Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach

Abstract

Mental health intervention can help to release individuals’ mental symptoms like anxiety and depression. A typical mental health intervention program can last for several months, people may lose interests along with the time and cannot insist till the end. Accurately predicting user dropout is crucial for delivering timely measures to address user disengagement and reduce its adverse effects on treatment. We develop a temporal deep learning approach to accurately predict dropout, leveraging advanced data augmentation and feature engineering techniques. By integrating interaction metrics from user behavior logs and semantic features from user self-reflections over a nine-week intervention program, our approach effectively characterizes user’s mental health intervention behavior patterns. The results validate the efficacy of temporal models for continuous dropout prediction.