Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission.

Enhanced cognitive-behavioral therapy (CBT-E) is one of the primary evidence-based treatments for adults with eating disorders (EDs). However, up to 50% of individuals do not respond to CBT-E, likely because of the high heterogeneity present even within similar diagnoses. This high heterogeneity, especially in regard to presenting pathology, makes it difficult to develop a treatment based "on averages" and for clinicians to accurately pinpoint which symptoms should be targeted in treatment. As such, new models based at both the group, and individual level, are needed to more accurately refine targets for personalized evidence-based treatments that can lead to full remission. The current study (Expected N = 120 anorexia nervosa, atypical anorexia nervosa, and bulimia nervosa) will build both group and individual longitudinal models of ED behaviors, cognitions, affect, and physiology. We will collect data for 30 days utilizing a mobile application to assess behaviors, cognition, and affect and a sensor wristband that assesses physiology (heart rate, acceleration). We will also collect outcome data at 1- and 6-month follow-ups to assess ED outcomes and remission status. These data will allow for identification of "on average" and "individual" targets that maintain ED pathology and test if these targets predict outcomes, including ED remission.

Small-Group Technology-Assisted Instruction: Virtual Teacher and Robot Peer for Individuals with Autism Spectrum Disorder.

The authors combined virtual reality technology and social robotics to develop a tutoring system that resembled a small-group arrangement. This tutoring system featured a virtual teacher instructing sight words, and included a humanoid robot emulating a peer. The authors used a multiple-probe design across word sets to evaluate the effects of the instructional package on the explicit acquisition and vicarious learning of sight words instructed to three children with autism spectrum disorder (ASD) and the robot peer. Results indicated that participants acquired, maintained, and generalized 100% of the words explicitly instructed to them, made fewer errors while learning the words common between them and the robot peer, and vicariously learned 94% of the words solely instructed to the robot.