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

> **NIH NIH R01** · DUKE UNIVERSITY · 2021 · $303,091

## Abstract

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
exis...

## Key facts

- **NIH application ID:** 10247759
- **Project number:** 5R01MH122370-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** GUILLERMO R SAPIRO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $303,091
- **Award type:** 5
- **Project period:** 2019-09-23 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247759

## Citation

> US National Institutes of Health, RePORTER application 10247759, SCH: INT: Computational Tools for Avoidaint/Restrictive Food Intake Disorder (5R01MH122370-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10247759. Licensed CC0.

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