Sensors help predict falls up to 3 weeks in advance

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The sensor system produces images and sends automatic email alerts that can be used to predict falls within a three-week period. (Photo: MU Center for Eldercare and Rehabilitation Technology)
The sensor system produces images and sends automatic email alerts that can be used to predict falls within a three-week period. (Photo: MU Center for Eldercare and Rehabilitation Technology)

Sensors that measure gait speed and stride length help predict the likelihood that an older adult will fall up to three weeks in advance, according to University of Missouri researchers who are testing the technology.

“Assessment of these functions through the use of sensor technology is improving coordinated healthcare for older adults,” said Marjorie Skubic, Ph.D., director of the university's Center for Eldercare and Rehabilitation Technology and a professor of electrical and computer engineering.

The investigators collected data via sensor systems placed at TigerPlace, a Colubmia, MO, independent living community operated by Americare and the University of Missouri Sinclair School of Nursing, which provides home health and personal care services there as needed.

Data from the sensors revealed that a gait speed decline of 5 cm per second was associated with an 86.3% probability of falling within the next three weeks. Also, a shortened stride length was associated with a 50.6% probability of falling within the next three weeks.

The sensor system generates images and alert emails for nurses when it detects irregular motion. Nurses could use this information to assess functional decline, provide treatment and prevent falls, according to the researchers.

“For many older adults, the risk of falling impacts how long seniors can remain independent,” said Marilyn Rantz, Ph.D., RN, the Curators' Professor Emerita of Nursing at the nursing school. “Being able to predict that a person is at risk of falling will allow caretakers to intervene with the necessary care to help seniors remain independent as long as possible.”

Their findings were published by the Western Journal of Nursing Research.

The researchers plan to study how nurses can best use fall prediction statistics from the sensor system to intervene before falls occur.

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