TY - JOUR
T1 - From smartphone data to clinically relevant predictions
T2 - A systematic review of digital phenotyping methods in depression
AU - Leaning, Imogen E.
AU - Ikani, Nessa
AU - Savage, Hannah S.
AU - Leow, Alex
AU - Beckmann, Christian
AU - Ruhé, Henricus G.
AU - Marquand, Andre F.
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4
AB - 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. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4
KW - Digital phenotyping
KW - Major Depressive Disorder
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85182911048&partnerID=8YFLogxK
U2 - 10.1016/j.neubiorev.2024.105541
DO - 10.1016/j.neubiorev.2024.105541
M3 - Review article
C2 - 38215802
SN - 0149-7634
VL - 158
JO - Neuroscience and Biobehavioral Reviews
JF - Neuroscience and Biobehavioral Reviews
M1 - 105541
ER -