Network-based methods for psychometric data of eating disorders: A systematic review
<h4>Background</h4> Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected sym...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621460/?tool=EBI |
| _version_ | 1798044718794276864 |
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| author | Clara Punzi Manuela Petti Paolo Tieri |
| author_facet | Clara Punzi Manuela Petti Paolo Tieri |
| author_sort | Clara Punzi |
| collection | DOAJ |
| description | <h4>Background</h4> Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person’s eating behavior. <h4>Aims</h4> We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. <h4>Methods</h4> A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. <h4>Results</h4> Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. <h4>Conclusions</h4> We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement. |
| first_indexed | 2024-04-11T23:08:46Z |
| format | Article |
| id | doaj.art-737f61ff53e542d393e6b7523f32f277 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T23:08:46Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-737f61ff53e542d393e6b7523f32f2772022-12-22T03:57:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710Network-based methods for psychometric data of eating disorders: A systematic reviewClara PunziManuela PettiPaolo Tieri<h4>Background</h4> Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person’s eating behavior. <h4>Aims</h4> We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. <h4>Methods</h4> A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. <h4>Results</h4> Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. <h4>Conclusions</h4> We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621460/?tool=EBI |
| spellingShingle | Clara Punzi Manuela Petti Paolo Tieri Network-based methods for psychometric data of eating disorders: A systematic review PLoS ONE |
| title | Network-based methods for psychometric data of eating disorders: A systematic review |
| title_full | Network-based methods for psychometric data of eating disorders: A systematic review |
| title_fullStr | Network-based methods for psychometric data of eating disorders: A systematic review |
| title_full_unstemmed | Network-based methods for psychometric data of eating disorders: A systematic review |
| title_short | Network-based methods for psychometric data of eating disorders: A systematic review |
| title_sort | network based methods for psychometric data of eating disorders a systematic review |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621460/?tool=EBI |
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