Emotion-Focused Therapy. Current trends and efficacy studies

Terapie zaměřená na emoce (Emotion-Focused Therapy, dále jen EFT) je integrativní neohumanistický psychoterapeutický přístup, jehož vznik a vývoj je podstatně spjat s empirickým výzkumem psychoterapeutického procesu a mechanismu terapeutické změny. Tato přehledová studie se věnuje výzkumu účinnosti EFT při léčbě úzkostných a dalších duševních poruch či obtíží, které byly publikovány ve druhé dekádě 21. století. Dosavadní výzkumná zjištění jsou prezentována v kontextu individuální, rodinné i skupinové formy EFT. Prostor je věnován i některým výzkumným trendům a inovacím, které se v rámci ověřování účinnosti EFT objevily teprve v posledních letech – především transdiagnostickému přístupu.Emotion-Focused Therapy is (understood as) an integrative, neo-humanistic, therapeutic approach whose formation and further development is substantially bound with empirical research of psychotherapeutic processes and mechanisms of change in therapy. This paper deals with empirical studies that were published during the second decade of the 21st century, focused on the efficacy of EFT used in the therapy of anxiety disorder, as well as other mental disorders. Current findings are being presented in the context of an individual-focused, family-focused and group-focused form of EFT. Particular attention is paid to selected new directions and innovations in the field of research that emerged only in recent years, during the verification process of the EFT – especially the transdiagnostic approach

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way

A Czech version of the Overall Anxiety Severity and Impairment Scale (OASIS): Standardization and psychometric properties

Background - The Overall Anxiety Severity and Impairment Scale (OASIS) is a transdiagnostic measure that assesses severity and impairment associated with anxiety disorders. However, its psychometric properties were primarily examined in English-speaking or western countries. Therefore, this study aims to examine its psychometric parameters in the Czech Republic. Methods - A large representative sample (n=1769), a clinical sample (n=60) and a retest sample (n=20) were used. In addition to the OASIS, conventional measures of anxiety, depression, personality traits, self-esteem, life satisfaction, and other scales were also administered. Moreover, we examined the latent structure, reliability, validity, and the cut-off score for the Reliable Change Index (RCI) and the Clinically Significant Change Index (CSI). Results - Higher anxiety was found in females, religious non-church members, and students. The Confirmatory Factor Analysis supported the adequate fit of the unidimensional solution: x2(4)=3.20; p<0.525; CFI=1.000; TLI=1.000;RMSEA=0, SRMR=0. The measurement equivalence examination indicated that the OASIS measures anxiety invariantly between males and females. The validity of the OASIS was supported by positive associations with neuroticism, depression, perceived stress, guilt, shame, and the established anxiety measures. The internal consistency was excellent (Cronbach’s alpha=0.96, McDonald’s omega=0.96). The test-retest reliability was acceptable (r=0.66). The cut-off for the CSI is 6 and the RCI is 5.32. Limitations - The main limitation is the use of self-report questionnaires for validity testing and lower test-retest reliability. Conclusions - The OASIS represents a reliable and valid instrument for assessing anxiety. Due to its shortness, excellent psychometric properties and percentile norms, it is especially useful for short and accurate screening of anxiety and mapping therapeutic changes in clinical practice

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way