A Web Tutor to Help Women Decide About Testing for Genetic Breast Cancer Risk

帮助女性决定是否进行遗传性乳腺癌风险检测的网络导师

• 批准号: 8046102
• 负责人: Valerie Frances Reyna
• 金额: $ 20.24万
• 依托单位: MIAMI UNIVERSITY OXFORD
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8046102
• 关键词: Address; Architecture; BRCA1 Mutation; BRCA1 gene; BRCA2 Mutation; BRCA2 gene; Back; Breast; Calculi; Cereals; Cognitive; Cognitive Science; Conscious; Control Groups; Decision Making; Development; Dimensions; Educational process of instructing; Empirical Research; Evaluation; Event; Family member; Foundations; Gene Mutation; Generations; Genetic; Genetic Risk; Genetic screening method; Goals; Human; Human Resources; Individual; Internet; Intervention; Joints; Judgment; Learning; Light; Logic; Maine; Malignant Neoplasms; Malignant neoplasm of ovary; Medical; Medical center; Methods; Modeling; National Cancer Institute; Online Systems; Outcome; Outcomes Research; Participant; Patients; Persons; Phase; Portraits; Probability; Process; Production; Professional counselor; Psyche structure; Psychology; Randomized; Relative (related person); Research; Research Methodology; Risk; Science; Series; System; Technology; Test Result; Testing; Universities; Woman; Work; base; cancer risk; computerized; computerized data processing; experience; improved; innovation; literacy; malignant breast neoplasm; preference; research and development; research study; response; stem; web site

DESCRIPTION (provided by applicant): Decisions about whether to be tested for genetic risk of breast cancer are difficult. There are qualitative and quantitative dimensions of this decision. Quantitative dimensions include understanding conditional probabilities, relative and absolute risk, and the logic of statistical risk models. Qualitative dimensions include understanding what is breast cancer, what does genetic risk for breast cancer mean, what people should do in the event of positive and negative test results, and deciding under what circumstances a person should consider being tested. Aims. The goals of this project are to understand how women who have never had cancer themselves decide about whether to undergo predictive testing for genetic risk of breast cancer, and to develop and test a web-based computerized Intelligent Tutoring System (ITS) to help women make this decision using information already vetted, approved, and available on the National Cancer Institute web site. The first aim is better understand decision-making processes. The second aim is to develop a web- based AutoTutor, a sophisticated ITS with an animated conversational agent. Innovation. This is, we believe, the first use of an ITS to improve patients' medical decision making. These tutorials will teach women about the qualitative and quantitative concepts related to predictive testing. The ultimate goal is helping women make better decisions about genetic testing for breast cancer risk. Methods. Dimensions of this research and development project are developing the web-based AutoTutor; conducting randomized controlled experiments; and carrying out fine-grained cognitive analyses. The fine-grained analysis will integrate detailed process data with outcomes and posttest responses from 120 participants. The AutoTutor will be developed and tested in three phases corresponding to two tutor modules emphasizing qualitative and quantitative content, and a post-production phase. This will be accomplished through an iterative process with cycles of (1) preliminary research, (2) tutor development, (3) empirical research, and (4) tutor revision. New dependent measurers will be developed in a study with 60 participants. Three controlled experiments will empirically test the AutoTutor and assess decision-making. Two experiments of 120 participants each will address each module and a third web-based experiment with 80 participants will test the complete tutor. Participants will be randomly assigned to the AutoTutor, the National Cancer Institute web site or a control group receiving unrelated information. We will work from the beginning to lay the foundations for the next, more sophisticated generation of the AutoTutor. Personnel. PIs Christopher Wolfe at Miami University and Valerie Reyna at Cornell University have considerable experience with research on medical decision-making, learning technologies and web-based interventions, web-based psychology experiments, quantitative decision making, and verbal reasoning. Expert consultants are Nananda Col MD, breast cancer expert and director of the Center for Outcomes Research and Evaluation, Maine Medical Center, and genetic counselor Sara Knapke. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop a web-based Intelligent Tutor about qualitative and quantitative dimensions of the decision to undergo predictive testing for genetic risk of breast cancer. The purpose is to understand how women make this decision and help improve decision making. Research methods include randomized controlled experiments and fine-grained cognitive analysis.

描述（由申请人提供）：决定是否进行乳腺癌遗传风险测试是困难的。这一决定有质量和数量两个方面。定量维度包括理解条件概率，相对和绝对风险以及统计风险模型的逻辑。定性维度包括了解什么是乳腺癌，乳腺癌的遗传风险意味着什么，人们在阳性和阴性检测结果的情况下应该做什么，以及决定在什么情况下一个人应该考虑接受检测。目标。该项目的目标是了解从未患过癌症的妇女如何决定是否接受乳腺癌遗传风险的预测性测试，并开发和测试基于网络的计算机化智能辅导系统（ITS），以帮助妇女使用已经审查，批准和国家癌症研究所网站上提供的信息做出决定。第一个目标是更好地理解决策过程。第二个目标是开发一个基于网络的AutoTutor，一个复杂的智能教学系统，带有一个动画会话代理。创新我们相信，这是首次使用ITS来改善患者的医疗决策。这些教程将教妇女有关的定性和定量的概念预测测试。最终目标是帮助女性对乳腺癌风险的基因检测做出更好的决定。方法.这个研究和开发项目的维度是开发基于网络的AutoTutor;进行随机对照实验;并进行细粒度的认知分析。细粒度分析将把详细的过程数据与来自120名参与者的结果和后测响应整合在一起。AutoTutor将分三个阶段进行开发和测试，分别对应于两个强调定性和定量内容的导师模块以及一个后期制作阶段。这将通过一个迭代的过程来完成，周期为（1）初步研究，（2）导师开发，（3）实证研究，（4）导师修订。将在一项有60名参与者的研究中开发新的依赖性测量器。三个对照实验将对AutoTutor进行实证测试并评估决策。两个实验的120名参与者将解决每个模块和第三个基于网络的实验与80名参与者将测试完整的导师。参与者将被随机分配到AutoTutor，国家癌症研究所网站或接受无关信息的对照组。我们将从一开始就为下一代更复杂的AutoTutor奠定基础。人员的迈阿密大学的Christopher Wolfe和康奈尔大学的Valerie Reyna在医疗决策、学习技术和基于网络的干预、基于网络的心理学实验、定量决策和口头推理方面有相当丰富的研究经验。专家顾问是Nananda Col医学博士，乳腺癌专家和缅因州医学中心成果研究和评估中心主任，以及遗传顾问Sara Knapke。 公共卫生关系：该项目的目标是开发一个基于网络的智能导师，关于进行乳腺癌遗传风险预测测试的决定的定性和定量方面。目的是了解女性如何做出这一决定，并帮助改善决策。研究方法包括随机对照实验和细粒度认知分析。