SCH: EAGER: Improving Nutritional Literacy and Decision Making with Learner-Centered Crowdsourcing

SCH：EAGER：通过以学习者为中心的众包提高营养素养和决策制定

• 批准号: 1551708
• 负责人: Lena Mamykina
• 金额: $ 20.29万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551708&HistoricalAwards=false
• 关键词: SCH; EAGER; Improving; Nutritional; Literacy

This EAGER project is funded from the Smart and Connected Health portfolio which aims to support a varied set of health and healthcare related research projects focusing on fundamental work in these multi-disciplinary fields. This project carries out basic research in the nutritional habits of certain segments of the population. There exist robust empirical evidence suggesting the benefits of healthy diet for prevention and management of a variety of health conditions. At the same time, adopting a healthy diet and making daily healthy nutritional choices is an uphill battle for many individuals, particularly those residing in economically disadvantaged communities and among ethnic minorities. Many factors contribute to the poor eating habits of adult Americans; among them are poor nutritional literacy, limited ability to critically appraise nutritional value of meals, and limited ability to apply these skills to personal dietary choices. While there exist computing technologies for facilitating healthy nutrition, no solution developed to date addresses all of these challenges. The goal of this research is to investigate a new breed of social computing mechanism that blend together the benefits of online health communities, crowdsourcing, and online learning environments. Specifically it develops new learner-centered nutrition crowdsourcing tools that are grounded in the theory of self-regulated learning and that 1) help individuals to develop better nutritional judgment by assessing nutritional value of each otherÕs meals and receiving tailored feedback; 2) provide in-the-moment nutritional decision support through community-generated feedback on prospective meals; 3) encourage long-term engagement using reciprocity principle and simple award structure. The proposed technologies are evaluated in a pilot study with individuals with diabetes recruited from an online diabetes community TuDiabetes.

该EAGER项目由智能和互联健康投资组合资助，旨在支持一系列专注于这些多学科领域基础工作的健康和医疗保健相关研究项目。该项目对某些人群的营养习惯进行基础研究。有强有力的经验证据表明，健康饮食对预防和管理各种健康状况有好处。与此同时，采用健康饮食和选择健康的日常营养对许多人来说是一场艰苦的战斗，特别是那些生活在经济上处于不利地位的社区和少数民族的人。许多因素导致了美国成年人不良的饮食习惯；其中包括营养知识贫乏，批判性评估膳食营养价值的能力有限，以及将这些技能应用于个人饮食选择的能力有限。虽然已有促进健康营养的计算技术，但迄今为止尚未开发出解决所有这些挑战的解决方案。本研究的目标是研究一种新型的社会计算机制，它将在线健康社区、众包和在线学习环境的优势融合在一起。具体来说，它开发了新的以学习者为中心的营养众包工具，这些工具以自我调节学习理论为基础，1)通过评估每种otherÕs膳食的营养价值并接收量身定制的反馈，帮助个人做出更好的营养判断；2)通过社区对预期膳食的反馈提供即时营养决策支持；3)利用互惠原则和简单的奖励结构鼓励长期参与。在一项试点研究中，从在线糖尿病社区TuDiabetes招募的糖尿病患者对拟议的技术进行了评估。