Evaluating the Feasibility of Emotion Expressions in Avatars Created From Real Person Photos: Pilot Web-Based Survey of Virtual Reality Software.

BACKGROUND: The availability and potential of virtual reality (VR) has led to an increase of its application. VR is suggested to be helpful in training elements of social competence but with an emphasis on interventions being tailored. Recognizing facial expressions is an important social skill and thus a target for training. Using VR in training these skills could have advantages over desktop alternatives. Children with autism, for instance, appear to prefer avatars over real images when assessing facial expressions. Available software provides the opportunity to transform profile pictures into avatars, thereby giving the possibility of tailoring according to an individual's own environment. However, the emotions provided by such software should be validated before application. OBJECTIVE: Our aim was to investigate whether available software is a quick, easy, and viable way of providing emotion expressions in avatars transformed from real images. METHODS: A total of 401 participants from a general population completed a survey on the web containing 27 different images of avatars transformed, using a software, from real images. We calculated the reliability of each image and their level of difficulty using a structural equation modeling approach. We used Bayesian confirmatory factor analysis testing under a multidimensional first-order correlated factor structure where faces showing the same emotions represented a latent variable. RESULTS: Few emotions were correctly perceived and rated as higher than other emotions. The factor loadings indicating the discrimination of the image were around 0.7, which means 49% shared variance with the latent factor that the face is linked with. The standardized thresholds indicating the difficulty level of the images are mostly around average, and the highest correlation is between faces showing happiness and anger. CONCLUSIONS: Only using a software to transform profile pictures to avatars is not sufficient to provide valid emotion expressions. Adjustments are needed to increase faces' discrimination (eg, increasing reliabilities). The faces showed average levels of difficulty, meaning that they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments should be made for specific populations and when applying this technology in clinical practice.

Guided Internet-Delivered Treatment for Depression: Scoping Review.

BACKGROUND: Studies on guided internet-delivered treatment have demonstrated promising results for patients with depressive disorder. OBJECTIVE: The aim of this study was to provide an overview of this research area and identify potential gaps in the research. METHODS: In this scoping review, web-based databases were used to identify research papers published between 2010 and 2022 where guided internet-delivered treatment was administered to participants with depressive disorders, a standardized rating scale of depressive symptoms was used as the primary outcome measure, and the treatment was compared with a control condition. RESULTS: A total of 111 studies were included, and an overview of the studies was provided. Several gaps in the research were identified regarding the design of the studies, treatments delivered, participant representation, and treatment completion. CONCLUSIONS: This review provides a comprehensive overview of the research area, and several research gaps were identified. The use of other designs and active control conditions is recommended. Future studies should provide access to treatment manuals, and more replications should be conducted. Researchers should aim to include underrepresented populations and provide reports of comorbidities. Definitions of adequate dosage, reports of completion rates, and reasons for treatment dropout are recommended for future studies.

Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter.

BACKGROUND: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people's perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. OBJECTIVE: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk's disclosure. METHODS: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms "Aspergers" or "Aspie." The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. RESULTS: We extracted a total of 227 popular tweets (34 posted the week before Musk's announcement and 193 posted the week after). We classified 210 (92.5%) of the tweets as neutral, 13 (5.7%) tweets as informative, and 4 (1.8%) as containing misinformation. Both informative and misinformative tweets were posted after Musk's disclosure. Popular tweets posted before Musk's disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. CONCLUSIONS: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition.

Confirming the Validity of the School-Refusal Assessment Scale-Revised in a Sample of Children With Attention-Deficit/Hyperactivity Disorder.

Children with developmental disorders, such as attention-deficit/hyperactivity disorder (ADHD), are at high risk of school-refusal behavior (SRB) compared with their peers. One of the most used scales to assess SRB is the school refusal behavior scale - revised (SRAS-R). The SRAS-R has demonstrated good psychometric properties when used with the general population of children, but, recently, its validity has been questioned when used with children with developmental disorders. We tested the psychometric properties of the SRAS-R parental reports in 96 children with ADHD (Mage = 12.4; SD = 1.7, 61.5% boys). Results showed good model fit and internal consistency for the original four-factor structure. Three of the factors were strongly correlated, suggesting that SRB among children with ADHD is caused by several factors.

Virtual and Augmented Reality in Social Skills Interventions for Individuals with Autism Spectrum Disorder: A Scoping Review.

