Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Katharina Senger
  • Jens Heider
  • Maria Kleinstaeuber
  • Matthias Sehlbrede
  • Michael Witthoft
  • Annette Schroeder
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000730736200013&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1097/PSY.0000000000000999
eISSN
1534-7796
Externe Identifier
  • Clarivate Analytics Document Solution ID: XP2YK
  • PubMed Identifier: 34428004
ISSN
0033-3174
Ausgabe der Veröffentlichung
1
Zeitschrift
PSYCHOSOMATIC MEDICINE
Schlüsselwörter
  • network analyses
  • persistent somatic symptoms
  • somatic symptom disorder
  • SOMS-7T
  • clinical sample
  • CI = confidence interval
  • CS = correlation stability coefficients
  • EBIC = extended Bayesian information criterion
  • PSS = persistent physical symptoms
  • SOMS-7T=Screening of Somatoform Disorder
  • SSD = somatic symptom disorder
Paginierung
74 - 85
Datum der Veröffentlichung
2022
Status
Published
Titel
Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples
Sub types
  • Article
Ausgabe der Zeitschrift
84

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:title>ABSTRACT</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>Previous attempts to group persistent somatic symptoms (PSSs) with factor-analytic approaches have obtained heterogeneous results. An alternative approach that seems to be more suitable is the network theory. Compared with factor analysis, which focuses on the underlying factor of symptoms, network analysis focuses on the dynamic relationships and interactions among different symptoms. The main aim of this study is to apply the network approach to examine the heterogeneous structure of PSS within two clinical samples.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>The first data set consisted of <jats:italic toggle="yes">n</jats:italic> = 254 outpatients who were part of a multicenter study. The second data set included <jats:italic toggle="yes">n</jats:italic> = 574 inpatients, both with somatoform disorders. Somatic symptom severity was assessed with the Screening of Somatoform Disorder (SOMS-7T).</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Results indicate that there are five main symptom groups that were found in both samples: neurological, gastrointestinal, urogenital, cardiovascular, and musculoskeletal symptoms. Although patterns of symptoms with high connection to each other look quite similar in both networks, the order of the most central symptoms (e.g., symptoms with a high connection to other symptoms in the network) differs.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>This work is the first to estimate the structure of PSS using network analysis. A next step could be first to replicate our findings before translating them into clinical practice. Second, results may be useful for generating hypotheses to be tested in future studies, and the results open new opportunities for a better understanding for etiology, prevention, and intervention research.</jats:p> </jats:sec>
Autoren
  • Katharina Senger
  • Jens Heider
  • Maria Kleinstäuber
  • Matthias Sehlbrede
  • Michael Witthöft
  • Annette Schröder
DOI
10.1097/psy.0000000000000999
eISSN
1534-7796
ISSN
0033-3174
Ausgabe der Veröffentlichung
1
Zeitschrift
Psychosomatic Medicine
Sprache
en
Online publication date
2021
Paginierung
74 - 85
Datum der Veröffentlichung
2022
Status
Published
Herausgeber
Ovid Technologies (Wolters Kluwer Health)
Herausgeber URL
http://dx.doi.org/10.1097/psy.0000000000000999
Datum der Datenerfassung
2023
Titel
Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples
Ausgabe der Zeitschrift
84

