Within-person prediction of depressive symptom change using year-long Screenome data and CES-D assessments

TL;DR

This study demonstrates that combining digital phenotyping data from smartphone screenshots with CES-D assessments can effectively predict individual depressive symptom trajectories over time, enabling earlier intervention.

Contribution

It introduces a novel within-person prediction model using Screenomics data and CES-D assessments, achieving high accuracy in forecasting symptom changes.

Findings

XGBoost model achieved AUC of 0.906 for severity band crossings.

Behavioral features like social media use and overnight activity predicted worsening.

Model generalized well to unseen individuals with AUC of 0.821.

Abstract

Predicting whether an individual's depressive symptoms will worsen, remain stable, or improve over the coming weeks can enable earlier and more targeted care, yet prospective within-person trajectory prediction remains largely unaddressed in digital phenotyping. We combine fortnightly CES-D assessments with over 100 million screenshots captured every five seconds via the Stanford Screenomics platform from 96 adults followed for approximately one year (M = 20.9, SD = 3.9 assessments per participant, 2,002 total observations). We frame prediction as a within-person classification task: whether symptoms will worsen, remain stable, or improve over the subsequent fortnight, operationalized in three ways to capture clinically meaningful change. Under temporal holdout, XGBoost achieves an AUC of 0.906 for crossings of established CES-D severity bands and 0.755 for change relative to each participant's own within-person variability, generalizing to unseen individuals (AUC = 0.821). Each person's typical symptom level was the only statistically significant predictor above the most recent CES-D score; without it, the most consequential worsening transitions go undetected. Screenome-derived behavioral features revealed prodromal patterns of worsening, including escalating social media use, fragmented device engagement, and changes in overnight activity, with substantial individual heterogeneity. These findings establish a proof-of-concept foundation for monitoring systems that could identify individuals approaching clinical deterioration before symptoms reach a crisis point.