150 Psychologie
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Background: A growing number of studies are questioning the validity of current DSM diagnoses, either as "discrete" or distinct mental disorders and/or as phenotypically homogeneous syndromes. In this study, we investigated how symptom domains in patients with a main diagnosis of obsessive-compulsive disorder (OCD), panic disorder (PD) and social anxiety disorder (SAD) coaggregate. We predicted that symptom domains would be unrelated to DSM diagnostic categories and less likely to cluster with each other as severity increases.
Methods: One-hundred eight treatment seeking patients with a main diagnosis of OCD, SAD or PD were assessed with the Dimensional Obsessive-Compulsive Scale (DOCS), the Social Phobia Inventory (SPIN), the Panic and Agoraphobia Scale (PAS), the Anxiety Sensitivity Index-Revised (ASI-R), and the Beck Depression and Anxiety Inventories (BDI and BAI, respectively). Subscores generated by each scale (herein termed "symptom domains") were used to categorize individuals into mild, moderate and severe subgroups through K-means clusterization and subsequently analysed by means of multiple correspondence analysis.
Results: Broadly, we observed that symptom domains of OCD, SAD or PD tend to cluster on the basis of their severities rather than their DSM diagnostic labels. In particular, symptom domains and disorders were grouped into (1) a single mild "neurotic" syndrome characterized by multiple, closely related and co-occurring mild symptom domains; (2) two moderate (complicated and uncomplicated) "neurotic" syndromes (the former associated with panic disorder); and (3) severe but dispersed "neurotic" symptom domains.
Conlusion: Our findings suggest that symptoms domains of treatment seeking patients with OCD and anxiety disorders tend to be better conceptualized in terms of severity rather than rigid diagnostic boundaries.
Background: Prolonged Grief Disorder (PGD) is a distinct syndrome that follows bereavement. It is different from other mental disorders and is characterized by symptoms such as yearning for the bereaved, or intense emotional pain or distress. Violent loss is one major risk factor for the development of PGD.
Objectives: PGD has been studied in different populations, mostly in small samples, with only a few of them being representative. Although research highlighted that traumatic experiences paired with challenges related to migration make refugees particularly vulnerable to PGD, PGD has only rarely been studied in refugees. Thus, this article a) examines the prevalence of PGD in female refugees in Germany according to the criteria proposed by Prigerson and colleagues in 2009, and b) associates PGD with other common psychopathology (e.g. anxiety, depression, somatization and trauma).
Method: A total of 106 female refugees were assessed for bereavement and PGD. Of these 106 individuals, 85 were interviewed using the Prolonged Grief Disorder Scale (PG-13). Symptoms of anxiety and depression were assessed by the Hopkins Symptom Checklist-25 (HSCL-25), somatization was assessed by the Somatization Subscale of the Symptom-Checklist-90 (SCL-90), and the number of witnessed and experienced trauma was assessed by the Posttraumatic Diagnostic Scale (PDS/HTQ).
Results: Ninety of the 106 participants had experienced bereavement, and among those, 9.41% met criteria for PGD. The most frequent PGD symptoms were bitterness, longing or yearning for the bereaved, and lack of acceptance of the loss. Furthermore, grief symptoms were significantly associated with symptoms of depression, anxiety, somatization, and the number of experienced traumatic events.
Conclusion: The PGD prevalence rate found corresponds with previous studies, demonstrating that prevalence rates for PGD are especially high in refugees. High prevalence rates of bereavement as well as PGD highlight the need for assessment and specifically tailored treatment of PGD in refugees. PGD goes along with significant psychopathology, which further emphasizes the need for treatment.
In psychotherapy, movement synchrony seems to be associated with higher patient satisfaction and treatment outcome. However, it remains unclear whether movement synchrony rated by humans and movement synchrony identified by automated methods reflect the same construct. To address this issue, video sequences showing movement synchrony of patients and therapists (N = 10) or not (N = 10), were analyzed using motion energy analysis. Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). The concordance of ratings (human rating vs. automatic assessment) was computed for 600 different parameter configurations of the WCLC/WCLR to identify the parameter settings that measure movement synchrony best. A parameter configuration was rated as having a good identification rate if it yields high concordance with human-rated intervals (Cohen’s kappa) and a low amount of over-identified data points. Results indicate that 76 configurations had a good identification rate (IR) in the least complex condition (artificial). Two had an acceptable IR with regard to the naturally isolated condition. Concordance was low with regard to the most complex (naturally embedded) condition. A valid identification of movement synchrony strongly depends on parameter configuration and goes beyond the identification of synchrony by human raters. Differences between human-rated synchrony and nonverbal synchrony measured by algorithms are discussed.