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new research shows.
An analysis of wearable motion-tracking data from UK Biobank participants showed a strong correlation between reduced daytime movement over 1 week and a clinical diagnosis of PD up to 7 years later.
“Smartwatch data is easily accessible and low cost. 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,” lead researcher Cynthia Sandor, PhD, from Cardiff (Wales) University, said in a statement.
“We have shown here that a single week of data captured can predict events up to 7 years in the future. With these results we could develop a valuable screening tool to aid in the early detection of Parkinson’s,” she added.
“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,” said Dr. Sandor.
The study was published online in Nature Medicine.
Novel biomarker for PD
Using machine learning, the researchers analyzed accelerometry data from 103,712 UK Biobank participants who wore a medical-grade smartwatch for a 7-day period during 2013-2016.
At the time of or within 2 years after accelerometry data collection, 273 participants were diagnosed with PD. An additional 196 individuals received a new PD diagnosis more than 2 years after accelerometry data collection (the prodromal group).
The patients with prodromal symptoms of PD and those who were diagnosed with PD showed a significantly reduced daytime acceleration profile up to 7 years before diagnosis, compared with age- and sex-matched healthy control persons, the researchers found.
The reduction in acceleration both before and following diagnosis was unique to patients with PD, “suggesting this measure to be disease specific with potential for use in early identification of individuals likely to be diagnosed with PD,” they wrote.
Accelerometry data proved more accurate than other risk factors (lifestyle, genetics, blood chemistry) or recognized prodromal symptoms of PD in predicting whether an individual would develop PD.
“Our results suggest that accelerometry collected with wearable devices in the general population could be used to identify those at elevated risk for PD on an unprecedented scale and, importantly, individuals who will likely convert within the next few years can be included in studies for neuroprotective treatments,” the researchers conclude in their article.
High-quality research
In a statement from the U.K.-based nonprofit Science Media Centre, José López Barneo, MD, PhD, with the University of Seville (Spain), said this “good quality” study “fits well with current knowledge.”
Dr. Barneo noted that other investigators have also observed that slowness of movement is a characteristic feature of some people who subsequently develop PD.
But these studies involved preselected cohorts of persons at risk of developing PD, or they were carried out in a hospital that required healthcare staff to conduct the movement analysis. In contrast, the current study was conducted in a very large cohort from the general U.K. population.
Also weighing in, José Luis Lanciego, MD, PhD, with the University of Navarra (Spain), said the “main value of this study is that it has demonstrated that accelerometry measurements obtained using wearable devices (such as a smartwatch or other similar devices) are more useful than the assessment of any other potentially prodromal symptom in identifying which people in the [general] population are at increased risk of developing Parkinson’s disease in the future, as well as being able to estimate how many years it will take to start suffering from this neurodegenerative process.
“In these diseases, early diagnosis is to some extent questionable, as early diagnosis is of little use if neuroprotective treatment is not available,” Dr. Lanciego noted.
“However, it is of great importance for use in clinical trials aimed at evaluating the efficacy of new potentially neuroprotective treatments whose main objective is to slow down – and, ideally, even halt ― the clinical progression that typically characterizes Parkinson’s disease,” Dr. Lanciego added.
The study was funded by the UK Dementia Research Institute, the Welsh government, and Cardiff University. Dr. Sandor, Dr. Barneo, and Dr. Lanciego have no relevant disclosures.
A version of this article originally appeared on Medscape.com.
new research shows.
An analysis of wearable motion-tracking data from UK Biobank participants showed a strong correlation between reduced daytime movement over 1 week and a clinical diagnosis of PD up to 7 years later.
“Smartwatch data is easily accessible and low cost. 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,” lead researcher Cynthia Sandor, PhD, from Cardiff (Wales) University, said in a statement.
“We have shown here that a single week of data captured can predict events up to 7 years in the future. With these results we could develop a valuable screening tool to aid in the early detection of Parkinson’s,” she added.
“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,” said Dr. Sandor.
The study was published online in Nature Medicine.
Novel biomarker for PD
Using machine learning, the researchers analyzed accelerometry data from 103,712 UK Biobank participants who wore a medical-grade smartwatch for a 7-day period during 2013-2016.
At the time of or within 2 years after accelerometry data collection, 273 participants were diagnosed with PD. An additional 196 individuals received a new PD diagnosis more than 2 years after accelerometry data collection (the prodromal group).
The patients with prodromal symptoms of PD and those who were diagnosed with PD showed a significantly reduced daytime acceleration profile up to 7 years before diagnosis, compared with age- and sex-matched healthy control persons, the researchers found.
The reduction in acceleration both before and following diagnosis was unique to patients with PD, “suggesting this measure to be disease specific with potential for use in early identification of individuals likely to be diagnosed with PD,” they wrote.
Accelerometry data proved more accurate than other risk factors (lifestyle, genetics, blood chemistry) or recognized prodromal symptoms of PD in predicting whether an individual would develop PD.
“Our results suggest that accelerometry collected with wearable devices in the general population could be used to identify those at elevated risk for PD on an unprecedented scale and, importantly, individuals who will likely convert within the next few years can be included in studies for neuroprotective treatments,” the researchers conclude in their article.
