Daily Affect Fluctuations in Phone Screen Content Predict Anxiety and Depressive Symptoms

TL;DR

This study used extensive smartphone data and deep learning to show that daily fluctuations in digital media affect mental health, with negative content linked to increased anxiety and depression.

Contribution

It provides the first large-scale, longitudinal analysis linking real-time digital media affect to mental health symptoms, highlighting individual variability.

Findings

Within-person fluctuations in media affect predict mental health changes.

Negative, low-arousal content correlates with higher anxiety and depression.

Digital behavior's dynamic nature requires personalized measurement approaches.

Abstract

The relationship between digital media use and mental health remains poorly understood, in part because real-world digital behavior is rarely captured at scale. This intensive longitudinal study tracked participants' complete natural smartphone interactions over one year. We collected screenshots every 5 seconds from 145 adults (yielding 111 million screenshots), alongside biweekly assessments of anxiety and depression (mean = 24 surveys). The valence and arousal of each screenshot were assessed using a deep learning affect model. Individuals showed highly idiosyncratic media patterns, with substantially more variance in anxiety and depression accounted for within-person than between-person. Day-to-day fluctuations in the valence and arousal of a person's screen content predicted subsequent changes in depression and anxiety, whereas between-person differences did not. Specifically, greater exposure to low-arousal negative content was associated with higher depression and anxiety. These findings underscore the dynamic, idiosyncratic nature of digital consumption and the need for targeted measurement and intervention.