Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation.

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

Design of the Healthcare Worker Exposure Response and Outcomes (HERO) research platform.

BACKGROUND: The SARS CoV-2 virus has caused one of the deadliest pandemics in recent history, resulting in over 170 million deaths and global economic disruption. There remains an urgent need for clinical trials to test therapies for treatment and prevention. DESIGN: An online research platform was created to support a registry community of healthcare workers (HCWs) to understand their experiences and conduct clinical studies to address their concerns. The first study, HERO-HCQ, was a double-blind, multicenter, randomized, pragmatic trial to evaluate the superiority of hydroxychloroquine (HCQ) vs placebo for pre-exposure prophylaxis (PrEP) of COVID-19 clinical infection in HCWs. Secondary objectives were to assess the efficacy of HCQ in preventing viral shedding of COVID-19 among HCWs and to assess the safety and tolerability of HCQ. METHODS: HCWs joined the Registry and were pre-screened for trial interest and eligibility. Trial participants were randomized 1:1 to receive HCQ or placebo. On-site baseline assessment included a COVID-19 nasopharyngeal PCR and blood serology test. Weekly follow-up was done via an online portal and included screening for symptoms of COVID-19, self-reported testing, adverse events, and quality of life assessments. The on-site visit was repeated at Day 30. DISCUSSION: The HERO research platform offers an approach to rapidly engage, screen, invite and enroll into clinical studies using a novel participant-facing online portal interface and remote data collection, enabling limited onsite procedures for conduct of a pragmatic clinical trial. This platform may be an example for future clinical trials of common conditions to enable more rapid evidence generation.