Innovative moments in low-intensity, telephone-based cognitive-behavioral therapy for depression

BackgroundInnovative moments (IMs), defined as moments in psychotherapy when patients’ problematic patterns change toward more elaborated and adaptive patterns, have been shown to be associated with a clinical change in patients with depression. Thus, far IMs have been studied in face-to-face settings but not in telephone-based cognitive-behavioral therapy (t-CBT). This study investigates whether IMs occur in t-CBT and examines the association between IMs and symptom improvement, and reconceptualization and symptom improvement.MethodsThe therapy transcripts of n = 10 patients with mild to moderate depression (range: 7–11 sessions, in total 94 sessions) undergoing t-CBT were qualitatively and quantitatively analyzed. Symptom severity (Patient Health Questionnaire-9) and IMs (levels and proportions) were assessed for each therapy session. Hierarchical linear models were used to test the prediction models.ResultsThe rating of IMs was shown to be feasible and reliable using the Innovative Moments Coding System (IMCS) (84.04% agreement in words coded), which is indicative of the applicability of the concept of IMs in t-CBT. Only reconceptualization IMs were shown to have a predictive value for treatment success (R$^{2}$ = 0.05, p = 0.01).DiscussionThe results should be interpreted with caution due to the exploratory nature of this study. Due to the telephone setting, it was necessary to adapt the IMCS. Nonetheless, the extent of IMs identified in the low-intensity t-CBT investigated was comparable to IMs in face-to-face therapy. Further studies are needed to clarify the association between IMs and treatment success as a change process, especially for low-intensity treatments

Innovative moments in low-intensity, telephone-based cognitive-behavioral therapy for depression.

BACKGROUND Innovative moments (IMs), defined as moments in psychotherapy when patients' problematic patterns change toward more elaborated and adaptive patterns, have been shown to be associated with a clinical change in patients with depression. Thus, far IMs have been studied in face-to-face settings but not in telephone-based cognitive-behavioral therapy (t-CBT). This study investigates whether IMs occur in t-CBT and examines the association between IMs and symptom improvement, and reconceptualization and symptom improvement. METHODS The therapy transcripts of n = 10 patients with mild to moderate depression (range: 7-11 sessions, in total 94 sessions) undergoing t-CBT were qualitatively and quantitatively analyzed. Symptom severity (Patient Health Questionnaire-9) and IMs (levels and proportions) were assessed for each therapy session. Hierarchical linear models were used to test the prediction models. RESULTS The rating of IMs was shown to be feasible and reliable using the Innovative Moments Coding System (IMCS) (84.04% agreement in words coded), which is indicative of the applicability of the concept of IMs in t-CBT. Only reconceptualization IMs were shown to have a predictive value for treatment success (R2 = 0.05, p = 0.01). DISCUSSION The results should be interpreted with caution due to the exploratory nature of this study. Due to the telephone setting, it was necessary to adapt the IMCS. Nonetheless, the extent of IMs identified in the low-intensity t-CBT investigated was comparable to IMs in face-to-face therapy. Further studies are needed to clarify the association between IMs and treatment success as a change process, especially for low-intensity treatments

Gesunde Ärztinnen und Ärzte für eine gesunde Versorgung

Einführung In den letzten Jahren ist das Wohlbefinden der Ärzteschaft vermehrt in den Vordergrund gerückt. Dies unter anderem, weil Ärztinnen und Ärzte ein höheres Risiko für psychische Erkrankungen, Suizid und stressbedingte Probleme haben als die Allgemeinbevölkerung [1–3]. Die Arbeitsbedingungen der Ärzteschaft ist seit Jahren geprägt von langen Schichten, Konkurrenzkampf und Leistungsdruck [4]. Dabei haben stressbedingte Erkrankungen wie beispielweise Burnout nicht nur eine Auswirkung auf das betroffene Individuum, sondern auch auf die Versorgungsqualität, die Arbeitssicherheit und die Zufriedenheit der Patientinnen und Patienten [5]. Dies zeigt sich im Spital beispielhaft durch mehr vermeidbare Fehler und eine höhere Mortalitätsrate der Patientinnen und Patienten [6]. Ausserdem kann es sein, dass Ärztinnen und Ärzte stressbedingt ausfallen oder im schlimmsten Fall den Beruf aufgeben [7], was das Gesundheitssystem vor ernsthafte Herausforderungen stellt [8]. Durch COVID-19 hat sich diese Problematik noch verschärft, aber auch an Bedeutung gewonnen, und daher wird dem ärztlichen Wohlbefinden vermehrt Aufmerksamkeit geschenkt [9, 10]

Acceptance of E-Mental Health Services for Different Application Purposes Among Psychotherapists in Clinical Training in Germany and Switzerland: Secondary Analysis of a Cross-Sectional Survey

