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

描述(由申请者提供)：决定是否接受乳腺癌遗传风险测试是很困难的。这一决定有定性和定量两个方面。量化维度包括理解条件概率、相对风险和绝对风险，以及统计风险模型的逻辑。定性方面包括了解什么是乳腺癌，乳腺癌的遗传风险意味着什么，在检测结果呈阳性和阴性的情况下人们应该做些什么，以及决定在什么情况下应该考虑接受检测。目标。该项目的目标是了解从未患过癌症的女性如何决定是否接受乳腺癌遗传风险的预测测试，并开发和测试基于网络的计算机化智能辅导系统(ITS)，以帮助女性使用国家癌症研究所网站上已经审查、批准和提供的信息做出这一决定。第一个目标是更好地理解决策过程。第二个目标是开发一种基于网络的AutoTutor，这是一种带有动画对话代理的复杂智能交通系统。创新。我们相信，这是第一次使用智能交通系统来改善患者的医疗决策。这些教程将向女性传授与预测性测试相关的定性和定量概念。最终目标是帮助女性在乳腺癌风险基因检测方面做出更好的决定。方法：研究方法。这个研发项目的维度包括开发基于网络的AutoTutor；进行随机对照实验；以及进行细粒度的认知分析。细粒度的分析将把详细的过程数据与120名参与者的结果和测试后反应结合起来。AutoTutor的开发和测试将分三个阶段进行，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。这将通过一个迭代过程完成，包括(1)初步研究，(2)导师发展，(3)实证研究，和(4)导师修订周期。在一项有60名参与者的研究中，将开发新的依赖测量工具。三个对照实验将对AutoTutor进行经验性测试，并评估决策。每个模块将有两个实验，每个实验有120名参与者，第三个有80个参与者的网络实验将测试完整的导师。参与者将被随机分配到自动家教、国家癌症研究所网站或接受无关信息的对照组。我们将从一开始就努力为下一代更复杂的AutoTutor奠定基础。人事部。迈阿密大学的PiS 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、定量决策和言语推理的研究方面拥有丰富的经验。专家顾问是缅因州医学中心乳腺癌专家兼结果研究和评估中心主任Nananda Col MD，以及遗传顾问Sara Knapke。 公共卫生相关性：这个项目的目标是开发一个基于网络的智能家教，关于接受乳腺癌遗传风险预测测试的决策的定性和定量方面。其目的是了解女性是如何做出这一决定的，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。

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.