Citation

Phillips LJ, DeRoche CB, Rantz M, Alexander GL, Skubic M, Despins L, Abbott CC, Harris BH, Galambos C, Koopman R. West. J. Nurs. Res. 2016; ePub(ePub): ePub.

Affiliation

University of Missouri, Columbia, USA.

Copyright

(Copyright © 2016, SAGE Publishing)

DOI

10.1177/0193945916662027

PMID

27470677

Abstract

This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell (n = 13) and those who did not fall (n = 10). We analyzed associations between participants' fall events (n = 69) and pre-fall changes in in-home gait speed and stride length (n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall (p <.0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed.

RESULTS demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.

© The Author(s) 2016.

Language: en