title: A statistical approach to discriminate between non-fallers, rare fallers and frequent fallers in older adults based on posturographic data.
abstract: BACKGROUND
  Identification of future non-fallers, infrequent and frequent fallers among older people would permit focusing the delivery of prevention programs on selected individuals. Posturographic parameters have been proven to differentiate between non-fallers and frequent fallers, but not between the first group and infrequent fallers.
  
  
  METHODS
  In this study, postural stability with eyes open and closed on both a firm and a compliant surface and while performing a cognitive task was assessed in a consecutive sample of 130 cognitively able elderly, mean age 77(7)years, categorized as non-fallers (N=67), infrequent fallers (one/two falls, N=45) and frequent fallers (more than two falls, N=18) according to their last year fall history. Principal Component Analysis was used to select the most significant features from a set of 17posturographic parameters. Next, variables derived from principal component analysis were used to test, in each task, group differences between the three groups.
  
  
  RESULTS
  One parameter based on a combination of a set of Centre of Pressure anterior-posterior variables obtained from the eyes-open on a compliant surface task was statistically different among all groups, thus distinguishing infrequent fallers from both non-fallers (P<0.05) and frequent fallers (P<0.05).
  
  
  CONCLUSIONS
  For the first time, a method based on posturographic data to retrospectively discriminate infrequent fallers was obtained. The joint use of both the eyes-open on a compliant surface condition and this new parameter could be used, in a future study, to improve the performance of protocols and to verify the ability of this method to identify new-fallers in elderly without cognitive impairment.
authors.affiliation: Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. Electronic address: e.maranesi@univpm.it.
authors.forename: E
authors.initials: E
authors.lastname: Maranesi
authors.affiliation: LAM - Motion Analysis Laboratory - AUSL of Reggio Emilia, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Merlo
authors.affiliation: Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Fioretti
authors.affiliation: Servizio Sottocenerino di Geriatria, Regional Hospitals of Lugano and Mendrisio, Switzerland.
authors.forename: D D
authors.initials: DD
authors.lastname: Zemp
authors.affiliation: LAM - Motion Analysis Laboratory - AUSL of Reggio Emilia, Italy.
authors.forename: I
authors.initials: I
authors.lastname: Campanini
authors.affiliation: Servizio Sottocenerino di Geriatria, Regional Hospitals of Lugano and Mendrisio, Switzerland.
authors.forename: P
authors.initials: P
authors.lastname: Quadri
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000369:Aged, 80 and over; D003071:Cognition / Q000502:physiology; D005123:Eye; D005260:Female; D015577:Geriatric Assessment / Q000379:methods; D006801:Humans; D008297:Male; D004856:Postural Balance; D011312:Pressure; D025341:Principal Component Analysis; D012189:Retrospective Studies; D018570:Risk Assessment; D014785:Vision, Ocular
keywords: Principal component analysis; center of pressure; elderly; fall risk assessment; posture