Alzheimer’s disease (AD) causes symptoms such as dementia, memory loss, disorientation, and even aggressiveness, and is more common in women than in men. AD may also manifest itself in changes in sleep patterns. However, the relationship between AD (in all stages) and bedtime behavior has not been thoroughly investigated.
In a prospective, cross-sectional survey, we evaluated 74 women categorized in two different stages of cognitive decline associated with AD (mild and severe) along with 37 women with no cognitive decline who served as controls. We obtained demographic and medical information such as age, health status, and medication, as well as psychiatrically confirmed staging of AD. We also collected actigraphy data for several nights in a row with a medical grade wristband using a 3-axis accelerometer and solid-state on-board memory. These data served as parameters for a clustering machine learning (ML) algorithm.
The ML process was able to unsupervisedly identify 85% of the participants according to their pre-assigned degree of dementia. When the clustering was carried out in a binary fashion (i.e., only taking into account healthy members vs. severely affected AD patients), it was possible to correctly classify 91% of the cases.
More information at https://link.springer.com/article/10.1007%2Fs11325-021-02327-x