Characterizing and predicting challenging behavior in autism spectrum disorder using medical and environmental data
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Authors
Ferina, Jennifer
Issue Date
2025-05
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Biomedical engineering
Alternative Title
Abstract
Autism Spectrum Disorder (ASD) continues to increase in prevalence in the population. However, about a quarter of this population meets the criteria for profound ASD, a new categorization of individuals with ASD and an IQ <50, that cannot live independently. Those with profound ASD are underserved in the literature, and have a higher rate of challenging behaviors than the overall population with ASD. Challenging behaviors may occur in response to physiological or psychological stimuli, and prevent individuals from being able to function safely in society by causing harm to themselves, peers, or caregivers. Many of those with profound ASD must live at residential facilities because of the severity of these behaviors and medical needs. Individuals' safety and daily outcomes may be better if these behaviors can be predicted in advance, and even prevented. First, this thesis explores how physiological variables may be leveraged to predict challenging behaviors of individuals with ASD living in a residential facility. Due to the many co-occurring conditions in individuals with ASD, there are many potential physiological causes of pain or discomfort. Residential facilities enable consistent recording of both physiological and behavioral data. In addition to physiological discomfort, changes in the environment may cause discomfort to these individuals. Environmental data are also examined as a predictive tool in this dissertation. The physiological data and environmental data are used in individual classification models to predict day-to-day behavioral episodes.
Next, this dissertation validates and extends these models for a subgroup of individuals. Further validation of these models is explored in several ways. Several model types are examined and compared for accuracy. The cohort is then reduced to a subgroup that can be reliably predicted by these models. The subgroup is used to examine whether the models can be improved by adding an autocorrelation component: using prior behavioral data to predict future behavior. The subgroup is also used to evaluate how many data points are needed to train the models, and how long the model predictions continue to be valid. These questions are important for implementing these models in practice.
Finally, the impact of the COVID-19 pandemic on challenging behaviors is examined. Previous work has not examined the impact of the COVID-19 pandemic on pediatric patients with ASD in a residential facility. A mixed generalized linear model was used to characterize the impact of the COVID-19 pandemic on behavior, and also included other contextual variables, including school days, weekends, and unexpected closures.
By further understanding both physiological and environmental associations with challenging behaviors, it is possible to reduce harmful impacts of the challenging behavior if it can be reliably predicted, and may be possible to prevent the behavior altogether. This dissertation examines several aspects of challenging behavior prediction in ASD, and characterizes the relationship between the COVID-19 pandemic and challenging behaviors in a residential setting.
Description
May2025
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY