Parkinson’s Disease Can be Detected through Daily Activity Data, Research Shows
The presence of Parkinson’s disease can now be predicted by analyzing activity data observed in daily life, according to recent research. As a condition associated with movement disorders, Parkinson’s disease can be diagnosed by studying a person’s movements and sleep patterns throughout the day.
Leading a research team, Cynthia Sander, a senior researcher at the Dementia Institute in the UK, recently published a study in the journal Nature Medicine, revealing that activity data collected from an individual’s movements throughout the day can detect the presence of Parkinson’s disease.
Parkinson’s disease, well-known for affecting historical figures such as Pope John Paul II and Muhammad Ali, is a representative degenerative cranial nerve disease. It is caused by the gradual death of dopamine-secreting neurons in the brain. Although it only affects approximately 1% of the elderly population worldwide, there is currently no definitive treatment available.
The symptoms of Parkinson’s disease gradually develop over several years. In this condition, 50-70% of the nerve cells responsible for motor control gradually degenerate, resulting in weakened limbs, stiffness in the muscles, and difficulties in movement, such as tremors in the limbs.
Dr. Sander’s research team suggests that early symptoms of Parkinson’s disease can be predicted by comparing and analyzing the average amount of acceleration a person undergoes throughout the day. To support their findings, the team utilized machine learning models to analyze activity tracking data collected between 2006 and 2010 from 103,000 individuals aged 40-69 in the UK, which was obtained through the UK Biobank.
Their analysis revealed that the difference in average acceleration during physical activity could potentially serve as a diagnostic marker for Parkinson’s disease. By studying the movements of 273 current Parkinson’s patients from 7 am to midnight, the researchers found that the observed acceleration during activity began to decrease several years before the appearance of Parkinson’s symptoms. Similar results were observed in 196 individuals who exhibited early symptoms of the disease; they also experienced sleep disturbances prior to being diagnosed.
The research team emphasized the need for further investigation, stating, “It is a step that requires further research.” They also acknowledged the significance of this achievement, highlighting that “analyzing activity data offers a less expensive alternative to surgical diagnosis of Parkinson’s disease.”
Parkinson’s disease can be detected with the activity data moved throughout the day. Courtesy of Getty Image Bank.
Research has shown that Parkinson’s disease can only be predicted with activity data that can be seen in daily life. As it is a disease associated with movement disorders, Parkinson’s disease can be diagnosed by analyzing movements and sleep patterns throughout the day.
A research team led by Cynthia Sander, senior researcher at the Dementia Institute in the UK, announced on the 4th in the journal Nature Medicine that Parkinson’s disease can be detected by analyzing activity data that a person moves throughout the day.
Parkinson’s disease, known to have been suffered by Pope John Paul II and Muhammad Ali, is a representative degenerative cranial nerve disease. It occurs when dopamine-secreting neurons in the brain gradually die. Although it is a disease that affects approximately 1% of the world’s elderly population, there is still no definitive treatment.
Symptoms of Parkinson’s disease appear over many years. In Parkinson’s disease, 50-70% of the nerve cells responsible for motor nerves are gradually lost, so you can’t give strength to your arms and legs, your muscles become stiff, and you have movement difficulties, such as shaking your legs and arms.
The research team of Dr. Sander believes that it would be possible to predict the precursor symptoms of Parkinson’s disease by comparing and analyzing the amount of acceleration a person does on average throughout the day. The research team used machine learning models to analyze activity tracking data between 2006 and 2010 of 103,000 people aged 40-69 in the UK collected by UK Biobank and their health status.
As a result, it was found that the difference in average acceleration during activity could be a marker for the diagnosis of Parkinson’s disease. An analysis of the movements of 273 patients currently suffering from Parkinson’s disease between 7 am and midnight showed that the acceleration observed during activity began to decrease several years before the symptoms of Parkinson’s disease appeared. Similar results were seen in 196 patients who started to develop prodromal symptoms of Parkinson’s disease. He also had trouble sleeping before he was diagnosed with Parkinson’s disease.
The research team added, “It is a step that requires further research,” and revealed the significance of the achievement by saying, “By analyzing activity data, Parkinson’s disease can be diagnosed at a lower cost than before without surgery.”
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