scholarly journals Integration of Cognitive and Emotional Processing Predicts Poor and Good Outcomes of Psychotherapy

2021 ◽  
Author(s):  
Giulio de Felice ◽  
Alessandro Giuliani ◽  
Silvia Andreassi ◽  
Franco Orsucci ◽  
Helmut Schöller ◽  
...  
Keyword(s):  
Emotional Processing ◽  
Principal Component ◽  
Well Being ◽  
University Hospital ◽  
Linear Discriminant ◽  
Daily Monitoring ◽  
Good Outcome ◽  
Poor Outcome ◽  
Monitoring Procedure ◽  
Affective Components

Abstract With the aim of investigating analogies and differences between psychotherapeutic processes, ten good-outcome and ten poor-outcome cases were selected from a sample of patients treated at the University Hospital of Psychiatry, Salzburg, Austria, and the Department of Psycho-Traumatology of the Clinic St. Irmingard, Prien am Chiemsee, Germany. They were monitored daily using the Therapy Process Questionnaire (TPQ), and their evolution over time was analyzed by means of Principal Components Analysis and Linear Discriminant Analysis. The results highlight that poor-outcome patients show a separation between cognitive processes (Principal Component 1) and relational-emotional processes (Principal Component 2) (r = − 0.25; p = n.s.), while in the good-outcome patients these aspects are well integrated (r = 0.70; p = 0.02). These results corroborate the validity of the daily monitoring procedure and also indicate the need for greater attention to the relational and emotional aspects of the patients rather than merely to their cognitive functioning and well-being. Key Message In poor-outcome cases, burdensome emotions and interpersonal experiences on the one hand and cognitive/well-being aspects of the mental processing on the other, stay unrelated. Successful therapeutic processing, as in good-outcome cases, requires an integration of cognitive and affective components.

scholarly journals What Differentiates Poor and Good Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research

2019 ◽  
Author(s):  
Giulio De Felice ◽  
Franco Orsucci ◽  
Andrea Scozzari ◽  
Omar Gelo ◽  
Gabriele Serafini ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Multivariate Time Series ◽  
Specific Effect ◽  
Psychotherapy Research ◽  
Principal Component ◽  
Coarse Grained ◽  
Emergent Properties ◽  
Good Outcome ◽  
Poor Outcome ◽  
Abstract Language

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. Static analysis (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the dynamic analysis, based on five coarse-grained descriptors related to variability, degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature to shift from a metaphorical to a fully quantitative status.

scholarly journals Impact of early-onset seizures on grading and outcome in patients with subarachnoid hemorrhage

2015 ◽  
Vol 122 (2) ◽  
pp. 408-413 ◽  
Author(s):  
Christian Fung ◽  
Mathias Balmer ◽  
Michael Murek ◽  
Werner J. Z'Graggen ◽  
Janine Abu-Isa ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Intracerebral Hemorrhage ◽  
Early Onset ◽  
Demographic Data ◽  
University Hospital ◽  
World Federation ◽  
Poor Grade ◽  
Good Outcome ◽  
Poor Outcome ◽  
The Impact

