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Wearable technology could help to predict who is likely to develop Parkinson’s disease, according to new research by scientists at Cardiff University and the UK Dementia Research Institute.
Parkinson’s disease is predominantly recognised for symptoms such as tremors and muscle stiffness, but slowness of movement is also a common early symptom.
The researchers therefore set out to discover whether accelerometery – the acceleration of motion – could be a prodromal (early) marker for Parkinson’s disease.
Early diagnosis of Parkinson’s disease typically difficult
To do this, they used data from roughly 104,000 Biobank participants who wore a medical-grade smart watch for a 7-day period in 2013-2016.
The devices measured average acceleration, meaning speed of movement, continuously over the week-long period.
They then compared the data from a subset of participants who had already been diagnosed with Parkinson’s disease, to another group who received a diagnosis up to seven years after the smart watch data was collected.
The results of the research, published in Nature Medicine, showed that monitoring patients with smartwatches could help to decipher between who has clinically diagnosed Parkinson’s disease, who has prodromal Parkinson’s disease and health controls.
The researchers were then able to predict time to diagnosis, and they estimate that smart watches could predict who is likely to develop Parkinson’s disease up to seven years before clinical diagnosis.
This is an invaluable finding considering that early diagnosis of Parkinson’s is largely uncommon, as Dr Kathryn Peall, Clinical Senior Lecturer in the NMHII, explains: “Parkinson’s disease is a progressive movement disorder caused by the loss of brain cells that use dopamine. However, by the time of clinical diagnosis approximately 50-70% of these brain cells will have been lost. This makes early diagnosis of the disease difficult.”
Potential for a new screening tool
Dr Cynthia Sandor, Cardiff University’s Dementia Research Institute said the study is the first demonstration of the clinical value of accelerometery-based biomarkers for prodromal Parkinson’s disease in the general population.
“Our results showed a pre-diagnosis reduction in acceleration was unique to Parkinson’s disease and was not observed for any other disorder that we examined.
“It suggests that accelerometery could be used to identify those at elevated risk for Parkinson’s disease on an unprecedented scale.
“In a clinical setting, continuous or semi-continuous monitoring of individuals can’t be achieved because of time, cost, accessibility and sensitivity; but smart devices capable of collecting accelerometer data are worn daily by millions of people,” she said.
Dr Sandor said more work will need to be done before this technology is expanded into clinical practice, but the research represents a “significant leap forward in the early diagnosis of Parkinson’s disease, and suggests that devices such as activity trackers and smartwatches could play a key role in clinical monitoring.”
She says that the results could also be used to develop a “valuable screening tool” to aid in the early detection of Parkinson’s.
“This has implications both for research, in improving recruitment into clinical trials, and in clinical practice, in allowing patients to access treatments at an earlier stage, in future when such treatments become available,” she said.