title: Evaluation of falls risk in community-dwelling older adults using body-worn sensors.
abstract: BACKGROUND
  Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. This study aimed to determine if a method based on body-worn sensor data can prospectively predict falls in community-dwelling older adults, and to compare its falls prediction performance to two standard methods on the same data set.
  
  
  METHODS
  Data were acquired using body-worn sensors, mounted on the left and right shanks, from 226 community-dwelling older adults (mean age 71.5 ± 6.7 years, 164 female) to quantify gait and lower limb movement while performing the 'Timed Up and Go' (TUG) test in a geriatric research clinic. Participants were contacted by telephone 2 years following their initial assessment to determine if they had fallen. These outcome data were used to create statistical models to predict falls.
  
  
  RESULTS
  Results obtained through cross-validation yielded a mean classification accuracy of 79.69% (mean 95% CI: 77.09-82.34) in prospectively identifying participants that fell during the follow-up period. Results were significantly (p < 0.0001) more accurate than those obtained for falls risk estimation using two standard measures of falls risk (manually timed TUG and the Berg balance score, which yielded mean classification accuracies of 59.43% (95% CI: 58.07-60.84) and 64.30% (95% CI: 62.56-66.09), respectively).
  
  
  CONCLUSIONS
  Results suggest that the quantification of movement during the TUG test using body-worn sensors could lead to a robust method for assessing future falls risk.
authors.affiliation: Technology Research for Independent Living, Leixlip, Ireland. barry.r.greene@intel.com
authors.forename: Barry R
authors.initials: BR
authors.lastname: Greene
authors.forename: Emer P
authors.initials: EP
authors.lastname: Doheny
authors.forename: Cathal
authors.initials: C
authors.lastname: Walsh
authors.forename: Clodagh
authors.initials: C
authors.lastname: Cunningham
authors.forename: Lisa
authors.initials: L
authors.lastname: Crosby
authors.forename: Rose A
authors.initials: RA
authors.lastname: Kenny
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D000465:Algorithms; D015331:Cohort Studies; D005260:Female; D005684:Gait / Q000502:physiology; D015577:Geriatric Assessment / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D004856:Postural Balance; D011446:Prospective Studies; D012111:Residence Characteristics; D012307:Risk Factors