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An investigation of clinical and sensor-based fall-risk assessment in community-dwelling older adults

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dc.contributor.advisor Clifford, Amanda M.
dc.contributor.advisor van de Ven, Pepijn
dc.contributor.advisor Nelson, John Power, Valerie 2015-01-22T14:17:56Z 2015-01-22T14:17:56Z 2014
dc.description peer-reviewed en_US
dc.description.abstract Accurate, efficient methods of assessing fall-risk are required to identify at-risk community-dwelling older adults and implement timely falls prevention interventions. Sensor-based fall-risk assessment (SBFRA) methods have been developed to objectively assess and quantify fall-risk by analysing functional task performance, but research exploring their clinical applications is lacking. The current research aimed to investigate if SBFRA could perform clinically-meaningful fall-risk assessment in community-dwelling older adults (i.e. could it accurately classify older adults according to their level of fall-risk), and to explore its use among high-risk older adults participating in a community-based falls prevention intervention. Following thorough examination of current evidence and issues of feasibility, clinical and SBFRA was carried out among High-Risk (n=38) and Low-Risk (n=33) groups of older adults in the community, the High-Risk group being participants in a community-based falls prevention intervention. An array of sensor-derived variables extracted from static and dynamic task performances distinguished between High-Risk and Low-Risk groups; among them, novel sensor-derived variables that quantified standing balance and TUG performance strategy and quality, as well as simple sensor-derived gait variables e.g. cadence, mean step time. Some improvements in sensor-based indicators of standing balance performance were observed following intervention. Simple sensor-derived gait variables classified High-Risk participants more accurately than clinical tools e.g. cadence (sensitivity/specificity: 90.9%) and mean step time (sensitivity: 87.9%, specificity: 90.9%) versus Berg Balance Scale (sensitivity: 86.8%, specificity: 81.8%). Classification tree models comprised of 1) clinical, 2) sensor-based variables and 3) both combined, exhibited excellent fall-risk classification properties, with 95.8% accuracy for all models. This research confirms that SBFRA can be used to perform clinically-meaningful fall-risk assessment among community-dwelling older adults. With further research to develop specific evidence-based and user-friendly methods, SBFRA could be used to augment clinical fall-risk assessments, thereby assisting healthcare providers with clinical reasoning and outcome measurement in community falls prevention. en_US
dc.language.iso eng en_US
dc.publisher University of Limerick en_US
dc.subject fall-risk assessment en_US
dc.subject older adults en_US
dc.subject high risk en_US
dc.subject community en_US
dc.title An investigation of clinical and sensor-based fall-risk assessment in community-dwelling older adults en_US
dc.type info:eu-repo/semantics/doctoralThesis en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.supercollection ul_theses_dissertations en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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