Remote Monitoring in PSP (NCT04753320)

Remote Monitoring in Progressive Supranuclear Palsy (NCT04753320)

Overview

This is a single-arm, longitudinal, observational study using wearable sensors and digital health technology to measure fall frequency, motor function, speech, and cognitive function in PSP patients over approximately one year["@nct"].

Remote Monitoring in Progressive Supranuclear Palsy (NCT04753320)

Overview

This is a single-arm, longitudinal, observational study using wearable sensors and digital health technology to measure fall frequency, motor function, speech, and cognitive function in PSP patients over approximately one year["@nct"].

The study addresses a critical gap in PSP care: the inability to capture the day-to-day variability and subtle progression that occurs between clinical visits. Traditional assessment approaches rely on periodic clinic visits, which may miss important fluctuations in symptoms.

Trial Details

| Field | Value |
|-------|-------|
| NCT ID | NCT04753320 |
| Status | Active, Not Recruiting |
| Phase | Observational |
| Study Design | Single-arm, longitudinal |
| Duration | 12 months |
| Assessments | Monthly remote + every 6 months in-person |
| Primary Outcome | Fall frequency, motor function |
| Sponsor | Academic medical center |

Study Objectives

Primary Outcomes

• Measured using wearable accelerometers on wrists, ankles, and trunk
• Daily fall counts recorded automatically
• Correlation with clinical measures

• Timed Up and Go (TUG) test
• 10-meter walk test
• Postural sway measurements
• Gait analysis during daily activities

• Acoustic analysis of speech samples
• Voice quality metrics
• Speech rate and fluency

• Digital cognitive assessments via smartphone
• Attention and processing speed tests
• Memory tasks

Secondary Outcomes

• PSP-specific quality of life questionnaires
• Daily activity monitoring
• Sleep quality tracking

• Caregiver-reported burden scales
• Time spent on caregiving activities
• Caregiver wellbeing measures

• Relationship between digital and clinical measures
• Validation of digital biomarkers
• Comparison with standard clinical assessments

Technology Platform

Wearable Sensors

The study utilizes multiple sensor modalities:

• Accelerometers: On wrists, ankles, and trunk for activity monitoring
• Gyroscopes: For rotation and movement detection
• Magnetometers: For orientation tracking
• Pressure sensors: In insoles for gait analysis

Digital Applications

• Smartphone Apps: Daily symptom tracking and cognitive tests
• Custom Software: Automated fall detection algorithms
• Cloud Platform: Data storage and analysis

Assessment Schedule

| Timepoint | Assessment Type |
|-----------|-----------------|
| Baseline | In-person comprehensive evaluation |
| Monthly | Remote monitoring via sensors |
| Every 6 months | In-person clinical assessment |
| Monthly | Virtual check-ins |

Rationale

Limitations of Traditional Monitoring

PSP progression is often measured through clinic visits every 6-12 months, but this approach has significant limitations:

• Symptom Fluctuations: Daily variation in motor symptoms may be missed
• Falls Between Visits: Important fall events occur at home, not in clinic
• Subtle Changes: Small but meaningful changes may not be detectable
• Recall Bias: Patient-reported outcomes rely on memory of symptoms

Advantages of Remote Monitoring

Continuous monitoring allows for[@berkowsky2021][@senson2020][@kluge2021]:

PSP-Specific Considerations

PSP presents unique challenges for remote monitoring:

• Frequent Falls: Characteristic feature requiring careful monitoring
• Postural Instability: Balance deficits need quantitative assessment
• Oculomotor Impairment: Can affect device interaction
• Cognitive Changes: May influence compliance with monitoring

Technical Implementation

Fall Detection Algorithm

The system uses machine learning algorithms trained on:

• Characteristic fall patterns in PSP
• Activity context (sitting, standing, walking)
• Environmental factors
• Individual baseline activity

Data Quality Assurance

• Automatic sensor calibration
• Data quality scoring
• Missing data handling protocols
• Privacy-preserving data collection

Integration with Clinical Care

Data from remote monitoring can:

• Alert clinicians to concerning trends
• Trigger early intervention for fall prevention
• Guide medication adjustments
• Support care planning

Current Status

The trial is active but not recruiting. Data collection is ongoing, with preliminary results expected in the coming years.

Clinical Implications

For Patients

Remote monitoring provides:

• More responsive care based on objective data
• Early detection of complications
• Reduced burden of frequent clinic visits
• Empowerment through self-monitoring

For Clinicians

The data supports:

• More informed clinical decisions
• Better understanding of individual patient patterns
• Objective documentation of progression
• Remote patient management capabilities

For Research

The initiative enables:

• Natural history studies
• Clinical trial endpoint development
• Biomarker validation
• Telemedicine research

Related Pages

• [Progressive Supranuclear Palsy](/diseases/progressive-supranuclear-palsy)
• [MOTIVE-PSP Initiative](/clinical-trials/motive-psp-initiative)
• [PSP Biomarkers](/diseases/progressive-supranuclear-palsy)
• [Digital Health Technologies in Neurology](/therapeutics/digital-health)
• [CBS/PSP Clinical Trials Guide](/clinical-trials/cbs-psp-clinical-trials-guide)

Future Directions

Digital Therapeutics Integration

The remote monitoring platform could eventually integrate with:

• Digital therapeutics for balance training
• Medication reminder systems
• Care coordination platforms
• Telehealth services

Multi-Disease Networks

Similar approaches are being developed for:

• [Parkinson's disease](/diseases/parkinsons-disease) Multiple system atrophy
• Corticobasal degeneration
• Other movement disorders

Regulatory Considerations

• FDA guidance on digital health endpoints
• Reimbursement for remote monitoring
• Data privacy and security requirements
• Integration with electronic health records

References