On capturing effects of medication change in Parkinson’s disease with wrist accelerometry-based digital biomarkers

<h2>Abstract:</h2> <div>Parkinson’s disease (PD) motor symptoms are characterized by fluctuations, rendering their monitoring and therapy personalization challenging. Wrist-worn accelerometers serve as objective digital biomarkers (dBMs) for continuous monitoring of PD symptoms. The aim of this study is to investigate if dBMs of upper limb mobility, passively captured via a single wrist-worn accelerometer, can be indicative of motor function improvement due to increase in Levodopa Equivalent Daily Dose (LEDD) in People with Parkinson’s (PwP). Daily accelerometer data of PwP from the Verily Study Watch cohort of the Parkinson’s Progression Markers Initiative (PPMI) study were filtered to isolate clinically relevant non-tremor gross movements and then processed to estimate digital biomarkers of mobility in periods before and after LEDD change. Repeated measures correlation analysis revealed significant (p < 0.05) positive correlations of range of motion (r = 0.32), peak acceleration (r =0.26) and jerk (r =0.38) with LEDD increase. Preliminary longitudinal and intraday analyses further point to the dBMs’ utility in tracking medication effects, revealing distinct changes in motor activity and symptom dynamics before and after medication adjustments. These findings suggest that wrist-based accelerometry features offer meaningful, continuous measures for remote monitoring and personalized PD management, potentially informing proactive treatment modifications and enhancing patient quality of life.</div>

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Published
2025-10-21
DOI
https://doi.org/10.1109/healthcom60686.2025.11342793
Primary Topic
Parkinson's Disease Mechanisms and Treatments
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article
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article

On capturing effects of medication change in Parkinson’s disease with wrist accelerometry-based digital biomarkers

Charalampos Sotirakis, Apostolos Moustaklis, Ioannis Gerasimou, Leontios J. Hadjileontiadis
Parkinson's Disease Mechanisms and Treatments
article

On capturing effects of medication change in Parkinson’s disease with wrist accelerometry-based digital biomarkers

Charalampos Sotirakis, Apostolos Moustaklis, Ioannis Gerasimou, Leontios J. Hadjileontiadis
article en

Abstract

<h2>Abstract:</h2> <div>Parkinson’s disease (PD) motor symptoms are characterized by fluctuations, rendering their monitoring and therapy personalization challenging. Wrist-worn accelerometers serve as objective digital biomarkers (dBMs) for continuous monitoring of PD symptoms. The aim of this study is to investigate if dBMs of upper limb mobility, passively captured via a single wrist-worn accelerometer, can be indicative of motor function improvement due to increase in Levodopa Equivalent Daily Dose (LEDD) in People with Parkinson’s (PwP). Daily accelerometer data of PwP from the Verily Study Watch cohort of the Parkinson’s Progression Markers Initiative (PPMI) study were filtered to isolate clinically relevant non-tremor gross movements and then processed to estimate digital biomarkers of mobility in periods before and after LEDD change. Repeated measures correlation analysis revealed significant (p < 0.05) positive correlations of range of motion (r = 0.32), peak acceleration (r =0.26) and jerk (r =0.38) with LEDD increase. Preliminary longitudinal and intraday analyses further point to the dBMs’ utility in tracking medication effects, revealing distinct changes in motor activity and symptom dynamics before and after medication adjustments. These findings suggest that wrist-based accelerometry features offer meaningful, continuous measures for remote monitoring and personalized PD management, potentially informing proactive treatment modifications and enhancing patient quality of life.</div>

Aristotle University of Thessaloniki (GR)
Openalex Percentile: Top 28%
Parkinson's Disease Mechanisms and Treatments
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On capturing effects of medication change in Parkinson’s disease with wrist accelerometry-based digital biomarkers — Charalampos Sotirakis, Apostolos Moustaklis, et al. · (2025) | TGRS Research Map | TGRS