Background and objectives: Children with autism spectrum disorder (ASD) experience challenges with social interactions, a core feature of the disorder. Social skills therapy has been shown to be helpful. Over the past several years, computer-assisted and robot-assisted therapies have been infiltrating the social skills teaching environment. Rapid progress in the field of technology, especially in the robotics area, offers tremendous possibilities for innovation and treatment or even education for individuals with ASD. This paper’s purpose is to drive awareness of these innovative interventions in order to support the social lives of children with ASD. The aims of the paper are identifying (1) the types of Information Technology platforms that are being evaluated in computer and robot-assisted therapies for children with ASD; (2) the various disciplines or professions studying and utilizing these computer and robot-assisted social skill therapies; (3) the outcomes being evaluated in each trial; and (4) if results demonstrate benefits to children with autism. Materials and Methods: PubMed, CINAHL, Science Direct, and Web of Science databases were searched for clinical trials published over the past five years. Search terms incorporated the subject intersection of autism, and computer or robot-assisted therapy. Results were mined for pediatric populations only and study designs establishing controlled comparisons. Results: Eighteen unique international studies were identified that utilize robot interventions (11 studies) and serious computer game interventions (seven studies). Most demonstrated promising results in improving outcomes for children with ASD. Study implications reveal a rapidly evolving assistive technology for ASD social skills therapy. Conclusions: These interventions show considerable promise, but more effectiveness and cost effectiveness research of high quality should be carried out with larger numbers of children. Also, further studies are necessary to evaluate these technologies’ effectiveness amongst adults with ASD and within unique subsets of the higher functioning autism population.

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Long glances away from the road present a significant risk to driving safety. The tail of the distribution for in-vehicle glance duration has been proposed to be more relevant to crash risk than the mean of the distribution. Using data collected in the 100-Car Naturalistic Driving Study (Dingus et al., 2006), this study examined the changes in the distribution of driver off-road glance duration, as well as in the frequency of such glances, at different time points before crash/near-crash events and compared with baseline (i.e., normal) driving. The results showed that the shape of the distribution of off-road glance duration at the onset of the precipitating factor of crashes/near-crashes was similar to the distribution in epochs during the preceding 25 seconds; nonetheless, the frequency of off-road glances increased in the approach to crashes/near-crash events. Compared with baseline epochs, drivers in crashes/near-crash events tended to look away from the forward road more often and with longer duration (i.e., with thicker tail end of the distributions; exceeding 1.7 seconds as observed in the distributions). It suggests that frequent off-road glances longer than 1.7 seconds present a high-risk glance pattern in the seconds preceding a safety- critical event and that the 2.0 second-threshold that is frequently cited in defining dangerously long off-road glances might be a liberal estimation.

Background: Cardiac rehabilitation (CR) is an evidence-based approach for preventing secondary cardiac events. Smartphone apps are starting to be used in CR to give patients real-time feedback on their health, connect them remotely with their medical team, and allow them to perform their rehabilitation at home. The use of smartphone apps is becoming omnipresent and has real potential in impacting patients in need of CR. Objective: This paper provides critical examinations and summaries of existing research studies with an in-depth analysis of not only the individual studies but also the larger patterns that have emerged with smartphone apps in CR as well as their significance for practice change. Methods: A systematic review was conducted through broad database searches that focused on evaluating randomized controlled trials, in compliance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) expectations. A total of 43 articles were evaluated, and 6 were chosen for this review. The dates of the articles ranged from 2014-2020, and the studies focused on the population of cardiac outpatients who needed CR after suffering a cardiac event, with interventions using a smartphone that incorporated the CR standards of the American Heart Association. The outcomes measured were directed at focusing on improved exercise function capacity, valued at a significance level of P<.05, for improved 6-minute walk test (6MWT) and peak oxygen uptake (PVO2) results. Results: In the evaluated articles, the results were inconsistent for significant positive effects of CR smartphone apps on cardiac patients’ physical function capacity in terms of the 6MWT and PVO2 when using a smartphone app to aid in CR. Conclusions: Because evidence in the literature suggests nonhomogeneous results for successful use of smartphone apps in CR, it is crucial to investigate the potential reasons for this inconsistency. An important observation from this systematic review is that smartphone apps used in CR have better clinical outcomes related to physical function capacity if the app automatically records information or provides real-time feedback to participants about their progress, compared to apps that only educate and encourage use while requiring the participant to manually log their CR activities. Additional factors to consider during these studies include the starting health of the patients, the sample sizes, and the specific components of CR that the smartphone apps are using. Overall, more clinical trials are needed that implement smartphone apps with these factors in mind, while placing stronger emphasis on using biosensing capabilities that can automatically log results and send them to providers on a real-time dashboard.