Tools
Tools
Packer, E, Debelle, H, Bailey, HGB, Ciravegna, F, Ireson, N, Evers, J, Niessen, M, Shi, JQ, Yarnall, AJ, Rochester, L, Alcock, L and Del Din, S (2023) Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's. BMJ Open, 13 (9).
|
Text
Translating digital healthcare to enhance clinical management a protocol for an observational study using a digital health technology system to monitor medication adher.pdf - Published Version Available under License Creative Commons Attribution. Download (817kB) | Preview |
Abstract
Introduction In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP. Methods and analysis This single-centre, UK-based study, will recruit 55 participants with Parkinson's. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens. Ethics and dissemination Ethical approval was granted by London - 142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent. Trial registration number ISRCTN13156149.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans; Parkinson Disease; Algorithms; Technology; Biomedical Technology; Medication Adherence; Observational Studies as Topic; Aging; Parkinson-s disease; Protocols & guidelines; Quality of Life; Humans; Parkinson Disease; Technology; Algorithms; Biomedical Technology; Medication Adherence; Observational Studies as Topic; 1103 Clinical Sciences; 1117 Public Health and Health Services; 1199 Other Medical and Health Sciences |
| Subjects: | R Medicine > RC Internal medicine > RC1200 Sports Medicine |
| Divisions: | Sport & Exercise Sciences |
| Publisher: | BMJ Publishing Group |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 23 Sep 2024 11:23 |
| Last Modified: | 23 Sep 2024 11:30 |
| DOI or ID number: | 10.1136/bmjopen-2023-073388 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24221 |
 |
View Item |