OBJECT After subarachnoid hemorrhage (SAH), seizure occurs in up to 26% of patients. The impact of seizure on outcome has been studied, yet its impact on grading is unknown. The authors evaluated the impact of early-onset seizures (EOS) on grading of spontaneous SAH and on outcome. METHODS This retrospective analysis included consecutive patients with SAH who were treated at the NeuroCenter, Inselspital, University Hospital Bern, Switzerland, between January 2005 and December 2010. Demographic data, clinical data, and reports of EOS were recorded. The EOS were defined as seizures occurring within 24 hours after ictus. Patients were graded according to the World Federation of Neurosurgical Societies (WFNS) scale pre- and postresuscitation and dichotomized into good (WFNS I–III) and poor (WFNS IV–V) grades. Outcome was assessed at 6 months by using the modified Rankin Scale (mRS); an mRS score of 0–3 was considered a good outcome and an mRS score of 4–6 was considered a poor outcome. RESULTS Forty-one of 425 patients with SAH had EOS. Twenty-seven of those 41 patients (65.9%) had a poor WFNS grade. Twenty-eight (68.3%) achieved a good outcome, 11 (26.8%) had a poor outcome, and 2 (4.9%) were lost to followup. Early-onset seizures were proven in 9 of 16 electroencephalograms. The EOS were associated with poor WFNS grade (OR 2.81, 97.5% CI 1.14–7.46; p = 0.03) and good outcome (OR 4.01, 97.5% CI 1.63–10.53; p = 0.03). Increasing age, hydrocephalus, intracerebral hemorrhage, and intraventricular hemorrhage were associated with poor WFNS grade, whereas only age, intracerebral hemorrhage (p < 0.001), and poor WFNS grade (p < 0.001) were associated with poor outcome. CONCLUSIONS Patients with EOS were classified significantly more often in a poor grade initially, but then they significantly more often achieved a good outcome. The authors conclude that EOS can negatively influence grading. This might influence decision making for the care of patients with SAH, so grading of patients with EOS should be interpreted with caution.

scholarly journals What Differentiates Poor and Good Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Giulio Felice ◽  
Franco Orsucci ◽  
Andrea Scozzari ◽  
Omar Gelo ◽  
Gabriele Serafini ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Multivariate Time Series ◽  
Specific Effect ◽  
Psychotherapy Research ◽  
Principal Component ◽  
Coarse Grained ◽  
Emergent Properties ◽  
Good Outcome ◽  
Poor Outcome ◽  
Abstract Language

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. “Static analyses” (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the “dynamic analyses”, based on five coarse-grained descriptors related to variability, the degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature enough to shift from a metaphorical to a fully quantitative status.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

scholarly journals COVID-19 Pandemic and Helsinki University Hospital Personnel Psychological Well-Being: Six-Month Follow-Up Results

2021 ◽  
Vol 18 (5) ◽  
pp. 2524
Author(s):  
Tanja Laukkala ◽  
Jaana Suvisaari ◽  
Tom Rosenström ◽  
Eero Pukkala ◽  
Kristiina Junttila ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Psychosocial Support ◽  
Well Being ◽  
Incidence Rates ◽  
University Hospital ◽  
Hospital Personnel ◽  
Front Line ◽  
Hospital District ◽  
Variation Explained

The COVID-19 pandemic has caused an unequally distributed extra workload to hospital personnel and first reports have indicated that especially front-line health care personnel are psychologically challenged. A majority of the Finnish COVID-19 patients are cared for in the Helsinki University Hospital district. The psychological distress of the Helsinki University Hospital personnel has been followed via an electronic survey monthly since June 2020. We report six-month follow-up results of a prospective 18-month cohort study. Individual variation explained much more of the total variance in psychological distress (68.5%, 95% CI 65.2–71.9%) and negative changes in sleep (75.6%, 95% CI 72.2–79.2%) than the study survey wave (1.6%, CI 0.5–5.5%; and 0.3%, CI 0.1–1.2%). Regional COVID-19 incidence rates correlated with the personnel’s psychological distress. In adjusted multilevel generalized linear multiple regression models, potentially traumatic COVID-19 pandemic-related events (OR 6.54, 95% CI 5.00–8.56) and front-line COVID-19 work (OR 1.81, 95% CI 1.37–2.39) was associated with personnel psychological distress but age and gender was not. While vaccinations have been initiated, creating hope, continuous follow-up and psychosocial support is still needed for all hospital personnel.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals Prevalence and Clinical Significance of Drug–Drug and Drug–Dietary Supplement Interactions among Patients Admitted for Cardiothoracic Surgery in Greece

