Developing an Optimized Conversational Agent or "Chatbot" to Facilitate Mental Health Services Use in Individuals with Eating Disorders

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PROJECT SUMMARY/ABSTRACT Eating disorders (EDs) are serious mental illnesses associated with high morbidity and mortality, clinical impairment, and comorbid psychopathology. Although evidence-based treatments for EDs have been established, the treatment gap is wide. Indeed, <20% of individuals with EDs receive treatment. We need a novel solution not only to identify individuals with EDs but also to encourage mental health services use and address treatment barriers. I have established a strong working relationship with the National EDs Association (NEDA), the U.S.’s leading non-profit for EDs, to offer our group’s online, evidence-based EDs screen on the NEDA website. Over one year, the NEDA screen was completed >200,000 times. Among screen respondents, the vast majority screen positive for an ED, but of those, most are not in treatment, and only a small percentage click to learn more about one of the options for seeking intervention presented in the screening feedback. I propose a research agenda to design a conversational agent or “chatbot” that is optimized to increase mental health services use among individuals with EDs through such features as: 1) providing a personalized recommendation for seeking intervention; 2) engaging the user in motivational interviewing to overcome barriers to care; and 3) repeated check-ins with the user to encourage follow-up with care. This research agenda aligns with NIMH’s focus on services research and interest in technology-driven approaches to promote engagement with care. I will conduct two studies. First, I will utilize a user-centered design approach to create a prototype chatbot and conduct usability testing with adults with EDs to inform chatbot refinements (Aim 1). Second, I will conduct a randomized optimization trial with adults who have completed screening on the NEDA website and screen positive for an ED but are not in treatment to determine chatbot feasibility and to generate data on the effect of the chatbot on motivation for treatment post-initial chatbot use and motivation for treatment and mental health services use at 1- and 3-month follow-ups (Aims 2 & 3). This trial will employ the Multiphase Optimization Strategy framework, using a 23 full factorial design, to randomly assign participants to a combination of the three proposed intervention components (n=8 conditions) to isolate the active ingredients. These aims support my training plan in which I will receive expert mentorship and training in: services research and implementation science (Training Goal 1); user-centered design and usability testing, as well as exposure to machine learning (Training Goal 2); novel trial designs (Training Goal 3); and advanced statistical techniques for analyzing longitudinal trial and digital innovation data (Training Goal 4). My expert mentorship team, along with the environment of Washington University, will ensure my success. Results from the proposed study will be used to optimize the chatbot, which I will then test in a subsequent R01 randomized controlled trial. This K08 will equip me with the skills to become a leader in addressing the research-practice and treatment gaps for EDs, with a focus on digital innovations.