Falls are a major health problem in the elderly population and prevention is needed. One in three (3.4 million) individuals over 60 years of age experience at least one fall every year. Falls are more common amongst the elderly living in care. Falls cause long-term consequences such as hip fractures, injuries, reduced mobility and activity levels, destroys confidence, increases isolation and reduces independence. It is estimated to cost the NHS more than £2.3 billion every year to treat falls.
There is clear evidence that appropriately designed interventions programme can prevent falls in older adults. However, to provide interventions, the first step is to identify elderly adults who are at high risk of falls. Traditionally, assessments often involve questionnaires which are subjective and the results are limited. To overcome these limitations more advanced objective assessments using lab-based measurement and body-worn sensors have been used. However, current falls prediction can only predict if a person might fall in 3-12 months time.
This study aims to investigate the ability to predict when, over a period of days or weeks, an individual is likely to fall by continuously monitoring their physical behaviour using body-worn sensors.
Data will be collected from residents in care homes. Validated activity monitors worn on the person and attached to their walking aid will be used to monitor and characterise free-living physical behaviours. Features in the patterns of free-living behaviour will be used in building prediction models.
Improved falls prediction will greatly enhance the ability to prevent falls, leading to significantly improved safety and enhanced independent living.
PhD start date: July 2018, September 2018, January 2019 or May 2019