Population aging is a global experience. Currently healthy-span is not keeping pace with life-span due to the combined phenomena of ageing, frailty and chronic disease. Disability is common in later life, and is a major risk factor for dependency and one of the strongest predictors of institutionalisation. Effective interventions that target key risk factors and at-risk populations are essential to control the negative aspects of ageing. TILDA aims to improve population health by providing insights into potentially new modifiable risk factors that can be used in screening tests for clinical and public health practice, and new treatments. TILDA measures a wide number of novel objective autonomic function and vascular health measures (i.e. Heart Rate Variability, Orthostatic Intolerance, Pulse Wave Velocity, Orthostatic Blood Pressure Responses, Accelerometry, Near Infrared Spectroscopy) which will help provide a better understanding of the mechanisms underlying the association between autonomic function and disease processes and provide an opportunity to study disability associated with global cardiovascular risk.We will use Big Data and Machine Learning approaches to investigate, integrate and identify predictors of decline and protectors for physical function in a nationally representative population sample.Not only will relationships be determined between individual variables, but we can also assess how predictive they are in the population. This work may help us untangle the role of cardiovascular disease across all autonomic function measures in disability.We will carry out a feasibility study to determine if novel biomarkers identified in a research setting can be translated into clinical practice. These biomarkers can then serve as outcome measures for guiding prediction analyses to detect at-risk population early, and identify modifiable physical risk factors such as sleep and exercise, that can be used in screening tests for clinical and public health practice, and new treatments.
