Wearable Motion Sensors May Help Diagnose Fragile X

Margarida Maia PhD avatar

by Margarida Maia PhD |

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Wearable motion sensors

Wearable motion sensors someday may help doctors diagnose fragile X syndrome and similar disorders based on the way people walk, a study suggests.

The method may be able to identify problems in walking 15 to 20 years ahead of clinical symptoms; as such, it offers an opportunity to intervene sooner to preserve brain structure and function.

“Walking patterns can be a revealing trait of health, but gait symptoms of disorders like fragile X can escape the naked eye for years until they are visibly noticeable,” Elizabeth Torres, PhD, said in a University press release. Torres is one of the study’s lead authors and professor of psychology at Rutgers University-New Brunswick, New Jersey.

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“Given issues with anatomical differences — such as people with longer or shorter limbs — and disease complexity, it has remained challenging to use walking patterns to screen nervous system disorders more broadly, across disorders impacting people of different ages and developmental stages,” added Torres, who also directs the Sensory Motor Integration Lab.

The study, “Optimal time lags from causal prediction model help stratify and forecast nervous system pathology,” was published in Nature Scientific Reports as a collaborative effort of researchers at several U.S. universities.

Fragile X syndrome is caused by mutations in FMR1, a gene located on the X chromosome. People who develop the disease have more than 200 copies of a small stretch of DNA in the FMR1 gene, compared with 55 or fewer in healthy people, and are said to have the full mutation.

People with an intermediate number of repeats (55 to 200 extra copies) are considered to have a premutation. They are called premutation carriers because they do not have the disorder, but their offspring are at greater risk of fragile X-associated disorders. One such disorder is fragile X–associated tremor/ataxia syndrome (FXTAS), which is marked by abnormal gait, or a person’s manner of walking.

“In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems,” the researchers wrote.

Participants were asked to complete a simple walking task while wearing the XSens system of sensors to collect data on position, orientation, and acceleration across the body or SoleSound footwear to measure gait parameters.

Using a method of analysis that determines whether one series of events is useful in forecasting another, the team found the number of signals that upper and lower limbs exchange with one another during a walk increased with age. However, elderly participants seem to have an overall slowdown in information transmission between upper and lower extremities as part of the natural aging process.

In FMR1 premutation carriers, the crosstalk between upper and lower limbs was cut down in relation to controls of similar age. Moreover, the time between one event and the next was cut short in a way similar to that seen in patients with Parkinson’s.

“These patterns in the FMR1 premutation carriers forecast trouble on the horizon, of the type elderly PPD [patients with Parkinson’s] eventually experience,” the researchers wrote.

Next, the researchers compared the patterns across the different groups of participants. The findings suggest these patterns may be used to stratify a random draw of the population to help predict the early loss of normal gait.

“This is an important way to detect signs of abnormal patterns,” said Theodoros Bermperidis, the study’s lead author.

Overall, “the results from this study are amenable to build digital screening tools using parameter spaces derived from a simple walking task,” the researchers concluded. “The methods described here may offer a new way to detect these gait problems 15–20 years ahead of their clinical onset and as such, could help advance neuroprotective intervention models.”