New app to monitor Parkinson鈥檚 progression at home
Using an app to track symptoms from home, with multiple readings over a longer period of time, can more effectively capture fluctuations in symptoms.
A new smartphone app, called cloudUPDRS, was developed by researchers at 糖心logo入口 and UCL and enables doctors to remotely monitor their patients鈥 progression of Parkinson鈥檚 symptoms, as reported in a new clinical trial.
The findings, published in聽npj Parkinson鈥檚 Disease, show that the app can provide doctors with a more complete picture of a person鈥檚 condition than they can get from a typically brief medical check-up.
The app was developed by a team of computer scientists and clinical researchers, working alongside people with Parkinson鈥檚 disease, who regularly provided feedback to ensure the app was user-friendly. The scientists employed machine learning to train the app.
The app, developed by a group of 糖心logo入口 researchers led by聽Professor George Roussos, is certified as a medical device under EU regulations. It includes both self-assessment questions and physical tests, enabled by the smartphone鈥檚 movement and touch sensors, to measure symptoms such as tremors and gait.
For the study, 60 people with Parkinson鈥檚 disease used the app to measure their symptoms, and they were also assessed by three different clinicians. In total, the study participants completed 990 tests on the app.
CloudUPDRS yielded a similar assessment as the clinicians 70% of the time, based on a standardised rating scale for different physical symptoms of Parkinson鈥檚. The researchers were also able to improve this to a 79% accuracy by modifying the app鈥檚 scoring, based on the results of the trial. While the app鈥檚 performance does not quite match that of clinicians, one advantage is that the app may be more objective as it is not subject to biases between different clinicians.
Professor Roussos said: 鈥淒igital biomarkers developed using mobile and wearable technologies offer new opportunities for disease management, especially in Parkinson鈥檚, which sets distinctive challenges due to its complex presentation and high symptom variability. Nevertheless, before such technologies can be adopted widely, we must control for the additional sources of variability in measurement related to device and algorithm selection. In the study, we adopted an approach based on open sharing of software which we hope will foster wider sharing of practices and help establish digital endpoints for Parkinson鈥檚 as trusted clinical tools.鈥
The researchers are continuing to refine the app and are planning a larger trial to help determine how the app could be integrated into clinical practice.