Background: Despite solid evidence supporting the efficacy of electronic mental health (EMH) services, their acceptance among psychotherapists is limited and uptake rates remain low. However, the acceptance of different EMH services has yet barely been examined in future generations of psychotherapists in a differentiated manner. The aims of this study were (1) to elaborate the intention to use various EMH services for different application purposes and (2) to determine predictors of EMH service acceptance among psychotherapists in clinical training (PiT). Materials and Methods: Our paper is based on a secondary data analysis of a cross-sectional survey. Respondents were recruited via recognized educational institutions for psychotherapy within Germany and the German-speaking part of Switzerland between June and July of 2020. The survey contained items on the intention to use different EMH services (i.e., guided and unguided programs, virtual reality, psychotherapy by telephone and videoconference) for various application purposes (i.e., prevention, treatment addition, treatment substitute, aftercare). Potential predictors of EMH service acceptance (e.g., barriers and advantages) were examined based on an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT). Results: Most of the n = 216 respondents were female (88.4%) and located in Germany (72.2%). General acceptance of EMH was moderate (M = 3.4, SD = 1.12, range 1–5), while acceptance of psychotherapy via videoconference was highest (M = 3.7, SD = 1.15) and acceptance of unguided programs was lowest (M = 2.55, SD = 1.14). There was an interaction effect of EMH service and application purpose (η2 = 0.21). Barriers and advantages both had a uniform influence on EMH service acceptance (Pr > 0.999), while impersonality, legal concerns, concerns about therapeutic alliance, simplified information provision, simplified contact maintenance, time flexibility, and geographic flexibility were significant predictors (all p 0.999). Conclusions: The intention to use different EMH services varied between application purposes among PiT. To increase acceptance of EMH services and reduce misconceptions, we identified predictors that should be addressed in future acceptance-facilitating interventions when educating PiT

Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis.

THEORETICAL BACKGROUND Research of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics. METHODS A secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors. RESULTS Best model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors. CONCLUSION This study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH

Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis

Theoretical backgroundResearch of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics.MethodsA secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors.ResultsBest model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors.ConclusionThis study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH

DataSheet_1_Exploring subgroups of acceptance prediction for e-mental health among psychotherapists-in-training: a latent class analysis.docx

Theoretical backgroundResearch of E-Mental Health (EMH) interventions remains a much-studied topic, as does its acceptance in different professional groups as psychotherapists-in-training (PiT). Acceptance among clinicians may vary and depend on several factors, including the characteristics of different EMH services and applications. Therefore, the aims of this study were to investigate the factors that predict acceptance of EMH among a sample of PiT using a latent class analysis. The study will 1) determine how many acceptance prediction classes can be distinguished and 2) describe classes and differences between classes based on their characteristics.MethodsA secondary analysis of a cross-sectional online survey was conducted. N = 216 PiT (88.4% female) participated. In the study, participants were asked to rate their acceptance of EMH, as operationalized by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, along with its predictors, perceived barriers, perceived advantages and additional facilitators. Indicator variables for the LCA were eight items measuring the UTAUT-predictors.ResultsBest model fit emerged for a two-class solution; the first class showed high levels on all UTAUT-predictors, the second class revealed moderate levels on the UTAUT-predictors.ConclusionThis study was able to show that two classes of individuals can be identified based on the UTAUT-predictors. Differences between the classes regarding Performance Expectancy and Effort Expectancy were found. Interestingly, the two classes differed in theoretical orientation but not in age or gender. Latent class analysis could help to identify subgroups and possible starting points to foster acceptance of EMH.</p

Time-resolved scanning electron microscopy with polarization analysis

We demonstrate the feasibility of investigating periodically driven magnetization dynamics in a scanning electron microscope with polarizationanalysis based on spin-polarized low-energy electron diffraction. With the present setup, analyzing the time structure of the scattering events, we obtain a temporal resolution of 700 ps, which is demonstrated by means of imaging the field-driven 100 MHz gyration of the vortex in a soft-magnetic FeCoSiB square. Owing to the efficient intrinsic timing scheme, high-quality movies, giving two components of the magnetization simultaneously, can be recorded on the time scale of hours

Analysis of Risk Factors and Long-Term Outcomes in Kidney Transplant Patients with Identified Lymphoceles

The collection of lymphatic fluids (lymphoceles) is a frequent adverse event following renal transplantation. A variety of surgical and medical factors has been linked to this entity, but reliable data on risk factors and long-term outcomes are lacking. This retrospective single-center study included 867 adult transplant recipients who received a kidney transplantation from 2006 to 2015. We evaluated for patient and graft survival, rejection episodes, or detectable donor-specific antibodies (dnDSA) in patients with identified lymphoceles in comparison to controls. We identified 305/867 (35.2%) patients with lymphocele formation, of whom 72/867 (8.3%) needed intervention. Multivariate analysis identified rejection episode as an independent risk factor (OR 1.61, CI 95% 1.17-2.21, p = 0.003) for lymphocele formation, while delayed graft function was independently associated with symptomatic lymphoceles (OR 1.9, CI 95% 1.16-3.12, p = 0.011). Interestingly, there was no difference in detectable dnDSA between groups with a similar graft and patient survival in all groups after 10 years. Lymphoceles frequently occur after transplantation and were found to be independently associated with rejection episodes, while symptomatic lymphoceles were associated with delayed graft function in our cohort. As both are inflammatory processes, they might play a causative role in the formation of lymphoceles. However, development or intervention of lymphoceles did not lead to impaired graft survival in the long-term