The home device may be the best “gold standard” for monitoring PD

The home device may be the best “gold standard” for monitoring PD

A wireless home device that monitors the movements and walking speed of patients with Parkinson’s disease (PD) could allow clinicians to monitor disease progression using a type of ‘human radar’ , according to a new study.

About the size of a Wi-Fi router, the device is mounted on a wall in the house and collects data on movement and walking speed using radio signals that bounce off the body of the person. patient when moving. Changes in these movements are potential indicators of disease progression and severity and response to medication.

When researchers compared a year’s worth of data collected with the device to conventional clinical measures of Parkinson’s disease, they found that clinicians could track disease and drug response more effectively than they could with assessments. clinical periodicals.

About the size of a Wi-Fi router, the wireless home device is mounted on a wall and collects data on movement and walking speed using radio signals that bounce off the patient’s body. when he moves.

“Parkinson’s symptoms get worse over time, but the disease progresses slowly and it sometimes takes many years to reliably assess the change in disease severity,” said researcher Dina Katabi, PhD, professor of engineering. Electrical and Computer Science at the Massachusetts Institute of Technology in Cambridge. , Told Medscape Medical News. “We can shorten this period because we have more reliable and more sensitive measurements.”

The results were published online September 21 in Science Translational Medicine.

Meets gold standard

Oral levodopa is the gold standard drug for improving symptoms of Parkinson’s disease, which affects about 1% of people over the age of 60. The effectiveness of the drug decreases in most patients after a few years, however, regular monitoring of motor skills and cognitive function is critical.

Because most PD specialists are concentrated in urban areas, up to 40% of Medicare patients are rarely seen by a neurologist or specialist, if at all.

This has spurred the search for other ways to monitor the disease outside of the clinic. Although portable devices have shown some results, the age and frailty of many patients present challenges for their successful use.

The device used in this study, developed in Katabi’s lab, sends wireless signals that are about 1000 times weaker than those emitted by a home Wi-Fi system. Signals pass through walls and objects but bounce off a person’s body as they move.

The researchers collected data for 1 year from 1-2 devices placed in the homes of 34 patients with PD and 16 healthy participants. Using machine learning algorithms, investigators analyzed more than 200,000 walking speed measurements.

Investigators found that home walking speed was strongly correlated with the subscore and total scores of the Movement Disorder Society-sponsored review of the Unified Parkinson’s Disease Rating Scale part III (P < 0.001 for both).

“The study shows that home gait is more correlated with the gold standard in measuring Parkinson’s disease,” Katabi said.

The researchers also found that annual walking speed decreased almost twice as fast in people with Parkinson’s disease as in those without (−0.026 m/s versus −0.015 m/s, respectively ; P = 0.04). Gait measurements were also aligned with medication use; walking speed improved shortly after a patient took their medication and decreased as the medication began to wear off.

“This allows doctors to assess the disease without asking patients to come to the clinic, which is usually difficult for elderly patients due to the motor impairment of Parkinson’s disease,” Katabi said.

Clinical impact

Researchers have conducted several studies on devices like the one used in this trial, including artificial intelligence (AI) technology that uses a neural network to mimic the human brain.

Researchers have used this device to successfully distinguish between patients with Parkinson’s disease and those without, based on nocturnal respiratory signals. Technology has even identified people with PD before they are diagnosed.

Using machine learning algorithms in this way has the potential to speed up drug development by simplifying clinical trials and reducing their cost, the researchers said. But for PD specialists, the most immediate interest is how the technology could improve patient care.

Commenting on the findings of Medscape Medical NewsRebecca Gilbert, MD, PhD, scientific director of the American Parkinson Disease Association, said the device in this new study could “add a vital component to monitoring PD, which at this stage consists of data collected at many wide intervals in the clinic.

“The use of home walking speed could be exceptionally useful for clinicians not only to understand their patient’s clinical status and progression, but also to review the response to their clinical decisions such as medication changes” , Gilbert added.

The study was funded by the National Institutes of Health and the Michael J. Fox Foundation. Katabi is co-founder of Emerald Innovations and sits on the Janssen Advisory Board and Amgen’s Data Science Advisory Board. Gilbert does not report any relevant financial relationships.

Sci Transl Med. Published online September 21, 2022. Full text

Kelli Whitlock Burton is a reporter for Medscape Medical News covering psychiatry and neurology.

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