collected data to predict and forecast daily PHQ-2
scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417
for daily prediction of PHQ-2 scores, which specifically in the used dataset
have a range of 0 to 12, using leave-one-subject-out cross-validation, as well
as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the
last 7 days. This illustrates the additive value that can be obtained by
incorporating actively-collected data in a depression monitoring application.
