title: Elderly fall risk prediction using static posturography.
abstract: Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older people at risk of multiple falls.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
authors.forename: Jennifer
authors.identifier: http://orcid.org/0000-0001-6055-9357
authors.initials: J
authors.lastname: Howcroft
authors.affiliation: Centre for Rehabilitation Research and Development, Ottawa Hospital Research Institute, Ottawa, Canada.|University of Ottawa, Faculty of Medicine, Ottawa, Canada.
authors.forename: Edward D
authors.identifier: http://orcid.org/0000-0003-4693-2623
authors.initials: ED
authors.lastname: Lemaire
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Kofman
authors.affiliation: Department of Kinesiology, University of Waterloo, Waterloo, Canada.
authors.forename: William E
authors.initials: WE
authors.lastname: McIlroy
mesh_terms: D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D003943:Diagnostic Techniques, Neurological / Q000295:instrumentation; D016002:Discriminant Analysis; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D004856:Postural Balance / Q000502:physiology; D012008:Recurrence; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D014785:Vision, Ocular