从智能手机数据到临床相关预测：抑郁症数字表型分析方法的系统评价

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

作者信息

Leaning Imogen E, Ikani Nessa, Savage Hannah S, Leow Alex, Beckmann Christian, Ruhé Henricus G, Marquand Andre F

机构信息

Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.

Department of Developmental Psychology, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, the Netherlands.

出版信息

Neurosci Biobehav Rev. 2024 Mar;158:105541. doi: 10.1016/j.neubiorev.2024.105541. Epub 2024 Jan 11.

DOI:10.1016/j.neubiorev.2024.105541

PMID:38215802

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. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.

摘要

背景

基于智能手机的数字表型分析能够在个体日常生活中收集具有潜在临床相关性的信息。这可能改善对重度抑郁症（MDD）患者的监测和干预。本系统评价的目的是调查目前在MDD中使用的数字表型分析特征和方法。

方法

我们检索了PubMed、PsycINFO、Embase、Scopus和Web of Science（2023年11月10日），查找包括以下内容的文章：（1）MDD人群，（2）基于智能手机的特征，（3）经过验证的评分。使用多种来源评估偏倚风险。在分析目标（将特征与抑郁相关联、预测症状严重程度、诊断、情绪状态/发作、其他）内对研究进行比较。纳入了24项研究（9801名参与者）。