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DC Field | Value | Language |
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dc.contributor.advisor | Beauchamp, Marla K | - |
dc.contributor.author | Saunders, Stephanie M | - |
dc.date.accessioned | 2025-08-26T18:41:39Z | - |
dc.date.available | 2025-08-26T18:41:39Z | - |
dc.date.issued | 2025 | - |
dc.identifier.uri | http://hdl.handle.net/11375/32247 | - |
dc.description.abstract | Background: Falls in older adulthood can lead to serious consequences, including disability and death. Although falls are preventable, current fall risk assessments lack evidence specific to community-dwelling older adults. The aim of this thesis, comprising four studies, was to advance the understanding of fall risk assessment in community-dwelling older adults. Methods: The first study was an umbrella review synthesizing prospective evidence on fall risk factors among community-dwelling older adults. Second, we developed standardized protocols for virtual balance and mobility tests and evaluated their feasibility and psychometric properties. Third, we described baseline data from a prospective cohort study (the INITIATE study) and examined associations between performance on 7 balance and mobility tests and 12-month fall history. Last, we evaluated the validity of these tests for predicting falls over 12-months and examined their utility within the World Falls Guidelines (WFG) algorithm for stratifying older adults into low, intermediate, and high-risk groups. Results: The umbrella review identified 29 unique risk factors across 57 systematic reviews, with mobility-related measures most frequently assessed; where clinical tests of balance, physical function, gait, and dual task ability were most consistently associated with falls. Frailty and chronic conditions were also consistently associated with falls. In study 2, virtual test administration was feasible and the tests demonstrated good to excellent psychometric properties. Study 3 described the baseline sample and found significant differences in test performance between individuals with and without a history of falls. Prospective analyses demonstrated poor predictive ability (all AUCs < 0.70), and incorporating these tests into the WFG algorithm did not improve fall risk stratification. Discussion: Balance and mobility tests offer limited value for fall prediction. Adding these tests to the WFG algorithm provides little additional utility. Future research is needed to identify the most effective strategies for fall risk screening in community-dwelling older adults. | en_US |
dc.language.iso | en | en_US |
dc.subject | falls | en_US |
dc.subject | older adults | en_US |
dc.subject | community dwelling | en_US |
dc.subject | independence | en_US |
dc.title | FALL RISK ASSESSMENT & PREDICTION IN COMMUNITY-DWELLING OLDER ADULTS | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Rehabilitation Science | en_US |
dc.description.degreetype | Dissertation | en_US |
dc.description.degree | Doctor of Rehabilitation (RhD) | en_US |
dc.description.layabstract | Falls are a serious problem for older adults and can lead to injury, disability, or even death. This work focused on identifying community-dwelling older adults at greatest risk of falling. Our work focused on synthesizing the evidence for fall risk, assessing the performance of tests designed to detect fall risk, and evaluating how these tests are used in clinical practice. The first study reviewed past research and identified 29 risk factors for falls, mostly related to mobility and frailty. Next, we examined whether balance and mobility tests could be done online. These tests worked well and were easy to use. In person, we found that those who had previous falls performed worse on the tests. However, the tests did not predict future falls. Adding them to a current fall risk tool did not improve their ability to identify who might fall. This suggests that healthcare workers may need better tools to detect fall risk early, and that more research is needed to find the most effective ways to prevent falls. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Saunders_Stephanie_M_202508_PhD.pdf | 3.32 MB | Adobe PDF | View/Open |
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