A new wearable device will now be able to predict if an elderly patient is at an increased risk of falling or not, finds a new study.
Highlights
- A new device will be able to predict the risk of fall in elderly
- This device is helpful because many older adults often don’t pay attention to the fact that they are unstable until after they fall
- This device will be able to warn the person before a fall and diminish their risk of injury. It also triggers such people to undertake rehabilitation exercises to reduce their chance of falling and increase their muscle strength
Sixty-seven women over the age of sixty were involved in this study. They were tested on their ability to walk; they were also asked to answer on the number of falls they had experienced in the previous year.
These participants had to wear a small device with motion sensors that measured their walking patterns and stability for a single week.
The data extracted from the devices were able to accurately predict that particular patients risk of falling as measured by physical examinations of unsteadiness in standing and walking.
"Our prediction showed that we could very accurately tell the difference between people that were really stable and people that were unstable in some way," said Bruce Schatz, Head of the Department of Medical Information Science at University of Illinois College of Medicine.
The researchers used a device called an accelerometer to measure the user’s walking patterns and their unsteadiness. They had combined this measurement with that individual’s history of fall to determine their risk of falling in the near future.
"I work a lot with primary care physicians, and they love this (idea) because they only see people after they start falling," Schatz said. "At that point, it’s already sort of too late."
Most people have smartphones these days which has an accelerometer; this same sensor can be used to constantly records their motion and notify the concerned person about their fall risk and start them on preventative exercises.
References
Accelerometer-based predictive models of fall risk in older women: a pilot study https://www.nature.com/articles/s41746-018-0033-5#Abs1
Source-Eurekalert