Detecting Risk Early Study - Healthy Group
STUDY BASICS
Are you 50 years old or older? Do you live without cognition problems? You may be able to participate in a research study to develop a new way to detect who is at higher risk for dementia without the need for testing. Compensation is provided.
STUDY PURPOSE
Detecting changes in cognition earlier may help some people delay or prevent dementia, but it typically involves extensive testing that is not available to most people. This study is trying to develop a new way to detect who is at a higher risk for dementia without the need for testing.
COULD THIS STUDY BE RIGHT FOR YOU?
Eligible participants:
- Are ages 50 and older
- Have never experienced problems with cognition
WHAT PARTICIPANTS CAN EXPECT
Screening will take place at your initial appointment. This will consist of completing several different assessments that will focus on your thinking, memory, sleep, and mood. This may take between 40-60 minutes of your time.
If eligible, you will be asked to wear an Apple watch 24/7 for one week. The watches and app record movement and acceleration intensity, i.e., how quickly you are moving. We will loan you with a current model Apple Watch and an iPhone. We will also help install/learn how to use the app. You will also be asked to complete a set of brief self-report and interview-based questionnaires about yourself including your sleep and mood that can be done at your own pace. This includes a self-reported sleep diary that you will be asked to fill out twice a day, in the morning and evening, while you are wearing the Apple watch. The diaries will take 4 minutes or less to complete each time.
IRB: STUDY22080033
- Detecting Risk Early StudyMEET THE RESEARCHER

Stephen Smagula
Stephen Smagula, PhD, is an Associate Professor in Psychiatry and Epidemiology at the University of Pittsburgh. A graduate of Lafayette College, Columbia University, and the University of Pittsburgh, Dr. Smagula’s research interests include clarifying how sleep-wake behaviors influence the biological mechanisms of depression. He uses activity tracking to better understand the sleep-wake patterns relevant to mood.