SCH: INT: Computational Tools for Avoidaint/Restrictive Food Intake Disorder

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Intellectual Merit: This project will for the first time provide the fundamental tools to integrate unique multimodal data toward screening, diagnosis, and intervention in eating disorders, with an initial focus on children with ARFID and related developmental and health disorders. This work is critical for enriching the understanding of healthy development and for broadening the foundations of behavioral data science. ARFID ·motivates the development of new computer vision and data analysis tools critical for the analysis of multidimensional behavioral data. The main aims are: 1. Develop and user individualized and integrated continuous facial affect coding from videos to discern affective motivations for food avoidance, critical due to the unique sensory aspects of eating disorders, and resulting from active stimulation via friendly and carefully designed images/videos and real food presentation; 2. Use data analysis and machine learning to derive sensory profiles based on patterns of food consumption and preference from existing unique datasets of selective eaters; and 3. Translate the tools developed in Aims 1 and 2 into the clinic and home to assess the capacity of these tools to define a threshold of clinically significant food avoidance, to detect change in acceptability of food with repeated presentations, and to examine and modify the accuracy of our food suggestion algorithms. Broader Impacts: The impact of this application comprises two broad domains. First is the derivation of processes, tools, and strategies to analyze very disparate data across multiple levels of analysis and to codify those strategies to inform similar future work, in particular incorporating automatic behavioral coding. Second is the exploitation of these tools to address questions about the emergence of healthy/unhealthy food selectivity across the lifespan, including recommendation delivery via apps and at-home recordings. The health impact of even partial success in this project is very broad and significant. Undergraduate students will be involved in this project via the 6-weeks summer research program at the Information Initiative at Duke, a center dedicated to the fundamentals of data science and its applications; via the co-Pl's research lab devoted to eating disorders; and via the Pl's project dedicated to training undergraduate students to address eating disorders of their friends via an anonymous app. Outreach and dissemination will follow the broad use of the developed app, both in the clinic and the general population, including the Pl's connections with low-income and under-represented bi-lingual preK. RELEVANCE (See instructions): Eating disorders are potentially life-threatening mental illnesses affecting the general population; -90% of individuals never receive treatment, in part due to lack of awareness and access. Individuals with eating disorders experience a diminished quality of life, high mental and physical illness comorbidities, and an existence marked by profound loneliness and isolation. Combining expertise in eating disorders with computer vision and machine learning, we bring for the first time data science to this health challenge. PROJECT/PERFORMANCE S1TE(S) (If addItIonal space Is needed use Project/Performance Stte Format Page)