Using technology to improve how Alzheimer's disease is detected
The DETECT-AD study (Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer's Disease) aims to improve how Alzheimer's disease is detected at early or preclinical stages.
Simulating a clinical trial, the study uses a combination of devices and sensors to see how well they can detect clinically meaningful changes in someone at risk of developing the disease. Staff at the Layton Aging and Alzheimer's Disease Research Center look for changes in mobility, cognition, sleep, and socialization.
Improving how Alzheimer's disease is detected early not only supports the development of much-needed new treatments but also helps patients and their families better plan for their future.
- Are at least 65 years old
- Use a computer, tablet, or smart phone and have an active internet connection
- Willing to have visits at OHSU that include cognitive assessments, an MRI, PET scan, and blood draw
- Willing to have devices installed and used in your home (including but not limited to): a scale, pillbox, movement sensors, and an activity watch
- Willing to participate in genetic research
- Normal cognition or MCI diagnosis
- Have a study partner
- Visits to OHSU for a cognitive assessment, brief medical exam, an MRI, a PET scan and a blood draw
- Technologies like an activity watch, wireless pillbox, wall sensors, scale, computer and phone software, and a driving sensor.
- Online health surveys and memory exercises
- Compensation is $100 for PET/MRI regardless of enrollment and, if enrolled, $50 per month
- Study duration is up to 36 months
Detecting behavioral activity
Devices and sensors measure behavioral activity as research participants go about their daily routines.
- Mobility - Walking speed is measured by the wrist-watch activity tracker
- Cognition - Computer use, measured by software installed in computers
- Sleep - Sleep times, measured by sleep sensors and activity trackers
- Socialization - Time spent out of home, measured by motion sensors installed in participants' homes