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The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer’s disease and estimate 5-year risks of cognitive decline and dementia

Citation

Alty, JE and Bai, Q and Li, R and Lawler, K and St George, RJ and Hill, E and Bindoff, A and Garg, S and Wang, X and Huang, G and Zhang, K and Rudd, KD and Bartlett, L and Goldberg, LR and Collins, JM and Hinder, MR and Naismith, SL and Hogg, DC and King, AE and Vickers, JC, The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer's disease and estimate 5-year risks of cognitive decline and dementia, BMC Neurology, 22 Article 266. ISSN 1471-2377 (2022) [Refereed Article]

DOI: doi:10.1186/s12883-022-02772-5

Abstract

Background

The worldwide prevalence of dementia is rapidly rising. Alzheimer’s disease (AD), accounts for 70% of cases and has a 10–20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify individuals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust ‘self-testing’ data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia.

Methods

Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD.

Discussion

This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen individuals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials.

Item Details

Item Type:Refereed Article
Keywords:Dementia, ageing, artificial intelligence, computer vision, screening, movement analysis, kinematics, finger tapping, dual-task, Alzheimers', dementia, pre-clinical, biomarker, movement analysis
Research Division:Biomedical and Clinical Sciences
Research Group:Clinical sciences
Research Field:Geriatrics and gerontology
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Alty, JE (Associate Professor Jane Alty)
UTAS Author:Bai, Q (Dr Quan Bai)
UTAS Author:Li, R (Mr Renjie Li)
UTAS Author:Lawler, K (Dr Katherine Lawler)
UTAS Author:St George, RJ (Dr Rebecca St George)
UTAS Author:Hill, E (Dr Edward Hill)
UTAS Author:Bindoff, A (Mr Aidan Bindoff)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Wang, X (Miss Xinyi Wang)
UTAS Author:Huang, G (Mr Guan Huang)
UTAS Author:Zhang, K (Miss Kaining Zhang)
UTAS Author:Rudd, KD (Ms Kaylee Rudd)
UTAS Author:Bartlett, L (Mrs Larissa Bartlett)
UTAS Author:Goldberg, LR (Associate Professor Lyn Goldberg)
UTAS Author:Collins, JM (Dr Jessica Collins)
UTAS Author:Hinder, MR (Associate Professor Mark Hinder)
UTAS Author:Naismith, SL (Professor Sharon Naismith)
UTAS Author:King, AE (Professor Anna King)
UTAS Author:Vickers, JC (Professor James Vickers)
ID Code:151120
Year Published:2022
Funding Support:National Health and Medical Research Council (2004051)
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2022-07-19
Last Modified:2022-08-02
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