Slow processing speed predicts falls in older adults with a falls history: 1-year prospective cohort study Academic Article uri icon

abstract

  • A previous fall is a strong predictor of future falls. Recent epidemiologic data suggest that deficits in processing speed predict future injurious falls. Our primary objective was to determine a parsimonious predictive model of future falls among older adults who experienced ≥1 fall in the past 12 months based on the following categories: counts of (1) total, (2) indoor, (3) outdoor or (4) non-injurious falls; (5) one mild or severe injury fall (yes vs no); (6) an injurious instead of a non-injurious fall; and (7) an outdoor instead of an indoor fall.12-month prospective cohort study.Vancouver Falls Prevention Clinic, Canada (www.fallsclinic.ca).Two-hundred and eighty-eight community-dwelling older adults aged ≥70 years with a history of ≥1 fall resulting in medical attention in the previous 12 months.We employed principal component analysis to reduce the baseline predictor variables to a smaller set of five factors (i.e., processing speed, working memory, emotional functioning, physical functioning and body composition/fall risk profile). Second, we used the extracted five factors as predictors in regression models predicting the incidence of falls over a 12-month prospective observation period. We conducted regression analyses for the seven falls-related categories (defined above).Among older adults with a falls history, processing speed was the most consistent predictor of future falls; poorer processing speed predicted a greater number of total, indoor, outdoor, and non-injurious falls, and a greater likelihood of experiencing at least one mild or severe injurious fall (all P values < .01).Poorer performance on the processing speed factor, a trainable factor, was independently associated with the most costly type of falls-injurious falls.

authors

  • Davis, Jennifer C
  • Best, John R
  • Khan, Karim M
  • Dian, Larry
  • Lord, Stephen
  • Delbaere, Kim
  • Hsu, Chun Liang
  • Cheung, Winnie
  • Chan, Wency
  • Liu-Ambrose, Teresa

publication date

  • 2017