SCH: Wearable Sensing and Visual Analytics to Estimate Receptivity to Just-In-Time Interventions for Eating Behavior

SCH：可穿戴传感和视觉分析来评估对饮食行为及时干预的接受度

• 批准号: 10601169
• 负责人: EDWARD S SAZONOV
• 金额: $ 28.62万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-26 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10601169
• 关键词: Adult; Awareness; Behavior; Behavioral Sciences; Body part; Computer Hardware; Computer software; Computing Methodologies; Consumption; Cues; Data; Data Analyses; Data Collection; Detection; Diet; Dietary Practices; Dietary intake; Disease; Eating; Eating Behavior; Environment; Environmental Exposure; Face; Feedback; Foundations; Grain; Health; Health Care Costs; Image; Image Compression; Individual; Intervention; Learning; Life; Methods; Monitor; Obesity; Outcome; Overweight; Participant; Pathway interactions; Pattern; Physical environment; Pilot Projects; Research; Research Personnel; Semantics; Social Interaction; Stream; Technology; Time; Training; Unhealthy Diet; United States; Vision; Visual; Work; base; design; dietary; dietary guidelines; food environment; human model; image processing; improved; novel; preventable death; privacy preservation; technology development; transmission process; wearable device; wearable sensor technology

Poor diet is a leading cause of preventable death and diseases, as well as preventable healthcare costs in the United States. Despite the importance of following a healthy dietary pattern, most U.S. adults do not meet national dietary guidelines and are either overweight or obese. There is a critical need for "just-in-time" (JIT) interventions to improve diet and eating behaviors as they occur. To maximize impact, JIT interventions should only be delivered when an individual is receptive, particularly when dietary quality is poor. However, which aspects of the food environment and dietary behavior have influence on dietary intake and quality are unknown, and how they relate to JIT intervention receptivity is unexplored. This would require collecting and analyzing near-continuous data about one's diet in the context of daily life, where behavior actually occurs, which is very challenging for researchers and burdensome for participants. Advances in wearable sensor technologies, equipped with novel computational methods could provide a pathway to capture and analyze the various exposures and patterns in the eating environment to fill this gap. The overall objective of this proposal is to create an integrated system of wearable sensor and computational methods to discover food environment exposures related to dietary quality that influence JIT intervention receptivity. Motivated by this vision, the objectives of this research include: 1) develop novel edge computing hardware and software for privacy-preserving compressive image capture and transmission, 2) develop new collaborative compression and analytics together with unsupervised continual learning to understand eating behavior, 3) determine whether sensed aspects of the environmental context during eating relate to dietary quality and receptivity to JIT interventions, particularly when the dietary quality is poor. The project is a collaborative effort combining expertise in wearable electronics, image processing, dietary patterns, and behavioral science.

不良饮食是可预防的死亡和疾病以及可预防的医疗费用的主要原因 美国。尽管遵循健康饮食模式很重要，但大多数美国成年人并不这样做 符合国家饮食指南并且超重或肥胖。迫切需要 “及时”（JIT）干预措施，以改善饮食和饮食行为。为了最大限度地发挥影响力， JIT 干预措施只能在个人愿意接受的情况下实施，特别是在饮食质量不佳的情况下 很穷。然而，食物环境和饮食行为的哪些方面对饮食产生影响？ 摄入量和质量尚不清楚，而且它们与 JIT 干预接受度之间的关系也尚未探索。这 需要收集和分析日常生活中有关饮食的近乎连续的数据， 行为实际发生的地方，这对研究人员来说非常具有挑战性，对研究人员来说也是负担 参与者。可穿戴传感器技术的进步，配备新颖的计算方法 可以提供一种捕获和分析饮食中各种暴露和模式的途径 环境来填补这一空白。该提案的总体目标是创建一个集成的系统 可穿戴传感器和计算方法来发现与饮食相关的食品环境暴露 影响 JIT 干预接受度的质量。在这一愿景的推动下，本研究的目标 包括：1）开发新颖的边缘计算硬件和软件以实现隐私保护压缩 图像捕获和传输，2) 开发新的协作压缩和分析 无监督的持续学习来理解饮食行为，3）确定是否感知到饮食行为 饮食期间的环境背景与饮食质量和对 JIT 干预措施的接受度有关， 尤其是当饮食质量较差时。该项目是一项协作努力，结合了以下方面的专业知识 可穿戴电子产品、图像处理、饮食模式和行为科学。