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.

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50th Annual Convention; Philadelphia, PA; 2024

Event Details


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B. F. Skinner Lecture Series Paper Session #434
CE Offered: BACB
Using AI, Machine Learning and Gamification to Enhance the Reach, Scale, Engagement and Effectiveness of Behavioral Interventions
Monday, May 27, 2024
10:00 AM–10:50 AM
Marriott Downtown, Level 5, Grand Ballroom Salon H
Area: PRA; Domain: Service Delivery
Chair: Claudia Drossel (Eastern Michigan University)
CE Instructor: Claudia Drossel, Ph.D.
Presenting Author: EVAN FORMAN (Drexel University)
Abstract:

Behavioral treatments help people modify their behavior in order to achieve greater psychological and physical well-being. These treatments can be implemented by a clinician and/or by automated systems. Each type of treatment has its strengths and weaknesses. This presentation considers ways that machine learning, AI, and gamification can partially negotiate these strengths and weaknesses to achieve optimal results. Behavioral treatments for lifestyle modification and weight control are good candidates for such technology because necessary behaviors run counter to biological and environmental forces, and thus require acquisition of specific behavioral skills and accountability normally provided by an expert clinician. Yet, there are a severe shortage of such clinicians relative to the tens of millions of people in needs of such services. In the NIH R01-funded Project ReLearn, we are evaluating an artificial intelligence (AI) system for optimizing the delivery of clinical services of varying cost in a manner that allows for scalability across large populations. Project LYRA, in contrast, is exploring whether a large language model-powered chatbot housed in a self-help behavioral weight loss program can successfully help individuals achieve weight loss without the need for human clinicians. Even when clinical services are being provided, a challenge is that skills and strategies taught in the clinic are not utilized by clients in the moment of need. We have been exploring whether a machine learning-power, smartphone-based just-in-time, adaptive intervention (JITAI) called OnTrack is able to predict and prevent dietary lapses and to facilitate weight loss. People struggle to maintain motivation to engage in the exceptionally difficult behavior changes necessary to achieve weight loss. The NIH R01-funded Project Neurofit evaluates whether gamification and neurocognitive training improve engagement and weight loss outcomes among men (who also reject traditional clinical interventions which they view as “feminine”).

Instruction Level: Intermediate
Target Audience:

Behavior therapists and researchers

Learning Objectives: At the conclusion of the presentation, participants will be able to: (1) describe how AI might be used to optimize, supplement and replace human-provided behavioral treatments; (2) describe how machine learning can be used to provide tailored behavioral skills and strategies in the moment of need; and (3) describe how gamification could be used to motivate difficult behavior change
 
EVAN FORMAN (Drexel University)
Evan M. Forman received his Ph.D. in Clinical Psychology from the University of Rochester, and completed clinical internships and fellowships at Albert Einstein College of Medicine, Harvard Medical School, and the University of Pennsylvania. He is currently a Professor of Psychological and Brian Sciences at Drexel University and is the Director of the Center for Weight Eating and Lifestyle Science (the WELL Center), and as such oversees 55 faculty, postdoc fellows, staff and students and a $27.2M research portfolio consisting of 21 projects. His primary interest is in the development and evaluation of innovative technological and behavioral approaches to health behavior change. He has received continuous NIH support to conduct research in these areas for over 12 years, and is currently the PI of three R01-funded clinical trials evaluating AI optimization, gamification and component efficacy of behavioral weight loss treatments. He has authored over 175 scientific papers, which have over 15,000 indexed citations. He is also the author of a clinician guide and workbook called Effective Weight Loss: An Acceptance-based Behavior Approach for Oxford Press’s Treatments that Work series.
 

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