IntroductionEmotional awareness is the ability to identify, interpret, and verbalize the emotional responses of oneself and those of others. The Levels of Emotional Awareness Scale (LEAS) is an objective performance inventory that accurately measures an individual's emotional awareness. LEAS assessments are typically scored manually and are therefore both time consuming and cognitively demanding. This study presents a German electronic scoring program for the LEAS (geLEAS), the first non-English computerized assessment approach of the LEAS.MethodsData were collected from a healthy German community sample (N = 208). We developed a modern software for computerizing LEAS scoring, an open-source text-based emotion assessment tool called VETA (Verbal Emotion in Text Assessment). We investigated if the software would arrive at similar results as hand scoring in German and if emotional awareness would show similar associations to sociodemographic information and psychometric test results as in previous studies.ResultsThe most frequently used scoring method of the geLEAS shows excellent internal consistency (α = 0.94) and high correlations with hand scoring (r = 0.97, p < 0.001). Higher emotional awareness measured by the geLEAS is associated with female gender, older age, and higher academic achievement (all p < 0.001). Moreover, it is linked to the ability to identify emotions in facial expressions (p < 0.001) and more accurate theory of mind functioning (p < 0.001).DiscussionAn automated method for evaluating emotional awareness greatly expands the ability to study emotional awareness in clinical care and research. This study aims to advance the use of emotional awareness as a clinical and scientific parameter.
Background: Digital acquisition of risk factors and symptoms based on patients self-reports represents a promising, cost-efficient and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. While the feasibility of digital data collection has been demonstrated across a range of psychiatric disorders, studies investigating digital data collection in schizophrenia spectrum disorder patients are scarce. Hence, up to now our knowledge about the acceptability and feasibility of digital data collection in patients with a schizophrenia spectrum disorder remains critically limited. Objective: The objective of this study was to explore the acceptance towards and performance with digitally acquired assessments of risk and symptom profiles in patients with a schizophrenia spectrum disorder in comparison with patients with an affective disorder. Methods: We investigated the acceptance, the required support and the data entry pace of patients during a longitudinal digital data collection system of risk and symptom profiles using self-reports on tablet computers throughout inpatient treatment in patients with a schizophrenia spectrum disorder. As a benchmark comparison, findings in patients with schizophrenia spectrum disorder were evaluated in direct comparison with findings in affective disorder patients. The influence of sociodemographic data and clinical characteristics on the assessment was explored. The study was performed at the Department of Psychiatry at the University of Muenster between February 2020 and February 2021. Results: Of 82 patients diagnosed with a schizophrenia spectrum disorder who were eligible for inclusion 59.8% (n=49) agreed to participate in the study of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates and subjective experience ratings did not differ between patients with a schizophrenia spectrum disorder and patients with an affective disorder. Out of all participating patients, 98% reported high satisfaction with the digital assessment. Patients with a schizophrenia spectrum disorder needed more support and more time for the assessment compared to patients with an affective disorder. The extent of support of patients with a schizophrenia spectrum disorder was predicted by age, whereas the feeling of self-efficacy predicted data entry pace. Conclusion: Our results indicate that, although patients with a schizophrenia spectrum disorder need more support and more time for data entry than patients with an affective disorder, digital data collection using patients self-reports is a feasible and well-received method. Future clinical and research efforts on digitized assessments in psychiatry should include patients with a schizophrenia spectrum disorder and offer adequate support to reduce digital exclusion of these patients.
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