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Examining dynamic symptom associations. the cases of post-traumatic stress disorder and repetitive negative thinking
Examining dynamic symptom associations. the cases of post-traumatic stress disorder and repetitive negative thinking
Conceptualization of mental disorders and changes in their diagnostic criteria have been present in research and practice for a long time. Recently, network analysis has been suggested as an alternative approach to explore the emergence of mental disorders. Namely, according to the network approach, symptoms and their associations are crucial for the development and maintenance of mental disorders. Estimated symptom networks provide an insight into the set of symptoms that characterize certain disorders and can help identify the core symptoms of the specific disorder, such as post-traumatic stress disorder (PTSD). The first evidence of the presence of post-traumatic stress symptoms comes from a few thousand years before Christ. Nevertheless, PTSD was first included in the diagnostic classification system in 1980. To date, there are still ongoing debates related to the number and types of symptoms that should be included in the diagnostic criteria. Those debates are best reflected in the different diagnostic criteria for PTSD in the current versions of the two major diagnostic systems: the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the eleventh version of the International Classification of Diseases (ICD-11). According to the most recent meta-analysis, since the first PTSD network study in 2015, more than 70 cross-sectional studies have been identified across non-clinical, subclinical, and clinical samples (for detailed information about these studies, please see: Isvoranu et al., 2021). Overall, there were inconsistent findings, which could be attributed to the heterogeneity of the sample. Furthermore, in contrast to cross-sectional studies, only a few studies investigated temporal dynamics between PTSD symptoms within a day using the experience sampling method (ESM; e.g., repeated, daily, symptom assessments via smartphone). Next to network approach which generally investigates symptom interrelations, it is also possible to examine specific association by focusing on the specific symptoms and/or processes. For example, the association between repetitive negative thinking (RNT) and negative affect (NA) was found across different mental disorders, including PTSD. There is an emerging question whether based on this association, it is possible to identify people of risk for psychopathology. This dissertation includes four empirical studies that address several issues. First, Study I tested whether trauma type is one of the potential moderators that could explain inconsistent findings in PTSD cross-sectional network literature to date. Specifically, it was investigated whether characteristics of the two trauma types (type I trauma = single event; sudden and unexpected, high levels of acute threat; N = 286 vs. type II trauma = repeated and/or protracted; anticipated; N=187) influence the symptom constellation in the cross-sectional PTSD networks across PTSD patients. Edges (symptom associations) that repeatedly emerged in the previous PTSD network studies were replicated. Furthermore, results showed that two networks globally differed. Additionally, specific edges (symptom associations) that differed between symptom networks of the two trauma types were identified. Results implicate that trauma type contributes to the inconsistent findings of the cross-sectional PTSD network literature to date. Second, PTSD symptom dynamics within the day were investigated using the experience sampling method (ESM; intensive, repeated smartphone assessments) for 15 days in a row, four time per day. Namely, Study II was designed to investigate temporal PTSD networks (illustrating how symptoms influence each other at the subsequent assessment) and contemporaneous PTSD networks (illustrating how symptoms influence each other within the same assessment). This study focused on PTSD patients (N=48) who were in the diagnostic phase but had not yet started with the trauma focused treatment. Results implicated the importance of estimating both contemporaneous and temporal networks, as they differed in important ways. In the temporal network, it was identified that changes in hypervigilance predicted changes in the most symptoms at the next assessment. As the focus of the second study was on the within-day dynamics, items related to sleep disturbances were excluded since they only referred to sleep at night and were assessed just once in the morning. Therefore, Study III focused specifically on the temporal association of trauma-related sleep disturbances, namely insomnia symptoms and nightmares, on PTSD symptoms in the following day. This study analyzed the same sample as in Study II. Multilevel model analyses showed that insomnia and nightmares were significant predictors of PTSD symptomatology on the following day, but that this association was unidirectional, as PTSD symptoms did not significantly predict insomnia and nightmares. Finally, Study I investigated generally interrelations between PTSD symptoms. In addition, it is possible to specifically examine dynamic symptom associations by focusing on the specific symptoms interaction and explore their predictive value. Therefore, Study IV used a statistical clustering algorithm, specifically focusing on the association between RNT and NA, in order to investigate the predictive value of this association. Study IV looked at three experience-sampling data sets across a young population (N=130; N=120; N=186;). The analysis showed that two groups of individuals were repeatedly identified. One group had a higher bidirectional association between RNT and NA (and also higher inertia) than the other group. Additionally, results implied that it is possible to identify individuals at risk of developing depressive symptoms during the 3-month follow-up based on the interaction between dynamic associations between RNT and NA and levels of NA over the experience sampling phase. Lastly, this dissertation outlines limitations and as well as practical and methodical directions for future research in the ESM and network analysis field generally and for PTSD in particular. Overall, the obtained results from this dissertation and the implications for future research should contribute to the general improvement of the diagnostic process and to treatment, specifically for PTSD patients.