In the last decade, there has been an increase in publications on technology-based interventions for autism spectrum disorder (ASD). Virtual reality based assessments and intervention tools are promising and have shown to be acceptable amongst individuals with ASD. This scoping review reports on 49 studies utilizing virtual reality and augmented reality technology in social skills interventions for individuals with ASD. The included studies mostly targeted children and adolescents, but few targeted very young children or adults. Our findings show that the mode number of participants with ASD is low, and that female participants are underrepresented. Our review suggests that there is need for studies that apply virtual and augmented realty with more rigorous designs involving established and evidenced-based intervention strategies.

Online Attitudes and Information-Seeking Behavior on Autism, Asperger Syndrome, and Greta Thunberg.

The purpose of this study was to examine Internet trends data and sentiment in tweets mentioning autism, Asperger syndrome, and Greta Thunberg during 2019. We used mixed methods in analyzing sentiment and attitudes in viral tweets and collected 1074 viral tweets on autism that were published in 2019 (tweets that got more than 100 likes). The sample from Twitter was compared with search patterns on Google. In 2019, Asperger syndrome was closely connected to Greta Thunberg, as of the tweets specifically mentioning Asperger (from the total sample of viral tweets mentioning autism), 83% also mentioned Thunberg. In the sample of tweets about Thunberg, the positive sentiment expressed that Greta Thunberg was a role model, whereas the tweets that expressed the most negativity used her diagnosis against her and could be considered as cyberbullying. The Google Trends data also showed that Thunberg was closely connected to search patterns on Asperger syndrome in 2019. The study showed that being open about health information while being an active participant in controversial debates might be used against you but also help break stigmas and stereotypes.

Virtual reality and naturalistic developmental behavioral interventions for children with autism spectrum disorder.

BACKGROUND: Naturalistic Developmental Behavioral Interventions (NDBI) have been evaluated as the most promising interventions for children with autism spectrum disorder. In recent years, a growing body of literature suggests that technological advancements such as Virtual Reality (VR) are promising intervention tools. However, to the best of our knowledge no studies have combined evidence-based practice with such tools. AIM: This article aims to review the current literature combining NDBI and VR, and provide suggestions on merging NDBI-approaches with VR. METHODS: This article is divided into two parts, where we first conduct a review mapping the research applying NDBI-approaches in VR. In the second part we argue how to apply the common features of NDBI into VR-technology. RESULTS: Our findings show that no VR-studies explicitly rely on NDBI-approaches, but some utilize elements in their interventions that are considered to be common features to NDBI. CONCLUSIONS AND IMPLICATIONS: As the results show, to date, no VR-based studies have utilized NDBI in their intervention. We therefore, in the second part of this article, suggests ways to merge VR and NDBI and introduce the term Virtual Naturalistic Developmental Behavioral Interventions (VNDBI). VNDBI is an innovative way of implementing NDBI which will contribute in making interventions more accessible in central as well as remote locations, while reducing unwanted variation between service sites. VNDBI will advance the possibilities of individually tailoring and widen the area of interventions. In addition, VNDBI can provide the field with new knowledge on effective components enhancing the accuracy in the intervention packages and thus move forward the research field and clinical practice.

Expansion of arbitrary stimulus classes and function-transfer measured by sorting performances.

Sorting (SRT) and matching-to-sample (MTS) tests have measured the formation of arbitrary stimulus classes. This experiment used SRT and MTS tests to document the expansion of class size. Thirty-two participants learned 12 conditional discriminations with a linear series training structure (A➔B➔C➔D➔E). SRT tests documented the formation of 5-member classes by 17 of the participants. Thereafter, 6-member class expansion was implemented by FC training. Nine of these 17 participants showed class expansion when tracked with a sequence of an SRT, MTS, and a final SRT test, and the other 8 showed expansion when tracked with a sequence of MTS and SRT tests. Thus, SRT tests documented class expansion, and the sequence of tests did not influence class expansion. The 15 participants who did not form the 5-member classes learned the baselines for new 3-member classes (A➔B➔C) and formed them as documented by an SRT test. Then, 4-member class expansion was implemented by FB training. Expansion was assessed using the above-mentioned testing sequences. All 15 showed class expansion with 100% correspondence between the SRT and MTS performances. Sorting documented the expansion of arbitrary stimulus classes, while the MTS tests showed that the stimuli also functioned as members of equivalence classes.