Longitudinal PTSD network structure: measuring PTSD symptom networks over 5 years
Network modeling has been applied in a range of trauma-exposed samples, yet results are limited by an over reliance on cross-sectional data. The current analyses used posttraumatic stress disorder (PTSD) symptom data collected over a 5-year period to estimate a more robust between-subject network an...
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Published in | Psychological medicine Vol. 53; no. 8; pp. 3525 - 3532 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Cambridge, UK
Cambridge University Press
01.06.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Network modeling has been applied in a range of trauma-exposed samples, yet results are limited by an over reliance on cross-sectional data. The current analyses used posttraumatic stress disorder (PTSD) symptom data collected over a 5-year period to estimate a more robust between-subject network and an associated symptom change network.
A PTSD symptom network is measured in a sample of military veterans across four time points (
s = 1254, 1231, 1106, 925). The repeated measures permit isolating between-subject associations by limiting the effects of within-subject variability. The result is a highly reliable PTSD symptom network. A symptom slope network depicting covariation of symptom change over time is also estimated.
Negative trauma-related emotions had particularly strong associations with the network. Trauma-related amnesia, sleep disturbance, and self-destructive behavior had weaker overall associations with other PTSD symptoms.
PTSD's network structure appears stable over time. There is no single 'most important' node or node cluster. The relevance of self-destructive behavior, sleep disturbance, and trauma-related amnesia to the PTSD construct may deserve additional consideration. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 0033-2917 1469-8978 1469-8978 |
DOI: | 10.1017/S0033291722000095 |