Little is known about the Quality of Life (QOL) in parents of children with developmental diseases as compared to other severe neurological or psychiatric disorders. Aims of the present study were: to evaluate QOL in parents of children affected by Pervasive Development Disorder (PDDs), Cerebral Palsy (CP) or Mental Retardation (MR) as compared to a control group (CG); to evaluate QOL of parents of patients with different types of PDDs, namely Autistic Disorder (AD), High Function Autism/Asperger Syndromes (HFA/AS) and Pervasive Developmental Disorder Not Otherwise Specified (PPD-NOS); and to compare the level of impairment in QOL of mothers and fathers within PDDs, CP, MR groups and between AD, HFA/AS, PDD-NOS sub-groups.

Several psychiatric conditions, both internalizing and externalizing, have been documented in comorbidity with Asperger Syndrome (AS) and High Functioning Autism (HFA). In this review we examine the interplay between psychiatric comorbidities and AS/HFA. In particular, we will focus our attention on three main issues. First, we examine which psychiatric disorders are more frequently associated with AS/HFA. Second, we review which diagnostic tools are currently available for clinicians to investigate and diagnose the associated psychiatric disorders in individuals with AS/HFA. Third, we discuss the challenges that clinicians and researchers face in trying to determine whether the psychiatric symptoms are phenotypic manifestations of AS/HFA or rather they are the expression of a distinct, though comorbid, disorder. We will also consider the role played by the environment in the manifestation and interpretation of these symptoms. Finally, we will propose some strategies to try to address these issues, and we will discuss therapeutic implications.

The higher prevalence of Asperger Syndrome (AS) and other autism spectrum conditions in males has been known for many years. However, recent multiplex immunoassay profiling studies have shown that males and females with AS have distinct proteomic changes in serum.

Background: Recently, there has been an increased interest in the efficacy of mindfulness-based interventions (MBI) for people with cardiovascular diseases (CVD), although the exact beneficial effects remain unclear. Methods: This review aims to establish the role of MBI in the management of wellbeing for patients with CVD. Seventeen articles have been included in this systematic synthesis of the literature and eleven in the meta-analysis. Results: Considering physical (i.e., heart rate, blood pressure) and psychological outcomes (i.e., depression, anxiety, stress, styles of coping), the vast majority of studies confirmed that MBI has a positive influence on coping with psychological risk factors, also improving physiological fitness. Random-effects meta-analysis models suggested a moderate-to-large effect size in reducing anxiety, depression, stress, and systolic blood pressure. Conclusions: Although a high heterogeneity was observed in the methodological approaches, scientific literature confirmed that MBI can now be translated into a first-line intervention tool for improving physical and psychological wellbeing in CVD patients.

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.

Background: Acceptance and Commitment Therapy (ACT) has been demonstrated as effective in improving psychological well-being in several clinical domains, but there is no evidence regarding the parents of children with Autism Spectrum Disorder (ASD). Methods: In this randomized controlled trial, we evaluated the efficacy of the ACT matrix behavioral protocol in comparison to the Parent Training (PT) program, measuring several primary and secondary outcomes prior to and following treatments. Twelve parents were randomly and equally assigned to two demographically matched groups wherein individuals underwent 24 weekly meetings of ACT protocol (experimental group) or conventional PT (control group). Results: Parents enrolled in the ACT protocol demonstrated significant improvement in psychological flexibility, awareness states, personal values in everyday life, and parental stress, whereas reduced scores were elicited in parents’ perceptions of their child’s disruptive behaviors. Conclusions: The results of this randomized controlled trial, if repeated with a large number of subjects, could open the way to include ACT protocols in daily practice to support the development of new parenting skills.