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Dynamic networks of PTSD symptoms during conflict.
Psychol Med 2018; 48(14):2409-2417PM

Abstract

BACKGROUND

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict.

METHODS

Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network.

RESULTS

Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms.

CONCLUSIONS

This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Authors+Show Affiliations

Department of Community Mental Health,University of Haifa,Haifa,Israel.Department of Community Mental Health,University of Haifa,Haifa,Israel.Department of Psychology,University of Amsterdam,Amsterdam,The Netherlands.Department of Psychology,University of Amsterdam,Amsterdam,The Netherlands.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29486811

Citation

Greene, Talya, et al. "Dynamic Networks of PTSD Symptoms During Conflict." Psychological Medicine, vol. 48, no. 14, 2018, pp. 2409-2417.
Greene T, Gelkopf M, Epskamp S, et al. Dynamic networks of PTSD symptoms during conflict. Psychol Med. 2018;48(14):2409-2417.
Greene, T., Gelkopf, M., Epskamp, S., & Fried, E. (2018). Dynamic networks of PTSD symptoms during conflict. Psychological Medicine, 48(14), pp. 2409-2417. doi:10.1017/S0033291718000351.
Greene T, et al. Dynamic Networks of PTSD Symptoms During Conflict. Psychol Med. 2018;48(14):2409-2417. PubMed PMID: 29486811.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dynamic networks of PTSD symptoms during conflict. AU - Greene,Talya, AU - Gelkopf,Marc, AU - Epskamp,Sacha, AU - Fried,Eiko, Y1 - 2018/02/28/ PY - 2018/3/1/pubmed PY - 2019/6/22/medline PY - 2018/3/1/entrez KW - ESM KW - intensive longitudinal assessment KW - multilevel VAR KW - network analysis KW - posttraumatic stress KW - trauma KW - war SP - 2409 EP - 2417 JF - Psychological medicine JO - Psychol Med VL - 48 IS - 14 N2 - BACKGROUND: Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict. METHODS: Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network. RESULTS: Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms. CONCLUSIONS: This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions. SN - 1469-8978 UR - https://www.unboundmedicine.com/medline/citation/29486811/Dynamic_networks_of_PTSD_symptoms_during_conflict_ L2 - https://www.cambridge.org/core/product/identifier/S0033291718000351/type/journal_article DB - PRIME DP - Unbound Medicine ER -