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 239
Author(s):  
Marios Spanakis ◽  
Maria Melissourgaki ◽  
George Lazopoulos ◽  
Athina E. Patelarou ◽  
Evridiki Patelarou
Keyword(s):  
Dietary Supplement ◽  
Clinical Significance ◽  
Healthcare Providers ◽  
Well Being ◽  
Cross Sectional Study ◽  
Medical Advice ◽  
Cardiothoracic Surgery ◽  
University Hospital ◽  
Cross Sectional ◽  
Evidenced Based

Background: Drug interactions represent a major issue in clinical settings, especially for critically ill patients such as those with cardiovascular disease (CVD) who require cardiothoracic surgery (CTS) and receive a high number of different medications. Methods: A cross-sectional study aimed at evaluating the exposure and clinical significance of drug–drug (DDIs) and drug–dietary supplement interactions (DDSIs) in patients admitted for CTS in the University Hospital of Crete Greece. DDIs were evaluated regarding underlying pharmacological mechanisms upon admission, preoperation, postoperation, and discharge from CTS clinic. Additionally, upon admission, the use of dietary supplements (DSs) and if patients had informed their treating physician that they were using these were recorded with subsequent analysis of potential DDSIs with prescribed medications. Results: The study employed 76 patients who were admitted for CTS and accepted to participate. Overall, 166 unique DDIs were identified, with 32% of them being related to pharmacokinetic (PK) processes and the rest (68%) were related to possible alterations of pharmacodynamic (PD) action. CVD medications and drugs for central nervous system disorders were the most frequently interacting medications. In total, 12% of the identified DDIs were of serious clinical significance. The frequency of PK-DDIs was higher during admission and discharge, whereas PD-DDIs were mainly recorded during pre- and postoperation periods. Regarding DS usage, 60% of patients were using DSs and perceived them as safe, and the majority had not informed their treating physician of this or sought out medical advice. Analysis of medical records showed 30 potential combinations with prescribed medications that could lead in DDSIs due to modulation of PK or PD processes, and grapefruit juice consumption was involved in 38% of them. Conclusions: An increased burden of DDIs and DDSIs was identified mostly upon admission for patients in CTS clinics in Greece. Healthcare providers, especially prescribing physicians in Greece, should always take into consideration the possibility of DDIs and the likely use of DS products by patients to promote their well-being; this should only be undertaken after receiving medical advice and an evidenced-based evaluation.

scholarly journals Effectiveness of a short Yoga Nidra meditation on stress, sleep, and well-being in a large and diverse sample

2020 ◽  
Author(s):  
Esther N. Moszeik ◽  
Timo von Oertzen ◽  
Karl-Heinz Renner
Keyword(s):  
Structural Equation ◽  
Well Being ◽  
Equation Model ◽  
Future Research ◽  
Control Group ◽  
Core Element ◽  
Waitlist Control Group ◽  
Affective Components ◽  
Meditation Group ◽  
Yoga Nidra

Abstract Previous studies have shown that meditation-based interventions can have a significant impact on stress and well-being in various populations. To further extend these findings, an 11-min Yoga Nidra meditation that may especially be integrated in a busy daily schedule by people who can only afford short time for breaks was adapted and analyzed in an experimental online study design. The effects of this short meditation on stress, sleep, well-being and mindfulness were examined for the first time. The meditation was provided as audio file and carried out during a period of 30 days by the participants of the meditation group. A Structural Equation Model (SEM) was used to analyze the data with Full Information Maximum Likelihood (FIML) in order to cope with missing data. As expected, the meditation group (N = 341) showed lower stress, higher well-being and improved sleep quality after the intervention (very small to small effect sizes) compared with a waitlist control group (N = 430). It turned out that the meditation had a stronger impact on the reduction of negative affect than on the increase of positive affect and also a stronger effect on affective components of well-being. Mindfulness, as a core element of the meditation, increased during the study within the meditation group. All effects remained stable at follow-up six weeks later. Overall, a large, heterogeneous sample showed that already a very short dose of meditation can positively influence stress, sleep, and well-being. Future research should consider biological markers as well as active control groups.

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