Technical Core

技术核心摘要 技术核心将提供通用基础设施和工具，以应对采用 移动研究方法，包括：参与者招募、保留和支持；验证和 了解新的认知和非认知测试以及其他生理和环境数据；以及 在重大参与者隐私问题的背景下适当保护数据。在最初的移动设备下 工具箱(MTB1)资助，我们成功地构建并部署了一个系统，允许研究人员从 经过验证的测量库，设计虚拟研究方案，通过以下方式向参与者提供这些测量 在他们的iOS或Android智能手机上本地运行的应用程序，并通过检索聚合的参与者数据 Web或编程接口。我们对MTB平台的目标是提供完整的端到端解决方案 研究团队使用远程管理的认知和其他非认知措施。我们会 实现支持继续扩展和传播远程移动措施的总体目标 通过两个目标：目标1：我们将继续扩大MTB图书馆的远程、自我管理 措施；目标2：我们将继续支持和维护移动工具箱平台 并扩展其功能，使研究人员管理虚拟交互变得越来越容易 与研究参与者一起提高招募和遵从性。 我们将专注于扩展到非认知领域，包括语境因素和心理， 物理和行为功能，以及包括被动传感器数据流，以提供更深层次的 已验证的度量的上下文。我们还将简化研究经理之间的沟通 和远程参与者，包括对通过MTB应用程序交付的自定义调查问题的支持，以及 采用参与未来研究设计的最佳实践。我们将使硬件和软件保持最新，并 确保平台的持续可靠性、性能和安全性。

Technical Core Summary The Technical Core will provide general infrastructure and tools to address technical challenges in adopting mobile approaches to research including: participant recruitment, retention, and support; validating and understanding new cognitive and non-cognitive tests and other physiological and environmental data; and securing data appropriately in the context of significant participant privacy concerns. Under the original Mobile Toolbox (MTB1) grant, we successfully built and deployed a system that allows researchers to select from a library of validated measures, design a virtual study protocol that delivers these measures to participants through an app running natively on their iOS or Android smartphone, and retrieve aggregated participant data through web or programmatic interfaces. Our goal for the MTB platform is to provide a complete end-to-end solution for the use of remotely administered cognitive and other non-cognitive measures by researcher teams. We will achieve our overall goal to support the continued expansion and dissemination of remote mobile measures through two aims: Aim 1: We will continuously expand the MTB library of remote, self-administered measures; Aim 2: We will provide continued support and maintenance of the Mobile Toolbox platform and extend its capabilities to make it increasingly easy for researchers to manage virtual interactions with study participants to improve recruitment and adherence. We will focus on expansion into non-cognitive domains including contextual factors, and psychological, physical, and behavioral functions, as well as the inclusion of passive sensor data streams to provide deeper context for already validated measures. We will also streamline the communication between study managers and remote participants, include support for custom survey questions to be delivered through the MTB app, and adopt best practices on engagement for future study design. We will keep hardware and software up to date and ensure continued reliability, performance, and security of the platform.