This study aims to assess the feasibility and acceptability of real-world activity monitoring using the Syde® wearable device in patients with Progressive Supranuclear Palsy Richardson Syndrome (PSP-R). The Syde® device collects continuous movement and activity data from patients in their natural environment, which is then compared to conventional on-site clinical endpoints including functional capacity assessments, cognitive testing, and standardized PSP rating scales 1. This research addresses a critical gap in neurological disease monitoring: the ability to capture meaningful, objective data between clinical visits rather than relying solely on periodic clinic-based assessments.
Current approaches to monitoring disease progression in PSP have significant limitations:
Infrequent Assessment: Standard clinical visits occur every 3-6 months, leaving large gaps where disease progression and functional changes go unmeasured. This sparse data collection obscures the true disease trajectory and may miss important fluctuations.
Recall Bias: Patients and caregivers must recall and report symptoms that occurred between visits, which introduces significant bias. Subtle changes in gait, balance, or activity levels are often not remembered or considered important enough to report.
Clinic-Based Testing Artifacts: The artificial environment of the clinic may not reflect true functional capabilities. Patients may perform differently in the familiar surroundings of their home versus the unfamiliar clinical setting, a phenomenon known as the "white-coat effect."
Subjective Measures: Traditional rating scales like the PSP Rating Scale, while validated, remain somewhat subjective and can be influenced by rater variability. Different clinicians may score the same patient differently.
Ceiling and Floor Effects: Standard scales may not capture changes in patients with very mild or very severe disease, limiting their utility across the full disease spectrum.
Digital health technologies offer a transformative approach to neurological disease monitoring:
Continuous Monitoring: Wearable devices can collect data continuously, providing a comprehensive picture of function rather than a snapshot. This enables detection of subtle changes that would be missed by periodic assessments.
Objective Measurements: Accelerometers, gyroscopes, and other sensors provide quantified, objective data that is not subject to interpretation bias. Numbers don't lie—the device measures what it measures regardless of patient or clinician perception.
Real-World Data: Unlike clinic-based testing, wearables capture how patients function in their natural environment—their home, their community, their daily life. This ecological validity is crucial for understanding true disease impact.
High Temporal Resolution: Data can be collected at millisecond resolution, capturing brief events like falls, freezes, or fluctuations that would be impossible to document in clinic.
Remote Monitoring: Data can be transmitted remotely, reducing the burden of travel for patients who may have difficulty getting to appointments. This is particularly relevant for PSP patients who often have mobility limitations.
SYSNAV's Syde® wearable device represents the next generation of digital health monitors:
Sensors Included:
Key Features:
Data Outputs:
Feasibility Assessment: Evaluate whether PSP patients can and will use the Syde® device as intended:
Acceptability Evaluation: Assess patient and caregiver perspectives:
Data Validity: Compare Syde® measurements to conventional clinical endpoints:
Endpoint Validation: Assess whether digital endpoints can serve as reliable measures of:
Digital Biomarker Discovery: Identify novel metrics that may be more sensitive than existing measures:
Algorithm Development: Refine algorithms for:
Real-World Activity Monitoring:
Specific Metrics Collected:
PSP Rating Scale (PSPRS):
Comprehensive assessment across six domains:
Total score range 0-100, higher scores indicate greater impairment
Functional Capacity Evaluation:
Cognitive Assessments:
Quality of Life Measures:
The study directly compares:
Postural Instability: PSP patients characteristically have severe postural instability with early falls. Digital devices can quantify:
Ocular Motor Dysfunction: While not directly measurable by wearable sensors, eye movement abnormalities affect navigation and may be reflected in:
Gait Characteristics: PSP has distinct gait patterns:
Disease-Specific Algorithm Needs: The study must develop PSP-specific algorithms because:
This study builds on digital health research in PD while addressing PSP-specific needs:
Shared Technologies: Many sensors and algorithms can be adapted from PD research
Differences from PD:
PSP-Specific Contributions:
This feasibility study is particularly timely given evolving regulatory perspectives:
FDA Guidance: The FDA has published guidance on digital health technologies and has expressed interest in digital endpoints as potentially more sensitive measures than traditional clinical outcomes 2.
EMA Perspectives: European regulators similarly support exploration of digital endpoints in neurological disease trials.
Clinical Trial Applications: If successful, digital endpoints could:
The digital endpoints being validated could support future therapeutic trials:
Successful digital monitoring could transform clinical practice:
Enhanced Monitoring: Clinicians could see how patients function between visits, enabling more informed decisions about treatment adjustments
Early Intervention: Subtle changes detected by devices could prompt earlier intervention before significant decline
Personalized Care: Individual activity patterns could inform personalized management strategies
Caregiver Support: Objective data could help caregivers understand disease progression and plan for increasing care needs
Digital endpoints could accelerate therapeutic development:
Patient benefits include:
Parkinson's Disease Digital Outcomes: The Parkinson's Foundation and Michael J. Fox Foundation have supported large-scale digital health initiatives that provide templates for PSP research. Studies like the mHealth2020 initiative have established feasibility and validity of smartphone-based assessments 3.
Gait Analysis Algorithms: Machine learning approaches have significantly improved gait analysis, enabling classification of Parkinsonian gait from normal with high accuracy and differentiation between PD and PSP 4.
Fall Detection: Recent advances in fall detection algorithms have reduced false positives and improved sensitivity, particularly important for PSP where falls are common and potentially dangerous 5.
Activity Classification: Deep learning models now classify activities with >95% accuracy in older adults, enabling reliable monitoring of functional status 6.
FDA's Digital Health Center of Excellence: Established to support development and validation of digital health technologies, providing guidance and resources for researchers and industry.
Software as Medical Device (SaMD): Evolving regulatory frameworks enable more rapid approval of digital health tools.
Real-World Evidence: Growing acceptance of real-world data from digital devices for regulatory decision-making.
Miniaturization: Sensors continue to shrink while improving in precision
Battery Technology: New battery technologies extend device life
Connectivity: 5G enables more reliable real-time data transmission
AI/ML: Sophisticated algorithms extract more meaningful insights from raw data