Datenquelle: Crossref

Abstract
<h4>Objective</h4>Previous attempts to group persistent somatic symptoms (PSSs) with factor-analytic approaches have obtained heterogeneous results. An alternative approach that seems to be more suitable is the network theory. Compared with factor analysis, which focuses on the underlying factor of symptoms, network analysis focuses on the dynamic relationships and interactions among different symptoms. The main aim of this study is to apply the network approach to examine the heterogeneous structure of PSS within two clinical samples.<h4>Methods</h4>The first data set consisted of n = 254 outpatients who were part of a multicenter study. The second data set included n = 574 inpatients, both with somatoform disorders. Somatic symptom severity was assessed with the Screening of Somatoform Disorder (SOMS-7T).<h4>Results</h4>Results indicate that there are five main symptom groups that were found in both samples: neurological, gastrointestinal, urogenital, cardiovascular, and musculoskeletal symptoms. Although patterns of symptoms with high connection to each other look quite similar in both networks, the order of the most central symptoms (e.g., symptoms with a high connection to other symptoms in the network) differs.<h4>Conclusions</h4>This work is the first to estimate the structure of PSS using network analysis. A next step could be first to replicate our findings before translating them into clinical practice. Second, results may be useful for generating hypotheses to be tested in future studies, and the results open new opportunities for a better understanding for etiology, prevention, and intervention research.
Addresses
  • From the Department of Psychology (Senger, Heider, Schröder), University of Koblenz-Landau, Landau, Germany; Department of Psychology (Kleinstäuber), Emma Eccles Jones College of Education and Health Services, Utah State University, Logan, Utah; Section of Health Care Research and Rehabilitation Research, Faculty of Medicine and Medical Centre (Sehlbrede), University of Freiburg, Freiburg im Breisgau; and Division of Clinical Psychology and Psychotherapy (Witthöft), Johannes Gutenberg University of Mainz, Mainz, Germany.
Autoren
  • Katharina Senger
  • Jens Heider
  • Maria Kleinstäuber
  • Matthias Sehlbrede
  • Michael Witthöft
  • Annette Schröder
DOI
10.1097/psy.0000000000000999
eISSN
1534-7796
Externe Identifier
  • PubMed Identifier: 34428004
Open access
false
ISSN
0033-3174
Ausgabe der Veröffentlichung
1
Zeitschrift
Psychosomatic medicine
Schlüsselwörter
  • Humans
  • Somatoform Disorders
  • Medically Unexplained Symptoms
Sprache
eng
Medium
Print
Paginierung
74 - 85
Datum der Veröffentlichung
2022
Status
Published
Datum der Datenerfassung
2021
Titel
Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples.
Sub types
  • Research Support, Non-U.S. Gov't
  • Multicenter Study
  • Journal Article
Ausgabe der Zeitschrift
84

Datenquelle: Europe PubMed Central

Abstract
OBJECTIVE: Previous attempts to group persistent somatic symptoms (PSSs) with factor-analytic approaches have obtained heterogeneous results. An alternative approach that seems to be more suitable is the network theory. Compared with factor analysis, which focuses on the underlying factor of symptoms, network analysis focuses on the dynamic relationships and interactions among different symptoms. The main aim of this study is to apply the network approach to examine the heterogeneous structure of PSS within two clinical samples. METHODS: The first data set consisted of n = 254 outpatients who were part of a multicenter study. The second data set included n = 574 inpatients, both with somatoform disorders. Somatic symptom severity was assessed with the Screening of Somatoform Disorder (SOMS-7T). RESULTS: Results indicate that there are five main symptom groups that were found in both samples: neurological, gastrointestinal, urogenital, cardiovascular, and musculoskeletal symptoms. Although patterns of symptoms with high connection to each other look quite similar in both networks, the order of the most central symptoms (e.g., symptoms with a high connection to other symptoms in the network) differs. CONCLUSIONS: This work is the first to estimate the structure of PSS using network analysis. A next step could be first to replicate our findings before translating them into clinical practice. Second, results may be useful for generating hypotheses to be tested in future studies, and the results open new opportunities for a better understanding for etiology, prevention, and intervention research.
Autoren
  • Katharina Senger
  • Jens Heider
  • Maria Kleinstäuber
  • Matthias Sehlbrede
  • Michael Witthöft
  • Annette Schröder
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/34428004
DOI
10.1097/PSY.0000000000000999
eISSN
1534-7796
Ausgabe der Veröffentlichung
1
Zeitschrift
Psychosom Med
Schlüsselwörter
  • Humans
  • Medically Unexplained Symptoms
  • Somatoform Disorders
Sprache
eng
Country
United States
Paginierung
74 - 85
PII
00006842-202201000-00009
Datum der Veröffentlichung
2022
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Titel
Network Analysis of Persistent Somatic Symptoms in Two Clinical Patient Samples.
Sub types
  • Journal Article
  • Multicenter Study
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
84

Datenquelle: PubMed

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