Wearable Computing Technology for Assessment of Cognitive Functioning of Bipolar Patients and Healthy Controls

Abstract

Mobile cognitive tests have been emerged to first, bring the assessments outside the clinics and second, frequently measure individuals' cognitive performance in their free-living environment. Patients with Bipolar Disorder (BD) suffer from cognitive impairments and poor sleep quality negatively affects their cognitive performance. Wearables are capable of unobtrusively collecting multivariate data including activity and sleep features. In this study, we analyzed daily attention, working memory, and executive functions of patients with BD and healthy controls by using a smartwatch-based tool called UbiCAT to 1) investigate its concurrent validity and feasibility, 2) identify digital phenotypes of mental health using cognitive and mobile sensor data, and 3) classify patients and healthy controls on the basis of their daily cognitive and mobile data. Our findings demonstrated that UbiCAT is feasible with valid measures for in-the-wild cognitive assessments. The analysis showed that the patients responded more slowly during the attention task than the healthy controls, which could indicate a lower alertness of this group. Furthermore, sleep duration correlated positively with participants' working memory performance the next day. Statistical analysis showed that features including cognitive measures of attention and executive functions, sleep duration, time in bed, awakening frequency and duration, and step counts are the digital phenotypes of mental health diagnosis. Supervised learning models was used to classify individuals' mental health diagnosis using their daily observations. Overall, we achieved accuracy of approximately 74% using K-Nearest Neighbour (KNN) method.