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Interventions based on applied behaviour analysis are considered evidence based practice for autism spectrum disorders. Due to the shortage of highly qualified professionals required for their delivery, innovative models should be explored, such as telehealth. Telehealth utilises technology for remote training and supervision. The purpose of our study was to systematically review the literature researching telehealth and ABA. We analysed intervention characteristics, outcomes and research quality in 28 studies and identified gaps. Intervention characteristics were: (1) research design (2) participants (3) technology (4) dependent variables (5) aims. Outcomes were favourable with all studies reporting improvements in at least one variable. Quality ratings were significantly low. Implications for future research and practice are discussed in light of identified methodological downfalls.

There are three branches of science of behavior analysis [i.e., experimental analysis of behavior, behavior analysis, and applied behavior analysis (ABA)]. ABA can be defined as a systematic approach to understanding behavior of social interest. For the past 40 plus years, researchers have evaluated ABA and ABA based procedures (e.g., behavioral intervention) as they relate to individuals diagnosed with autism spectrum disorder (ASD) and have implemented behavioral intervention in clinical settings for individuals diagnosed with ASD. In this paper, we discuss some of the pivotal contributions in the field of ABA in research and clinical practice. Additionally, we provide recommendations for the science and clinical practice of behavioral intervention in the next 40 years.

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go ‘deeper’ than tracking, and address automated recognition of animals’ internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of pain and emotional states in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic—classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

Deep learning-based methods have advanced animal pose estimation, enhancing accuracy, and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compromise reliability. Here, we present the anti-drift pose tracker (ADPT), a transformer-based tool that mitigates tracking drift in behavioral analysis. Extensive experiments across cross-species datasets—including proprietary mouse and monkey recordings and public Drosophila and macaque datasets—demonstrate that ADPT significantly reduces drift and surpasses existing models like DeepLabCut and SLEAP in accuracy. Moreover, ADPT achieved 93.16% identification accuracy for 10 unmarked mice and 90.36% accuracy for freely interacting unmarked mice, which can be further refined to 99.72%, enhancing both anti-drift performance and pose estimation accuracy in social interactions. With its end-to-end design, ADPT is computationally efficient and suitable for real-time analysis, offering a robust solution for reproducible animal behavior studies. The ADPT code is available at https://github.com/tangguoling/ADPT .

PurposeCourse recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approachThe study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.FindingsThe main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implicationsThe findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/valueThis study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

The Behavior Analyst Certification Board (BACB®) created a third level of certification, the Registered Behavior Technician™ (RBT®) in 2014. The RBT® was created based upon the requests of stakeholders who wanted to credential those individuals who make direct contact with clients under the supervision of a Board Certified Behavior Analyst®. There has been tremendous growth in the number of RBTs® with over 60,000 individuals certified to date. The BACB® recently sent out a newsletter outlining changes to the RBT® certification, including the processes of training, supervising, and becoming an RBT®. These changes represent a number of potential concerns. The purpose of this paper is to highlight these concerns and to propose solutions to improve the RBT® certification.

Accreditation is typically a voluntary process that involves a thorough evaluation of an organization’s policies, procedures, and practices. Much like a colonoscopy, the evaluation process probes deep and can be uncomfortable. With the discomfort, time, cost, and effort it takes to undergo evaluation for accreditation, the natural question is whether it is worth doing. In this paper, I will review the history of accreditation and the results of systematic literature reviews focused on the impact of accreditation. I will also discuss how accreditation may help provide quality control in behavior analysis and safeguard against service providers’ behaviors being solely shaped by funding sources, such as insurance providers. Lastly, I will provide critical questions consumers can ask to assess accrediting bodies’ transparency, objectivity, and fairness when they are seeking accreditation. Accreditation is typically a voluntary process that involves a thorough evaluation of an organization’s policies, procedures, and practices. Much like a colonoscopy, the evaluation process probes deep and can be uncomfortable. With the discomfort, time, cost, and effort it takes to undergo evaluation for accreditation, the natural question is whether it is worth doing. In this paper, I will review the history of accreditation and the results of systematic literature reviews focused on the impact of accreditation. I will also discuss how accreditation may help provide quality control in behavior analysis and safeguard against service providers’ behaviors being solely shaped by funding sources, such as insurance providers. Lastly, I will provide critical questions consumers can ask to assess accrediting bodies’ transparency, objectivity, and fairness when they are seeking accreditation.

This study investigates demographic and clinical factors associated with initiation, continuation, and adherence to behavioral health treatment (BHT) among children with autism spectrum disorder. Among 293 insured children referred for applied behavior analysis (ABA) based BHT, 23% never initiated treatment. Among those initiating treatment, 31% discontinued treatment within 1 year of treatment initiation, and only 15% received 80% or more of recommended treatment hours. Younger age at referral to treatment, private health insurance, and receiving more than 10 h/week of BHT were associated with treatment engagement. Co-occurring psychiatric and medical conditions were related to treatment discontinuation among children 5 years or older. These findings suggest specific subgroups that may benefit from additional support with engaging in recommended behavioral health treatment.

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

For security and justice professionals (e.g., police officers, lawyers, judges), the thousands of peer-reviewed articles on nonverbal communication represent important sources of knowledge. However, despite the scope of the scientific work carried out on this subject, professionals can turn to programs, methods, and approaches that fail to reflect the state of science. The objective of this article is to examine (i) concepts of nonverbal communication conveyed by these programs, methods, and approaches, but also (ii) the consequences of their use (e.g., on the life or liberty of individuals). To achieve this objective, we describe the scope of scientific research on nonverbal communication. A program (SPOT; Screening of Passengers by Observation Techniques), a method (the BAI; Behavior Analysis Interview) and an approach (synergology) that each run counter to the state of science are examined. Finally, we outline five hypotheses to explain why some organizations in the fields of security and justice are turning to pseudoscience and pseudoscientific techniques. We conclude the article by inviting these organizations to work with the international community of scholars who have scientific expertise in nonverbal communication and lie (and truth) detection to implement evidence-based practices.