The RADAR-CNS consortium focused on evaluating the feasibility, adherence and personal satisfaction with remote measurement technologies in people with three different conditions. They also addressed the clinical harmonisation required to assess sleep, physical activity, speech, mood, and cognition in everyday life across clinical disorders.
Major Depressive Disorder: The multicentre observational cohort study (RADAR-MDD) followed patients with recurrent depressive disorder over the course of approximately 18 months as they used remote measurement technologies. This study recruited participants across three sites and engagement was excellent, resulting in RADAR-MDD being the largest multiparametric remote measurement study in depressed individuals in the world.
Multiple Scelrosis: The workstream focusing on MS collected data from participants at three sites. It explored whether remote measurement technologies can be used to characterise depression and gait disturbance in people with MS.
Epilepsy: The project’s research into epilepsy determined the feasibility, acceptability of, and adherence to, remote measurement technologies in people with the condition to provide real-time objective, multidimensional indications of seizure occurrence and clinical state. Researchers recruited participants from two sites and assessed seven wearable devices.
The patient involvement team worked with patient stakeholders to understand issues such as privacy, usability, and acceptability of remote measurement technologies. The Patient Advisory Board were integral through the project and worked with researchers to identify clinical endpoints which are most relevant to patients, and identify facilitators and challenges for engagement and adherence that were further tested in this project.
One of the major achievements of the programme was the development of the RADAR-base platform to collect the varied data. RADAR-base is open source and now in use by multiple research projects. Alongside this, the data analysis team used both statistical methods and artificial intelligence models to gain understanding into the association between these data and the remission, relapse, and recurrence of the conditions studied.