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The Use of Wearable in Parkinson's Disease Research

Naomi Limbachiya

Research Fellow at IoPPN and King's College Hospital

26 June 2024

Parkinson’s Disease (PD) is the most common neurodegenerative disease, but it is currently incurable. Our recent publication looks at the use of wearable technology to learn more about PD symptoms and the efficiency of therapeutic approaches.

Parkinson’s Disease (PD) is the most common neurodegenerative disease, sequential to Alzheimer’s disease, and is the fastest-growing neurological condition. Cardinal motor symptoms include bradykinesia (BK; slowness of movement), resting tremor, rigidity, and gait deficits. 

PD is still incurable, with the primary therapeutic approach being Levodopa (LD) for symptom alleviation. LD use, however,  is challenged by a short half-life and reduced efficacy over time. This contributes to the emergence of 'wearing off' phenomena that include motor and/or non-motor symptoms. Patients may also develop dyskinesia (involuntary motor movement) with prolonged LD use.

'Wearing off' can happen during the Early Morning Off (EMO) periods, occurring from 02.00 to 06.00. EMO affects approximately 59.7 per cent of individuals across all PD stages. Given the variability in EMO symptom presentation, precise diagnosis and effective management require accurate patient self-reporting and clinician observation. This poses a significant challenge as clinical visits are infrequent and brief. Patients may also have trouble recalling symptoms between appointments, making it difficult to assess patients' symptoms comprehensively. 


Recognizing this challenge and the heterogeneous nature of PD, wearable technologies for continuous symptom monitoring have become increasingly valuable. The Parkinson’s KinetiGraph™ (PKG™) is one such device. It can monitor motor fluctuations (including tremors, dyskinesia, bradykinesia, and immobility) over 24 hours for six days. The device then creates reports that score bradykinesia and dyskinesia to complement routine clinical assessments.

We recently conducted and published a retrospective analysis of PKG records from over 200 cases to evaluate the effectiveness of this device in monitoring EMO. The records come from multiple centres such as King's College Hospital, King's Parkinson’s Centre of Excellence, and Poznan University in Poland.

Additionally, we reviewed clinical records including Parkinson’s Disease Sleep Scale (PDSS) scores, which indicate overall sleep disturbances and score the patient's EMO symptoms.

We identified five cases with severe early-morning bradykinesia (BK) patterns during the EMO period, consistent with patient-reported symptoms and PDSS scores. Before PKG use, EMO had not been diagnosed in these cases, resulting in ineffective treatment.


The PKG graph above illustrates motor fluctuation patterns over 24 hours. The shaded areas indicate varying levels of bradykinesia, with darker shades representing higher bradykinesia. Vertical lines indicate scheduled medication reminders, showing the correlation between medication intake and symptom control.

The combination of PDSS insights and PKG data enabled tailored care strategies for these individuals. Post PKG assessment, the patient's treatment plans were adjusted to include night-time continuous drug delivery that effectively alleviated early morning BK.

Our findings indicate that routine PKG monitoring provides objective data that complements patient self-reports and clinical assessments, aiding in the identification of EMO and adjustment of medication regimens as needed.

Integrating PKG into NHS PD monitoring frameworks could enhance symptom management through continuous and objective monitoring.

This approach would likely improve patient outcomes by facilitating timely treatment adjustments based on real-time data, promoting medication adherence through device reminders, and optimizing the efficiency of clinical visits, thereby reducing the burden on NHS resources.

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K Ray Chaudhuri

K Ray Chaudhuri

Professor of Movement Disorders and Neurology

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