Pilot work to develop in-home passive digital outcome measures in Parkinson’s disease

Abstract

Clinical trials testing disease-modifying therapies in Parkinson’s disease (PD) have been hampered by the flaws of the rating scales used to evaluate patients’ symptom progression. Technology-assisted outcome measures at home show promise in addressing the challenge of producing functionally relevant, sensitive and reliable symptom measurements. However, knowledge gaps around sensor platform implementation remain. This thesis seeks to systematically investigate the barriers and facilitators affecting acceptability of a multimodal sensor platform, evaluate which outcome measures are feasible, clinically useful and functionally relevant to measure and highlight the pitfalls associated with sharing of such data. 

A systematic review of the topic is presented to provide context. Next, qualitative work investigates acceptability and design modifications of a multimodal in-home sensor platform from the viewpoint of people living with PD. This is then followed by three quantitative studies, each evaluating a promising set of parameters within a mobility outcome domain – first turning of gait, then sit-to-stand and finally room use and room transition duration. Next, perceptions from people with PD around cross-sectoral data sharing are explored, privacy issues relating to such data dissemination are examined and conclusions are drawn including the decisions taken in publication of the main study dataset.

The next few years will see increased testing, validation, and hopefully widespread deployment of digital outcome measures to clinical trials in PD. This may help to finally realise the aim of seeing disease-modifying therapies reach the patient in the clinic. However, this exciting future must be approached with cautious optimism, recognising that such tools cannot yet be fully depended upon until they have been carefully validated and tested for reliability, and being mindful that technology may also cause harm which needs to be understood, measured, and mitigated. 

Date of Award	10 Dec 2024
Original language	English
Awarding Institution	University of Bristol
Supervisor	Alan L Whone (Supervisor) & Ian J Craddock (Supervisor)