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Personalized mood prediction from patterns of behavior collected with smartphones

Authors :
Brunilda Balliu
Chris Douglas
Darsol Seok
Liat Shenhav
Yue Wu
Doxa Chatzopoulou
William Kaiser
Victor Chen
Jennifer Kim
Sandeep Deverasetty
Inna Arnaudova
Robert Gibbons
Eliza Congdon
Michelle G. Craske
Nelson Freimer
Eran Halperin
Sriram Sankararaman
Jonathan Flint
Source :
npj Digital Medicine, Vol 7, Iss 1, Pp 1-14 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R 2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Details

Language :
English
ISSN :
23986352
Volume :
7
Issue :
1
Database :
Directory of Open Access Journals
Journal :
npj Digital Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.06a6b1f5fd1e4f3a8d2ecf4755c7b8a7
Document Type :
article
Full Text :
https://doi.org/10.1038/s41746-024-01035-6