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.

翻译：数字健康应用对于评估和监测患有抑郁等精神疾病的人的福利越来越重要,所述应用的一个共同目标是预测自评病人健康问卷的结果,表明抑郁症患者目前症状的严重性。在这项工作中,我们探索利用积极收集的数据预测和预测新收集的纵向数据集每日PHQ-2分数的可能性。我们获得了每天预测PHQ-2分数的最佳MAE为1.417, 具体地说,在使用过的数据集中,PHQ-2分数的数值在0到12之间,使用留置单单交叉校验以及1.914的1.914最佳MAE,用过去7天的数据预测PHQ-2分数。这说明了将积极收集的数据纳入抑郁症监测应用中可以获得的添加值。