title: Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls.
abstract: Falls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest.
  
  
  
  This study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model.
  
  
  
  We found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults.
  
  
  
  The interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.
authors.affiliation: Research group Lifestyle and Health, Utrecht University of Applied Sciences, Bolognalaan 101, Utrecht, 3584 JW, The Netherlands. Michiel.punt@hu.nl.
authors.forename: Michiel
authors.initials: M
authors.lastname: Punt
authors.affiliation: Move Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|Department of Orthopedics, first affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China.
authors.forename: Sjoerd M
authors.initials: SM
authors.lastname: Bruijn
authors.affiliation: Move Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
authors.forename: Kimberley S
authors.initials: KS
authors.lastname: van Schooten
authors.affiliation: Move Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
authors.forename: Mirjam
authors.initials: M
authors.lastname: Pijnappels
authors.affiliation: Revant Rehabilitation Center Breda, Breda, The Netherlands.
authors.forename: Ingrid G
authors.initials: IG
authors.lastname: van de Port
authors.affiliation: Research group Lifestyle and Health, Utrecht University of Applied Sciences, Bolognalaan 101, Utrecht, 3584 JW, The Netherlands.
authors.forename: Harriet
authors.initials: H
authors.lastname: Wittink
authors.affiliation: Move Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
authors.forename: Jaap H
authors.initials: JH
authors.lastname: van Dieën
mesh_terms: D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D020521:Stroke / Q000150:complications / Q000503:physiopathology; D017741:Survivors
keywords: Accelerometry; Accidental falls; Fall risk; Gait quality; Gait quantity; Stability