Depression Screening from Text Message Reply Latency

Depression is both debilitating and prevalent. While treatable, it is often undiagnosed. Passive depression screening is crucial, but leveraging data from Smartphones and social media has privacy concerns. Inspired by the known relationship between depression and slower information processing speed, we hypothesize the latency of texting replies will contain useful information in screening for depression. Specifically, we extract nine reply latency related features from crowd-sourced text message conversation meta-data. By considering text metadata instead of content, we mitigate the privacy concerns. To predict binary screening survey scores, we explore a variety of machine learning methods built on principal components of the latency features. Our findings demonstrate that an XGBoost model built with one principal component achieves an F1 score of 0.67, AUC of 0.72, and Accuracy of 0.69. Thus, we confirm that reply latency of texting has promise as a modality for depression screening.