Pairing wearable sensors with a computer program enables effective monitoring of the way multiple sclerosis (MS) patients walk in “real life,” potentially helping clinicians to better evaluate treatments and judge disability, a small U.K. study reports.
The research, “Free-living and laboratory gait characteristics in patients with multiple sclerosis,” was published in the journal PLOS ONE. Its authors are hoping to further test their approach in a larger study.
About 75 to 90 percent of all MS patients experience mobility problems, and analyzing gait — the way a person walks — is a common indicator of disease stage, especially in early stages. But such analysis is currently done in medical settings.
Aiming to develop an alternative that mimics “real life” conditions more closely, doctors at Sheffield Teaching Hospitals and scientists at the University of Sheffield developed an algorithm (computer program) specific to MS that, when paired with wearable sensors, may enable more informative and effective monitoring of MS patients’ gait in their daily life.
“The measurements we take of people with MS in a lab may not be an accurate representation of their everyday condition,” Claudia Mazzà, PhD, the study’s senior author, said in a press release. “Having data from real life scenarios will help clinical staff assess a patient’s condition more accurately. For patients this will mean better treatment as a result of clinicians being more informed about their condition.”
The researchers first checked whether their portable sensors — waist and shank (leg) mounted inertial sensors — were accurate and comfortable, and that results were the same as a lab-based sensor. They then created an algorithm to process measurements collected.
A total of 14 MS patients with moderate to severe ambulatory impairment were tested for gait under both laboratory and daily life conditions.
Analysis found that patients walked at a slower pace in daily living than in the laboratory. The study also reported that assessing gait in short walking bouts, defined as between five and 50 steps at a time, may be the best approach to quantify disability level and treatment effectiveness “in patients moderately affected by MS.”
Overall, the scientists observed that their algorithm could handle and process data from complex movements performed outside a lab.
“Although this is a small study, the results are encouraging and it gives us enough information to progress to a large-scale clinical trial,” Mazzà said.
This work “provides a robust approach for the quantification of recognized clinically relevant outcomes and an innovative perspective in the study of real life walking,” the researchers wrote.
Added Sivaraman Nair, a consultant neurologist at Sheffield Teaching Hospitals: “Assessing the changes in the way patients with MS walk is key to understanding the progression of disability. It is particularly important to look at these indicators at an early stage as it can also tell us about the effectiveness of the medication they are taking.”
Nair also underscored the study’s importance as a potentially effective and low-cost way of conducting MS assessments, and one that could be applied to other conditions where monitoring gait would be beneficial, such as Parkinson’s disease.
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