Global dementia prevalence is rising. Alzheimer's disease (AD), the most common cause, has devastating effects on people's quality of life. AD has a preclinical (pre-AD) period of 10-20 years when brain pathology silently progresses before any cognitive symptoms appear. Current tests for pre-AD are invasive, costly and unsuitable for screening at population level. Similar to screening for pre-diabetes and carcinoma in situ, it is important to detect AD at the preclinical stage in order to offer early interventions before the pathology progresses to the irrerversible degenerative stage. In the study, research will develop a new scalable test (TAS Test) by combining two innovative ideas: hand-movement tests to detect pre-AD \>10 years before cognitive symptoms begin; and computer vision so people can self-test online using home computers. This unique approach builds on recent discoveries that hand-movement patterns change in pre-AD. The research team will use exquisitely precise computer vision methods to automatically analyse movement data from thousands of participants, and combine this with machine learning of overall motor-cognitive performance. The project team has access to 3 well-phenotyped cohorts, \>10,000 existing participants and a cutting-edge assay for a blood AD biomarker, ptau181. The research team will develop a TAS Test algorithm to classify hand-movement and cognitive test data for pre-AD risk (p-taua181 levels) and determine TAS Test's precision to prospectively predict 5-year risks of cognitive decline and AD.