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

描述（由申请人提供）：决定是否进行乳腺癌遗传风险检测是困难的。这一决定有定性和定量两个方面。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，在检测结果呈阳性和阴性的情况下人们应该怎么做，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测试，并开发和测试基于网络的计算机智能辅导系统（ITS），以帮助妇女利用已经审查、批准并可在国家癌症研究所网站上获得的信息做出决定。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自动导师，一个具有动画会话代理的复杂ITS。创新。我们相信，这是第一次使用智能信息系统来改善患者的医疗决策。这些教程将教妇女有关预测测试的定性和定量概念。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。这个研究和发展项目的维度是开发基于网络的自动导师；进行随机对照实验；进行细致的认知分析。细粒度分析将把详细的过程数据与120名参与者的结果和测试后的反应结合起来。AutoTutor将分三个阶段进行开发和测试，分别是两个强调定性和定量内容的辅导模块，以及一个后期制作阶段。这将通过(1)初步研究、(2)导师发展、(3)实证研究和(4)导师修订的循环迭代过程来完成。将在一项有60名参与者的研究中开发新的依赖测量方法。三个对照实验将对AutoTutor进行实证测试并评估决策。两个实验分别有120名参与者，每个实验将涉及每个模块，第三个基于网络的实验有80名参与者，将测试完整的导师。参与者将被随机分配到自动导师、国家癌症研究所网站或接受不相关信息的对照组。我们将从一开始就为下一代更复杂的AutoTutor打下基础。人员。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着丰富的研究经验。专家顾问是Nananda Col MD，乳腺癌专家和缅因州医学中心结果研究和评估中心主任，以及遗传咨询师Sara Knapke。

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.