The Use of Home-Based Behaviours for Detecting Early Dementia: Protocol for the CUBOId Study

IntroductionThere is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. The ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) project specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimers disease (AD) in the early stages of the disease. Bespoke behavioural models will be introduced and deployed on a novel dataset of longitudinal sensor data from persons with MCI and AD to analyse key symptoms of the disease. Methods and analysisCUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 2-22 months. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Machine learning will be used to generate fused multimodal sensor data for optimisation of diagnostic and predictive performance from localisation, activity, and speech together. Ethics and disseminationCUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by the University of Bristol. It is also supported by the National Institute for Health Research (NIHR) Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.