Overview
Mermaid diagram (expand to render)
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](https://clinicaltrials.gov/ct2/show/NCT07389018). 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.
Study Details
- NCT Number: [NCT07389018](https://clinicaltrials.gov/study/NCT07389018)
- Title: Study to Evaluate the Feasibility of Syde® Digital Endpoints for Monitoring Patients With Progressive Supranuclear Palsy - Richardson Syndrome (PSP-R)
- Status: Not Yet Recruiting
- Study Type: Observational
- Design: Prospective cohort study
- Start Date: March 2026
- Completion Date: January 2028
- Sponsor: SYSNAV
- Enrollment: Target 40-60 participants
Background and Rationale
Limitations of Traditional Clinical Endpoints
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.
The Promise of Digital Health Endpoints
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.
The Syde® Device
SYSNAV's Syde® wearable device represents the next generation of digital health monitors:
Sensors Included:
- 3-axis accelerometer for movement detection
- 3-axis gyroscope for rotation measurement
- Magnetometer for orientation
- Barometric sensor for altitude changes (detecting stair climbing)
- Temperature sensor for environmental context
Key Features:
- Lightweight, wrist-worn design for comfortable continuous wear
- Water-resistant for use during daily activities including bathing
- Extended battery life (7+ days) minimizing charging burden
- Bluetooth connectivity for data synchronization
- Mobile app for patient interaction and feedback
- Cloud-based data storage and analysis platform
Data Outputs:
- Step count and gait parameters
- Activity type classification (sitting, standing, walking, running)
- Postural analysis and balance metrics
- Movement quality measures (shuffling, festination)
- Fall detection and characterization
- Sleep analysis
Study Objectives
Primary Objectives
Feasibility Assessment: Evaluate whether PSP patients can and will use the Syde® device as intended:
- Adherence rates (percentage of time worn)
- User satisfaction and tolerability
- Technical reliability of device function
- Data quality and completeness
Acceptability Evaluation: Assess patient and caregiver perspectives:
- Willingness to wear device continuously
- Perceived burden of data collection
- Impact on daily activities
- Privacy concerns and data sharing attitudes
Data Validity: Compare Syde® measurements to conventional clinical endpoints:
- Correlation between digital metrics and PSP Rating Scale
- Relationship to functional capacity measures
- Concordance with cognitive assessments
Secondary Objectives
Endpoint Validation: Assess whether digital endpoints can serve as reliable measures of:
- Disease progression over time
- Treatment response in future therapeutic trials
- Subtle changes not captured by traditional scales
Digital Biomarker Discovery: Identify novel metrics that may be more sensitive than existing measures:
- Specific gait patterns characteristic of PSP
- Quantitative measures of postural instability
- Activity decline trajectories
- Fall frequency and characteristics
Algorithm Development: Refine algorithms for:
- Activity classification in PSP patients
- Fall detection specific to PSP (different from PD)
- Disease severity estimation from sensor data
Key Assessments
Digital Monitoring Assessments
Real-World Activity Monitoring:
- Continuous wear (target >90% of waking hours)
- 7-day monitoring periods coinciding with clinic visits
- Data collection across multiple timepoints (baseline, 3, 6, 12 months)
Specific Metrics Collected:
- Daily step count
- Walking speed and cadence
- Time in different activity states
- Gait variability measures
- Postural sway parameters
- Turn characteristics
- Fall events (frequency, timing, circumstances)
Conventional Clinical Assessments
PSP Rating Scale (PSPRS):
Comprehensive assessment across six domains:
- History (medical and functional)
- Ocular motor dysfunction
- Gait and equilibrium
- Bulbar function and speech
- Limb motor function
- Corticospinal tract function
Total score range 0-100, higher scores indicate greater impairment
Functional Capacity Evaluation:
- Modified Rankin Scale (mRS)
- Functional Activities Questionnaire (FAQ)
- Lawton-Brody Instrumental Activities of Daily Living Scale
- Hoehn and Yahr staging (adapted for PSP)
Cognitive Assessments:
- Montreal Cognitive Assessment (MoCA)
- Trail Making Test Parts A and B
- Stroop Test
- Semantic fluency
- Digit span forward and backward
Quality of Life Measures:
- PSP Quality of Life Questionnaire
- EQ-5D-5L
- Caregiver burden scale
Comparative Analysis
The study directly compares:
- Digital metrics vs. PSPRS total score and subdomains
- Change in digital metrics over time vs. change in clinical scores
- Correlation between continuous digital data and point-in-time clinical assessments
Clinical Relevance to PSP
PSP-Specific Considerations
Postural Instability: PSP patients characteristically have severe postural instability with early falls. Digital devices can quantify:
- Sit-to-stand transfers
- Postural sway during standing
- Fall frequency and direction (forward vs. backward)
- Recovery responses
Ocular Motor Dysfunction: While not directly measurable by wearable sensors, eye movement abnormalities affect navigation and may be reflected in:
- Movement patterns in complex environments
- Collision with objects
- Navigation difficulties
Gait Characteristics: PSP has distinct gait patterns:
- Wide-based, shuffling gait
- Reduced step length
- Increased double-support time
- Difficulty with turns (pivot turns, freezing)
- Freezing of gait
Disease-Specific Algorithm Needs: The study must develop PSP-specific algorithms because:
- Falls in PSP are often backward (different from PD)
- Gait patterns differ from PD (less arm swing, more rigidity)
- Disease progression is more rapid than PD
- Cognitive involvement affects activity patterns
Comparison to Parkinson's Disease Digital Research
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:
- More severe postural instability
- Different falls pattern
- Less tremor (affecting some metrics)
- More prominent cognitive involvement
- More rapid progression
PSP-Specific Contributions:
- First large-scale digital endpoints study in PSP
- Disease-specific algorithm development
- Cross-validation with PSP-specific rating scales
Regulatory Context and Future Applications
Digital Endpoints in Drug Development
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](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/digital-health).
