Free-living Motor Symptoms Evaluation in Parkinson's patients through Smartwatches: Protocol for defining digital biomarkers (Preprint)
Authors: Polvorinos-Fernández Carlos , Sigcha Luis , Centeno-Cerrato María , de Arcas Guillermo , Grande Miriam , Marín Mayca , Pareés Isabel , Martínez-Castrillo Juan Carlos , Pavón Ignacio 
Published: February 2025

BACKGROUND

Monitoring motor symptoms in patients with Parkinson’s disease (PD) presents significant challenges due to the complex nature of symptom progression, variations in medication responses, and the fluctuations that can occur throughout the day. Traditional neurological visits provide only a limited perspective of a patient’s overall condition, with challenges in achieving accurate and objective assessments of symptoms. To bridge this gap, extended monitoring in non-clinical settings could play a critical role in personalizing treatments and improving their efficacy. Wearable devices have emerged as potential tools for assessing PD symptom severity; however, studies integrating both in-clinic and free-living conditions, as well as multi-day monitoring, remain scarce. Defining digital biomarkers that provide valuable insights into motor symptoms could enable comprehensive monitoring and tracking of PD in various contexts, facilitating more precise medication adjustments and the implementation of advanced therapeutic strategies.

OBJECTIVE

The objective of this study is to collect a dataset for the proposal and definition of digital biomarkers of PD motor symptoms using wearable devices. Data will be gathered in a supervised setting and continuously in a remote free-living context during their normal daily activities, including PD patients and healthy controls. The goal is to identify reliable digital biomarkers that can effectively distinguish PD patients from healthy controls and classify disease severity in both supervised and unsupervised free-living environments.

METHODS

This article outlines a protocol for an observational case-control study aimed at assessing motor symptoms in PD patients using a smartwatch. The smartwatch will record accelerometer, gyroscope, and physical activity data. Participants will be instructed to perform a series of exercises guided via an smartphone. Measurements will be collected in two settings: a supervised clinical environment, with motor symptoms assessments conducted at the beginning and end of the study, and in an unsupervised free-living context for one week. In both settings, participants will be required to wear the smartwatch while performing the same set of exercises. In their daily routine, participants will be required to wear the smartwatch continuously throughout the day, removing it only at night for charging.

RESULTS

Subject recruitment and data collection started in December 2024 and will proceed until the spring of 2025. The planned sample size includes 15 participants with PD and 15 healthy controls.

CONCLUSIONS

This study aims to generate a dataset of accelerometer and gyroscope signal data recorded from PD patients at various stages of the disease, alongside data from a control group, enabling robust comparative and impactful analyses. Additionally, the study seeks to develop analytical techniques capable of tracking PD symptoms in real-life scenarios, both in everyday settings and clinical environments.

CLINICALTRIAL

ClinicalTrials.gov NCT06817772; https://clinicaltrials.gov/ct2/show/NCT06817772