Shillong, July 4: New research suggests that smartwatches, combined with artificial intelligence (AI), have the potential to identify individuals who will develop Parkinson’s disease up to seven years before the onset of hallmark symptoms and clinical diagnosis.
A study conducted by researchers at Cardiff University in the UK demonstrated that by analyzing just one week of smartwatch data and utilizing AI algorithms, it is possible to identify people who will later develop Parkinson’s disease.
This discovery could pave the way for a new screening tool for early detection of Parkinson’s, surpassing current diagnostic methods and allowing intervention at an earlier stage.
Dr. Cynthia Sandor from the UK Dementia Research Institute at Cardiff University highlighted the accessibility and affordability of smartwatch data, stating that it could help identify individuals in the early stages of Parkinson’s within the general population. The study showed that data from a single week could predict events occurring up to seven years in the future, making it a valuable tool for early detection.
Parkinson’s disease affects dopaminergic neurons in the brain’s substantia nigra region and leads to motor symptoms such as tremors, rigidity, and slowed movement. However, by the time these symptoms become apparent and a clinical diagnosis is made, more than half of the substantia nigra cells have already died.
The researchers emphasized the need for inexpensive and easily accessible methods to detect early changes in order to intervene before extensive brain damage occurs.
The study involved analyzing data from 103,712 participants in the UK who wore medical-grade smartwatches for seven days between 2013 and 2016. The smartwatches continuously measured average acceleration, which indicates the speed of movement.
By comparing data from individuals who were already diagnosed with Parkinson’s disease to those who received a diagnosis up to seven years later, the researchers established the potential of smartwatch data in predicting the development of the disease.
