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From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression
Leaning,I. Ikani,N. Savage,H. Leow,A. Beckmann,C. Ruhé,H. Marquand,A.
Leaning,I.
Ikani,N.
Savage,H.
Leow,A.
Beckmann,C.
Ruhé,H.
Marquand,A.
Abstract
Background Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. Methods We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. Results Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. Discussion: Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring.
Description
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Digital phenotyping, Major Depressive Disorder, Smartphone
Citation
Leaning, I, Ikani, N, Savage, H, Leow, A, Beckmann, C, Ruhé, H & Marquand, A 2024, 'From smartphone data to clinically relevant predictions : A systematic review of digital phenotyping methods in depression', Neuroscience and Biobehavioral Reviews, vol. 158, 105541. https://doi.org/10.1016/j.neubiorev.2024.105541