title: A novel approach to falls classification in Parkinson's disease: development of the Fall-Related Activity Classification (FRAC).
abstract: BACKGROUND
  Falls are a major problem for people with Parkinson's disease (PD). Despite years of focused research knowledge of falls aetiology is poor. This may be partly due to classification approaches which conventionally report fall frequency. This nosology is blunt, and does not take into account causality or the circumstances in which the fall occurred. For example, it is likely that people who fall from a postural transition are phenotypically different to those who fall during high level activities. Recent evidence supports the use of a novel falls classification based on fall related activity, however its clinimetric properties have not yet been tested.
  
  
  OBJECTIVE
  This study describes further development of the Fall-Related Activity Classification (FRAC) and reports on its inter-rater reliability (IRR).
  
  
  METHODS
  Descriptors of the FRAC were refined through an iterative process with a multidisciplinary team. Three categories based on the activity preceding the fall were identified. PD fallers were categorised as: (1) advanced (2) combined or (3) transitional. Fifty-five fall scenarios were rated by 23 raters using a standardised process. Raters comprised 3 clinical subgroups: (1) physiotherapists, (2) physicians, (3) non-medical researchers. IRR analysis was performed using weighted kappa coefficients and included sub group analysis based on clinical speciality.
  
  
  RESULTS
  Excellent agreement was reached for all clinicians, κ=0.807 (95% CI 0.732 to 0.870). Clinical subgroups performed similarly well (range of κ=0.780 to 0.822).
  
  
  CONCLUSIONS
  The FRAC can be reliably used to classify falls. This may discriminate between phenotypically different fallers and subsequently strengthen falls predictors in future studies.
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Aging, Newcastle upon Tyne, UK. Electronic address: annie.ross@nuth.nhs.uk.
authors.forename: Annie
authors.initials: A
authors.lastname: Ross
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Aging, Newcastle upon Tyne, UK. Electronic address: alison.yarnall@newcastle.ac.uk.
authors.forename: Alison J
authors.initials: AJ
authors.lastname: Yarnall
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Aging, Newcastle upon Tyne, UK. Electronic address: lynn.rochester@newcastle.ac.uk.
authors.forename: Lynn
authors.initials: L
authors.lastname: Rochester
authors.affiliation: Institute of Neuroscience, Newcastle University Institute for Aging, Newcastle upon Tyne, UK. Electronic address: sue.lord@ncl.ac.uk.
authors.forename: Sue
authors.initials: S
authors.lastname: Lord
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000203:Activities of Daily Living; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D015588:Observer Variation; D010300:Parkinson Disease / Q000503:physiopathology*; D004856:Postural Balance / Q000502:physiology; D015203:Reproducibility of Results; D012307:Risk Factors; D012720:Severity of Illness Index
keywords: Classification; Falls; Reliability