Syde® Digital Endpoints for PSP (NCT07389018)

Overview

Overview

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

• 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

• 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

• Adherence rates (percentage of time worn)
• User satisfaction and tolerability
• Technical reliability of device function
• Data quality and completeness

• Willingness to wear device continuously
• Perceived burden of data collection
• Impact on daily activities
• Privacy concerns and data sharing attitudes

• Correlation between digital metrics and PSP Rating Scale
• Relationship to functional capacity measures
• Concordance with cognitive assessments

Secondary Objectives

• Disease progression over time
• Treatment response in future therapeutic trials
• Subtle changes not captured by traditional scales

• Specific gait patterns characteristic of PSP
• Quantitative measures of postural instability
• Activity decline trajectories
• Fall frequency and characteristics

• 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)

• 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

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)

• Montreal Cognitive Assessment (MoCA)
• Trail Making Test Parts A and B
• Stroop Test
• Semantic fluency
• Digit span forward and backward

• 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

• Movement patterns in complex environments
• Collision with objects
• Navigation difficulties

• Wide-based, shuffling gait
• Reduced step length
• Increased double-support time
• Difficulty with turns (pivot turns, freezing)
• Freezing of gait

• 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

• 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

Statistical 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:

For Research

Digital endpoints could accelerate therapeutic development:

For Patients

Patient benefits include:

Emerging 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

Related Research Areas

Key Pathways

• [Tauopathy Mechanisms](/mechanisms/tau-pathology)
• [Basal Ganglia Circuitry](/mechanisms/basal-ganglia-circuits)
• [Postural Control Systems](/mechanisms/postural-control)

Related Conditions

• [Progressive Supranuclear Palsy](/diseases/progressive-supranuclear-palsy)
• [Parkinson's Disease](/diseases/parkinsons-disease)
• [Corticobasal Syndrome](/diseases/corticobasal-syndrome)

Related Trials

• [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

Related Hypotheses

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) &#x1f504;

Pathway Diagram

The following diagram shows the key molecular relationships involving Syde® Digital Endpoints for PSP (NCT07389018) discovered through SciDEX knowledge graph analysis: