Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting

TL;DR

This paper investigates using actively-collected data to predict and forecast daily PHQ-2 depression scores, demonstrating improved accuracy in a longitudinal dataset for mental health monitoring.

Contribution

It introduces a novel approach for depression score prediction using actively-collected data and provides empirical results showing its effectiveness.

Findings

Best MAE of 1.417 for daily prediction

Best MAE of 1.914 for forecasting up to 7 days

Active data improves depression monitoring accuracy

Abstract

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.