Validating a Scalable, Open Science Framework for Collecting Laboratory-Grade Data Remotely in Specialized Populations

项目摘要 适用于智力障碍儿童的临床结局指标有限， 发育障碍（IDD）正在损害临床试验的前景。不规范 捕获“光谱”信号的实验室技术--如心理生理学分析-- 越来越多地被认为是用于随时间监测患者反应的有前景的方法。 然而，这些工具需要专门的设备和人员来管理， 通过远程医疗轻松部署，使光谱研究偏向能够并愿意 前往诊所。因此，可用的基于远程医疗的协议存在显着差距， 远程收集缺碘症人群的实验室级数据。本研究探讨了这一问题 通过为PANDABox（家长辅助）制定全面的培训协议， 神经发育评估），这是一项开放科学、基于远程医疗的评估协议， PI Kelleher开发用于远程收集高质量，综合的临床，行为， IDD参与者的光谱分析。发表的研究结果表明，PANDABox是高度 可行的和可接受的照顾者，并产生高质量的，综合的，“实验室级” 低成本的数据。PANDABox已经被部署在各种治疗和自然疗法中， 正在将其翻译成西班牙文，以促进 可访问性。本研究的目的是提高PANDABox的可扩展性，通过实现 两个具体目标。首先，我们将开发和验证开放科学培训协议， 对等可靠性网络，以促进标准化，跨实验室实施 IDD中的PANDABox协议。该方案将包括虚拟培训中心， 培训，以及一个可靠性网络，以促进跨站点校准。第二，我们将创造 一个用户友好的软件程序，支持用户高效地处理PANDABox注意事项 分析，这在临床试验中产生了有希望的结果，但需要计算 专业知识进行分析，限制了其可扩展性。解决这些差距将改变现状， IDD的临床科学，通过（1）提供一个可扩展的，可访问的协议，收集实验室- 远程对缺碘症人群的数据进行分级;（2）产生必要的标准化数据输出 用于大规模、多地点、协作科学。

PROJECT SUMMARY The limited repertoire of clinical outcome measures suitable for children with intellectual and developmental disabilities (IDD) is compromising the promise of clinical trials. Non-standardized laboratory techniques that capture “spectral” signals – such as psychophysiological assays – are increasingly recognized as promising methods for monitoring patient responses over time. However these tools require specialized equipment and personnel to administer and are not easily deployed via telehealth, biasing spectral studies toward patients who are able and willing to travel to clinics. Thus, there is a significant gap in available telehealth-based protocols for collecting laboratory-grade data remotely in IDD populations. The present study addresses this gap by developing a comprehensive training protocol for PANDABox (Parent Assisted Neurodevelopmental Assessment), an open-science, telehealth-based assessment protocol that PI Kelleher developed for remotely collecting high quality, integrated clinical, behavioral, and spectral assays from participants with IDD. Published findings indicate that PANDABox is highly feasible and acceptable to caregivers and generates high quality, integrated, “laboratory-grade” data at low cost. Already, PANDABox is being deployed in a variety of treatment and natural history studies across IDD populations and is being translated to Spanish to promote accessibility. The present study aims to enhance the scalability of PANDABox by accomplishing two specific aims. First, we will develop and validate an open-science training protocol and peer-to-peer reliability network to facilitate standardized, cross-laboratory implementation of the PANDABox protocol in IDD. This protocol will include both a virtual training hub for self-paced training, as well as a reliability network to facilitate cross-site calibration. Second, we will create user-friendly software program to support users to efficiently process the PANDABox attention assay, which has produced promising outcomes in clinical trials but requires computational expertise to analyze, limiting its scalability. Addressing these gaps would shift the status quo of clinical science in IDD by (1) providing a scalable, accessible protocol for collecting laboratory- grade data remotely in IDD populations and (2) producing standardized data outputs necessary for large scale, multi-site, collaborative science.