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

SCH：INT：避免/限制性食物摄入障碍的计算工具

• 批准号: 10228145
• 负责人: GUILLERMO R SAPIRO
• 金额: $ 5.92万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228145
• 关键词: Address; Affect; Affective; Algorithms; Anxiety; Assessment tool; Attention; Awareness; Behavior Therapy; Behavioral; Caregivers; Child; Childhood; Clinic; Clinical; Code; Computer Vision Systems; Data; Data Analyses; Data Science; Data Set; Depressed mood; Derivation procedure; Development; Diagnosis; Diet; Disease; Distress; Eating; Eating Disorders; Emotional; Emotions; Evolution; Exposure to; Face; Family; Food; Food Patterns; Food Preferences; Foundations; Friends; Fright; Future; General Population; Goals; Health; Health Personnel; Home environment; Image; Impairment; Individual; Industry; Instruction; Intervention; Life; Link; Literature; Loneliness; Longevity; Low income; Machine Learning; Maps; Mathematics; Measures; Mental disorders; Monitor; Motion; Motivation; Parents; Performance; Phenotype; Primary Health Care; Process; Psyche structure; Quality of life; Reaction; Recommendation; Research; Scientist; Sensory; Severities; Smell Perception; Standardization; Structure; Suggestion; System; Taste aversion; Time; Training; Translating; Uncertainty; Work; analytical tool; base; behavior change; clinically significant; comorbidity; computerized tools; design; experience; food avoidance; food consumption; gaze; improved; indexing; mathematical algorithm; multimodal data; novel; outreach; precision medicine; preference; programs; relating to nervous system; response; screening; success; summer research; tool; undergraduate student; wasting; willingness

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)

智力优势：该项目将首次提供基本工具， 多模态数据对筛查，诊断和干预饮食失调，初步重点是 患有ARFID和相关发育和健康障碍的儿童。这项工作对于丰富 健康发展的理解和拓宽行为数据科学的基础。 ARFID促进了新的计算机视觉和数据分析工具的开发，这些工具对分析 多维行为数据主要目的是：1.开发和用户个性化和集成 从视频中连续的面部情感编码来辨别避免食物的情感动机， 饮食失调的独特感官方面，并通过友好和积极的刺激产生， 精心设计的图像/视频和真实的食物展示; 2.使用数据分析和机器学习， 根据现有的独特的食物消费模式和偏好， 选择性进食者的数据集;以及3.将目标1和2中开发的工具应用于诊所和家庭 评估这些工具的能力，以确定临床上显著的食物回避阈值， 改变食品的可接受性与重复介绍，并检查和修改我们的准确性 食物建议算法 更广泛的影响：这一应用的影响包括两个广泛的领域。首先是推导 流程、工具和策略，以跨多个分析级别分析非常不同的数据， 编纂这些战略，为今后类似的工作提供信息，特别是纳入自动行为编码。 其次是利用这些工具来解决健康/不健康的出现问题， 整个生命周期的食物选择性，包括通过应用程序和在家录音提供建议。 该项目即使部分成功，对健康的影响也是非常广泛和重大的。 本科生将通过为期6周的夏季研究计划参与该项目， 杜克的信息倡议，致力于数据科学及其应用的基础知识中心; 通过共同Pl的研究实验室致力于饮食失调;通过Pl的项目致力于培训 本科生通过匿名应用程序解决他们朋友的饮食失调问题。 外联和传播将遵循开发的应用程序的广泛使用，无论是在诊所和 一般人口，包括PL与低收入和代表性不足的双语学前教育的联系。 相关性（参见说明）： 饮食失调是影响普通人群的潜在危及生命的精神疾病; 个人从未得到治疗，部分原因是缺乏认识和机会。个人饮食 精神疾病患者的生活质量下降，精神和身体疾病合并症高， 以深刻的孤独和孤立为特点的存在。将饮食失调方面的专业知识与 通过计算机视觉和机器学习，我们首次将数据科学带入这一健康挑战。 项目/绩效表（如果需要额外的空间，请使用项目/绩效表格式页）