Power_2014_predicting.pdf (570.89 kB)
Predicting falls in community-dwelling older adults: A systematic review of task performance-based assessment tools
journal contributionposted on 2022-12-12, 09:43 authored by Valerie Power, PEPIJN VAN DE VENPEPIJN VAN DE VEN, JOHN NELSONJOHN NELSON, AMANDA CLIFFORDAMANDA CLIFFORD
INTRODUCTION: Falls among community-dwelling older adults are a common yet often preventable occurrence. Clinicians frequently use task-based assessment tools to evaluate clients' balance and mobility with the aim of predicting falls and providing targeted fall prevention interventions, but no consensus exists on the optimum tool(s) to use for this purpose. This review aims to identify the task-based assessment tools that can best predict falls among community-dwelling older adults. METHODS: Online databases Academic Search Complete, AMED, Biomedical Reference Collection: Expanded, CINAHL Plus, MEDLINE, General Science, and SPORTDiscus were searched from 1983 to 2013 to identify prospective studies assessing the performance of specific tasks in order to predict falls. Following screening, the methodological quality of studies included for review was appraised using a checklist based on the Critical Appraisal Skills Programme tool for cohort studies . RESULTS: Thirty-seven studies, dating from 1996 to 2013 and largely of high methodological quality, were included in this review. A range of task performance-based assessment tools suitable for use in both clinical and laboratory settings were identified. CONCLUSIONS: Strong evidence in favour of using the Timed Up-and-Go test, Five Times Sit-to-Stand test and assessments of gait speed to predict falls among this population in clinical settings was found, along with weaker evidence for tests of standing balance and reaching task performance. Laboratory-based assessments of postural sway and gait variability were also found to predict falls. Incorporating the recommended assessment tools into comprehensive assessments of community-dwelling older clients can lead to improved falls prediction by clinicians.