Research Project
10289016
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.
