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J Gerontol A Biol Sci Med Sci. 2019 May 16. pii: glz128. doi: 10.1093/gerona/glz128. [Epub ahead of print]

When will my patient fall? Sensor-based in-home walking speed identifies future falls in older adults.

Author information

1
Oregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland, USA.
2
Internal medicine and Gerontology, University Hospital of Toulouse, UPS, Toulouse, France.

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 vs. 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 three months prior to a fall in those destined to fall as compared to controls, p<0.01(i.e. mean walking speed declined 0.1 cm.s-1 per week). We also found pre-fall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just prior to the fall compared to three months prior to the fall, both p<0.01.

CONCLUSIONS:

While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics prior to a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

KEYWORDS:

digital biomarkers; pervasive computing; technology

PMID:
31095283
DOI:
10.1093/gerona/glz128

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