Predicting Adverse Behavior in Individuals with Autism Spectrum Disorder Through Off-body Sleep Analysis.

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2,000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74 in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value<0.05), with 3 showing significant trends (p-value<0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data.

Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.

Observational study of medical marijuana as a treatment for treatment-resistant epilepsies.

OBJECTIVES: Medical cannabis formulations with cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) are widely used to treat epilepsy. We studied the safety and efficacy of two formulations. METHODS: We prospectively observed 29 subjects (12 to 46 years old) with treatment-resistant epilepsies (11 Lennox-Gastaut syndrome; 15 with focal or multifocal epilepsy; three generalized epilepsy) were treated with medical cannabis (1THC:20CBD and/or 1THC:50CBD; maximum of 6 mg THC/day) for ≥24 weeks. The primary outcome was change in convulsive seizure frequency from the pre-treatment baseline to the stable optimal dose phase. RESULTS: There were no significant differences during treatment on stable maximal doses for convulsive seizure frequency, seizure duration, postictal duration, or use of rescue medications compared to baseline. No benefits were seen for behavioral disorders or sleep duration; there was a trend for more frequent bowel movements compared to baseline. Ten adverse events occurred in 6/29 patients, all were transient and most unrelated to study medication. No serious adverse events were related to study medication. INTERPRETATION: Our prospective observational study of two high-CBD/low-THC formulations found no evidence of efficacy in reducing seizures, seizure duration, postictal duration, or rescue medication use. Behavioral disorders or sleep duration was unchanged. Study medication was generally well tolerated. The doses of CBD used were lower than prior studies. Randomized trials with larger cohorts are needed, but we found no evidence of efficacy for two CBD:THC products in treating epilepsy, sleep, or behavior in our population.

Examining the Association Between Electrodermal Activity and Problem Behavior in Severe Autism Spectrum Disorder: A Feasibility Study.

Background: Many individuals with autism spectrum disorder (ASD) engage in problem behavior, presenting significant challenges for those providing care and services for this population. Psychophysiological measures of arousal, such as electrodermal activity (EDA), may provide an early indication of subsequent problem behavior. However, variability in EDA patterns associated with behaviors may limit this predictive ability. Methods: EDA data was sampled from eight individuals with severe ASD in a naturalistic setting, while participating in educational programming in a school setting at a residential facility for severely affected individuals with developmental disabilities, to examine variability in EDA patterns. Results: An anticipatory rise in EDA only occurred 60% of the time prior to the problem behavior. Additionally, EDA after a problem behavior returned to median baseline levels only 45% of the time. Conclusions: Heterogeneity of EDA responses in those with the most severe forms of ASD will be an important consideration in future studies utilizing psychophysiological tools such as EDA to anticipate problem behavior, including the need for monitoring of return to baseline after problem behaviors. Incorporation of this consideration may lead to greater reliability of these approaches to help anticipate and manage problem behaviors.

Improving social functioning and challenging behaviors in adolescents with ASD and significant ID: A randomized pilot feasibility trial of reciprocal imitation training in a residential setting.

There is a lack of effective social interventions for youths with ASD and co-morbid intellectual disability (ID). A previous single-case design study indicated that reciprocal imitation training (RIT) may improve social interaction and challenging behavior in this population. The current pilot study examined the feasibility of conducting an RCT to investigate the effectiveness of RIT for improving social functioning and challenging behaviors in 20 adolescents with ASD and severe ID in a residential program. The assessment protocol was feasible. RIT was well-tolerated by the adolescents and implemented with fidelity by teaching staff. Preliminary findings indicate that treatment had moderate to large effects on social functioning and challenging behavior, with mixed findings for imitation skills. A larger RCT of RIT for this population is feasible and warranted.

Social intervention for adolescents with autism and significant intellectual disability: initial efficacy of reciprocal imitation training.

Individuals with autism have difficulty with social skills across the lifespan. Few social interventions have been examined for older individuals with autism who also have significant intellectual disabilities (ID). Previous research suggests that reciprocal imitation training (RIT) improves imitation and social engagement in young children with autism. This study used a multiple-baseline design to examine whether RIT could improve social behaviors in four adolescents with autism and significant ID. All adolescents improved their spontaneous imitation and two improved their joint engagement. In addition, two adolescents decreased their rate of self-stimulatory behaviors over the course of treatment. Overall, these results suggest that RIT may be effective at improving social interaction and decreasing self-stimulatory behavior in adolescents with autism and significant ID.