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.
@article{arxiv.2603.13511,
title = {Daily Affect Fluctuations in Phone Screen Content Predict Anxiety and Depressive Symptoms},
author = {Christopher A. Kelly and Yikun Chi and Nicholas Haber and Byron Reeves and Mu-Jung Cho and Thomas N. Robinson and Nilam Ram and Johannes C. Eichstaedt},
journal= {arXiv preprint arXiv:2603.13511},
year = {2026}
}