Smart watches could detect Parkinson’s before symptoms appear

Mature and young man jogging on a bridge taking a break looking at smart watch

Smart watches could identify Parkinson’s disease up to seven years before hallmark symptoms appear and a clinical diagnosis can be made, new research reveals.

The study was led by scientists at the Medical Research Council-funded (MRC) UK Dementia Research Institute at Cardiff University and Dr Kathryn Peall, an MRC Clinician-Scientist Fellow at Cardiff University.

The researchers analysed data collected by smart watches over a seven-day period measuring participants’ speed of movement.

They found that they could accurately predict, using artificial intelligence (AI), those who would go on to later develop Parkinson’s disease.

Researchers say this could be used as a new screening tool for Parkinson’s disease, which would enable detection of the disorder at a much earlier stage than current methods allow.

Need for better detection

Parkinson’s affects cells in the brain called dopaminergic neurons, located in an area of the brain known as the substantia nigra.

It causes motor symptoms such as tremor, rigidity (stiffness), and slowness of movement.

By the time these hallmark symptoms of Parkinson’s begin to show, and a clinical diagnosis can be made, more than half of the cells in the substantia nigra will already have died.

Therefore, there is a need for cheap, reliable and easily accessible methods to detect early changes so that intervention can be made before the disease causes extensive damage to the brain.

UK Biobank data

The researchers analysed data collected from 103,712 UK Biobank participants who wore a medical-grade smart watch for a seven-day period in 2013 to 2016.

The devices measured average acceleration, meaning speed of movement, continuously over the week-long period.

They compared 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.

These groups were also compared to age and sex-matched healthy people.

Use of AI

The researchers showed that, using AI, it is possible to identify participants who would later go on to develop Parkinson’s disease, from their smart watch data.

Not only could these participants be distinguished from healthy controls in the study, but the researchers then extended this to show that the AI could be used to identify individuals who would later develop Parkinson’s in the general population.

They found that this was more accurate than any other risk factor or other recognised early sign of the disease in predicting whether someone would develop Parkinson’s disease.

The machine learning model was also able to predict time to diagnosis.

A valuable screening tool

Study leader Dr Cynthia Sandor, Emerging Leader at the UK Dementia Research Institute at Cardiff University, said:

Smart watch data is easily accessible and low-cost. As of 2020, around 30 per cent of the UK population wear smart watches. By using this type of data, we would potentially be able to identify individuals in the very early stages of Parkinson’s disease within the general population.

We have shown here that a single week of data captured can predict events up to seven years in the future. With these results we could 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.

A limitation to the study is the lack of replication using another data source, as there are currently no other comparable data sets that would allow for similar analysis. However, extensive evaluation was performed to mitigate any biases.

The study was funded by the UK Dementia Research Institute, the Welsh Government and Cardiff University.

Further information

The study was published in the journal Nature Medicine.

Top image:  Credit: Zorica Nastasic, iStock, Getty Images Plus via Getty Images

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