Machine Learning to Detect Motor Agitation in People With Dementia Using Real-Time Location Data

Motor agitation is a neuropsychiatric symptom of dementia that signals distress and poses a safety risk. Development of digital phenotypes for this symptom could enable better detection, monitoring, and personalized care. Our aim is to establish whether data from a location-monitoring system, alone or combined with clinical assessments, can be used in a machine learning model to classify 8-hour shifts as motor agitation or normal motor activity in individuals with dementia.

A prospective, longitudinal observational study.

A psychogeriatric unit for individuals with behavioral and psychological symptoms of dementia.

47 older adults with dementia.

Motor agitation was rated on a shift-by-shift basis by clinical staff using the Pittsburgh Agitation Scale. Information about movement in the unit was extracted from a real-time clinical safety system. Models were developed to predict the presence of motor agitation using combinations of clinical and location-based features.

Our findings indicate that using location-based trajectory metrics with clinical measures significantly improves the predictability of motor agitation beyond individual baselines. Our best model achieves an area under the curve of the receiver operating characteristic of 0.81 in classifying presence of motor agitation. Explainability analysis identified that trajectory-based features were the most important features for correctly classifying motor agitation.

Location data can be used to distinguish clinically important motor agitation from normal motor activities on a shift-by-shift basis. Behavioral analytics derived from location data are informative for clinical assessment of neuropsychiatric symptoms in dementia.