Parkinson’s Disease is a progressive neurodegenerative disorder characterized by motor impairments and is the second most common neurodegenerative disease after Alzheimer’s disease. It affects 2-3% of the population aged 65 and above, with a long latency period. Currently, there is no known cure for Parkinson’s disease, and reliable predictive biomarkers are yet to be identified.
Diagnosing Parkinson’s disease typically takes several years as symptoms such as bradykinesia, involuntary tremors, and muscle stiffness develop slowly. However, if the disease is diagnosed early enough, the quality of life for patients can be improved through medication, treatments, and, in some cases, surgery.
A research study titled “Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis” was recently published by Cardiff University in Nature Medicine. The researchers utilized commonly used smartwatches to diagnose the disease up to seven years before clinical diagnosis. They used accelerometry, a measurement of movement acceleration, on the movement data of 103,712 smartwatch wearers in the UK.
The participants wore medical-grade smartwatches for seven days between 2013 and 2016. By tracking their movement speed during that week, the computer program was able to identify not only patients already diagnosed with Parkinson’s disease but also those in the early stages of the disease who had not yet been diagnosed.
The algorithm developed by the research team utilized data from the UK Biobank, the only UK database with the scale and volume of data necessary to run the computer program. The algorithm studied the predictive value of accelerometry in identifying prodromal Parkinson’s disease in the general population and compared this digital biomarker to models based on genetics, lifestyle, blood biochemistry, or prodromal symptom data.
