New Method Developed to Identify Fear of Falling Among Parkinson’s Patients

New Method Developed to Identify Fear of Falling Among Parkinson’s Patients

A new tool to measure fear of falling may help improve patients’ personalized care, reduce the risk of falls, and enhance physical and social activity among patients with Parkinson’s disease, a study reports.

The study, “Differentiating cognitive or motor dimensions associated with the perception of fall-related self-efficacy in Parkinson’s disease,” was published in npj Parkinson’s Disease.

Fear of falling is a common concern among the elderly that can present a significant barrier to their physical and social activities, negatively affecting their quality of life and general health.

Parkinson’s disease carries one of the highest risks of falls among neurological illnesses. The decline of both cognitive and motor function, which characterize this disease, can significantly limit a person’s ability to perform daily tasks. It can also increase the fear of falling — a result of a person’s perceived risk of falling and cognitive functioning.

While it has been frequently proposed that the therapeutic management of fear of falling should focus on improving lower-level mobility problems, Parkinson’s patients can have cognitive dysfunction early in the course of the disease that may occur independently from motor system pathology.

As a result, a fear of falling may arise due to poor cognitive functioning rather than an underlying motor system impairment.

“It would therefore be highly desirable to develop precision medicine methods that would be able to differentiate domain-specific contributions of cognitive or motor impairments with respect to fear of falling,” the researchers wrote.

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Researchers used analytical and machine-learning approaches to analyze data collected from 57 Parkinson’s patients who were registered in an ongoing trial (ISRCTN06023392) assessing a home-based music walking program called Ambulosono.

An integrated analysis revealed that data on Hoehn & Yahr (which measures Parkinson’s severity divided into five stages), UPDRS-III scores (a 50-question assessment of both motor and non-motor symptoms associated with Parkinson’s), and gait speed would all provide valuable information for the motor component domain. Visuospatial, attention, and memory retrieval test scores would make up the cognition domain.

Results showed that decreases in both motor and cognitive domain functioning were associated with an increased probability of fear of falling. In addition, the extracted cognitive components could significantly and independently predict fear of falling.

“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,” study senior author Bin Hu, PhD, a professor at the University of Calgary and member of the Hotchkiss Brain Institute, said in a university news story.

Based on patients’ cognitive and motor characteristics, researchers built a predictive model that could separate patients with a fear of falling into different categories: those with mobility issues, those with cognitive dysfunction with relatively mild motor impairment, and those with a combination of the two. The model had an accuracy greater than 92%.

“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,” Hu said. “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.”

These results suggest that both motor and cognitive domains may have “important, yet different, roles” in fear of falling, according to the study.

“As the nature of fear of falling is multi-factorial, it is difficult for a single assessment tool, such as the Falls Efficacy Scale-International (FES-I), to capture the underlying contributing factors,” the researchers wrote.

“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,” said Taylor Chomiak, PhD, an adjunct assistant professor at the University of Calgary and lead author of the study.

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