Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


43rd Annual Convention; Denver, CO; 2017

Event Details

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Symposium #280
CE Offered: BACB — 
Emerging Practices in Assessment and Treatment of Disruptive Behavior: Novel Applications of Telehealth and Exploratory Data Analysis
Sunday, May 28, 2017
3:00 PM–4:50 PM
Convention Center Mile High Ballroom 1A/B
Area: DDA; Domain: Translational
Chair: Joy Pollard (Behavior Change Institute; Stanford University)
Discussant: Scott S. Hall (Stanford University)
CE Instructor: Joy Pollard, Ph.D.

Functional analysis (FA) is the gold standard for the assessment of severe disruptive behavior. Researchers in this field are actively working to refine the assessment and methods of interpretation of FAs in an effort to improve client outcomes. In this symposium, we will discuss methods that may be used to enhance clinical practices by way of improved efficiencies and access to care. The first paper will review the use of exploratory data analysis (EDA) to increase the time efficiency and objectivity of functional analysis (FA) interpretation. The second paper applies the EDA method to interpret FA data and subsequently coach parents via telehealth to implement a FCT protocol to reduce challenging behavior in children with Fragile X syndrome. The third paper utilizes an automated telehealth messaging system to obtain electronic data to assess generality of child outcomes outside of treatment sessions. Finally, we will conclude with an overview of ethical considerations and guidance on the development of the clinical and business infrastructure for telehealth service delivery.

Instruction Level: Intermediate
Keyword(s): functional analysis, problem behavior, technology, telehealth

Enhancing the Efficiency and Objectivity of Functional Analysis Data Interpretation: A Step-by-Step Guide

SCOTT S. HALL (Stanford University), Joy Pollard (Behavior Change Institute; Stanford University), Katerina Monlux (Stanford University)

Functional analysis (FA) is a well-established assessment procedure designed to facilitate the selection of function-based treatments for problem behavior. Despite recent efforts to improve the objectivity of FA data interpretation, visual analysis of FA data requires applying a large set of complicated decision rules and subjective judgments that could result in interpretation errors and compromise subsequent treatment selection. In this article, we examined whether a common data analysis procedure employed in other areas of scientific inquiry - Exploratory Data Analysis (EDA) - could enhance the efficiency and objectivity of FA data interpretation. We first demonstrate how EDA plots can be generated from FA data using an example dataset. We then devise operational definitions to identify differentiated outcomes, the highest condition, and downward and upward trends, to facilitate the interpretation of the EDA plots. Finally, we generate EDA plots from the example FA datasets presented in Roane et al. (2013) and use the operational definitions we developed to interpret each FA. In each case, outcomes were consistent with those reported by Roane et al. Importantly, EDA plots significantly reduced the number of data points to be examined, allowing the FA data to be interpreted more efficiently and objectively. EDA techniques could therefore be employed as an adjunct or alternative to other visual analysis approaches designed to augment FA data interpretation. Continued refinement of the methods by which FA data are interpreted will likely result in improved treatment selection and greater acceptance of FA procedures by the wider scientific community in general.


Preliminary Findings of a Telehealth Model to Treat Problem Behaviors in Boys With Fragile X Syndrome

(Applied Research)
KATERINA MONLUX (Stanford University), Arlette Bujanda (Behavior Change Institute; Stanford University), Joy Pollard (Behavior Change Institute; Stanford University), Scott S. Hall (Stanford University)

Many individuals with fragile X syndrome (FXS), a rare genetic disorder associated with Autism Spectrum Disorder (ASD), commonly show severe problem behaviors such as self-injury and aggression that can be extremely distressing to families and can severely impact the child�s quality of life and educational placement. Although pharmacotherapies are commonly prescribed to treat problem behaviors in this population, evidence suggests that social-environmental factors play a significant role in the development and maintenance of these behaviors. We therefore evaluated whether targeted function-based behavioral treatments for problem behaviors in FXS, conducted via telehealth, could reduce problem behaviors in this disorder. Following in home assessments to identify the function of disruptive behaviors, caregivers received daily coaching via telemedicine to implement function-based treatments over a 12-week period. Preliminary findings suggest that telehealth behavioral treatment is an effective model for reducing problem behavior in children with FXS. This study will therefore help inform treatment decisions and aid clinicians in determining the appropriateness of pharmacotherapies in genetic conditions such as FXS.


Evaluating the Generality of Therapuetic Gains via Telehealth

(Applied Research)
NEALETTA HOUCHINS-JUAREZ (Vanderbilt University), Abigail Morgan (Vanderbilt University), Joseph Michael Lambert (Vanderbilt University), Mary Matthews (Vanderbilt University), Somer Wiggins (Vanderbilt University), Kayla Rechelle Randall (Vanderbilt University )

Generalization is essential to the social validity of effective intervention. However, it is difficult to evaluate the generality of therapeutic gains across all facets of a clients life because therapists are not available to collect data at these times. One solution is parent report; however, ensuring consistent and accurate data without presenting undue burden to family is challenging. In our study, we employed an automated texting system to send parents daily individualized-behavioral questions at prescribed times during all phases of intervention (i.e., assessment through discharge). Responding remained high throughout the investigation, suggesting texting may be a viable reporting option (although questions about reliability/accuracy remain). Importantly, obtained data indicate that problem behavior persisted outside of therapeutic sessions for the duration of the study; even after it had been eliminated during these sessions by parents who were trained to fidelity via behavior-skills training. These results suggest a greater focus on generalization is merited.


Ethical Considerations in the Development of a Telehealth Service Delivery Model: Recommendations for Clinicians and Behavior Analytic Organizations

(Service Delivery)
JOY POLLARD (Behavior Change Institute), Kathleen Karimi (Behavior Change Institute), Michelle Ficcaglia (Behavior Change Institute)

Telehealth service delivery models have become increasingly popular in the provision of behavior analytic services. Telehealth provides an opportunity to enhance care by providing clinicians and consumers with the ability to bridge issues related to geography by improving access to behavioral healthcare and reducing health disparities between urban and rural populations. As technology advances, this raises for consideration ethical challenges that may arise within this new model. Further, changes in the clinical and business infrastructure may be warranted to ensure safe, effective, and quality treatment for consumers. This paper explores ethical concerns when designing a telehealth service model within a behavior analytic organization. Recommendations related to the development of clinical and business infrastructure are provided to guide clinicians and organizations to promoting ethically sound services.




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