A longitudinal observational study of home-based conversations for detecting early dementia: protocol for the CUBOId TV task

Author:

Kumpik Daniel PaulORCID,Santos-Rodriguez Raul,Selwood James,Coulthard Elizabeth,Twomey Niall,Craddock Ian,Ben-Shlomo Yoav

Abstract

IntroductionLimitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the ‘TV task’, designed to track changes in ecologically valid conversations with disease progression.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 8–25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone.Ethics and disseminationCUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.

Funder

EPSRC SPHERE Interdisciplinary Research Collaboration

EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol

MRC Momentum award

UKRI Turing AI Fellowship

BRACE

Publisher

BMJ

Subject

General Medicine

Reference67 articles.

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1. Research hotspots and trends of monitoring technologies in dementia: A Scientometric analysis;2023 4th International Conference on Machine Learning and Computer Application;2023-10-27

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