Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study.

BACKGROUND: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking. OBJECTIVE: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups. METHODS: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender. RESULTS: Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P<.003). CONCLUSIONS: These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.

Investigating the Effects of Spacing on Working Memory Training Outcome: A Randomized, Controlled, Multisite Trial in Older Adults.

OBJECTIVE: The majority of the population will experience some cognitive decline with age. Therefore, the development of effective interventions to mitigate age-related decline is critical for older adults' cognitive functioning and their quality of life. METHODS: In our randomized controlled multisite trial, we target participants' working memory (WM) skills, and in addition, we focus on the intervention's optimal scheduling in order to test whether and how the distribution of training sessions might affect task learning, and ultimately, transfer. Healthy older adults completed an intervention targeting either WM or general knowledge twice per day, once per day, or once every-other-day. Before and after the intervention and 3 months after training completion, participants were tested in a variety of cognitive domains, including those representing functioning in everyday life. RESULTS: In contrast to our hypotheses, spacing seems to affect learning only minimally. We did observe some transfer effects, especially within the targeted cognitive domain (WM and inhibition/interference), which remained stable at the 3-month follow-up. DISCUSSION: Our findings have practical implications by showing that the variation in training schedule, at least within the range used here, does not seem to be a crucial element for training benefits.

Exploring the relationship of digital information sources and medication adherence.

We present a retrospective analysis of data collected in the United States from the 2015 National Consumer Survey on the Medication Experience and Pharmacists' Role in order to model the relationship between health information sources and medication adherence and perception. Our results indicate that while the digital age has presented prescription users with many non-traditional alternatives for health information, the use of digital content has a significant negative correlation with pharmaceutical adherence and attitudes toward medication. These findings along with previous research suggest that in order to fully realize the potential benefits of the digital age in regards to patient health, positive patient-provider discussions regarding information found online, efforts to improve general health literacy and improvements in the quality and accuracy of the information found are key. Given that higher reliance on digital content is correlated with younger age, the analysis suggests that proactive measures should be taken to educate younger prescription users about the merits and pitfalls of information seeking techniques as they pertain to health literacy.

Exploring Age-Related Metamemory Differences using Modified Brier Scores and Hierarchical Clustering.

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs' ability to assess their own memory functions-Metamemory (MM) - find little evidence that this function is susceptible to age-related decline. Our study examines OAs' and young adults' (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM-the modified Brier-in order to adjust for differences in scale usage between participants. Our data indicate that OAs and YAs differ in the strategies they use to assess their memory and in how well their MM matches with memory performance. However, there was no evidence that the chosen strategies were associated with differences in MM match, indicating that there are multiple strategies that might be effective (i.e. lead to similar match) in this MM task.