Case Studies: Real-World Examples of StrivePD Sensor Data in Managing Parkinson’s On-Off Cycle
Below are three specific examples illustrating how StrivePD sensor data and AI interpretation help individuals with Parkinson’s disease manage their on-off fluctuations more effectively.
Case Study 1: Optimizing Levodopa Dosage with StrivePD
Patient: Mark, a 62-year-old diagnosed with Parkinson’s disease 8 years ago
Challenge: Mark experiences sudden “off” periods in the afternoon, making it difficult to move and increasing fall risk.
StrivePD Data Findings:
- Tremors (Hz) and Bradykinesia (movement speed) worsened around 2 PM
- Gait analysis showed increased shuffling and shorter stride length after 1:30 PM
- Medication intake at 8 AM, 12 PM, and 4 PM showed symptom worsening before the next dose
Intervention:
- Doctor adjusted levodopa intake from every 4 hours to every 3.5 hours
- Added a COMT inhibitor to extend levodopa’s duration
- StrivePD continued monitoring symptom improvement in real-time
Outcome:
- Mark’s “off” episodes reduced from 3 per day to just 1 mild episode
- Improved walking stability and tremor control
- Medication adjustments were personalized based on objective AI data
Case Study 2: Identifying Protein Interference with Medication Absorption
Patient: Susan, a 58-year-old with Parkinson’s for 6 years
Challenge: Despite taking levodopa every 5 hours, Susan noticed her medications weren’t always effective and had unpredictable on-off periods.
StrivePD Data Findings:
- AI detected increased freezing episodes within 30 minutes of her lunch at 1 PM
- Bradykinesia symptoms were significantly worse post-meal
- Tremor frequency increased from 3 Hz (mild) to 5 Hz (moderate) after eating
Intervention:
- Dietitian reviewed Susan’s meal timing and found that high-protein intake (chicken and eggs) at lunch blocked levodopa absorption
- Advised her to shift protein consumption to dinner
- Adjusted levodopa schedule to 30 minutes before meals
Outcome:
- Tremors and freezing reduced by 50% in the afternoon
- On-off fluctuations became more predictable
- AI-assisted data helped pinpoint dietary impact on medication effectiveness
Case Study 3: Predicting and Preventing Fall Risk Using AI Sensors
Patient: Robert, a 70-year-old retired teacher diagnosed with Parkinson’s 10 years ago
Challenge: Robert experienced frequent falls due to unexpected “off” periods, despite taking medications regularly.
StrivePD Data Findings:
- AI detected progressive decline in stride length (step size reduced by 40%) in the hours before falls
- Gait irregularities were highest between 4-6 PM
- Rigidity severity worsened (score increased from 2 to 4) leading up to a fall
Intervention:
- Doctor introduced Deep Brain Stimulation (DBS) as an alternative therapy
- StrivePD used to monitor DBS effectiveness in real time
- AI-based alerts helped predict fall risk and notify caregivers
Outcome:
- Robert’s fall frequency reduced by 75%
- Increased walking stability and reduced rigidity symptoms
- Caregivers received real-time alerts before potential falls, improving safety
Key Takeaways from These Examples
- AI-driven symptom tracking allows for precise medication timing adjustments
- StrivePD’s wearable sensors can detect dietary effects on medication absorption
- AI can predict falls before they happen, preventing injuries
- Objective data replaces symptom diaries, reducing reliance on patient recall errors
Final Thoughts
These real-world applications of StrivePD and AI-driven monitoring illustrate the potential of wearable technology in Parkinson’s management. Whether adjusting medications, diet, or advanced therapies like DBS, continuous symptom tracking helps personalize treatments, reducing on-off fluctuations and improving quality of life