- A recent study explored the use of wrist accelerometers to detect Parkinson’s disease prior to clinical diagnosis.
- Researchers have found that a decrease in the speed of movement could be observed several years before a person is diagnosed with Parkinson’s.
- The accelerometer data has surpassed other models based on medical symptoms, genetics, lifestyle or blood biochemistry data and may be incorporated into clinical practice in the future.
In Parkinson’s disease, the deterioration of specific brain cells causes movement problems and other health problems that get worse over time. Unfortunately, there is still no treatment that reverses or stops the disease.
Before someone is diagnosed with Parkinson’s disease, they may have had other symptoms for several years (known as prodromal symptoms). Researchers have studied these symptoms, as well as genetics, lifestyle and blood biochemistry data, to see how well they can predict the development of Parkinson’s disease. The results are promising, but there is still room for improvement.
Research has also shown that impairment of daily activities and signs of sluggishness can appear years before a person is diagnosed with Parkinson’s. This has inspired researchers to use wearable digital sensors that monitor walking patterns as a tool for detecting Parkinson’s.
Most smartwatches contain a sensor that measures the acceleration of a moving body, known as an accelerometer. A 2021 study showed that wrist accelerometers can detect Parkinson’s with high accuracy. However, the utility of these findings was limited by the fact that the study focused on people already diagnosed with Parkinson’s.
Building on this work, a new study led by researchers at the UK’s Dementia Research Institute and the Neuroscience and Mental Health Innovation Institute in Cardiff has explored the possibility of using wrist accelerometers to identify Parkinson’s years before clinical diagnosis.
The study is published in
The study used data from the UK Biobank study, which has collected data from over 500,000 individuals aged 4,069 since 2006.
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To assess whether data from these accelerometers could be used as early markers for Parkinson’s, researchers at Cardiff University compared accelerometer data from people with Parkinson’s, people without the disease, and individuals with other neurodegenerative or movement disorders.
They also compared the Parkinson’s prediction model based on accelerometer data to other models trained on known medical symptoms, genetics, lifestyle, or blood biochemistry data to see which combination of data sources was most effective at identifying the former. signs of Parkinson’s in the general population.
Researchers have found that a decrease in movement speed (or acceleration) can be seen several years before a person is diagnosed with Parkinson’s. This reduction in acceleration was unique to Parkinson’s and was not seen in other neurodegenerative or movement disorders studied.
Sleep characteristics derived from acceleration data indicated lower sleep quality and duration in people diagnosed with Parkinson’s or in the prodromal phase than in those without the disease.
The results showed that accelerometer data can predict Parkinson’s even before it is diagnosed clinically. Additionally, the model based on accelerometer data outperformed other models trained on known medical symptoms, genetics, lifestyle, or blood biochemistry data.
Additionally, the researchers were able to use accelerometry to estimate the time a Parkinson’s diagnosis could be expected.
Dr. Walter Maetzler, a full professor of neurogeriatrics and deputy director of the department of neurology at Kiel University Hospital in Germany, who was not involved in the study, expressed surprise at the strong results of this study.
Some changes in people’s mobility and agility in a prodromal stage of [Parkinsons], up to about five years before the clinically possible diagnosis, one could already suspect on the basis of the existing literature. What is surprising about the current study is that they find reduced mobility up to 7 years before the clinical diagnosis of Parkinson’s and can even predict [the] moment when clinic [Parkinsons] diagnosis is possible.
Dr. Walter Maetzler
In their paper, the study authors note that the results of this study were not validated using another dataset due to a lack of large-scale equivalent datasets that capture the prodromal phase of multiple disorders.
The UK biobank dataset had some limitations, such as the availability of accelerometer data for only seven days and the absence of clinically recognized prodromal markers such as dopamine transporter imaging or motor exams.
Another limitation of this study is that the models were trained on a subset of individuals who had complete information, which artificially reduced the sample size and may limit the generalizability of the results.
Ms. Schalkamp also noted that we’ve currently only tested our tool on one specific device, the Activity Ax3, and can’t draw any conclusions about how well it would work on other devices.
In the comments to Medical News TodayAnn-Kathrin Schalkamp, first author of the study and Ph.D. student at the UK’s Dementia Research Institute at Cardiff University, clarified they don’t intend for people to be able to use smartwatches to measure their risk to develop Parkinson’s.
Once these findings are confirmed in an independent cohort, the ultimate goal would be to incorporate smart watch-based risk scoring for Parkinson’s into clinical practice, Schalkamp said.
A 2022 study reported that using smartwatches to detect atrial fibrillation generates a high false-positive rate and inconclusive results in some patients with certain heart conditions. MNT extension asked the study author whether detecting Parkinson’s disease using smartwatches could present a similar problem.
Since we aimed to design a screening tool rather than a diagnostic one, our model training choice prioritized sensitivity over specificity, leading to a higher number of false positives. When an individual is recognized by the screening tool a[s] being at high risk of developing Parkinson’s in the future, further tests would be needed to confirm a Parkinson’s diagnosis later, Schalkamp explained.
Schalkamp believes that in the future, neurologists will not rely solely on the smartwatch data, but will consider them as additional indicators in their decision-making process.
Dr. Maetzler said MNT extension that the findings will likely not immediately change our clinical practice.
Confirmatory studies are needed and the protocol may also need to be refined (e.g. with longer and more repeated measurement steps, alternative device positions on the body, e.g. at the non-dominant wrist, lower back or foot, even combinations of devices could be beneficial and boost results), he added.
Dr. Maetzler believes the findings will be of great interest to pharmaceutical companies studying potential neuroprotective drugs. Using this accelerometry-based prediction model, they can increase the likelihood of including subjects who are actually in the prodromal phase [Parkinsons] in their studies and clinical trials.
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