The potential of AI inspired approaches for patient monitoring in drug development for Huntington's disease: capturing the digital exhaust for activities of daily living.

Diagnosis, assessment and monitoring of Huntington's disease (HD) requires measurement of a complex array of motor, cognitive and psychiatric symptoms over sustained periods. However, the prodromal stages often span over a decade with symptoms presenting in a non-linear manner with short term fluctuations. This presents challenges for investigational drug development as treatment effects may become masked. To help address this issue, a number of digital health technology tools are being investigated for use in HD studies with increasing degrees of utility and sophistication. Herein we outline how remote passive monitoring technologies could be integrated into managed care of HD patients, and have the potential to inform algorithms and digital tools which could be used to improve our understanding of disease trajectories and improve early detection rates. Steps to implementation and the potential to derive learnings from adjacent technologies is presented, affording a key opportunity for next- generation approaches to patient monitoring. Measures derived from such technologies will also provide data to capture disease progression and treatment effects with high fidelity and granularity, and complement existing clinical research outcomes (clinROs) and patient reported outcomes (PRO's). Given the ethical, economic, and societal burden presented by neurodegenerative diseases, such developments could have long term impact in managed healthcare.