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New, Multi-Sensor Tool Measures Slight Changes in Multiple Sclerosis Patients

Multiple sclerosis, or MS, is a progressive, chronic disorder where the immune system of the body attacks the central nervous system. This leads to several detrimental effects, such as fatigue, numbness, loss of muscle control and vision, and impaired speech. MS cannot be cured, and treatment focus is only on managing symptoms and delaying advancement.

Consequently, the potential to accurately evaluate the extent of MS-associated disability and disease progression is crucial for effective treatment.

A new study by an international group of researchers, headed by scientists at University of California San Diego School of Medicine and described in the February 26th, 2020, online issue of Annals of Clinical and Translational Neurology, reports an innovative, multi-sensor tool with the ability to measure even slight changes in MS patients, enabling physicians to more often and more rapidly respond to variations in symptoms or patient condition.

We currently lack reliable measures of subtle MS disability progression over short time intervals,” stated senior study author Jennifer Graves, MD, PhD, a neurologist at UC San Diego Health and associate professor of neurosciences at UC San Diego School of Medicine.

For example, a patient may tell us that she can no longer play piano, but our 150-year-old bedside neurological exam techniques can’t quantify this. In a standard clinical trial, this patient would be rated stable and not progressing. Developing tools that can capture MS progression reliably within six to 12 months instead of three to five years will drive faster drug development for the most disabling forms of MS.

Jennifer Graves, Neurologist, UC San Diego Health; and Associate Professor of Neurosciences; UC San Diego School of Medicine

Nearly one million people in the United States and 2.5 million across the world are affected by MS. Although the cause has not been identified yet, the condition usually appears between the ages of 20 and 40 and is more common in women. MS is one of the most leading causes of non-traumatic disability in young and middle-aged adults.

Conventional evaluation of MS involves regular clinical tests, which may only generate actionable outcomes over a period of several years. Tools to identify smaller, more subtle changes in the disease that could happen in shorter intervals are still lacking.

The new device makes use of a combination of sensors, like gyroscopes, accelerometers, and surface electromyography (for recording nerve electrical impulses with the help of electrodes placed on the skin), which have been redeployed from commercial application.

The use of multi-sensors allows for use of complementary data-types that can be employed for a more comprehensive view of the movement,” stated Graves.

The types of sensors we used are widely available in different hardware products. We used a product that could be purchased off Amazon and was originally used for gaming and other gesture control tasks. The critical steps in our work involved the data processing and analyses, including use of artificial intelligence approaches.

Jennifer Graves, Neurologist, UC San Diego Health; and Associate Professor of Neurosciences; UC San Diego School of Medicine

The device includes a small, sensor-loaded band worn on the calf or forearm, following which 20 finger or foot taps are completed. Data is wirelessly downloaded to a computer in real-time. The process is repeated on all four limbs of the patient, and requires less than 5 minutes to complete.

A great advantage is potential use by non-experts and even non-clinicians, such as medical assistants or research coordinators.

Jennifer Graves, Neurologist, UC San Diego Health; and Associate Professor of Neurosciences; UC San Diego School of Medicine

At present, the scientists are making arrangements to publish a longitudinal analysis that shows the sensitivity of the device to within-patient changes over short periods of time. Further steps involve validation in a multi-site study and creating commercial-grade software to enable more expansive dissemination.

Alireza Akhbardeh from Johns Hopkins University, Jennifer K. Arjona and Kristen M. Krysko from UC San Francisco, Bardia Nourbakhsh from UCSF and Johns Hopkins, and Pierre Antoine Gourraud from Nantes University, France are the co-authors of the study.

This study was partially funded by the UCSF Clinical and Translational Science Institute Resource Allocation Pilot program and Genentech.

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Video Credit: Caltech.


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