Neural Conversational Agent for Automated Weight Loss Counseling

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Obesity is one of the most important medical and public health problems in the United States. According to recent nationally representative studies, every third adult in the U.S. is obese. Motivational Interviewing (MI), a client-centered and directive approach to behavior change counseling, has been widely adapted for treating obesity. Despite the evidence presented in published meta-reviews that suggests that MI is effective at activating behavioral changes, anthropometric changes are less significant. At the same time, there are several access barriers to this type of behavioral health care, such as shortage of human counselors in certain geographical areas, long wait times, cost, and fear of judgment. Recent advances in deep learning have allowed artificial intelligence (AI) methods to expand into the areas of health care that were previously thought to be the exclusive province of human experts, such as clinical diagnostics. Behavioral health and MI, however, are the areas of medicine that have not yet substantially benefitted from modern AI technologies, such as neural conversational agents. To address this limitation, the proposed project aims to test the feasibility and usability of using neural conversational agents for automated behavioral counseling with a focus on weight loss. Specifically, we build on recent advances in deep learning, such as conversational agents, neural attention, transformers, supervised policy learning, variational autoencoders and adversarial training, and aim to develop and validate Neural Agent for Obesity Motivational Interviewing (NAOMI), a mobile device (smartphone or tablet) application to conduct automated MI counseling focused on weight loss. NAOMI is based on a novel neural architecture, which consists of neural networks that can be independently and collectively trained using the proposed multi-stage procedure to learn communication behaviors, which should be strategically utilized during different stages of an MI counseling session depending on the observed interactions and generate responses that are grounded in session context and reflect patient’s language. We will recruit 40 obese adults, who will interact with NAOMI and provide their feedback through semi-structured qualitative interviews. We plan on conducting at most 4 iterative development cycles of NAOMI with 10 patients participating in each cycle. We will conduct a mixed-methods sub-study after each development cycle. Quantitative evaluation of NAOMI’s MI counseling skills will be conducted based on the transcripts of participants’ interactions by a coder trained in using the MI Treatment Integrity (MITI) coding system, a standard instrument for assessing MI fidelity. Qualitative interviews with the participants will be analyzed using Framework Matrix Analysis. The methods and techniques proposed in this project can be adapted to other types of psychotherapeutic interventions besides MI and to other conditions besides obesity.