Digital biomarkers in Parkinson's disease

Area of Science:

• Neurology
• Biomedical Engineering
• Digital Health

Background:

• Digital biomarkers (DBs) offer objective, real-world measures for Parkinson's disease (PD) assessment.
• Smartphone-based inertial sensors show promise for monitoring PD symptoms and progression.
• Current limitations include a lack of standardized validation and consensus for DB use in clinical practice.

Purpose of the Study:

• To provide an updated review of digital biomarker applications in Parkinson's disease.
• To highlight the potential benefits and limitations of using DBs for PD diagnosis and monitoring.
• To suggest future research directions for the clinical validation of DBs.

Main Methods:

• Review of current evidence on digital biomarker use in Parkinson's disease.
• Analysis of clinical trials evaluating DB performance against traditional assessment tools.
• Discussion of analytical and clinical validation challenges for DBs.

Main Results:

• Digital biomarkers show potential for improving the monitoring, treatment, and outcomes of PD patients.
• AI and Machine Learning can leverage DB data for advanced patient analytics.
• Significant challenges remain in standardizing and validating DBs for widespread clinical use.

Conclusions:

• Digital biomarkers hold considerable promise for enhancing Parkinson's disease management, especially in advanced stages.
• Standardization, harmonization, and robust clinical validation are essential for the successful integration of DBs into routine care.
• Further research is needed to overcome current limitations and fully realize the potential of DBs in PD.