Parkinson’s disease, a progressive nervous system disorder that affects movement, causes one of the highest risks of falling among all neurological conditions. Due to this, many patients develop a fear of falling (FOF), even if they have never fallen. For some the fear can be excessive. Patients become prisoners in their own homes, scared to venture out despite the fact that they are physically able to do so. Others can develop a “fearlessness” putting themselves at high risk of falling.

Drs. Bin Hu, Ph.D., and Taylor Chomiak, Ph.D., with the Cumming School of Medicine (CSM) have developed a way to measure different types of FOF in hopes of improving treatment and quality of life for patients. Traditionally, FOF is considered to be a problem with motor function. Standard treatment focuses on improving a patient’s gait, balance and muscle strength. However, in a recent multi-centre study, these researchers discovered that cognitive function plays an important role.

Vivien Poon, who was diagnosed with Parkinson’s disease 10 years ago, underwent a six-minute walking test using an Ambulosono wearable sensor system (seen on Vivien’s right leg). (c) University of Calgary

“The findings indicate the current standard treatment for fear of falling may not be effective for all patients. Many may benefit from treatments aimed at addressing their fear and improving their level of confidence to get up and be active,” says Hu, professor in the departments of Clinical Neurosciences and Cell Biology and Anatomy and member of the Hotchkiss Brain Institute.

“Some patients have developed an excessive fear of falling that’s keeping them from participating in activities, but physically, they have no reason to be afraid,” says Hu. “On the opposite end of the spectrum we discovered patients who are physically at a high-risk of falling, but cognitively don’t recognize their weaknesses and aren’t taking proper precautions.”

The researchers incorporated machine learning to compare cognitive and mobility tests from 57 patients. This aspect of artificial intelligence allows computer systems to learn from the data and find hidden patterns. The algorithms produced visual maps that helped separate the patients with FOF into different categories: those with mobility issues, those with cognitive dysfunction with relatively mild motor impairment, and those with a combination of the two.

“Up to now there has been no generally accepted scientific method that can be used to diagnose patients with different types of fear of falling,” says Chomiak, an adjunct assistant professor in the Department of Clinical Neurosciences. “This is the first step toward the development of an effective diagnostic tool to identify types of FOF that combines conventional clinical assessments with mobile and computer technology.”

Source: University of Calgary