Alzheimer's Disease: A Step Towards Prognosis Using Smart Wearables ^†.

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Neurodegenerative Disease Research

Background:

• Alzheimer's disease (AD) is a leading cause of dementia.
• Hemodynamic factors like aging, arterial stiffness, high blood pressure, and hypoperfusion are linked to AD risk.
• Current methods for assessing these risks can be invasive or limited.

Purpose of the Study:

• To explore the feasibility of using arterial pulse wave (PW) analysis from wearable sensors for assessing AD hemodynamic risk factors.
• To determine if PW features measured non-invasively can help stratify individuals at risk for AD.

Main Methods:

• A numerical model simulated pulse wave propagation in virtual subjects aged 25-75.
• Variations in arterial stiffness were incorporated to mimic normal aging.
• Key PW features were extracted and analyzed for their relationship with AD risk factors.

Main Results:

• Pulse waves measured at the wrist showed significant changes with age and arterial stiffness.
• Several candidate PW features demonstrated significant age-related changes.
• The findings suggest that smart wearables can potentially detect hemodynamic changes relevant to AD risk.

Conclusions:

• Non-invasive assessment of hemodynamic risk factors for AD using pulse waves is potentially feasible.
• Photoplethysmography (PPG) pulse waves, obtainable from smartwatches and phones, may be utilized for this assessment.
• Further clinical studies are needed to validate these findings and explore patient self-monitoring for lifestyle adjustments.