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Introduction. The transition from childhood to adulthood is one of the main critical points in the network of services for taking care of people with autism spectrum disorder (ASD). Within the framework of the national research programs on autism, an exploratory longitudinal multicentre study was conducted. This research program, called “Ev.A Project (Developmental and Adult Age)”, was proposed by the Italian National Institute of Health (Istituto Superiore di Sanità, ISS) and the aim was the development and testing of a diagnostic, therapeutic, assistance and educational pathway (PDTAE) for autism. Aim. The present study aimed to evaluate two impact outcomes of the care protocol: the response obtained by the ASD person, and the perception of the change in the family context. Methods. Participants underwent an initial clinical evaluation and then after one year. Over the course of the year, participants undertook a program of intervention. The measures of adaptive functioning, need for support, psychiatric symptomatology and family quality of life were used for the outcome assessment. Linear mixed models were constructed for each measure to estimate the explanatory/predictive behavior of the intensity of the interventions, adjusted for the participant’s level of symptom severity. Results. The results estimate a main effect of Intervention Group (b=-27.22, p

The COVID-19 pandemic has fast-tracked interest in telehealth methods to guarantee the continuity of care of children with Autism Spectrum Disorder (ASD). Store-and-forward telehealth approaches offer the opportunity to facilitate timely screening of ASD, allowing parents to record videos of their child's behaviors, subsequently shared with clinicians that provide an assessment remotely. This study aimed to examine the psychometric properties of a new telehealth screening tool, the teleNIDA, administered in home settings for remote observation of early signs of ASD in toddlers aged 18--30 months. Results showed good psychometric properties of the teleNIDA, as compared to the gold standard in-person assessment, and the predictive validity on the diagnosis of ASD at 36 months was demonstrated. This study supports the teleNIDA as a promising level 2 screening tool for ASD able to speed up diagnostic and intervention processes.

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers.Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children.Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained.Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills.Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

Feasibility, acceptability and effectiveness data of a virtual adaptation of the WHO Caregiver Skills Training (CST; n = 25) were compared with those of a pilot RCT of CST delivered in person (n = 43) against treatment as usual (TAU; n = 43). Virtual CST was delivered with high levels of integrity, but received lower ratings in some caregiver- and facilitator-rated acceptability and feasibility dimensions. Qualitative analysis identified both benefits (flexibility, convenience, clinical usefulness) and challenges, (technological issues, distraction from family members, emotional distance). Virtual and in-person CST improved significantly more on caregiver competence than TAU; there were no other significant effects. Potential for use of virtual CST as a clinical response in contexts where in-person delivery is not possible is discussed.