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.

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.

Acceptance of E-mental health interventions and its determinants among psychotherapists-in-training during the first phase of COVID-19.

Background: Although E-mental health (EMH) interventions have been shown to be effective in the treatment of mental health problems and empirical knowledge regarding EMH acceptance for different occupations in health care is established, little is known regarding EMH and psychotherapists-in-training. This seems particularly relevant as psychotherapists-in-training will shape the future health care system since they are as being the next generation of psychotherapists. With social distancing measures in place, COVID-19 has led to an increased demand for EMH, which is broadening the way psychological treatments are delivered. Objective: The present study aims to assess the acceptance of EMH and its determinants among psychotherapists-in-training of different EMH modalities and to retrospectively compare current acceptance with pre-COVID-19 times. Methods: Altogether, 29 training institutions in Switzerland and 232 training institutions in Germany were contacted, resulting in a sample of N = 216 psychotherapists-in-training (88.4 % female) who filled out the self-administered web-based questionnaire in summer 2020. The acceptance of EMH was assessed considering several different modalities (e.g., videoconference, guided self-help programs) as well as further possible predictors of EMH acceptance based on the Unified Theory of Acceptance and Use of Technology. Acceptance scores were categorized as low, moderate or high based on prior research and predicted using multiple regression. Results: Acceptance of EMH was moderate (M = 3.40, SD = 1.11) and increased significantly (t(215) = 12.03, p < .01; d = 0.88) compared to pre-COVID-19 (M = 2.67, SD = 1.11); however, acceptance varied significantly between modalities (F(2.6, 561.7) = 62.93, p < .01, partial η2 = 0.23), with videoconferencing being the most accepted and unguided programs the least. Stepwise regression including three of 14 variables (R2 = 0.55, F (14, 201) = 17.68, p < .001) identified performance expectancy, social influence and concerns about the therapeutic alliance as significant determinants of EMH acceptance. Discussion: Acceptance by psychotherapists-in-training was moderate and in line with prior research and comparable with other clinicians' acceptance scores. Performance expectancy, social influence and concerns about the therapeutic alliance were predictive of EMH acceptance, indicating their significance in the implementation of EMH in health care. Conclusion: These findings underline the importance of the aforementioned determinants of EMH acceptance and the need for further studies investigating EMH acceptance in order to derive adequate educational programs and to facilitate dissemination among psychotherapists-in-training.

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 < 0.05). Results showed that the extended UTAUT model was the best fitting model to predict EMH service acceptance (Pr > 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.