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Stefanović, Mina
2022
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Stefanović, Mina (2022): Examining dynamic symptom associations: the cases of post-traumatic stress disorder and repetitive negative thinking. Dissertation, LMU München: Faculty of Psychology and Educational Sciences
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Abstract

Conceptualization of mental disorders and changes in their diagnostic criteria have been present in research and practice for a long time. Recently, network analysis has been suggested as an alternative approach to explore the emergence of mental disorders. Namely, according to the network approach, symptoms and their associations are crucial for the development and maintenance of mental disorders. Estimated symptom networks provide an insight into the set of symptoms that characterize certain disorders and can help identify the core symptoms of the specific disorder, such as post-traumatic stress disorder (PTSD). The first evidence of the presence of post-traumatic stress symptoms comes from a few thousand years before Christ. Nevertheless, PTSD was first included in the diagnostic classification system in 1980. To date, there are still ongoing debates related to the number and types of symptoms that should be included in the diagnostic criteria. Those debates are best reflected in the different diagnostic criteria for PTSD in the current versions of the two major diagnostic systems: the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the eleventh version of the International Classification of Diseases (ICD-11). According to the most recent meta-analysis, since the first PTSD network study in 2015, more than 70 cross-sectional studies have been identified across non-clinical, subclinical, and clinical samples (for detailed information about these studies, please see: Isvoranu et al., 2021). Overall, there were inconsistent findings, which could be attributed to the heterogeneity of the sample. Furthermore, in contrast to cross-sectional studies, only a few studies investigated temporal dynamics between PTSD symptoms within a day using the experience sampling method (ESM; e.g., repeated, daily, symptom assessments via smartphone). Next to network approach which generally investigates symptom interrelations, it is also possible to examine specific association by focusing on the specific symptoms and/or processes. For example, the association between repetitive negative thinking (RNT) and negative affect (NA) was found across different mental disorders, including PTSD. There is an emerging question whether based on this association, it is possible to identify people of risk for psychopathology. This dissertation includes four empirical studies that address several issues. First, Study I tested whether trauma type is one of the potential moderators that could explain inconsistent findings in PTSD cross-sectional network literature to date. Specifically, it was investigated whether characteristics of the two trauma types (type I trauma = single event; sudden and unexpected, high levels of acute threat; N = 286 vs. type II trauma = repeated and/or protracted; anticipated; N=187) influence the symptom constellation in the cross-sectional PTSD networks across PTSD patients. Edges (symptom associations) that repeatedly emerged in the previous PTSD network studies were replicated. Furthermore, results showed that two networks globally differed. Additionally, specific edges (symptom associations) that differed between symptom networks of the two trauma types were identified. Results implicate that trauma type contributes to the inconsistent findings of the cross-sectional PTSD network literature to date. Second, PTSD symptom dynamics within the day were investigated using the experience sampling method (ESM; intensive, repeated smartphone assessments) for 15 days in a row, four time per day. Namely, Study II was designed to investigate temporal PTSD networks (illustrating how symptoms influence each other at the subsequent assessment) and contemporaneous PTSD networks (illustrating how symptoms influence each other within the same assessment). This study focused on PTSD patients (N=48) who were in the diagnostic phase but had not yet started with the trauma focused treatment. Results implicated the importance of estimating both contemporaneous and temporal networks, as they differed in important ways. In the temporal network, it was identified that changes in hypervigilance predicted changes in the most symptoms at the next assessment. As the focus of the second study was on the within-day dynamics, items related to sleep disturbances were excluded since they only referred to sleep at night and were assessed just once in the morning. Therefore, Study III focused specifically on the temporal association of trauma-related sleep disturbances, namely insomnia symptoms and nightmares, on PTSD symptoms in the following day. This study analyzed the same sample as in Study II. Multilevel model analyses showed that insomnia and nightmares were significant predictors of PTSD symptomatology on the following day, but that this association was unidirectional, as PTSD symptoms did not significantly predict insomnia and nightmares. Finally, Study I investigated generally interrelations between PTSD symptoms. In addition, it is possible to specifically examine dynamic symptom associations by focusing on the specific symptoms interaction and explore their predictive value. Therefore, Study IV used a statistical clustering algorithm, specifically focusing on the association between RNT and NA, in order to investigate the predictive value of this association. Study IV looked at three experience-sampling data sets across a young population (N=130; N=120; N=186;). The analysis showed that two groups of individuals were repeatedly identified. One group had a higher bidirectional association between RNT and NA (and also higher inertia) than the other group. Additionally, results implied that it is possible to identify individuals at risk of developing depressive symptoms during the 3-month follow-up based on the interaction between dynamic associations between RNT and NA and levels of NA over the experience sampling phase. Lastly, this dissertation outlines limitations and as well as practical and methodical directions for future research in the ESM and network analysis field generally and for PTSD in particular. Overall, the obtained results from this dissertation and the implications for future research should contribute to the general improvement of the diagnostic process and to treatment, specifically for PTSD patients.