Constraint-induced movement therapy (CIMT) has been demonstrated to yield functional improvements for children with spastic hemiplegic cerebral palsy (CP); however, many studies have reported inconsistent findings with regards to the extent of the benefits observed following the intervention. This study sought to examine the effects of CIMT in the context of a day camp in this population; it also examined the child-therapist (C-T) interaction during the assessment sessions as a potential factor influencing the child’s scores on tests of motor performance. This interaction has not yet been formally measured in the literature, and may influence the perceived outcomes of CIMT. Motor Outcomes: Results demonstrated significant improvements in quality of use of the upper extremity following the intervention; the improvements were maintained at the 3-month follow-up. Analysis of each individual participant yielded additional information on clinically significant improvements. C-T Outcomes: Results demonstrated that the strength of the C-T interaction was significantly and positively correlated with the scores obtained by participants on motor assessments. The results of this study indicate that modified CIMT is effective in inducing lasting and meaningful changes in children with spastic hemiplegic CP. They also suggest that the C-T interaction may contribute to a participant’s performance during the assessment session, which may ultimately affect the perceived outcomes of CIMT. / UOIT

Clinical and research practice in the context of Autism rapidly evolved in the last decades. Finer diagnostic procedures, evidence-based models of intervention and higher social inclusivity significantly improved the possibility for autistic children to participate in the fabric of social life. In terms of health best practices, gold-standard procedures still need to be improved, and bridging research and clinical practice still presents several challenges. From the clinical standpoint, the role of process variables, predictors, mechanisms, and timing of change still requires extensive investigation in order to explain response variability and design optimized interventions, tailored to individual needs and maximally effective. Observational techniques represent the elective research methods in child development, especially in clinical contexts, due to their non-invasiveness. However, they still suffer from limited objectivity and poor quantification. Further, their main disadvantage is that they are highly time-consuming and labor-intensive. The aim of this thesis was moving forward to promote translational research in clinical practice of Autism intervention with preschool children. At first, we tried to design and apply quantitative observational techniques to longitudinally study treatment response trajectories during developmental intervention. We tried to characterize different response profiles, and which baseline predictors were able to predict the response over time. Secondly, we investigated mechanisms of change. In particular, we focused on the role of the child-therapist interaction dynamics as a possible active mediator of the process of intervention, especially in the developmental framework that stresses the importance of interpersonal aspects. We also aimed at understanding whether certain time-windows during the intervention were particularly predictive of the response, as well as which specific interaction aspects played a role. Finally, to promote the translational application of observational methods and to improve objective quantification, we proposed and validated an Artificial Intelligence (AI) system to automate data annotation in unconstrained clinical contexts, remaining completely non-invasive and dealing with the specific noisy data that characterize them, for the analysis of the child-therapist acoustic interaction. This effort represents a base building block enabling to employ downstream computational techniques greatly reducing the need for human annotation that usually prevents the application of observational research to large amounts of data . We discuss our findings stressing the importance of assuming a developmental framework in Autism, the key role of the interpersonal experience also in the clinical context, the importance of focusing on trajectories of change and the important need to promote the acquisition of large amounts of quantitative data from the clinical contexts exploiting AI-based systems to assist clinicians, improving objectivity, enabling treatment monitoring, and producing precious data-driven knowledge on treatment efficacy.