Mild cognitive impairment (MCI) often serves as an early warning sign of Alzheimer’s disease or dementia. Early detection of MCI can open doors to timely interventions, potentially slowing or halting disease progression. However, diagnosing MCI can be a long and challenging process, especially in rural areas where access to licensed neuropsychologists may be limited. To address this issue, researchers at the University of Missouri have developed a portable system that efficiently assesses multiple aspects of motor function, offering an affordable and accessible tool for identifying cognitive decline.
A New Approach to Cognitive Assessment
The research team, led by Trent Guess, an associate professor in the College of Health Sciences, Jamie Hall, an associate teaching professor, and Praveen Rao, an associate professor in the College of Engineering, has created a device that combines several advanced technologies, including a depth camera, a force plate, and an interface board. This portable system is designed to provide accurate motor function assessments with ease and at a fraction of the cost of traditional methods.
In a recent study, the team tested the system on older adults, including individuals diagnosed with MCI. The participants were asked to perform three tasks: standing still, walking, and standing up from a bench. These activities were paired with a cognitive challenge—participants were required to count backwards in intervals of seven while completing the tasks.
The data collected from these tasks were fed into a machine learning model, an artificial intelligence system that helped to analyze the results and identify signs of cognitive impairment. Remarkably, the system was able to accurately identify 83% of the participants with MCI.
The Impact of Early Detection
As the number of Alzheimer’s disease cases in the U.S. is expected to more than double by 2060, according to the Centers for Disease Control and Prevention (CDC), this portable system could prove invaluable in identifying individuals at risk for Alzheimer’s and dementia early. Early diagnosis of MCI could lead to better access to treatments that slow the progression of the disease.
According to Hall, “Alzheimer’s disease is a significant problem here in the U.S. We know that if we can identify people early, we can provide early intervention to halt or slow the progression of the disease.” Hall also pointed out that currently, only about 8% of people in the U.S. who are believed to have MCI receive a clinical diagnosis.
The long-term goal of the team is to expand the use of this portable system into various settings, such as county health departments, assisted living facilities, community centers, physical therapy clinics, and senior centers. This would allow for more widespread screenings and easier access to cognitive assessments, particularly for older adults in underserved communities.
A Simple Yet Effective Tool
The system is designed to detect subtle motor differences that may not be apparent in traditional assessments. For example, it can identify if a person walks slower, takes shorter steps, or exhibits signs of imbalance when standing up—all of which can be indicative of cognitive strain. “Our portable system can detect if a person walks slower or doesn’t take as big of a step because they are thinking very hard,” explained Hall. “Some people have more sway and are less balanced or are slower to stand up when they are sitting. Our technology can measure these subtle differences in a way that you could not with a stopwatch.”
Broader Applications Beyond MCI
While the primary focus of the system is on detecting MCI and Alzheimer’s risk, the technology has a wide range of other potential applications. Guess noted that the system could also be used to assess fall risk and frailty in older adults, as well as to monitor individuals with conditions such as concussions, sports injuries, ALS (Amyotrophic Lateral Sclerosis), Parkinson’s disease, and recovery after knee or hip replacements.
“Moving is an important part of who we are,” said Guess. “It’s rewarding to see that this portable system can be beneficial in a lot of different ways.”
The Personal Investment of Participants
The participants in the study were deeply invested in the research. Many of them had either been diagnosed with MCI themselves or had a family member with Alzheimer’s disease. Their involvement underscores the importance of this research, both for those directly affected by cognitive decline and for future generations.
“Many of those who came in to be tested either have been diagnosed with MCI or have a family member who has Alzheimer’s disease, so they feel strongly about helping us move this forward,” Hall added. “It really amplifies why this is so important to me.”
Next Steps and Future Potential
The study, titled “Feasibility of Using a Novel, Multimodal Motor Function Assessment Platform with Machine Learning to Identify Individuals with Mild Cognitive Impairment,” was published in Alzheimer’s Disease and Associated Disorders. The project was funded by the University of Missouri Coulter Biomedical Accelerator, which supports collaboration between engineers and clinicians at the university to develop devices that can benefit society.
Moving forward, the team plans to expand the research by including additional participants and exploring other uses of the portable system, including its ability to detect early signs of fall risk and frailty in older adults. The potential of this technology to improve the quality of life for those with cognitive impairments and other motor-related conditions is substantial, offering a promising, accessible, and affordable solution to a growing healthcare challenge.
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