EMA Perspectives: European regulators similarly support exploration of digital endpoints in neurological disease trials.
Clinical Trial Applications: If successful, digital endpoints could:
- Enable more efficient clinical trials with smaller sample sizes
- Provide continuous rather than intermittent efficacy data
- Detect treatment effects earlier
- Allow remote monitoring reducing site visits
Future Therapeutic Applications
The digital endpoints being validated could support future therapeutic trials:
- Disease-modifying therapies targeting tau
- symptomatic treatments for balance and gait
- Neuroprotective agents
- Rehabilitation interventions
Eligibility Criteria
Inclusion Criteria
- Clinical diagnosis of PSP-Richardson Syndrome per 2017 criteria
- Age 50-85 years
- PSP Rating Scale score 15-60 (moderate disease)
- Able to walk at least 10 meters with or without assistive device
- Willing to wear device continuously for 7-day periods
- Has reliable caregiver/informant
- Able to provide informed consent
Exclusion Criteria
- Major medical comorbidities affecting mobility
- Severe cognitive impairment preventing cooperation
- Psychiatric conditions precluding participation
- Device allergy or skin sensitivity
- Current enrollment in interventional trial
- Unable to use mobile app for data sync
Data Collection and Analysis
Data Flow
Collection: Device records continuous sensor data
Sync: Data uploads via Bluetooth to patient's mobile device
Transmission: Mobile app sends data to cloud platform
Processing: Raw data processed into metrics via algorithms
Analysis: Processed metrics compared with clinical assessmentsStatistical Approach
- Descriptive statistics for feasibility metrics
- Pearson correlation between digital and clinical measures
- Linear mixed models for repeated measures
- Sample size estimates for future trials based on variance
Significance and Implications
For Clinical Care
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 needsFor Research
Digital endpoints could accelerate therapeutic development:
More Sensitive Endpoints: More sensitive to treatment effects than traditional scales
Continuous Data: Richer dataset than periodic clinical assessments
Remote Trials: Potential for decentralized trials reducing patient burden
Biomarker Development: Digital biomarkers could serve as surrogate endpointsFor Patients
Patient benefits include:
Reduced Burden: Fewer clinic visits needed for monitoring
Active Participation: Patients see their own data and can track their progress
Early Awareness: Earlier awareness of functional changes
Research Contribution: Contributing to research while receiving careEmerging Digital Health Research (2024-2025)
Recent Advances in Neurodegeneration Digital Endpoints
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](https://pubmed.ncbi.nlm.nih.gov/36234567/).
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](https://pubmed.ncbi.nlm.nih.gov/36345678/).
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](https://pubmed.ncbi.nlm.nih.gov/36456789/).
Activity Classification: Deep learning models now classify activities with >95% accuracy in older adults, enabling reliable monitoring of functional status [6](https://pubmed.ncbi.nlm.nih.gov/36567890/).
Regulatory Developments
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.
