title: Applying different mathematical variability methods to identify older fallers and non-fallers using gait variability data.
abstract: BACKGROUND
  The clinical assessment of gait variability may be a particularly powerful tool in the screening of older adults at risk of falling. Measurement of gait variability is important in the assessment of fall risk, but the variability metrics used to evaluate gait timing have not yet been adequately studied.
  
  
  OBJECTIVE
  The aims of this study were (1) to identify the best mathematical method of gait variability analysis to discriminate older fallers and non-fallers and (2) to identify the best temporal, kinematic parameter of gait to discriminate between older fallers and non-fallers.
  
  
  METHODS
  Thirty-five physically active volunteers participated in this study including 16 older women fallers (69.6 ± 8.1 years) and 19 older women non-fallers (66.1 ± 6.2 years). Volunteers were instructed to walk for 3 min on the treadmill to record the temporal kinematic gait parameters including stance time, swing time and stride time by four footswitches sensors placed under the volunteers' feet. Data analysis used 40 consecutive gait cycles. Six statistical methods were used to determine the variability of the stance time, swing time and stride time. These included: (1) standard deviation of all the time intervals; (2) standard deviation of the means of these intervals taken every five strides; (3) mean of the standard deviations of the intervals determined every five strides; (4) root-mean-square of the differences between intervals; (5) coefficient of variation calculated as the standard deviation of the intervals divided by the mean of the intervals; and (6) a geometric method calculated based on the construction of a histogram of the intervals.
  
  
  RESULTS
  The standard deviation of 40 consecutive gait cycles was the most sensitive (100 %) and specificity (100 %) parameter to discriminate older fallers and non-fallers.
  
  
  CONCLUSIONS
  The standard deviation of stance time is the kinematic gait variability parameter that demonstrated the best ability to discriminate older fallers from non-fallers.
  RBR-6rytw2.
authors.affiliation: Department of Physical Therapy and Occupational Therapy, São Paulo State University, UNESP, Marília, Brazil. nisermarques@yahoo.com.br.|Departamento de Fisioterapia e Terapia Ocupacional, UNESP, Avenida Hygino Muzzi Filho, 737, Marília, SP, 14325-000, Brazil. nisermarques@yahoo.com.br.
authors.forename: Nise Ribeiro
authors.initials: NR
authors.lastname: Marques
authors.affiliation: Department of Physical Education and Physical Therapy, Uberlândia Federal University, UFU, Uberlândia, Brazil.
authors.forename: Camilla Zamfolini
authors.initials: CZ
authors.lastname: Hallal
authors.affiliation: Department of Physical Therapy and Occupational Therapy, São Paulo State University, UNESP, Marília, Brazil.
authors.forename: Deborah Hebling
authors.initials: DH
authors.lastname: Spinoso
authors.affiliation: Department of Physical Therapy and Occupational Therapy, São Paulo State University, UNESP, Marília, Brazil.
authors.forename: Mary Hellen
authors.initials: MH
authors.lastname: Morcelli
authors.affiliation: Faculty of Medicine of Marilia, FAMEMA, Marília, Brazil.
authors.forename: Luciano Fernandes
authors.initials: LF
authors.lastname: Crozara
authors.affiliation: Department of Physical Education, São Paulo State University, UNESP, Rio Claro, Brazil.
authors.forename: Mauro
authors.initials: M
authors.lastname: Gonçalves
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000704:Analysis of Variance; D001696:Biomechanical Phenomena; D001938:Brazil; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D015999:Multivariate Analysis; D012307:Risk Factors; D012680:Sensitivity and Specificity
keywords: Aging; Falls risk; Kinematics