Harnessing Smart Watches: A New Dawn in Early Parkinson's Detection

The early detection of diseases often spells the difference between successful intervention and debilitating progression. Parkinson's disease, a neurodegenerative disorder that affects dopaminergic neurons in the brain, is no exception. The disease is characterised by motor symptoms such as tremors, rigidity, and slowness of movement. However, by the time these symptoms become apparent, more than half of the cells in the substantia nigra, a brain region, have already been lost. This makes the pursuit of early detection methods a crucial endeavour.

A recent study led by scientists at the Medical Research Council-funded UK Dementia Research Institute at Cardiff University and Dr Kathryn Peall, an MRC Clinician-Scientist Fellow at Cardiff University, has shed light on a promising new avenue for early detection of Parkinson's disease: smartwatches. The researchers analysed data collected by smartwatches over seven days, focusing on the speed of movement of the participants. The results were nothing short of groundbreaking.

The study found that artificial intelligence (AI) could accurately predict, based on the smartwatch data, those who would later develop Parkinson's disease. This discovery could pave the way for a new screening tool for Parkinson's, enabling the detection of the disorder much earlier than current methods allow. The implications of this are profound, as it could allow for interventions to be made before the disease causes extensive damage to the brain.

The data analysed by the researchers was collected from 103,712 UK Biobank participants who wore a medical-grade smartwatch for seven days between 2013 and 2016. The devices continuously measured average acceleration, or speed of movement, over the week. The researchers compared data from a subset of participants who had already been diagnosed with Parkinson's to another group who received a diagnosis up to seven years after the smartwatch data was collected. These groups were also compared to age and sex-matched healthy people.

The researchers demonstrated that using AI could identify participants who would later develop Parkinson's disease from their smartwatch data. Not only could these participants be distinguished from healthy controls in the study, but the researchers extended this to show that the AI could identify individuals who would later develop Parkinson's in the general population. They found this was more accurate than any other risk factor or recognised early signs of the disease in predicting whether someone would develop Parkinson's. The machine learning model was also able to predict the time to diagnosis.

Study leader Dr Cynthia Sandor, Emerging Leader at the UK Dementia Research Institute at Cardiff University, highlighted the accessibility and low cost of smartwatch data. As of 2020, around 30% of the UK population wears smartwatches. By using this type of data, we could potentially identify individuals in the very early stages of Parkinson's disease within the general population. She further emphasised 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 for research, improving recruitment into clinical trials and in clinical practice, allowing patients to access treatments at an earlier stage when such treatments become available.

The study has limitations, such as the lack of replication using another data source, as there are currently no comparable data sets that would allow for a similar analysis. However, extensive evaluation was performed to mitigate any biases. Despite these limitations, the study represents a significant step forward in the early detection of Parkinson's disease and underscores the potential of wearable technology in healthcare.

Source:?UKRI

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