BACKGROUND: Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall. METHOD: In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period. RESULTS: We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01. CONCLUSIONS: While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.
BACKGROUND: Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall. METHOD: In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period. RESULTS: We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01. CONCLUSIONS: While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.
Authors: Jochen Klenk; Clemens Becker; Pierpaolo Palumbo; Lars Schwickert; Kilan Rapp; Jorunn L Helbostad; Chris Todd; Stephen R Lord; Ngaire Kerse Journal: J Am Med Dir Assoc Date: 2017-09-12 Impact factor: 4.669
Authors: John P K Bernstein; Katherine Dorociak; Nora Mattek; Mira Leese; Chelsea Trapp; Zachary Beattie; Jeffrey Kaye; Adriana Hughes Journal: Neuropsychol Dev Cogn B Aging Neuropsychol Cogn Date: 2021-04-18
Authors: Zachary Beattie; Lyndsey M Miller; Carlos Almirola; Wan-Tai M Au-Yeung; Hannah Bernard; Kevin E Cosgrove; Hiroko H Dodge; Charlene J Gamboa; Ona Golonka; Sarah Gothard; Sam Harbison; Stephanie Irish; Judith Kornfeld; Jonathan Lee; Jennifer Marcoe; Nora C Mattek; Charlie Quinn; Christina Reynolds; Thomas Riley; Nathaniel Rodrigues; Nicole Sharma; Mary Alice Siqueland; Neil W Thomas; Timothy Truty; Rachel Wall; Katherine Wild; Chao-Yi Wu; Jason Karlawish; Nina B Silverberg; Lisa L Barnes; Sara Czaja; Lisa C Silbert; Jeffrey Kaye Journal: Digit Biomark Date: 2020-11-26
Authors: Luc Jw Evers; Yordan P Raykov; Jesse H Krijthe; Ana Lígia Silva de Lima; Reham Badawy; Kasper Claes; Tom M Heskes; Max A Little; Marjan J Meinders; Bastiaan R Bloem Journal: J Med Internet Res Date: 2020-10-09 Impact factor: 5.428
Authors: Björn Friedrich; Carolin Lübbe; Enno-Edzard Steen; Jürgen Martin Bauer; Andreas Hein Journal: Sensors (Basel) Date: 2022-01-10 Impact factor: 3.576