Background: When COVID-19 was declared as a pandemic, many countries imposed severe lockdowns that changed families’ routines and negatively impacted on parents’ and children’s mental health. Several studies on families with children with autism spectrum disorder (ASD) revealed that lockdown increased the difficulties faced by individuals with ASD, as well as parental distress. No studies have analyzed the interplay between parental distress, children’s emotional responses, and adaptive behaviors in children with ASD considering the period of the mandatory lockdown. Furthermore, we compared families with children on the spectrum and families with typically developing (TD) children in terms of their distress, children’s emotional responses, and behavioral adaptation. Methods: In this study, 120 parents of children aged 5–10 years (53 with ASD) participated. Results: In the four tested models, children’s positive and negative emotional responses mediated the impact of parental distress on children’s playing activities. In the ASD group, parents reported that their children expressed more positive emotions, but fewer playing activities, than TD children. Families with children on the spectrum reported greater behavioral problems during the lockdown and more parental distress. Conclusions: Our findings inform the interventions designed for parents to reduce distress and to develop coping strategies to better manage the caregiver–child relationship.

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In this paper, a computational approach is proposed and put into practice to assess the capability of children having had diagnosed Autism Spectrum Disorders (ASD) to produce facial expressions. The proposed approach is based on computer vision components working on sequence of images acquired by an off-the-shelf camera in unconstrained conditions. Action unit intensities are estimated by analyzing local appearance and then both temporal and geometrical relationships, learned by Convolutional Neural Networks, are exploited to regularize gathered estimates. To cope with stereotyped movements and to highlight even subtle voluntary movements of facial muscles, a personalized and contextual statistical modeling of non-emotional face is formulated and used as a reference. Experimental results demonstrate how the proposed pipeline can improve the analysis of facial expressions produced by ASD children. A comparison of system’s outputs with the evaluations performed by psychologists, on the same group of ASD children, makes evident how the performed quantitative analysis of children’s abilities helps to go beyond the traditional qualitative ASD assessment/diagnosis protocols, whose outcomes are affected by human limitations in observing and understanding multi-cues behaviors such as facial expressions.

The present study focused on the psychological impact that the lockdown due to coronavirus disease-19 (COVID-19) had on families in Italy. During the COVID-19 pandemic, the Italian government imposed a strict lockdown for all citizens. People were forced to stay at home, and the length of the lockdown was uncertain. Previous studies analyzed the impact of social distance measures on individuals' mental health, whereas few studies have examined the interplay between the adults' functioning, as parents, during this period and the association with the child's adjustment. The present study tested if maternal distress/coping predicts children's behaviors during the COVID-19 lockdown, hypothesizing a mediation effect via children's emotional experience. Participants were 144 mothers (Mage = 39.3, 25–52, SD = 5.6) with children aged 5–10 years (Mage = 7.54, SD = 1.6, 82 boys); mothers answered to an online survey. Results indicated that mothers with higher exposure to COVID-19 showed higher levels of distress and higher display of coping attitudes, even if in the structural equation modeling model, the COVID-19 exposure was not a predictor of mothers' distress. Compared with mothers with good coping skills, mothers with higher stress levels were more likely to attribute negative emotions to their children at the expense of their positive emotions. Moreover, children's emotions acted as mediators between maternal distress/coping and children's adaptive/maladaptive behaviors. In conclusion, it is important to support parents during pandemic emergence, by providing them with adequate information to manage the relationship with their children, to reduce their level of distress and to enhance their coping abilities.

The present study provides a systematic review of level 1 and level 2 screening tools for the early detection of autism under 24 months of age and an evaluation of the psychometric and measurement properties of their studies. Methods: Seven databases (e.g., Scopus, EBSCOhost Research Database) were screened and experts in the autism spectrum disorders (ASD) field were questioned; Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and Consensus-based Standard for the selection of health Measurement INstruments (COSMIN) checklist were applied. Results: the study included 52 papers and 16 measures; most of them were questionnaires, and the Modified-CHecklist for Autism in Toddler (M-CHAT) was the most extensively tested. The measures’ strengths (analytical evaluation of methodological quality according to COSMIN) and limitations (in term of Negative Predictive Value, Positive Predictive Value, sensitivity, and specificity) were described; the quality of the studies, assessed with the application of the COSMIN checklist, highlighted the necessity of further validation studies for all the measures. According to COSMIN results, the M-CHAT, First Years Inventory (FYI), and Quantitative-CHecklist for Autism in Toddler (Q-CHAT) seem to be promising measures that may be applied systematically by health professionals in the future.

Time spent outdoors and physical activity (PA) promote mental health. To confirm this relationship in the aftermath of COVID-19 lockdowns, we explored individual levels of anxiety, depression, stress and subjective well-being (SWB) in a cohort of academic students and staff members and tested their association with sport practice, PA at leisure time and time spent outdoors. Our cross-sectional study collected data during the COVID-19 outbreak (April–May 2021) on 939 students and on 238 employees, who completed an online survey on sociodemographic and lifestyle features, depression, anxiety, stress, and SWB. Results showed that the students exhibited higher levels of anxiety, depression, and stress, and lower levels of SWB (p < 0.001 for all domains) compared to the staff members. Correlation analysis confirmed that PA and time spent in nature were associated to high mental health scores among staff and, more consistently, among students. Finally, mediation analyses indicated that the time spent in nature, social relationships, and levels of energy play a mediator role in the relationship between sport practice and SWB. Our evidence reinforces the protective role of time spent in nature in improving mental health, and provides support for policymakers to make appropriate choices for a better management of COVID-19 pandemic consequences.

The computational analysis of facial expressions is an emerging research topic that could overcome the limitations of human perception and get quick and objective outcomes in the assessment of neurodevelopmental disorders (e.g., Autism Spectrum Disorders, ASD). Unfortunately, there have been only a few attempts to quantify facial expression production and most of the scientific literature aims at the easier task of recognizing if either a facial expression is present or not. Some attempts to face this challenging task exist but they do not provide a comprehensive study based on the comparison between human and automatic outcomes in quantifying children's ability to produce basic emotions. Furthermore, these works do not exploit the latest solutions in computer vision and machine learning. Finally, they generally focus only on a homogeneous (in terms of cognitive capabilities) group of individuals. To fill this gap, in this paper some advanced computer vision and machine learning strategies are integrated into a framework aimed to computationally analyze how both ASD and typically developing children produce facial expressions. The framework locates and tracks a number of landmarks (virtual electromyography sensors) with the aim of monitoring facial muscle movements involved in facial expression production. The output of these virtual sensors is then fused to model the individual ability to produce facial expressions. Gathered computational outcomes have been correlated with the evaluation provided by psychologists and evidence has been given that shows how the proposed framework could be effectively exploited to deeply analyze the emotional competence of ASD children to produce facial expressions.

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