What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis

萧条(经济学) 心理学 心情 精神科 抑郁症状 DSM-5 中心性 临床心理学 焦虑 数学 组合数学 宏观经济学 经济
作者
Eiko I. Fried,Sacha Epskamp,Randolph M. Nesse,Francis Tuerlinckx,Denny Borsboom
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:189: 314-320 被引量:770
标识
DOI:10.1016/j.jad.2015.09.005
摘要

The symptoms for Major Depression (MD) defined in the DSM-5 differ markedly from symptoms assessed in common rating scales, and the empirical question about core depression symptoms is unresolved. Here we conceptualize depression as a complex dynamic system of interacting symptoms to examine what symptoms are most central to driving depressive processes. We constructed a network of 28 depression symptoms assessed via the Inventory of Depressive Symptomatology (IDS-30) in 3,463 depressed outpatients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. We estimated the centrality of all IDS-30 symptoms, and compared the centrality of DSM and non-DSM symptoms; centrality reflects the connectedness of each symptom with all other symptoms. A network with 28 intertwined symptoms emerged, and symptoms differed substantially in their centrality values. Both DSM symptoms (e.g., sad mood) and non-DSM symptoms (e.g., anxiety) were among the most central symptoms, and DSM criteria were not more central than non-DSM symptoms. Many subjects enrolled in STAR*D reported comorbid medical and psychiatric conditions which may have affected symptom presentation. The network perspective neither supports the standard psychometric notion that depression symptoms are equivalent indicators of MD, nor the common assumption that DSM symptoms of depression are of higher clinical relevance than non-DSM depression symptoms. The findings suggest the value of research focusing on especially central symptoms to increase the accuracy of predicting outcomes such as the course of illness, probability of relapse, and treatment response.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
张路完成签到 ,获得积分10
1秒前
LSH慧发布了新的文献求助10
2秒前
英姑应助熊熊阁采纳,获得10
4秒前
Medici完成签到,获得积分10
5秒前
6秒前
科研通AI6.4应助taozi采纳,获得50
6秒前
6秒前
6秒前
年轻枕头完成签到,获得积分10
8秒前
EH完成签到,获得积分10
9秒前
小破孩发布了新的文献求助10
9秒前
神勇寄风完成签到,获得积分10
10秒前
haoshuo发布了新的文献求助10
11秒前
Yybe发布了新的文献求助10
11秒前
学无止境完成签到 ,获得积分10
12秒前
戊烷完成签到,获得积分10
14秒前
仄言完成签到,获得积分20
15秒前
song完成签到,获得积分10
15秒前
Lin完成签到,获得积分10
16秒前
诚心的小鸽子完成签到,获得积分10
16秒前
笑点低的云朵应助河马dd采纳,获得10
16秒前
跳跃飞瑶完成签到,获得积分10
17秒前
小美完成签到,获得积分10
17秒前
18秒前
19秒前
20秒前
20秒前
20秒前
科研通AI6.4应助洋1采纳,获得10
21秒前
22秒前
FashionBoy应助科研通管家采纳,获得10
22秒前
小二郎应助科研通管家采纳,获得10
22秒前
爆米花应助科研通管家采纳,获得10
22秒前
22秒前
丘比特应助科研通管家采纳,获得10
23秒前
molihuakai应助科研通管家采纳,获得10
23秒前
23秒前
情怀应助科研通管家采纳,获得10
23秒前
23秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Markov Chain Monte Carlo 5000
《上海道教》季刊 2200
Evidence Summary. Injection (subcutaneous):op- timal administration 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Curating Socialism: A Handbook of International Art Exhibitions 1947-1989 530
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7486158
求助须知:如何正确求助?哪些是违规求助? 9078096
关于积分的说明 19360178
捐赠科研通 7100594
什么是DOI,文献DOI怎么找? 3248356
关于科研通互助平台的介绍 2417656
邀请新用户注册赠送积分活动 2233782