Jennifer C Davis1,2,3, John R Best1,2,3, Karim M Khan2,4, Larry Dian5, Stephen Lord6, Kim Delbaere6, Chun Liang Hsu1,2,3, Winnie Cheung1,2,3, Wency Chan1,2,3, Teresa Liu-Ambrose1,2,3. 1. Aging, Mobility, and Cognitive Neuroscience Lab, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada. 2. Center for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada. 3. Djavad Mowafaghian Centre for Brain Health, Vancouver, British Columbia, Canada. 4. Department of Family Practice, University of British Columbia, Vancouver, British Columbia, Canada. 5. Division of Geriatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada. 6. Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, New South Wales, Australia.
Abstract
BACKGROUND/ OBJECTIVES: 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. DESIGN: 12-month prospective cohort study. SETTING: Vancouver Falls Prevention Clinic, Canada (www.fallsclinic.ca). PARTICIPANTS: 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. MEASUREMENTS: 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). RESULTS: 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). CONCLUSION: Poorer performance on the processing speed factor, a trainable factor, was independently associated with the most costly type of falls-injurious falls.
BACKGROUND/ OBJECTIVES: 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. DESIGN: 12-month prospective cohort study. SETTING: Vancouver Falls Prevention Clinic, Canada (www.fallsclinic.ca). PARTICIPANTS: 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. MEASUREMENTS: 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). RESULTS: 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). CONCLUSION: Poorer performance on the processing speed factor, a trainable factor, was independently associated with the most costly type of falls-injurious falls.
Authors: Deborah A Jehu; Jennifer C Davis; Kenneth Madden; Naaz Parmar; Teresa Liu-Ambrose Journal: Qual Life Res Date: 2022-08-23 Impact factor: 3.440
Authors: Katri M Turunen; Anna Tirkkonen; Tiina Savikangas; Tuomo Hänninen; Markku Alen; Roger A Fielding; Miia Kivipelto; Anna Stigsdotter Neely; Timo Törmäkangas; Sarianna Sipilä Journal: J Gerontol A Biol Sci Med Sci Date: 2022-07-05 Impact factor: 6.591
Authors: Andreas W Blomkvist; Fredrik Eika; Martin T Rahbek; Karin D Eikhof; Mette D Hansen; Malene Søndergaard; Jesper Ryg; Stig Andersen; Martin G Jørgensen Journal: PLoS One Date: 2017-12-29 Impact factor: 3.240