High-quality research
In a statement from the U.K.-based nonprofit Science Media Centre, José López Barneo, MD, PhD, with the University of Seville (Spain), said this “good quality” study “fits well with current knowledge.”
Dr. Barneo noted that other investigators have also observed that slowness of movement is a characteristic feature of some people who subsequently develop PD.
But these studies involved preselected cohorts of persons at risk of developing PD, or they were carried out in a hospital that required healthcare staff to conduct the movement analysis. In contrast, the current study was conducted in a very large cohort from the general U.K. population.
Also weighing in, José Luis Lanciego, MD, PhD, with the University of Navarra (Spain), said the “main value of this study is that it has demonstrated that accelerometry measurements obtained using wearable devices (such as a smartwatch or other similar devices) are more useful than the assessment of any other potentially prodromal symptom in identifying which people in the [general] population are at increased risk of developing Parkinson’s disease in the future, as well as being able to estimate how many years it will take to start suffering from this neurodegenerative process.
“In these diseases, early diagnosis is to some extent questionable, as early diagnosis is of little use if neuroprotective treatment is not available,” Dr. Lanciego noted.
“However, it is of great importance for use in clinical trials aimed at evaluating the efficacy of new potentially neuroprotective treatments whose main objective is to slow down – and, ideally, even halt ― the clinical progression that typically characterizes Parkinson’s disease,” Dr. Lanciego added.
The study was funded by the UK Dementia Research Institute, the Welsh government, and Cardiff University. Dr. Sandor, Dr. Barneo, and Dr. Lanciego have no relevant disclosures.
A version of this article originally appeared on Medscape.com.
new research shows.
An analysis of wearable motion-tracking data from UK Biobank participants showed a strong correlation between reduced daytime movement over 1 week and a clinical diagnosis of PD up to 7 years later.
“Smartwatch data is easily accessible and low cost. 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,” lead researcher Cynthia Sandor, PhD, from Cardiff (Wales) University, said in a statement.
“We have shown here that a single week of data captured can predict events up to 7 years in the future. With these results we could develop a valuable screening tool to aid in the early detection of Parkinson’s,” she added.
“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,” said Dr. Sandor.
The study was published online in Nature Medicine.
Novel biomarker for PD
Using machine learning, the researchers analyzed accelerometry data from 103,712 UK Biobank participants who wore a medical-grade smartwatch for a 7-day period during 2013-2016.
At the time of or within 2 years after accelerometry data collection, 273 participants were diagnosed with PD. An additional 196 individuals received a new PD diagnosis more than 2 years after accelerometry data collection (the prodromal group).
The patients with prodromal symptoms of PD and those who were diagnosed with PD showed a significantly reduced daytime acceleration profile up to 7 years before diagnosis, compared with age- and sex-matched healthy control persons, the researchers found.
The reduction in acceleration both before and following diagnosis was unique to patients with PD, “suggesting this measure to be disease specific with potential for use in early identification of individuals likely to be diagnosed with PD,” they wrote.
Accelerometry data proved more accurate than other risk factors (lifestyle, genetics, blood chemistry) or recognized prodromal symptoms of PD in predicting whether an individual would develop PD.
“Our results suggest that accelerometry collected with wearable devices in the general population could be used to identify those at elevated risk for PD on an unprecedented scale and, importantly, individuals who will likely convert within the next few years can be included in studies for neuroprotective treatments,” the researchers conclude in their article.
High-quality research
In a statement from the U.K.-based nonprofit Science Media Centre, José López Barneo, MD, PhD, with the University of Seville (Spain), said this “good quality” study “fits well with current knowledge.”
Dr. Barneo noted that other investigators have also observed that slowness of movement is a characteristic feature of some people who subsequently develop PD.
But these studies involved preselected cohorts of persons at risk of developing PD, or they were carried out in a hospital that required healthcare staff to conduct the movement analysis. In contrast, the current study was conducted in a very large cohort from the general U.K. population.
Also weighing in, José Luis Lanciego, MD, PhD, with the University of Navarra (Spain), said the “main value of this study is that it has demonstrated that accelerometry measurements obtained using wearable devices (such as a smartwatch or other similar devices) are more useful than the assessment of any other potentially prodromal symptom in identifying which people in the [general] population are at increased risk of developing Parkinson’s disease in the future, as well as being able to estimate how many years it will take to start suffering from this neurodegenerative process.
“In these diseases, early diagnosis is to some extent questionable, as early diagnosis is of little use if neuroprotective treatment is not available,” Dr. Lanciego noted.
“However, it is of great importance for use in clinical trials aimed at evaluating the efficacy of new potentially neuroprotective treatments whose main objective is to slow down – and, ideally, even halt ― the clinical progression that typically characterizes Parkinson’s disease,” Dr. Lanciego added.
The study was funded by the UK Dementia Research Institute, the Welsh government, and Cardiff University. Dr. Sandor, Dr. Barneo, and Dr. Lanciego have no relevant disclosures.
A version of this article originally appeared on Medscape.com.
FROM NATURE MEDICINE