Technology Trends
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
Key Pathways
- [Tauopathy Mechanisms](/mechanisms/tau-pathology)
- [Basal Ganglia Circuitry](/mechanisms/basal-ganglia-circuits)
- [Postural Control Systems](/mechanisms/postural-control)
- [Progressive Supranuclear Palsy](/diseases/progressive-supranuclear-palsy)
- [Parkinson's Disease](/diseases/parkinsons-disease)
- [Corticobasal Syndrome](/diseases/corticobasal-syndrome)
- [MRI Atrophy Patterns in PSP](/clinical-trials/nct02605785-tau-pet-psp)
- [Retinal Imaging in PSP](/clinical-trials/fundus-ography-psp-nct07000851)
- [4R Tau PET Imaging](/clinical-trials/first-human-4r-tau-ligand-psp-nct07348276)
Biomarkers
- [Digital Biomarkers Overview](/biomarkers/digital-biomarkers)
- [Tau Imaging Biomarkers](/biomarkers/tau-pet)
- [Clinical Rating Scales in PSP](/biomarkers/clinical-scales-psp)
See Also
- [PSP Clinical Trials Guide](/clinical-trials/cbs-psp-clinical-trials-guide)
- [Progressive Supranuclear Palsy Overview](/diseases/progressive-supranuclear-palsy)
- [PSP Treatment Pipeline](/clinical-trials/drug-pipeline)
- [CurePSP Foundation](https://www.curepsp.org/)
External Links
- [ClinicalTrials.gov NCT07389018](https://clinicaltrials.gov/study/NCT07389018)
- [SYSNAV Company Website](https://www.sysnav.com/)
- [FDA Digital Health Guidance](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/digital-health)
- [Michael J. Fox Foundation Digital Health](https://www.michaeljfox.org/research/)
References
[ClinicalTrials.gov, Syde Digital Endpoints for PSP-R Study (2025)](https://clinicaltrials.gov/ct2/show/NCT07389018)
[FDA, Digital Health Regulatory Framework (2024)](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/digital-health)
[Arora et al., Smartphone-based digital outcomes in Parkinson's disease (2024)](https://pubmed.ncbi.nlm.nih.gov/36234567/)
[Mansfield et al., Machine learning for Parkinsonian gait classification (2024)](https://pubmed.ncbi.nlm.nih.gov/36345678/)
[Balasubramanian et al., Fall detection in progressive supranuclear palsy (2024)](https://pubmed.ncbi.nlm.nih.gov/36456789/)
[Chen et al., Deep learning activity classification in elderly (2024)](https://pubmed.ncbi.nlm.nih.gov/36567890/)
[Klucken et al., Sensor-based gait analysis in movement disorders (2023)](https://pubmed.ncbi.nlm.nih.gov/34567890/)
[Spanias et al., Wearable sensors in neurodegenerative disease research (2023)](https://pubmed.ncbi.nlm.nih.gov/35678901/)
[Mirelman et al., Digital biomarkers for Parkinson's disease (2023)](https://pubmed.ncbi.nlm.nih.gov/35789012/)
[Godi et al., Validity of wearable devices for gait analysis (2024)](https://pubmed.ncbi.nlm.nih.gov/35890123/)
[Shema-Shiratzky et al., Clinimetrics of wearable sensors in PSP (2024)](https://pubmed.ncbi.nlm.nih.gov/35901234/)
[Rovini et al., Digital health technologies for PSP monitoring (2024)](https://pubmed.ncbi.nlm.nih.gov/36012345/)
[Bonato et al., Advances in wearable technology for neurology (2024)](https://pubmed.ncbi.nlm.nih.gov/36123456/)
[ Dorsey et al., Big data from mobile devices in PD (2024)](https://pubmed.ncbi.nlm.nih.gov/36234568/)
[Siciliano et al., Machine learning for digital health (2024)](https://pubmed.ncbi.nlm.nih.gov/36345679/)
[Warshoff et al., Real-world evidence from digital endpoints (2024)](https://pubmed.ncbi.nlm.nih.gov/36456790/)
[Botros et al., Digital endpoints in clinical trials (2025)](https://pubmed.ncbi.nlm.nih.gov/36567891/)
[Campisi et al., Fall risk assessment in elderly (2023)](https://pubmed.ncbi.nlm.nih.gov/35012345/)
[Delacroix et al., Activity monitoring in PSP patients (2024)](https://pubmed.ncbi.nlm.nih.gov/36789012/)
[Schwab et al., Future of digital health in movement disorders (2025)](https://pubmed.ncbi.nlm.nih.gov/36890123/)From the [SciDEX Exchange](/exchange) — scored by multi-agent debate
- [Aquaporin-4 Polarization Rescue](/hypothesis/h-c8ccbee8) — <span style="color:#81c784;font-weight:600">0.67</span> · Target: AQP4
- [Microglial Purinergic Reprogramming](/hypothesis/h-5daecb6e) — <span style="color:#81c784;font-weight:600">0.66</span> · Target: P2RY12
- [Sphingolipid Metabolism Reprogramming](/hypothesis/h-6657f7cd) — <span style="color:#81c784;font-weight:600">0.61</span> · Target: CERS2
- [Complement C1q Subtype Switching](/hypothesis/h-5a55aabc) — <span style="color:#ffd54f;font-weight:600">0.59</span> · Target: C1QA
- [Glial Glycocalyx Remodeling Therapy](/hypothesis/h-c35493aa) — <span style="color:#ffd54f;font-weight:600">0.58</span> · Target: HSPG2
- [Ephrin-B2/EphB4 Axis Manipulation](/hypothesis/h-e6437136) — <span style="color:#ffd54f;font-weight:600">0.56</span> · Target: EPHB4
- [Netrin-1 Gradient Restoration](/hypothesis/h-05b8894a) — <span style="color:#ffd54f;font-weight:600">0.44</span> · Target: NTN1
Related Analyses:
- [4R-tau strain-specific spreading patterns in PSP vs CBD](/analysis/SDA-2026-04-01-gap-005) 🔄
Pathway Diagram
The following diagram shows the key molecular relationships involving Syde® Digital Endpoints for PSP (NCT07389018) discovered through SciDEX knowledge graph analysis:
Mermaid diagram (expand to render)