L’analyse appliquée du comportement en autisme et ses enjeux : une évaluation du potentiel de la technologie pour améliorer la pratique et la recherche
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by significant deficits in social communication and social interactions and by the presence of restricted and repetitive behaviors or interests. Empirical evidence suggests that interventions based on applied behavior analysis (ABA) are the most effective for treating individuals with ASD. Nevertheless, interventions based on behavior analysis present some issues. In particular, intervention services are hard to access, knowledge about the underlying factors of the effectiveness of interventions is lacking and divergent perceptions about of ABA hamper the adoption of the science. This dissertation includes three studies in which technology is used to better understand or improve these issues regarding ABA. As part of my first study, the effects of a fully self-guided interactive web training (IWT) developed for teaching parents of children with ASD ABA-derived strategies to reduce their child's challenging behaviors were evaluated using a randomized waitlist trial. The results of this study support the effectiveness of the IWT for increasing the frequency of parents’ use of behavioral interventions as well as for reducing the frequency and severity of their child’s challenging behaviors. In contrast, no significant difference was observed for the measurement of parenting practices. Ethical and practical consideration regarding the dissemination of fully self-guided online trainings are discussed. The second study of my doctoral thesis aimed to show how to use machine learning algorithms to predict individuals who were most likely to improve following an intervention. Specifically, a demonstration of how to implement four machine learning algorithms to predict the participants from my first study who were the most likely to report a decrease in their child's iv challenging behaviors. This study argues that machine learning algorithms can be used with small samples to support clinicians’ and researchers’ decision making. The third study of my dissertation aimed to quantify the information about ABA published on four subforums of an internet forum; an online resource often used by families to identify potential interventions for their child. This goal was achieved through the use of a data mining procedure. The analyses showed that parents who visited the forum were exposed to a significant proportion of messages that disapproved of ABA for individuals with ASD or that inaccurately described its underlying principles, methods, procedures, or interventions. Together, the studies carried out as part of my doctoral dissertation highlight the benefits of technology to support assessments, interventions, and knowledge gains or transfer within psychosocial practices. As highlighted in the three studies of this dissertation, each of the tools used presents limitations and should therefore be used to support clinicians and researchers, and should not replace their interventions and clinical judgment. Future studies should continue to focus on the effectiveness of technological tools and on the underlying factors that will promote their use. Finally, researchers must reflect on the ethical considerations related to use of technology when working with humans.
Autism Challenging behavior Online parenting training Data mining Machine learning algorithms Applied behavior analysis