Falls Prevention

This study, a collaboration between CASALA, University College Dublin and the Health Service Executive proposes to observe how a volunteer group of older people identified as being at mild to moderate risk of falling move and behave over a 5-day period in the Great Northern Haven smart apartments.

This study, a collaboration between CASALA, University College Dublin and the Health Service Executive proposes to observe how a volunteer group of older people identified as being at mild to moderate risk of falling move and behave over a 5-day period in the Great Northern Haven smart apartments. The GNH demonstration apartment will be used as a transitional setting from which researchers can gather additional behavioral and technological data, which should assist in the care and management of older adults at risk of falls.

Falls in older people are a serious cause of injury, hospitalisation and loss of independence. The reasons for falling are diverse and include: problems with stability, mobility, vision and blood pressure control. Current risk assessments for falling are based on tests of such factors in the clinic, but little is objectively known on how these interact over a longer period of time and within a home environment.

This study, a collaboration between CASALA, University College Dublin and the Health Service Executive proposes to observe how a volunteer group of older people identified as being at mild to moderate risk of falling move and behave over a 5-day period in the Great Northern Haven smart apartments. The GNH demonstration apartment will be used as a transitional setting from which researchers can gather additional behavioural and technological data, which should assist in the care and management of older adults at risk of falls.

With advances in sensor technology, opportunities are now emerging to embed unobtrusive monitoring of human function, performance and behaviour into the home environment. It is possible to evaluate biometric data such as physiological functions, health indicators and movement continuously through wireless transmission to remote monitoring hubs and so the use of these novel technologies presents an opportunity for falls risk identification in the real world environment. With the anticipated growth in the older population and the concomitant increase in the at-risk groups, the community level identification and home based assessment of older people to determine falls risk with a view to prevention must be a priority to prevent an exponential growth in falls related morbidity and mortality. What is not yet known however is how the data derived from home based monitoring relates to the current ‘gold standard’ clinical risk assessment criteria, nor how the addition of home monitoring can enhance the clinical and psychosocial outcomes associated with current care pathways. This study proposes to integrate clinical and technological expertise to determine if an improved paradigm of care can be developed to prevent falls and enhance the wellbeing of older people.

The aims of this study are to:

Determine the efficacy of a combined clinical assessment plus home monitoring assessment ‘Enhanced Monitoring’ in optimising the care pathway and treatment outcomes of individuals at risk of falling, by comparison with the routine clinical pathway. Examine the criterion validity of ambient environmental sensor monitoring in measuring characteristics indicative of falling through correlation with a battery of clinical falls indicators. xamine the construct validity of ambient sensors through assessment of their ability to discriminate between groups of different levels of risk and later to examine the predictive and evaluative potential of these measures.