Development of an Online Course Suite in Tools for Analysis of Sensor-Based Behavioral Health Data (AHA!)

项目摘要/摘要 我们的社会在提供每个人都能获得的高质量医疗保健方面面临着重大挑战 对每个人的个人生活方式和个人健康需求都很敏感。由于最近 传感技术的进步，提高了精度，增加了吞吐量，以及 成本降低，收集高分辨率行为和个性化变得相对容易 大规模的健康数据。可以分析产生的大数据集，以了解 行为与健康，并设计健康行为干预措施。然而，在这个新兴领域， 目前很少有课程可供研究人员和从业者学习 收集、存储、分析和使用行为的基本原则和最佳实践- 基于传感器数据。教授这些技能可以帮助下一代学生在 日益数字化的世界。 这个应用程序的目标是设计在线课程，培训研究人员和实践者 基于传感器的行为健康。具体地说，我们将提供负责任的行为、收集 了解行为传感器数据、数据探索和统计推断、扩展 对海量数据集的行为分析，并介绍最新的机器学习和活动 学习技巧。这些课程将面授给威斯康星州立大学的教职员工，并与员工一起提供 通过MOOC提供支持，并通过我们的网页向普通公众提供。课程材料将 通过具体的临床案例研究加以加强和推动。此外，课程还将包括 辅以学生在课程结束后可以继续使用的实际数据集。 这一贡献意义重大，因为不仅是大型研究小组，甚至是个人 调查人员可以创建大型数据集，提供关于以下方面的有价值的即时信息 人类的行为。他们需要能够应对在使用传感器时出现的挑战- 基于行为数据。因为学生将接受实际传感器数据集和 分析工具，他们将知道如何从可用的工具中获得最佳结果，并将能够 解释分析结果的意义。 我们建议的在线课程计划，名为AHA！，建立在调查人员广泛的 华盛顿州立大学在SMART开发方面的经验和持续合作 家庭和移动健康应用程序的设计、活动识别、可扩展的生物数据挖掘，以及 这些技术的临床应用。我们的方法将是设计在线课程模块 使用临床案例研究培训个人分析基于行为的传感器数据(目标1)。 我们将设计一个教育项目，让来自不同背景的学生参与进来，这就是 可查找、可访问、可互操作和可重复使用(目标2)。最后，我们将进行一次彻底的 评估以监控项目的成功并逐步改进项目(目标3)。所有的材料 将被设计为在该计划的资助期之后继续使用。

PROJECT SUMMARY/ABSTRACT Our society faces significant challenges in providing quality health care that is accessible by each person and is sensitive to each person's individual lifestyle and individual health needs. Due to recent advances in sensing technologies that have improved in accuracy, increased in throughput, and reduced in cost, it has become relatively easy to gather high resolution behavioral and individualized health data at scale. The resulting big datasets can be analyzed to understand the link between behavior and health and to design healthy behavior interventions. In this emerging area, however, very few courses are currently available for teaching researchers and practitioners about the foundational principles and best practices behind collecting, storing, analyzing, and using behavior- based sensor data. Teaching these skills can help the next generation of students thrive in the increasingly digital world. The goal of this application is to design online courses that train researchers and practitioners in sensor-based behavioral health. Specifically, we will offer training in responsible conduct, collection and understanding of behavioral sensor data, data exploration and statistical inference, scaling behavioral analysis to massive datasets, and introducing state of the art machine learning and activity learning techniques. The courses will be offered in person to WSU faculty and staff, offered with staff support through MOOCs, and available to the general public from our web page. Course material will be enhanced and driven by specific clinical case studies. Additionally, the courses will be supplemented with actual datasets that students can continue to use beyond the course. This contribution is significant because not only large research groups but even individual investigators can create large data sets that provide valuable, in-the-moment information about human behavior. They need to be able to handle the challenges that arise when working with sensor- based behavior data. Because students will receive hands-on training with actual sensor datasets and analysis tools, they will know how to get the best results from available tools and will be able to interpret the significance of analysis results. Our proposed online course program, called AHA!, builds on the investigators' extensive experience and ongoing collaboration at Washington State University on the development of smart home and mobile health app design, activity recognition, scalable biological data mining, and the use of these technologies for clinical applications. Our approach will be to design online course modules to train individuals in the analysis of behavior-based sensor data using clinical case studies (Aim 1). We will design an educational program that involves students from diverse backgrounds and that is findable, accessible, interoperable, and reusable (Aim 2). Finally, we will conduct a thorough evaluation to monitor success and incrementally improve the program (Aim 3). All of the materials will be designed for continued use beyond the funding period of the program.