Identifying pre-agitation biometric signature in patients with dementia: A feasibility study

Abstract

Background:

Agitation and aggression (AA) occur frequently in patients with dementia (PwD), and cause distress to PwD and caregivers. This study will investigate whether physiological parameters, such as actigraphy, heart rate variability, temperature, and electrodermal activity, measured via wearable sensors, correlate with AA in PwD. It will also explore whether these parameters could be compiled to create a pre-agitation biometric marker capable of predicting episodes of AA in PwD.

Methods:

This study will take place at Ontario Shores Centre for Mental Health Sciences. Thirty inpatient participants who are inpatients, males, and females, aged 60 or older, with clinically significant AA, and diagnosis of Major Neurocognitive Disorder will be recruited. Participants will wear the device for 48 to 72 hours on three occasions during an 8-week study period. Participant demographics and clinical measures used to assess behavior will be collected at specific time intervals during the study period.

Ceiling mounted cameras and clinical data are collected to annotate episodes of AA, which will allow identification of peripheral physiological markers “signature” unique to the patient

Results:

the algorithm connecting wearable devices, cloud and cameras was tested on healthy volunteers and demonstrated feasibility and reliability. The feasibility of implementation in PwD has been demonstrated in our sample of PwD previously in a sample of 6 participants. Feasibility in this larger sample will be assessed. Correlation analysis between physiological measures, camera capture of agitation onset and clinical measures will be calculated to identify agitation events and pre-agitation triggers. Various machine learning and features extraction/exploration techniques will be used to test whether physiological measures can detect exact time of agitation and predict pre-agitation triggers. This study will provide a reasonable estimation of sample size needed to detect a meaningful effect size, which will be determined from the prediction model.

Conclusion:

Early detection of AA in PwD will allow caregivers to offer timely and personalized interventions which will help avoid crises and critical incidents and improve quality of life in PwD and their caregivers.