Background Sleep disturbances are both risk factors for and symptoms of dementia. Current methods assessing sleep largely based on either polysomnography (PSG) which is costly inconvenient, or self- care-giver reports prone to measurement error. Low-cost monitor longitudinally at scale can be useful symptom development. Here, we develop deep learning models that use multimodal variables (accele...