IntroductionParkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP).MethodsThirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback.ResultsAdherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS.ConclusionThis study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.

Parkinson's disease (PD) is a neurodegenerative condition where dopaminergic medication, such as levodopa, is typically used to improve motor symptoms, including mobility. Identifying the impact of levodopa on real-world motor state (e.g. ON/OFF/ DYSKINESIA) is important for both clinicians and people with PD. The aim of the present work was to automatically classify medication states using machine learning models. Continuous 7-day data were collected in 26 people with PD using an Inertial Measurement Unit (IMU) placed on the fifth lumbar vertebrae (L5) level. Over the week, each participant was asked to complete a diary by annotating medication states (offcondition and dyskinesias) with a 30-minute resolution. Diary entries were used as reference labels assigned to the processed IMU data. Two different networks were chosen for the classification: the k-Nearest Neighbors algorithm (kNN) to identify ON-OFF-DYSKINESIA classes and Fine Tree (FT) to identify only OFF and ON classes. Preliminary results demonstrate that IMU data paired with machine learning could accurately classify ON-OFF and DYSKINESIA with 84% accuracy and the ON-OFF states were classified with 95% accuracy. These results are encouraging and pave the way to a better understanding of the effect that medication has on motor symptoms in PD during everyday life and may serve as a useful tool for optimizing clinical management of people with PD.