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.
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.
| 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 |
-
Fall Frequency
- Measured using wearable accelerometers on wrists, ankles, and trunk
- Daily fall counts recorded automatically
- Correlation with clinical measures
-
Motor Function
- Timed Up and Go (TUG) test
- 10-meter walk test
- Postural sway measurements
- Gait analysis during daily activities
-
Speech Function
- Acoustic analysis of speech samples
- Voice quality metrics
- Speech rate and fluency
-
Cognitive Function
- Digital cognitive assessments via smartphone
- Attention and processing speed tests
- Memory tasks
-
Quality of Life Measures
- PSP-specific quality of life questionnaires
- Daily activity monitoring
- Sleep quality tracking
-
Caregiver Burden Assessment
- Caregiver-reported burden scales
- Time spent on caregiving activities
- Caregiver wellbeing measures
-
Correlation Analysis
- Relationship between digital and clinical measures
- Validation of digital biomarkers
- Comparison with standard clinical assessments
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
- Smartphone Apps: Daily symptom tracking and cognitive tests
- Custom Software: Automated fall detection algorithms
- Cloud Platform: Data storage and analysis
| Timepoint |
Assessment Type |
| Baseline |
In-person comprehensive evaluation |
| Monthly |
Remote monitoring via sensors |
| Every 6 months |
In-person clinical assessment |
| Monthly |
Virtual check-ins |
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
Continuous monitoring allows for:
- Continuous Data Collection: Objectively capture symptoms throughout daily life
- Detection of Subtle Changes: Identify small but progressive changes
- Better Understanding of Disease Trajectory: Characterize individual patterns
- Development of Digital Biomarkers: Create objective measures for clinical use
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
The system uses machine learning algorithms trained on:
- Characteristic fall patterns in PSP
- Activity context (sitting, standing, walking)
- Environmental factors
- Individual baseline activity
- Automatic sensor calibration
- Data quality scoring
- Missing data handling protocols
- Privacy-preserving data collection
Data from remote monitoring can:
- Alert clinicians to concerning trends
- Trigger early intervention for fall prevention
- Guide medication adjustments
- Support care planning
The trial is active but not recruiting. Data collection is ongoing, with preliminary results expected in the coming years.
Remote monitoring provides:
- More responsive care based on objective data
- Early detection of complications
- Reduced burden of frequent clinic visits
- Empowerment through self-monitoring
The data supports:
- More informed clinical decisions
- Better understanding of individual patient patterns
- Objective documentation of progression
- Remote patient management capabilities
The initiative enables:
- Natural history studies
- Clinical trial endpoint development
- Biomarker validation
- Telemedicine research
The remote monitoring platform could eventually integrate with:
- Digital therapeutics for balance training
- Medication reminder systems
- Care coordination platforms
- Telehealth services
Similar approaches are being developed for:
- FDA guidance on digital health endpoints
- Reimbursement for remote monitoring
- Data privacy and security requirements
- Integration with electronic health records