From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression

Abstract

AbstractBackgroundSmartphone-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.MethodsWe 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.ResultsStudies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation.DiscussionStudies 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.European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework:https://osf.io/s7ay4