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Prognostic value of movement quality parameters derived from wrist sensor data in a large population

Prognostic value of movement quality parameters derived from wrist sensor data in a large population

Principal Investigator: Mr Long Yu Chan
Approved Research ID: 56109
Approval date: March 2nd 2020

Lay summary

Falls, dementia, depression and Parkinson's disease are common in older age and often lead to long term disability, premature institutionalization and significant socio-economic burden. However, timely diagnosis and intervention are difficult and often relate to some older people's inability to report their signs and symptoms. Previous studies have demonstrated that clinical mobility tests, such as walking speed and sit-to-stand time, can predict the onset of geriatric conditions. This study aims to investigate whether more precise predictions can be achieved through using wearable sensor data. Compared to clinical tests, wearable sensors record usual performances in everyday living, collect more data on movement quality and document a longer exposures. UK Biobank has seven-day wrist-worn accelerometry data from around one hundred thousand participants with over three-years follow up on health outcomes. Through further processing and analysis of data, we aim to recognize daily activities, derive movement quality parameters, and predict onset of adverse events. These prediction models will form the basis for large scale screening programs, which will facilitate prevention and early intervention of age-related syndromes and diseases.