Scalable Multi‐Sensor At Home Assessment of Sleep and Activity in Adults At High Risk for Dementia
Andrew Lim, Andrew Peter Centen, Nasim Montazeri, Erin Gibson, Thien Thanh Dang‐Vu, Julie Carrier, Senny Chan, Sylvie Belleville, Haakon B. Nygaard, Manuel Montero‐Odasso, Howard H. Feldman, Howard Chertkow,- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
Sleep disruption is common in older adults, and emerging evidence suggests that differences in sleep whether measured by electroencephalography (EEG) or actigraphy, may be associated with clinical cognitive outcomes, and with specific dementia‐associated pathologies. However, whether EEG and actigraphic sleep features may predict cognitive response to lifestyle interventions for dementia, or whether they may themselves respond to these lifestyle interventions, is unknown. A major challenge is to develop sleep assessment in virtual studies where subjects do not attend a research center.
Methods
In the Canada‐wide Brain Health Support Program (BHSP), 350 adults at high risk for dementia have been recruited to receive an online lifestyle risk factors intervention. At baseline, and at 12‐month follow‐up, we are assessing 3 nights of sleep EEG and 10‐days of wrist accelerometry at home using a mailed‐out package. Wearable devices are mailed to subjects, who are instructed in their use, and data is collected remotely. The wearables are then mailed back to the study center. We used the MUSE‐S (Interaxon, Toronto, Canada), which is a multimodal wearable headband combining 4‐channel dry‐electrode EEG, with accelerometry and photoplethysmography, along with the AX3 wrist triaxial accelerometer (Axivity, Newcastle, UK).
Results
EEG headbands and wrist accelerometers were sent to 348 participants with a total of 1116 nights of EEG recording and obtained. Of the 348 participants, 15 withdrew from the EEG component of the study; of the remaining 333, 316 (95%) completed at least 1 night of recording, and 301 (90%) obtained at least 1 night of recording with signal quality meeting quality control criteria (adequate signal quality in at least 1 frontal and 1 temporal electrode for at least 80% of the recording). For the wrist acclerometry data, 342 completed at least 7 days of wear meeting QC criteria.
Conclusion
Fully remote sleep EEG and accelerometry data collection is feasible in a geographically dispersed cohort of older adults at high risk for dementia. The BHSP will provide an opportunity to assess whether EEG and accelerometric sleep features may predict the cognitive response to lifestyle interventions for dementia, or whether they themselves may respond to these lifestyle interventions.