When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults

Antoine Piau, Nora Mattek, Rachel Crissey, Zachary Beattie, Hiroko Dodge, Jeffrey Kaye

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)968-973
Number of pages6
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume75
Issue number5
DOIs
StatePublished - Apr 17 2020

Keywords

  • Digital biomarkers
  • Pervasive computing
  • Technology

ASJC Scopus subject areas

  • Aging
  • Geriatrics and Gerontology

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