Brief Report: A Mobile Application to Treat Prosodic Deficits in Autism Spectrum Disorder and Other Communication Impairments: A Pilot Study

This study examined the acceptability of a mobile application, SpeechPrompts, designed to treat prosodic disorders in children with ASD and other communication impairments. Ten speech-language pathologists (SLPs) in public schools and 40 of their students, 5-19 years with prosody deficits participated. Students received treatment with the software over eight weeks. Pre- and post-treatment speech samples and student engagement data were collected. Feedback on the utility of the software was also obtained. SLPs implemented the software with their students in an authentic education setting. Student engagement ratings indicated students\u27 attention to the software was maintained during treatment. Although more testing is warranted, post-treatment prosody ratings suggest that SpeechPrompts has potential to be a useful tool in the treatment of prosodic disorders

The Use of Mobile Technology in the Treatment of Prosodic Deficits in Autism Spectrum Disorders

Objectives: The purpose of this study is to assess the feasibility and preliminary utility of an application, SpeechPrompts, for iOS devices in the treatment of prosodic disorders in school-age children with ASD

Bridging the Research Gap: Making HRI Useful to Individuals with Autism

While there is a rich history of studies involving robots and individuals with autism spectrum disorders (ASD), few of these studies have made substantial impact in the clinical research community. In this paper we first examine how differences in approach, study design, evaluation, and publication practices have hindered uptake of these research results. Based on ten years of collaboration, we suggest a set of design principles that satisfy the needs (both academic and cultural) of both the robotics and clinical autism research communities. Using these principles, we present a study that demonstrates a quantitatively measured improvement in human-human social interaction for children with ASD, effected by interaction with a robot

Evaluating Human Eye Features for Objective Measure of Working Memory Capacity

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an objective measure of the working memory capacity and a study to investigate how working memory capacity is affected when reading AI-generated fake news

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.publishedVersio

Relationship Between Sleep and Behavior in Autism Spectrum Disorder: Exploring the Impact of Sleep Variability.

Objective:The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Methods:Caregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Results:Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Conclusion:Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD

Enhancing the Understanding of Clinically Meaningful Results: A Clinical Research Perspective

Published research often address aspects related to “statistical significance” but fail to address the clinical and practical importance and meaning of results. Our main objectives in this article are to investigate the merit of common measures of Effect Size in statistical research and to highlight the importance of the simple Relative Risk ratio. In this article we present data where we consider two widely utilized effect size measures (Cohen's d and Pearson's r) in relations to relative risk. We conclude that probability analyses of risk surpass the most commonly used statistical approach used in clinical trials today and should thus be the preferred compared to the misuse and misunderstanding of reporting for instance p-values alone.publishedVersio